TWI850531B - Method, system and device for monitoring battery impedance abnormality during charging process - Google Patents

Abstract

The present invention relates to the field of battery monitoring, and specifically to a method, system and device for monitoring abnormal battery impedance based on the charging process. The method includes: receiving the operation information of the power battery; calculating according to the operation signal data of the power battery in a complete charging process in the operation information to determine whether the internal resistance of the power battery changes with the change of the state of charge of the power battery; determining whether the battery impedance is abnormal according to the result of whether the resistance difference changes with the change of the state of charge and the preset judgment conditions. In order to solve how to more accurately monitor whether the power battery has impedance abnormality, and then find the location where the abnormality occurs, timely alarm, and realize cloud monitoring.

Description

Method, system and device for monitoring battery impedance abnormality during charging process

The present invention relates to the technical field of power battery impedance abnormality monitoring, and in particular to a method, system and device for monitoring battery impedance abnormality based on a charging process.

Due to the requirements of electric car driving experience and endurance, the power battery needs to have large capacity and high voltage, so many single cells are connected in series to increase capacity and in parallel to increase voltage. On the one hand, different battery cells are connected by copper bars, welding, etc. If welding is abnormal, the copper bar connection is loose, or the surface is oxidized, it will affect the battery current distribution, resulting in poor battery voltage consistency in the battery pack, and areas with large impedance are prone to heat generation and high temperatures. On the other hand, there are resistance differences in the battery cells themselves, including ohmic internal resistance (affected by welding, connectors, etc.) and polarization internal resistance. Large polarization internal resistance usually reflects changes in the internal electrochemical environment of the battery, such as local side reaction accumulation leading to obstruction of the conductive network, uneven distribution of battery electrolyte leading to concentration polarization, etc. Over a long period of use, the battery will accelerate attenuation changes. During fast charging, the battery consistency will also affect the amount of charge in the entire pack. Whether it is an impedance problem caused by battery connection or an impedance difference in the battery itself, it needs to be discovered and repaired and replaced in time to prevent the battery's consistency performance from deteriorating or even safety risks. The Hybrid Pulse Power Characteristic (HPPC) test method tests the voltage change after the power battery is subjected to a large current shock and calculates the battery's internal resistance. Some electric vehicles use the Battery Management System (BMS) to monitor and calculate the power battery impedance abnormality, which refers to the above HPPC test method, capturing the voltage change when the power battery is subjected to a large current change during driving and calculating the internal resistance. However, this treatment method cannot truly reflect the cumulative effects of polarization during continued use of the battery, such as what is reflected in the charging process.

Therefore, it is necessary to provide a solution for effectively monitoring abnormal battery impedance.

In order to overcome the above-mentioned defects and solve or at least partially solve the problem of how to accurately monitor the impedance abnormality of a power battery, a method, system and device for monitoring the impedance abnormality of a battery based on a charging process of the present invention are proposed.

In a first aspect, a method for monitoring battery impedance abnormality based on a charging process is provided, comprising: receiving operation information of a power battery; calculating based on operation signal data of the power battery in a complete charging process in the operation information to determine whether an internal resistance change of the power battery changes with a change in the state of charge of the power battery; and determining whether an abnormality occurs in the power battery impedance based on a result of whether the internal resistance changes with a change in the state of charge and a preset judgment condition.

Among them, the "receiving of power battery operation information" includes: the electric vehicle battery management system collects the battery operation signal data monitored in each complete charging process; the operation signal data at least includes: the current, single cell voltage, single cell voltage minimum value, single cell voltage maximum value, battery maximum value cell number, and battery charge state at each moment in a complete charging process; the electric vehicle battery management system forms the battery operation signal data into the operation information and sends it to the cloud through the network; the cloud receives and stores the operation information.

The "calculating according to the operation signal data of the power battery in a complete charging process in the operation information to determine whether the internal resistance of the power battery changes with the charge state of the power battery" includes: calculating the cell internal resistance deviation CRD of the power battery at any moment according to the cell voltage minimum, cell voltage maximum and current at any moment in the operation signal data of the power battery in a complete charging process; judging at any moment t according to the cell internal resistance deviation CRD of the power battery at all moments in the operation signal data of the power battery in a complete charging process and the charge state of the power battery at all moments Whether the cell internal resistance deviation CRD(i) corresponding to _i changes with the change of the charge state Soc(i); wherein i is a natural number greater than or equal to 1, representing the i-th, and _ti represents the i-th moment in any moment.

Among them, the "calculating the cell internal resistance deviation CRD of the power battery at any moment according to the cell voltage minimum, cell voltage maximum and current at any moment in the operation signal data of the power battery in a complete charging process" includes: calculating the cell voltage deviation CVD of the power battery at any moment according to the cell voltage minimum and cell voltage maximum corresponding to the any moment; calculating the cell internal resistance deviation CRD of the power battery corresponding to the any moment according to the current and the cell voltage deviation CVD corresponding to the any moment.

The “determining whether the cell internal resistance deviation CRD(i) corresponding to any moment ti changes with the change of the state of charge Soc(i) according to the cell internal resistance deviation CRD of the power battery at all moments in the operation signal _data of the power battery in the complete charging process” includes: calculating the average value of the cell internal resistance deviation CRD of the power battery at all moments , and make a trend judgment based on the linear fitting method to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i), where _CRDi represents the CRD at the i-th moment _ti ; or, make a judgment based on the classification method to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i); or, make a judgment based on the tree regression method to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i).

Wherein, the linear fitting method includes: according to the linear fitting formula Make a judgment; wherein k and b are constants in the linear fitting formula; if the constant k is determined according to the above linear fitting formula as: k1＜k＜k2, wherein the values of k1 and k2 are in the interval [-0.1,0.1], then it is determined that the single cell internal resistance deviation CRD(i) corresponding to any moment t _i does not change with the change of Soc(i); if the constant k is determined according to the above linear fitting formula as: k＞k3, wherein the value of k3 is in the interval [1,10], then it is determined that the single cell internal resistance deviation CRD(i) corresponding to any moment t _i increases with the increase of Soc(i).

Wherein, the “determining whether the battery impedance is abnormal according to the result of whether the internal resistance changes with the change of the charge state and the preset judgment condition” includes: when the cell internal resistance deviation CRD does not change with the change of the charge state, and the average value of the cell internal resistance deviation is greater than the threshold value in the preset judgment condition, it is determined that there is an abnormality in the connection impedance of the power battery; when the cell internal resistance deviation CRD increases with the increase of the charge state, and the average value of the cell internal resistance deviation If the value of the polarization resistance of the battery cell is greater than the threshold value in the preset judgment condition, it is determined that there is an abnormality of a large polarization internal resistance of the battery cell in the power battery.

