WO2021128116A1

WO2021128116A1 - Systems and methods for performing a health evaluation of a battery system

Info

Publication number: WO2021128116A1
Application number: PCT/CN2019/128521
Authority: WO
Inventors: Lei Yang
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2019-12-26
Filing date: 2019-12-26
Publication date: 2021-07-01
Anticipated expiration: 2022-06-26

Abstract

Embodiments of the disclosure provide systems and methods for performing a health evaluation of a battery system. An exemplary system may include a communication interface configured to receive operating data of the battery system acquired from one or more vehicles. The system may also include at least one processor coupled to the communication interface. The at least one processor is configured to determine a parameter indicative of a charging characteristic of the battery system based on the operating data. Moreover, the at least one processor is further configured to determine a trend of the parameter over a predetermined time period and evaluate a health of the battery system based on the trend of the parameter.

Description

SYSTEMS AND METHODS FOR PERFORMING A HEALTH EVALUATION OF A BATTERY SYSTEM

TECHNICAL FIELD

The present disclosure relates to systems and methods for performing a health evaluation of a battery system, and more particularly to systems and methods for evaluating the health of the battery system based on determining a trend of the Direct Current Internal Resistance (DCR) of the battery pack over a predetermined time period.

BACKGROUND

Electric power can be supplied by sources such as electric batteries. An electric battery is a device consisting of one or more electrochemical cells with external connections provided to power electrical devices such as mobile phones, flashlights and electric cars. When using electric batteries to provide electric power, electrochemical cells generate electrical energy from chemical reactions.

To provide enough electric power for driving large electrical apparatuses such as an electric car, many batteries are connected in series and/or in parallel to form a battery system. The state of health of each of the cells within the battery system will determine the overall performance and even the safety of the entire battery system. Thus, systems and methods for determining a malfunction of the cells within the battery system easily and accurately is desired.

In most cases, malfunction of the cells happens when the internal resistance of the battery system behaves abnormally. Current testing methods such as Hybrid Pulse Power Characteristic (HPPC) require a lot of offline data, which is very time consuming to collect. That makes the method not practical to perform outside the laboratory.

Embodiments of the disclosure address the above problems by providing systems and methods for evaluating a health of a battery method based on determining a trend of the DCR of the battery system, to make the testing and evaluation of the health more practical and cost-effective.

SUMMARY

Embodiments of the disclosure provide a system for performing a health evaluation of a battery system. An exemplary system may include a communication interface configured to receive operating data of the battery system acquired from one or more vehicles. The system may also include at least one processor coupled to the communication interface. The at least one processor is configured to determine a parameter indicative of a characteristic of the battery system charging based on the operating data. Moreover, the at least one processor is further configured to determine a trend of the parameter over a predetermined time period and evaluate a health of the battery system based on the trend of the parameter.

Embodiments of the disclosure also provide a method for performing a health evaluation of a battery system. An exemplary method may include receiving operating data of the battery system acquired from one or more vehicles and determining a parameter indicative of a charging characteristic of the battery system based on the operating data. The method may further include determining a trend of the parameter over a predetermined time period and evaluating a health of the battery system based on the trend of the parameter.

Embodiments of the disclosure further provide a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method for a method for performing a health evaluation of a battery system. The method may include determining a parameter indicative of a charging characteristic of the battery system based on operating data of the battery system acquired from one or more vehicles. The method may further include determining a trend of the parameter over a predetermined time period and evaluating a health of the battery system based on the trend of the parameter.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a schematic diagram of an exemplary battery system health evaluation system for performing a health evaluation of the battery system, according to embodiments of the disclosure.

FIG. 2 illustrates a block diagram of an exemplary battery system health evaluation server for performing a health evaluation of the battery system, according to embodiments of the disclosure.

FIG. 3 illustrates a flowchart of an exemplary method for performing a health evaluation of the battery system, according to embodiments of the disclosure.

FIG. 4 illustrates a flowchart of an exemplary method for determining a trend of the DCR, according to embodiments of the disclosure.

FIG. 5 illustrates an exemplary relationship between operating voltages and corresponding currents that characterize the charging of a battery system, according to embodiments of the disclosure.

FIG. 6 illustrates an exemplary trend of DCR ₁₀ over a time period, according to embodiments of the disclosure.

FIG. 7 illustrates an exemplary trend of multiple DCRs of a healthy battery system, according to embodiments of the disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.