The method of “determining whether the battery impedance is abnormal based on the result of whether the change of the internal resistance changes with the change of the state of charge and the preset judgment conditions” further includes: when it is determined that there is an abnormality in the connection impedance of the power battery or an abnormality in that the polarization internal resistance of the battery cell is relatively large, calculating the battery polarization resistance at each moment in the complete charging process; The plurality of large-value cell numbers Mode determines the connection point location where the abnormal connection impedance occurs, or the cell where the abnormal cell polarization internal resistance is relatively large; when the impedance abnormality occurs, the cloud sends an alarm to the electric vehicle where the power battery is located, and sends prompt information including the connection point where the abnormal connection impedance occurs or the cell where the abnormal cell polarization internal resistance is relatively large.

In a second aspect, a system for monitoring battery impedance abnormality based on a charging process is provided, comprising: a monitoring data receiving device for receiving operation information of a power battery; a monitoring data processing device for calculating based on the operation signal data of the power battery in a complete charging process in the operation information to determine whether the internal resistance of the power battery changes with the change of the state of charge of the power battery; a monitoring data judging device for determining whether the battery impedance is abnormal based on the result of whether the internal resistance changes with the change of the state of charge and a preset judging condition.

Among them, the monitoring data receiving device includes: the battery operation signal data monitored by the electric vehicle battery management system during each complete charging process; the operation signal data at least includes: the current, single cell voltage, single cell voltage minimum value, single cell voltage maximum value, battery maximum value cell number, and battery charge state at each moment in a complete charging process; the electric vehicle battery management system forms the battery operation signal data into the operation information and sends it to the cloud through the network; the cloud receives and stores the operation information.

Wherein, the monitoring data processing device also includes: calculating the cell internal resistance deviation CRD of the power battery at any moment according to the cell voltage minimum value, cell voltage maximum value and current at any moment in the operation signal data of the power battery in a complete charging process; judging whether the cell internal resistance deviation CRD(i) corresponding to any moment ti changes with the change of the charge state Soc(i) according to the cell internal resistance deviation CRD of the power battery at all moments in the operation signal data of the power battery _in a complete charging process and the various charge states of the power battery at all moments; wherein i is a natural number greater than or equal to 1, representing the i-th, and _ti represents the i-th moment in any moment.

Among them, the monitoring data processing "calculating the cell internal resistance deviation CRD of the power battery at any moment according to the cell voltage minimum, cell voltage maximum and current at any moment in the operation signal data of the power battery in a complete charging process" specifically includes: calculating the cell voltage deviation CVD of the power battery at any moment according to the cell voltage minimum and cell voltage maximum corresponding to the any moment; calculating the cell internal resistance deviation CRD of the power battery corresponding to the any moment according to the current corresponding to the any moment and the cell voltage deviation CVD.

The monitoring data processing device "determines whether the cell internal resistance deviation CRD(i) corresponding to any moment ti changes with the change of the charge state Soc(i) _according to the cell internal resistance deviation CRD of the power battery at all moments in the operation signal data of the power battery in the complete charging process, and the various charge states of the power battery at all moments", specifically includes: calculating the average value of the cell internal resistance deviation according to the cell internal resistance deviation CRD of the power battery at all moments , and make a trend judgment based on the linear simulation method to determine whether the single-cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i), where CRDi represents the CRD of the i-th moment _ti ; or, make a judgment based on the classification method to determine whether the single-cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i); or, make a judgment based on the tree regression method to determine whether the single-cell internal resistance deviation CRD(i) corresponding to any moment ti changes with the change of the charge state Soc(i).

The "linear fitting method" in the monitoring data processing device includes: according to the linear fitting formula Make a judgment; wherein k and b are constants in the linear fitting formula; if the constant k is determined according to the above linear fitting formula as: k1＜k＜k2, wherein the values of k1 and k2 are in the interval [-0.1,0.1], then it is determined that the single cell internal resistance deviation CRD(i) corresponding to any moment t _i does not change with the change of Soc(i); if the constant k is determined according to the above linear fitting formula as: k＞k3, wherein the value of k3 is in the interval [1,10], then it is determined that the single cell internal resistance deviation CRD(i) corresponding to any moment t _i increases with the increase of Soc(i).

Wherein, the monitoring data judgment device includes: when the cell internal resistance deviation CRD does not change with the change of the charge state, and the average value of the cell internal resistance deviation is greater than the threshold value in the preset judgment condition, it is determined that there is an abnormality in the connection impedance of the power battery; when the cell internal resistance deviation CRD increases with the increase of the charge state, and the average value of the cell internal resistance deviation If the value of the polarization resistance of the battery cell is greater than the threshold value in the preset judgment condition, it is determined that there is an abnormality of a large polarization internal resistance of the battery cell in the power battery.

Wherein, the monitoring data judgment device also includes: when it is determined that there is an abnormality in connection impedance or a large abnormality in cell polarization internal resistance in the power battery, according to calculating the number Mode of the maximum value cell numbers of the battery at each moment in the complete charging process, determining the connection point where the abnormality in connection impedance occurs or the cell where the abnormality in cell polarization internal resistance occurs; when the impedance abnormality occurs, an alarm is issued from the cloud to the electric vehicle where the power battery is located, and prompt information including the connection point where the abnormality in connection impedance exists or the cell where the abnormality in cell polarization internal resistance exists is sent.

In a third aspect, a storage device is provided, wherein a plurality of program codes are stored therein, wherein the program codes are suitable for being loaded and run by a processor to execute any of the above-mentioned methods for monitoring battery impedance abnormality based on a charging process.

In a fourth aspect, a control device is provided, which includes a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, and the program codes are suitable for being loaded and run by the processor to execute any of the above-mentioned methods for monitoring battery impedance abnormality based on a charging process.

The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

In the technical solution of the present invention, the battery management system BMS (BATTERY MANAGEMENT SYSTEM) in the electric vehicle collects the operation signal data of the power battery charging process of the electric vehicle in real time (such as: the current, cell voltage, cell voltage minimum value, cell voltage maximum value, battery maximum value, cell number, battery charge state, etc. corresponding to each moment in each complete charging process), transmits the data to the cloud through the vehicle network, and stores it by the cloud's powerful data storage capability to ensure that it can be obtained for a long time, including a The cloud can store historical data of several hours during a complete charging process, and use the powerful and fast computing and operation capabilities of the cloud to quickly process complex data to monitor and detect whether there is impedance abnormality in the power battery, and where the impedance abnormality occurs in the power battery (near a connection point or a certain battery cell). It can also issue an alarm when impedance abnormality is found, and return corresponding prompt information, thereby realizing cloud monitoring.

Furthermore, when executing calculations on the cloud, an appropriate complete charging process can be selected (such as the starting state of charge Soc (Soc_start) and the ending state of charge Soc (Soc_end)). Based on the consistency deviation method of calculating the internal resistance difference, the internal impedance difference ΔR (such as CRD) of the power battery is calculated through the change relationship between the single cell voltage and current in a complete charging process. By correlating the internal resistance difference in the charging process with the Soc change, it can be distinguished whether the power battery impedance difference is caused by the difference in the polarization internal resistance of the battery cell or the connection impedance problem.

In addition, in the existing HPPC test, the voltage of the power battery usually tested changes after being impacted by a large current, and the internal resistance of the power battery is calculated accordingly. Some BMSs will refer to this method to capture the voltage change when the power battery changes greatly during driving to calculate the internal resistance, but it cannot truly reflect the cumulative effect of polarization of the power battery during continuous charging (that is, the internal resistance is actually different but this method cannot reflect it). Only by using the internal resistance calculation method of the present invention can the abnormal battery impedance based on the charging process be truly and accurately reflected, reflecting the difference between the abnormal internal resistance in the actual charging process and the internal resistance test during driving.

Furthermore, in addition to being able to monitor abnormal power battery impedance from the cloud, the technical solution of the present invention can also accurately and effectively distinguish whether the power battery has abnormal connection impedance or abnormal cell polarization impedance by determining whether the internal resistance difference changes significantly with the change of the state of charge Soc.

Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

In the description of the present invention, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, storage, and may also include software parts, such as program code, or may be a combination of software and hardware. The processor may be a central processing unit, a microprocessor, a digital signal processor or any other suitable processor. The processor has data and/or signal processing functions. The processor may be implemented in software, hardware or a combination of the two. Non-temporary computer-readable storage media include any suitable media that can store program code, such as a disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, etc. The term "A and/or B" means all possible combinations of A and B, such as just A, just B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and/or B" and may include just A, just B, or A and B. The singular terms "one" and "the" may also include plural forms.

Here we first explain some terms involved in the present invention.

HPPC (Hybrid Pulse Power Characteristic) is a characteristic of the pulse charge and discharge performance of the power battery.

Soc (State of charge) is the ratio of the remaining capacity of a power battery after it has been used for a period of time or has been left unused for a long time to its capacity in a fully charged state, usually expressed as a percentage. Its value range is 0~1. When Soc=0, it means that the battery is fully discharged, and when Soc=1, it means that the battery is fully charged.

A single cell battery refers to the basic battery unit that constitutes a power battery. Multiple single cells are connected in series and parallel to form a power battery.

Cell voltage refers to the voltage of each cell in a battery pack composed of multiple cells, such as the power battery mentioned above. If it is a whole block, it refers to the voltage of this battery (power battery). For example, a 12V battery can be composed of 6 blocks, and it looks like there are 6 independent battery chambers. A cell can also be said to be a battery with independent positive and negative outputs. Most large-capacity batteries achieve rated voltage output through series and parallel connection of small-voltage battery modules, so cell voltage should refer to the voltage of a single battery module.

The minimum cell voltage (MinCellVoltage) is the minimum voltage value of a single cell battery. For a power battery with multiple single cells, it refers to the minimum value of the cell voltage data of all single cells at a certain moment.

The maximum cell voltage (MaxCellVoltage) is the minimum voltage value of a single cell battery. For a power battery with multiple single cells, it refers to the minimum value of the cell voltage data of all single cells at a certain moment.

Mode refers to the value with a clear concentrated trend point in the statistical distribution, representing the general level of the data. The mode can be non-existent or more than one. Specifically, the value that appears most frequently in a set of data (or the value that accounts for the largest proportion in a set of data) is called the mode (represented by M). Sometimes there are several modes in a set of numbers.

The maximum battery cell number (MaxCellVoltageNum) indicates the number of the battery cell with the maximum voltage value.

Referring to FIG. 1 below, a schematic diagram of an application scenario of an embodiment of the technical solution of the present invention is shown. The driving experience and endurance of electric vehicles require a power battery with large power and high voltage. The power battery increases the capacity by connecting single cells in series and increases the voltage by connecting them in parallel. Since abnormal connections between different cells will affect current distribution, resulting in poor consistency of voltages in the power battery, the area with increased impedance is prone to generate heat and high temperature; and the cell itself has resistance differences, ohmic internal resistance (affected by welding, connectors, etc.) and polarization internal resistance. When the polarization internal resistance is large, it usually reflects changes in the electrochemical environment inside the battery. Long-term use will accelerate battery attenuation changes. When charging quickly, the battery consistency will affect the charging capacity of the entire power battery. Therefore, whether it is a problem of impedance abnormality caused by battery connection or impedance abnormality caused by impedance differences in the cell itself, it needs to be discovered and repaired in time to prevent the deterioration of power battery performance and even safety risks caused by the deterioration of consistency.

In an application scenario of an embodiment of the present invention, one end of the electric vehicle can use the vehicle's data collection device, such as a battery management system BMS, to collect the operating signal data of the power battery in real time during each actual complete charging process of its power battery (such as a storage battery), collect the operating signal data at each moment in a complete charging process that lasts for several hours, and form these operating signal data into operating information, which is transmitted to the cloud through the vehicle network for storage and processing.

The cloud can monitor whether the power battery impedance is abnormal. The cloud can have servers and storage devices, which can store more data. In this way, historical data obtained over a long period of time can be recorded and stored all the time. For example, a complete charging process often takes several hours, and the amount of data is complex and large. In addition, the cloud can also perform complex, fast, and accurate calculations. The calculations implemented in the cloud are based on the method of calculating internal resistance based on consistency deviation. Determine a complete charging process, for example: select the state of charge Soc (Soc_start) and select the state of charge (Soc_end) to determine a complete charging process, calculate the cell internal resistance deviation (as internal resistance difference) of the battery, i.e., impedance difference, through the changing relationship between the cell voltage and current in the process (e.g., calculating the voltage difference), and associate the cell internal resistance deviation corresponding to each moment in the process with the corresponding state of charge Soc change (e.g., linear fitting), determine the changing relationship between the internal resistance difference and the state of charge Soc, thereby further distinguishing whether the impedance abnormality problem caused by the battery impedance difference is caused by the cell polarization internal resistance difference or the connection impedance problem. After the cloud completes the calculation and determines the impedance abnormality, it will send an alarm to the corresponding electric vehicle end for the monitored impedance abnormality; it can also send a prompt message to explain which position or component structure has the impedance abnormality. Therefore, the cloud can monitor the battery impedance abnormality during the charging process, the calculation is more accurate, and the battery impedance abnormality can be accurately identified. In addition, by calculating the data of the complete long-term charging process, it can distinguish the abnormalities such as the connection impedance of the battery and the consistency change caused by the increase of the battery polarization internal resistance.