FIG. 1 illustrates a schematic diagram of an exemplary battery health evaluation system 100 equipped with a battery system health evaluation server 150 (server 150 hereafter) for performing a health evaluation of the battery system, according to embodiments of the disclosure. As shown in FIG. 1, server 150 may request/download operating data of the battery system from a database 140 through a network (not shown) . The operating data of the battery system may be acquired from one or more vehicles 110. Server 150 may evaluate the health of the battery system of the at least one of vehicles 110 and transmit an evaluation result 103 to a display device 130 for further determination and/or processing (e.g., whether further actions such as a replacement of at least one cell of the battery system and/or a further diagnosis of the battery system are needed) .

Consistent with some embodiments, vehicle 110 may be a vehicle configured to provide transportation. It is contemplated that vehicle 110 may be an electric vehicle that has an electric motor, or a hybrid vehicle that includes an internal combustion engine and at least one electric motor (e.g., for driving the vehicle at different speeds) . Vehicle 110 may have a battery system for providing electrical power to the electric motor. The battery system may have more than one cells connected in series and/or in parallel to provide a larger electric power output. Vehicle 110 may have a body and at least one wheel. The body may be of any body style, such as a sports vehicle, a coupe, a sedan, a pick-up truck, a station wagon, a sports utility vehicle (SUV) , a minivan, or a conversion van. In some embodiments, vehicle 110 may include a pair of front wheels and a pair of rear wheels. However, it is contemplated that vehicle 110 may have more or less wheels or equivalent structures that enable vehicle 110 to move around. Vehicle 110 may be configured to be all-wheel drive (AWD) , front wheel drive (FWR) , or rear wheel drive (RWD) . In some embodiments, vehicle 110 may be configured to be operated by an operator occupying the vehicle, remotely controlled, and/or autonomous.

In some embodiments, vehicle 110 may be additionally equipped with sensor (s) (not shown) for detecting/measuring operating data 102 of the battery system. The sensor (s) may include an electrical sensor unit, such as a voltage sensor and/or a current sensor. In some embodiments, the sensor (s) may use screws, adhesives, or another mounting mechanism to be mounted to vehicle 110. It is contemplated that the manners in which the sensor (s) can be equipped on vehicle 110 are not limited by the example disclosed herein, and may be modified depending on the types of the battery system and the sensor (s) and/or vehicle 100 to achieve a desirable electrical data sensing performance.

In some embodiments, operating data 102 may be data indicative of the operative performance of cells within the battery system while vehicle 110 is operating. For example, operating data 102 may include partial and/or complete data acquired under the control of a battery management system (BMS) which manages the battery system, for protecting the battery system from operating outside its safe operating zone, monitoring its state, calculating secondary data, etc. Particularly, operating data 102 may include operating voltages and their corresponding operating currents of the battery system. In some embodiments, operating data 102 may include the operating voltages and their corresponding operating currents under both a charging status and an uncharged status. Operating data 102 that lacks data segments indicative of at least one of these two statuses (i.e., both the charging status and the uncharged status) may be cleaned/filtered during data pre-processing (also known as data cleaning) . The data cleaning process will be disclosed in greater details below.

In some embodiments, operating data 102 may be acquired by the sensor (s) mentioned above and be stored in a memory and/or a storage coupled to the sensor (s) . For example, operating data 102 may be stored in a format of ＊. xls, ＊. xlsx, ＊. csv, etc. It is understood that the format of storing operating data 102 is not limited to the formats disclosed herein and may be modified for other operating purposes.

In some embodiments, operating data 102 may be uploaded to database 140 in real-time (e.g., by streaming from the sensor (s) to database 140) , or collectively after a period of time (e.g., every few seconds, every few minutes, etc. ) through a network (not shown) . In some embodiments, the network may be a Wireless Local Area Network (WLAN) , a Wide Area Network (WAN) , wireless networks such as radio waves, a cellular network, a satellite communication network, and/or a local or short-range wireless network (e.g., Bluetooth ^TM or near-field communication) for transmitting operating-related information of the battery system of vehicle 110. In some other embodiments, operating data 102 may also be uploaded to database 140 via a direct link (e.g., through a communication cable) . For example, a user of vehicle 110 (i.e., the driver/operator) may periodically drive/direct vehicle 110 to a terminal where database 140 is located for uploading the data.