Please refer to FIG2, which is a schematic diagram of the main steps of an embodiment of the method of the present invention. The method at least includes the following steps:

Step S210, receiving the operation information of the power battery sent by the battery management system in the electric vehicle.

In one implementation, in conjunction with the aforementioned application scenario example, the received operation information is transmitted to the cloud via a network by an electric vehicle connected to a cloud network, for example, the network may be a vehicle-connected network. The electric vehicle has a battery management system BMS that can manage a power battery, such as a storage battery. During the charging of the power battery, the battery management system collects the operation signal data of the power battery.

Specifically, the electric vehicle battery management system collects the battery operation signal data monitored in each complete charging process. The operation signal data at least includes: the current/charging current (curent) _I (i), cell voltage (CellVoltage), cell voltage minimum value (MinCellVoltage), cell voltage maximum value (MaxCellVoltage), battery maximum value cell number (MaxCellVoltageNum), and battery state of charge (Soc) at each moment t i (i is a natural number greater than or equal to 0) in a complete charging process. Then, the electric vehicle battery management system converts the battery operation signal data into the operation information and sends it to the cloud through a network (such as a vehicle network), that is, the charging history data of several hours are transmitted to the cloud. The cloud can receive the sent operation information and store it, such as in a memory, a database, etc., so that the next step is to perform relatively complex calculations based on the complex data in the charging process to monitor the abnormal impedance of the battery.

For example, during a full charging process, the collected operating signal data may include:

At _t1 : I(0) = 5A, cell A voltage (CellVotage) _CV0 = 3v, cell B voltage _CV0 = 3v, cell C voltage _CV0 = 3.5v, cell voltage minimum value _MinCV0 = 3v, cell voltage maximum value _MaxCV0 = 3.5v, Soc = 30 (soc_start);

At t ₂ : I(1) = 5A, cell A voltage CV ₁ = 3.5v, cell B voltage CV ₁ = 3v, cell C voltage CV ₁ = 4v, cell voltage minimum value MinCV ₀ = 3v, cell voltage maximum value MaxCV ₀ = 4v, Soc = 60;

At t ₃ : I(2) = 5A, cell A voltage CV ₁ = 4v, cell B voltage CV ₁ = 3v, cell C voltage CV ₁ = 4.5v, cell voltage minimum value MinCV ₀ = 3v, cell voltage maximum value MaxCV ₀ = 4.5v, Soc = 90 (soc_end).

Another implementation method is that when the power battery is placed in a battery swap station or other places for charging, the battery management system set up in the dedicated battery swap station or other places can collect the information and transmit it to the cloud through the network. As long as the operation information of the power battery in a charging process, especially the complete charging process, can be collected.

Step S220, calculating based on the operation signal data of the power battery in a complete charging process in the operation information to determine whether the resistance difference of the power battery changes with the change of the charge state of the power battery.

In one embodiment, refer to FIG. 3 for an example flow chart of calculating the resistance difference of a power battery.

Step S221, according to the operation signal data of the power battery in a complete charging process, the minimum value of the cell voltage, the maximum value of the cell voltage and the current at any moment, calculate the cell internal resistance deviation CRD of the power battery at any moment as the resistance difference.

Specifically, first: the cell voltage deviation CVD of the power battery at any moment can be calculated according to the cell voltage minimum and cell voltage maximum corresponding to any moment. For example: the cloud can select the operation information corresponding to the power battery number from the stored data, and find the charge state of the start charging soc (Soc_start) and the charge state of the end charging soc (Soc_end) in a complete charging process in the operation information. Here, in order to obtain better calculation accuracy, it can be considered to select a complete charging process with soc_start < 40 and soc_end > 80. In this charging process, for any moment t _i , the voltage difference That is, the cell voltage deviation CVD is calculated by taking the difference between the minimum cell voltage MinCellVoltage and the maximum cell voltage MaxCellVoltage, as shown in the following (Formula 1):

(Formula 1)

Specifically, secondly, according to the current I(i) corresponding to any one moment _ti and the cell voltage deviation CVD, the cell internal resistance deviation in the power battery corresponding to any one moment _ti is calculated. The cell internal resistance deviation CRD of the battery is calculated as the resistance difference ΔR of the battery as follows (Formula 2):

(Formula 2)

Step S222, based on the cell internal resistance deviation CRD of the power battery at all moments in the operation signal data of the power battery in the complete charging process, and the various charge states of the power battery at all moments, determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i); wherein i is a natural number greater than or equal to 1, representing the i-th, and _ti represents the i-th moment in any moment.

For example, a complete charging process includes the 1st, 2nd, ..., i...n (i, n are natural numbers greater than or equal to 1, and i≤n) moments, and the cell internal resistance deviation at each moment is CRD(i). Then the cell internal resistance deviation (i.e., battery resistance difference) corresponding to all moments in the charging process is: {CRD(1), CRD(2)...CRD(i)...CRD(n)}; and the state of charge soc corresponding to all moments is {Soc(1), Soc(2)...Soc(i)...Soc(n)} in the previously collected operation signal data. Therefore, it is detected whether CRD is related to Soc changes.

Continuing with the above example, i=1, 2, 3, the CRD at each moment is calculated as: CRD(1): 3.5-3=0.5, 0.5/5=0.1; CRD(2): 4-3=1, 1/5=0.2; CRD(3): 4.5-3=1.5, 1.5/5=0.3, that is, the CRD set is {0.1, 0.2, 0.3}. Soc(1)=30, Soc(2)=60, Soc(3)=90, that is, the Soc set is {30, 60, 90}.

In a preferred embodiment, the average value of the cell internal resistance deviation CRD of the power battery at all times is calculated. , according to the above example, the AvgCRD is calculated as: 0.6/3=0.2. In addition, the linear simulation method is used to perform trend judgment to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i), where CRD _i represents the CRD at the i-th moment _ti .

For example, we can consider the linear fitting method to make trend judgment (sample data statistical analysis), and obtain the linear equation of the following one-dimensional linear fitting (Formula 3) and the average value of the single-cell internal resistance deviation avgCRD at all times in a complete charging process (Formula 4):

(Formula 3)

(Formula 4)

Wherein, k and b are parameters in the linear fitting formula. In the estimation after sample statistical analysis, when k is: k1＜k＜k2, where the values of k1 and k2 are between -0.1 and 0.1 (for example, in the interval [-0.1, 0.1]), it is determined that the single cell internal resistance deviation CRD(i) corresponding to any moment t _i does not change with the change of Soc(i), see Figure 4 Mode I. In the estimation after sample statistical analysis, when k is: k＞k3, where the value of k3 is between 1 and 10 (for example, in the interval [1, 10]), it is determined that the single cell internal resistance deviation CRD(i) corresponding to any moment t _i increases with the increase of Soc(i), see Figure 4 Mode II.

In addition, based on the two data sets (CRD and Soc), the classification algorithm can also be used for analysis and judgment to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment t _i changes with the change of the charge state Soc(i). For example, the classification algorithm uses the examples in the two data sets to infer the classification rules represented by the decision tree, and constructs the rules of the decision tree to find the relationship between the two, that is, to determine whether CRD(i) changes with Soc(i).

In addition, based on the two data sets (CRD and Soc), the tree regression method can also be used to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment t _i changes with the change of the charge state Soc(i). For example, the data set is divided into multiple pieces of data that are easy to model, and then linear regression is used for modeling and fitting to determine the fitting relationship between the two, that is, to determine whether CRD(i) changes with Soc(i).

Step S230, determining whether the power battery impedance is abnormal based on whether the resistance difference changes with the change of the charge state and a preset judgment condition.

In one embodiment, when the cell internal resistance deviation CRD as the resistance difference does not change with the change of the charge state Soc (as shown in mode I in FIG. 4 ), and the average value of the cell internal resistance deviation is If the value is greater than the threshold value in the preset judgment condition, such as AvgCRD＞c1, the value of c1 can be selected between 0 and 0.5 based on actual experiments and experience, such as c1 is 0.2. It is determined that there is an abnormality in the connection impedance of the power battery. At this time, it can be considered that there is a serious abnormal connection impedance problem near a voltage collection point in the power battery.

An example: If the average value of the internal resistance deviation of a single cell is calculated If 0.35Ohm and k = 0.01, the condition is met.

In one embodiment, when the cell internal resistance deviation CRD as the resistance difference increases with the increase of the charge state (as shown in mode II in FIG. 4 ), and the average value of the cell internal resistance deviation is If the value is greater than the threshold value in the preset judgment condition, for example: AvgCRD＞c1, for example, c1 is 0.2, it is determined that there is an abnormality of large polarization internal resistance of the battery cell in the power battery. At this time, it can be considered that a certain battery cell has a problem of large polarization internal resistance.

An example: If the average value of the internal resistance deviation of a single cell is calculated is 0.35Ohm and k = 2, then the condition is met.

In one embodiment, when it is determined that there is an abnormality in connection impedance or an abnormality in cell polarization internal resistance in the power battery, the connection point location where the abnormality in connection impedance occurs or the cell where the abnormality in cell polarization internal resistance is large can be determined based on calculating the number Mode of the battery maximum value cell numbers at each moment in the complete charging process.

That is, when the above judgment determines that the alarm condition of abnormal battery impedance has been triggered, the number of maximum battery cell numbers (MaxCellVoltageMode) of the battery in the complete charging process is used to lock the connection point or battery cell where the abnormal impedance occurs.

Furthermore, when the power battery impedance is abnormal, the cloud sends an alarm to the electric vehicle where the power battery is located, and sends prompt information including the connection point with abnormal connection impedance or the battery cell with abnormal large polarization internal resistance.

Furthermore, if the alarm condition is not triggered, the information that the power battery is normal can be fed back to the electric vehicle.

Please refer to FIG5 , which is a main structural block diagram of an embodiment of the system of the present invention. The system at least includes: a cloud 520, which includes at least a monitoring data receiving device 5201, a monitoring data calculating device 5202, and a monitoring data judging device 5203. The system also includes an electric vehicle 510 connected to the cloud 520 (such as a vehicle network connection), and the electric vehicle 510 includes at least a vehicle data collection device (including, for example, a BMS, etc., not shown).

The monitoring data receiving device 5201 is used to receive the operation information of the power battery sent by the battery management system in the electric vehicle.

In one implementation, in conjunction with the aforementioned application scenario example, the received operation information is transmitted to the cloud via a network by an electric vehicle connected to a cloud network, for example, the network may be a vehicle-connected network. The electric vehicle has a battery management system BMS that can manage a power battery, such as a storage battery. During the charging of the power battery, the battery management system collects the operation signal data of the power battery.

Specifically, the electric vehicle battery management system collects the battery operation signal data monitored in each complete charging process. The operation signal data at least includes: the current/charging current (curent) _I (i), cell voltage (CellVoltage), cell voltage minimum value (MinCellVoltage), cell voltage maximum value (MaxCellVoltage), battery maximum value cell number (MaxCellVoltageNum), and battery state of charge (Soc) at each moment t i (i is a natural number greater than or equal to 0) in a complete charging process. Then, the electric vehicle battery management system converts the battery operation signal data into the operation information and sends it to the cloud through a network (such as a vehicle network), that is, the charging history data of several hours are transmitted to the cloud. The cloud can receive the sent operation information and store it, such as in a memory, a database, etc., so that the next step is to perform relatively complex calculations based on the complex data in the charging process to monitor the abnormal impedance of the battery.

An example: During a complete charging process, the collected operation signal data may include, for example: at _t1 : I(0)=5A, cell A voltage (CellVotage) _CV0 =3v, cell B voltage _CV0 =3v, cell C voltage _CV0 =3.5v, cell voltage minimum value _MinCV0 =3v, cell voltage maximum value _MaxCV0 =3.5v, Soc=30 (soc_start); at _t2 : I(1)=5A, cell A voltage _CV1 =3.5v, cell B voltage _CV1 =3v, cell C voltage _CV1 =4v, cell voltage minimum value _MinCV0 =3v, cell voltage maximum value _MaxCV0 =4v, Soc=60; Moment ₃ : I(2) = 5A, cell A voltage CV ₁ = 4v, cell B voltage CV ₁ = 3v, cell C voltage CV ₁ = 4.5v, cell voltage minimum value MinCV ₀ = 3v, cell voltage maximum value MaxCV ₀ = 4.5v, Soc = 90 (soc_end).

Another implementation method is that when the power battery is placed in a battery swap station or other places for charging, the battery management system set up in the dedicated battery swap station or other places can collect the information and transmit it to the cloud through the network. As long as the operation information of the power battery in a charging process, especially the complete charging process, can be collected.

The monitoring data calculation device 5202 is used to calculate according to the operation signal data of the power battery in a complete charging process in the operation information to determine whether the resistance difference of the power battery changes with the change of the charge state of the power battery.

In one embodiment, first enter S1, and calculate the cell internal resistance deviation CRD of the power battery at any moment as the resistance difference according to the cell voltage minimum value, cell voltage maximum value and current at any moment in the operation signal data of the power battery in a complete charging process. Specifically, first: the cell voltage deviation CVD of the power battery at any moment can be calculated according to the cell voltage minimum value and cell voltage maximum value corresponding to any moment. For example: the cloud can select the operation information corresponding to the power battery number from the stored data, and find the charge state of the start charging soc (Soc_start) and the charge state of the end charging soc (Soc_end) in a complete charging process in the operation information. Here, in order to obtain better calculation accuracy, we can consider a complete charging process with soc_start＜40 and soc_end＞80. In this charging process, for any moment t _i , the voltage difference (CellVoltDifference, CVD), that is, the cell voltage deviation CVD, is calculated based on the minimum cell voltage MinCellVoltage and the maximum cell voltage MaxCellVoltage, as shown in the following (Formula 1) to calculate the cell voltage deviation CVD of the battery:

(Formula 1)

Specifically, secondly, the cell internal resistance deviation (CellRsiDifference(i), CRD) in the power battery corresponding to any one moment _ti is calculated based on the current I(i) corresponding to any one moment _ti and the cell voltage deviation CVD. The cell internal resistance deviation CRD of the battery is calculated as the resistance difference ΔR of the battery as follows (Formula 2):

(Formula 2)

In one embodiment, the process then enters S2, and determines whether the cell internal resistance deviation CRD(i) corresponding to any moment _{ti changes} with the change of the charge state Soc(i) based on the cell internal resistance deviation CRD of the power battery at all moments in the operation signal data of the power battery in the complete charging process, and the various charge states of the power battery at all moments; wherein i is a natural number greater than or equal to 1, representing the i-th, and _ti represents the i-th moment in any moment.

For example, a complete charging process includes the 1st, 2nd, ..., i...n (i, n are natural numbers greater than or equal to 1, and i≤n) moments, and the cell internal resistance deviation at each moment is CRD(i). Then the cell internal resistance deviation (i.e., battery resistance difference) corresponding to all moments in the charging process is: {CRD(1), CRD(2)...CRD(i)...CRD(n)}; and the state of charge soc corresponding to all moments is {Soc(1), Soc(2)...Soc(i)...Soc(n)} in the previously collected operation signal data. Therefore, it is detected whether CRD is related to Soc changes.

Continuing with the above example, i=1, 2, 3, the CRD at each moment is calculated as: CRD(1): 3.5-3=0.5, 0.5/5=0.1; CRD(2): 4-3=1, 1/5=0.2; CRD(3): 4.5-3=1.5, 1.5/5=0.3, that is, the CRD set is {0.1, 0.2, 0.3}. Soc(1)=30, Soc(2)=60, Soc(3)=90, that is, the Soc set is {30, 60, 90}.

In a preferred embodiment, the average value of the cell internal resistance deviation CRD of the power battery at all times is calculated. , according to the above example, the AvgCRD is calculated as: 0.6/3=0.2. In addition, the linear simulation method is used to perform trend judgment to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i), where CRD _i represents the CRD at the i-th moment _ti .

For example, the linear fitting method can be considered to make trend judgment (sample data statistical analysis), and the linear equation of the following one-dimensional linear fitting (Formula 3) and the average value of the single-cell internal resistance deviation avgCRD at all times in a complete charging process (Formula 4) can be obtained:

(Formula 3)

(Formula 4)

Wherein, k and b are parameters in the linear fitting formula. In the estimation after sample statistical analysis, when k is: k1＜k＜k2, where the values of k1 and k2 are between -0.1 and 0.1 (for example, in the interval [-0.1, 0.1]), it is determined that the single cell internal resistance deviation CRD(i) corresponding to any moment t _i does not change with the change of Soc(i), see Figure 4 Mode I. In the estimation after sample statistical analysis, when k is: k＞k3, where the value of k3 is between 1 and 10 (for example, in the interval [1, 10]), it is determined that the single cell internal resistance deviation CRD(i) corresponding to any moment t _i increases with the increase of Soc(i), see Figure 4 Mode II.

In addition, based on the two data sets (CRD and Soc), the classification algorithm can also be used for analysis and judgment to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment t _i changes with the change of the charge state Soc(i). For example, the classification algorithm uses examples in the two data sets to infer the classification rules represented by the decision tree, and constructs the rules of the decision tree to find the relationship between the two, that is, to determine whether CRD(i) changes with Soc(i).

In addition, based on the two data sets (CRD and Soc), the tree regression method can also be used to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment t _i changes with the change of the charge state Soc(i). For example, the data set is divided into multiple pieces of data that are easy to model, and then linear regression is used for modeling and fitting to determine the fitting relationship between the two, that is, to determine whether CRD(i) changes with Soc(i).

The monitoring data judgment device 5203 is used to determine whether the power battery impedance is abnormal based on the result of whether the resistance difference changes with the change of the charge state and the preset judgment conditions.

In one embodiment, when the cell internal resistance deviation CRD as the resistance difference does not change with the change of the state of charge Soc (as shown in mode I in FIG. 4 ), and the average value AvgCRD of the cell internal resistance deviation is greater than the threshold value in the preset judgment condition, for example, AvgCRD＞c1, where the value of c1 can be selected between 0 and 0.5 based on actual experiments and experience, for example, c1 is 0.2. It is determined that there is an abnormality in the connection impedance in the power battery. At this time, it can be considered that there is a serious abnormal connection impedance problem near a voltage collection point in the power battery.

For example, if the calculated average value of the cell internal resistance deviation AvgCRD is 0.35Ohm and k = 0.01, the condition is met.

In one embodiment, when the cell internal resistance deviation CRD as the resistance difference increases with the increase of the state of charge (as shown in mode II in FIG. 4 ), and the average value AvgCRD of the cell internal resistance deviation is greater than the threshold value in the preset judgment condition, for example: AvgCRD＞c1, for example, c1 is 0.2, it is determined that there is an abnormality of a large polarization internal resistance of the cell in the power battery. At this time, it can be considered that a certain cell has a problem of large polarization internal resistance.

For example, if the calculated average value of the cell internal resistance deviation AvgCRD is 0.35Ohm and k = 2, the condition is met.

In one embodiment, when it is determined that there is an abnormality in connection impedance or an abnormality in cell polarization internal resistance in the power battery, the connection point location where the abnormality in connection impedance occurs or the cell where the abnormality in cell polarization internal resistance is large can be determined based on calculating the number Mode of the battery maximum value cell numbers at each moment in the complete charging process.

That is, when the above judgment determines that the alarm condition of abnormal battery impedance has been triggered, the number of maximum battery cell numbers (MaxCellVoltageMode) of the battery in the complete charging process is used to lock the connection point or battery cell where the abnormal impedance occurs.

Furthermore, when the power battery impedance is abnormal, the cloud sends an alarm to the electric vehicle where the power battery is located, and sends prompt information including the connection point with abnormal connection impedance or the battery cell with abnormal large polarization internal resistance.

Furthermore, if the alarm condition is not triggered, the information that the power battery is normal can be fed back to the electric vehicle.

Based on the above method embodiment, the present invention also provides a storage device embodiment. In the storage device embodiment, the storage device stores multiple program codes, which are suitable for being loaded and run by a processor to execute the power battery insulation monitoring method of the above method embodiment. For the convenience of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention.

Based on the above method embodiment, the present invention also provides a control device embodiment. In the control device embodiment, the device includes a processor and a storage device, and the storage device stores a plurality of program codes, and the program codes are suitable for being loaded and run by the processor to execute the power battery insulation monitoring method of the above method embodiment. For the convenience of explanation, only the parts related to the embodiment of the present invention are shown. For the specific technical details not disclosed, please refer to the method part of the embodiment of the present invention.

It is understood by those skilled in the art that all or part of the processes in the method of implementing the above-mentioned embodiment of the present invention can also be completed by instructing related hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method embodiments can be implemented. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device, medium, flash drive, flash hard disk, magnetic disk, optical disk, computer storage, read-only storage, random access storage, electric carrier wave signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier wave signals and telecommunication signals.

Furthermore, it should be understood that since the configuration of each module is only for illustrating the functional units of the system of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic.

It is understood by those skilled in the art that each module in the system can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present invention, and therefore, the technical solution after splitting or merging will fall within the protection scope of the present invention.

So far, the technical solution of the present invention has been described in conjunction with an implementation shown in the attached drawings. However, it is easy for a person skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific implementations. Without departing from the principle of the present invention, a person skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

510: Electric vehicle 520: Cloud 5201: Monitoring data receiving device 5202: Monitoring data calculation device 5203: Monitoring data judgment device

The specific implementation of the present invention is described below with reference to the attached drawings, in which: Figure 1 is a schematic diagram of an application scenario example of the method for monitoring battery impedance abnormality based on the charging process according to the present invention; Figure 2 is a schematic diagram of the main steps of an implementation example of the method for monitoring battery impedance abnormality based on the charging process according to the present invention; Figure 3 is a schematic diagram of the main steps of an implementation example of the method for monitoring battery impedance abnormality based on the charging process according to the present invention. An example flow chart of calculating the resistance difference of the power battery; Figure 4 is a schematic diagram of the linear fitting trend judgment mode I and mode II of the internal resistance deviation CRD(i) and the state of charge Soc(i) of the power battery corresponding to each moment in a complete charging process according to an embodiment of the method of the present invention; Figure 5 is a main structural block diagram of an embodiment of the method of monitoring battery impedance abnormality based on the charging process according to the present invention.

Claims (16)

A method for monitoring battery impedance abnormality based on a charging process, comprising: receiving operation information of a power battery; performing calculations based on operation signal data of the power battery in a complete charging process in the operation information to determine whether the internal resistance of the power battery changes with the change of the state of charge of the power battery; wherein the operation signal data of the power battery in a complete charging process in the operation information includes the cell internal resistance deviation CRD of the power battery at all times and the various states of charge of the power battery at all times; determining the result based on whether the internal resistance changes with the change of the state of charge; The method comprises the following steps: determining whether the battery impedance is abnormal based on the result and the preset judgment condition, including: when the cell internal resistance deviation CRD does not change with the change of the charge state, and the average value AvgCRD of the cell internal resistance deviation CRD is greater than the threshold value in the preset judgment condition, determining that there is an abnormality in the connection impedance of the power battery; and when the cell internal resistance deviation CRD increases with the increase of the charge state, and the average value AvgCRD of the cell internal resistance deviation CRD is greater than the threshold value in the preset judgment condition, determining that there is an abnormality in the cell polarization internal resistance being relatively large in the power battery. A method for monitoring abnormal battery impedance based on the charging process as described in claim 1, wherein the "receiving operation information of the power battery" includes: the electric vehicle battery management system collects the battery operation signal data monitored in each complete charging process; the operation signal data at least includes: the current, cell voltage, cell voltage minimum value, cell voltage maximum value, battery maximum value cell number, and battery charge state at each moment in a complete charging process; the electric vehicle battery management system forms the battery operation signal data into the operation information and sends it to the cloud through the network; the cloud receives and stores the operation information. A method for monitoring battery impedance anomalies based on a charging process as described in claim 2, wherein the "calculating based on the operation signal data of the power battery in a complete charging process in the operation information to determine whether the internal resistance of the power battery changes with the change of the charge state of the power battery" includes: calculating the cell internal resistance deviation CRD of the power battery at any moment based on the cell voltage minimum, cell voltage maximum and current at any moment in the operation signal data of the power battery in a complete charging process; judging the cell internal resistance deviation CRD of the power battery at all moments in the operation signal data of the power battery in a complete charging process and the charge state of the power battery at all moments at any moment. Whether the cell internal resistance deviation CRD(i) corresponding to _i changes with the change of the charge state Soc(i); wherein i is a natural number greater than or equal to 0, representing the i-th, and _ti represents the i-th moment in any moment. As described in claim 3, the method for monitoring battery impedance abnormality based on the charging process, wherein the "calculating the cell internal resistance deviation CRD of the power battery at any moment according to the cell voltage minimum value, cell voltage maximum value and current at any moment in the operation signal data of the power battery in a complete charging process" includes: calculating the cell voltage deviation CVD of the power battery at any moment according to the cell voltage minimum value and cell voltage maximum value corresponding to the any moment; calculating the cell internal resistance deviation CRD of the power battery corresponding to the any moment according to the current and the cell voltage deviation CVD corresponding to the any moment. A method for monitoring battery impedance anomalies during the charging process as described in claim 4, wherein the "based on the cell internal resistance deviation CRD of the power battery at all times in the operation signal data of the power battery during the complete charging process, and the various charge states of the power battery at all times, judging whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i)" includes: calculating the average value of the cell internal resistance deviation based on the cell internal resistance deviation CRD of the power battery at all times

, and make a trend judgment based on the linear fitting method to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i), where CRD _i represents the CRD of the i-th moment _ti ; or, make a judgment based on the classification method to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i); or, make a judgment based on the tree regression method to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i). A method for monitoring battery impedance anomalies during charging process as described in claim 5, wherein the linear fitting method comprises: making judgments according to the linear fitting formula CRD ( i )= k * Soc ( i )+ b ; wherein k and b are constants in the linear fitting formula; if the constant k is determined according to the linear fitting formula as: k1<k<k2, wherein the values of k1 and k2 are in the interval [-0.1,0.1], then it is determined that the cell internal resistance deviation CRD(i) corresponding to any moment t does not change with the change of Soc(i); if the constant k is determined according to the linear fitting formula as: k>k3, wherein the value of k3 is in the interval [1,10], then it is determined that any moment t The cell internal resistance deviation CRD(i) corresponding to _i increases as Soc(i) increases. The method for monitoring battery impedance abnormality based on the charging process as described in claim 6, wherein the "determining whether the battery impedance is abnormal based on the result of whether the internal resistance changes with the change of the charge state and the preset judgment conditions" also includes: when it is determined that there is an abnormality in the connection impedance of the power battery, or there is an abnormality in the polarization internal resistance of the battery cell, according to the calculation of the complete charging process The number of the battery cell numbers with the maximum value at each moment in the process is determined to determine the connection point location where the abnormal connection impedance occurs, or the battery cell with the abnormal large cell polarization internal resistance; when the power battery impedance is abnormal, the cloud sends an alarm to the electric vehicle where the power battery is located, and sends prompt information including the connection point with the abnormal connection impedance or the battery cell with the abnormal large cell polarization internal resistance. A system for monitoring battery impedance abnormality based on a charging process comprises: a monitoring data receiving device for receiving operation information of a power battery; a monitoring data processing device for calculating according to the operation signal data of the power battery in a complete charging process in the operation information to determine whether the internal resistance of the power battery changes with the change of the state of charge of the power battery; wherein the operation signal data of the power battery in a complete charging process in the operation information includes the cell internal resistance deviation CRD of the power battery at all times and the charge state of the power battery at all times; a monitoring data judging device for calculating according to whether the internal resistance changes with the charge state The battery impedance is determined to be abnormal based on the result of the change of the charge state and the preset judgment condition; the monitoring data judgment device includes: when the cell internal resistance deviation CRD does not change with the change of the charge state, and the average value AvgCRD of the cell internal resistance deviation CRD is greater than the threshold value in the preset judgment condition, it is determined that there is an abnormality in the connection impedance of the power battery; when the cell internal resistance deviation CRD increases with the increase of the charge state, and the average value AvgCRD of the cell internal resistance deviation CRD is greater than the threshold value in the preset judgment condition, it is determined that there is an abnormality in the cell polarization internal resistance in the power battery. As claimed in claim 8, the system for monitoring abnormal battery impedance during the charging process, wherein the monitoring data receiving device includes: the battery operation signal data monitored during each complete charging process is collected by the electric vehicle battery management system; the operation signal data at least includes: the current, cell voltage, cell voltage minimum value, cell voltage maximum value, battery maximum value cell number, and battery charge state at each moment during a complete charging process; the electric vehicle battery management system converts the battery operation signal data into the operation information and sends it to the cloud through the network; the cloud receives and stores the operation information. A system for monitoring battery impedance abnormalities during a charging process as described in claim 9, wherein the monitoring data processing device further comprises: calculating the cell internal resistance deviation CRD of the power battery at any moment according to the cell voltage minimum, cell voltage maximum and current at any moment in the operation signal data of the power battery during a complete charging process; judging whether the cell internal resistance deviation CRD(i) corresponding to any moment t changes with the change of the charge state Soc(i) according to the cell internal resistance deviation CRD of the power battery at all moments in the operation signal data of the power battery during a complete charging process and the various charge states of the power battery at all moments; wherein i is a natural number greater than or equal to 1, indicating the i-th, t _i represents the i-th moment in any moment. As claimed in claim 10, a system for monitoring battery impedance abnormality during charging process, wherein the monitoring data processing "calculates the cell internal resistance deviation CRD of the power battery at any moment according to the cell voltage minimum value, cell voltage maximum value and current at any moment in the operation signal data of the power battery during a complete charging process", specifically includes: calculating the cell voltage deviation CVD of the power battery at any moment according to the cell voltage minimum value and cell voltage maximum value corresponding to any moment; calculating the cell internal resistance deviation CRD of the power battery corresponding to any moment according to the current and cell voltage deviation CVD corresponding to any moment. A system for monitoring battery impedance anomalies during the charging process as described in claim 11, wherein the monitoring data processing device "determines whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i) based on the cell internal resistance deviation CRD of the power battery at all moments in the operation signal data of the power battery during the complete charging process, and the various charge states of the power battery at all moments", specifically including: calculating the average value of the cell internal resistance deviation based on the cell internal resistance deviation CRD of the power battery at all moments

, and make a trend judgment based on the linear fitting method to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i), where CRD _i represents the CRD of the i-th moment _ti ; or, make a judgment based on the classification method to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i); or, make a judgment based on the tree regression method to determine whether the cell internal resistance deviation CRD(i) corresponding to any moment _ti changes with the change of the charge state Soc(i). A system for monitoring battery impedance anomalies during charging as described in claim 12, wherein the "linear fitting method" in the monitoring data processing device includes: making a judgment based on the linear fitting formula CRD ( i )= k * Soc ( i )+ b ; wherein k and b are constants in the linear fitting formula; if the constant k is determined according to the above linear fitting formula as: k1<k<k2, wherein the values of k1 and k2 are in the interval [-0.1,0.1], then it is determined that the cell internal resistance deviation CRD(i) corresponding to any moment t _i does not change with the change of Soc(i); if the constant k is determined according to the above linear fitting formula as: k>k3, wherein the value of k3 is in the interval [1,10], then it is determined that any moment t The cell internal resistance deviation CRD(i) corresponding to _i increases as Soc(i) increases. As described in claim 13, the system for monitoring battery impedance abnormality during the charging process, wherein the monitoring data judgment device further includes: when it is determined that there is an abnormality in the connection impedance in the power battery, or an abnormality in the polarization internal resistance of the battery cell is relatively large, according to the calculation of the number of the maximum value of the battery cell number Mode at each moment in the complete charging process, determine the connection point where the abnormality in the connection impedance occurs, or the battery cell where the abnormality in the polarization internal resistance of the battery cell is relatively large; when the impedance abnormality occurs, the cloud sends an alarm to the electric vehicle where the power battery is located, and sends prompt information including the connection point where the abnormality in the connection impedance exists or the battery cell where the abnormality in the polarization internal resistance of the battery cell is relatively large. A storage device storing a plurality of program codes, characterized in that the program codes are suitable for being loaded and run by a processor to execute the method for monitoring battery impedance abnormality based on the charging process as described in any one of claims 1 to 7. A control device includes a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, wherein the program codes are suitable for being loaded and run by the processor to execute the method for monitoring battery impedance abnormality based on the charging process as described in any one of claim items 1 to 7.