In some embodiments, server 150 may download operating data 102 from database 140 in real-time through the same and/or a different network through which operating data 102 is uploaded to database 140, or via communication cables for downloading operating data 102 collectively (e.g., every few seconds, every few minutes, etc. ) . In some embodiments, server 150 may process operating data 102 and generate an evaluation result 103 of the battery system based on the processed operating data 102. In some embodiments, battery health evaluation system 100 may optionally include a display device 130 for displaying evaluation result 103. Evaluation result 103 may be used for monitoring the health of the battery system and/or for further determinations/actions making to keep the safety and high performance of the battery system. It is contemplated that battery health evaluation system 100 may include more or less components compared to those shown in FIG. 1.

FIG. 2 illustrates a block diagram of an exemplary battery system health evaluation server 150 for performing a health evaluation of the battery system, according to embodiments of the disclosure. Consistent with the present disclosure, server 150 may receive operating data 102 and generate evaluation result 103 indicative of the health of the battery system based on operating data 102. Although as shown in FIG. 2, server 150 is a physical stand-along apparatus, it is contemplated that in some embodiments, server 150 may be implemented as a cloud software, an application on database 140 and/or display device 130, a virtual server, or a distributed server that is implemented multiple devices. For example, in some embodiments, the operating data cleaning and pre-processing may be implemented by the BMS within vehicle 110 and the remaining functions may be implemented by display device 130. Consistent with the present disclosure, server 150 may be a general-purpose server or a proprietary device specially designed for performing battery health evaluations.

In some embodiments, as shown in FIG. 2, server 150 may include a communication interface 202 and a processor 204. In some embodiments, server 150 may also include a memory 206, and a storage 208. In some embodiments, server 150 may have different modules in a single device, such as an integrated circuit (IC) chip (implemented as an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA) ) , or separate devices with dedicated functions. In some embodiments, one or more components of server 150 may be located in a cloud computing environment or may be alternatively in a single location or distributed locations. Components of server 150 may be in an integrated device or distributed at different locations but communicate with each other through a network (not shown) .

Communication interface 202 may receive data (e.g., operating data 102) from database 140 and transmit data (e.g., evaluation result 103) to display device 130 via communication cables, a Wireless Local Area Network (WLAN) , a Wide Area Network (WAN) , wireless networks such as radio waves, a cellular network, satellite communication links, and/or a local or short-range wireless network (e.g., Bluetooth ^TM) , or other communication methods. In some embodiments, communication interface 202 can be an integrated services digital network (ISDN) card, cable modem, satellite modem, or a modem to provide a data communication connection. As another example, communication interface 202 can be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links can also be implemented by communication interface 202. In such an implementation, communication interface 202 can send and receive electrical, electromagnetic or optical signals that carry digital data streams representing various types of information via a network.

Consistent with some embodiments, communication interface 202 may further provide the received data to storage 208 for storage or to processor 204 for processing. Communication interface 202 may also receive evaluation result 103 generated by processor 204 and provide the evaluation result 103 to display device 130.

Processor 204 may include any appropriate type of general-purpose or special-purpose microprocessor, digital signal processor, or microcontroller. Processor 204 may be configured as a separate processor module dedicated to process operating data 102. Alternatively, processor 204 may be configured as a shared processor module for performing other functions unrelated to battery system health evaluation (e.g., processor 204 may be a shared processor module on database 140 and/or a share processor module on display device 130) .

As shown in FIG. 2, processor 204 may include multiple modules, such as a data cleaning unit 210, a DCR determination unit 212, a health evaluation unit 214, and the like. These modules (and any corresponding sub-modules or sub-units) can be hardware units (e.g., portions of an integrated circuit) of processor 204 designed for use with other components or software units implemented by processor 204 through executing at least part of a program. The program may be stored on a computer-readable medium, and when executed by processor 204, it may perform one or more functions. Although FIG. 2 shows units 210-214 all within one processor 204, it is contemplated that these units may be distributed among multiple processors located near or remotely with each other.

After receiving operating data 102 from database 140, data cleaning unit 210 may clean and pre-process (e.g., filter) the data. In some embodiments, corrupt, incorrect and/or inaccurate data (i.e., data with errors) may be discarded. For example, data that has predetermined types of error such as having typographical errors and/or formality errors (e.g., repetitions of one or more packets, misplacement of one or more packets, wrong payload format, payloads with empty values, etc. ) may be cleaned by data cleaning unit 210. For example, data cleaning unit 210 may execute programs/applications programed using programing languages such as C or C ⁺⁺, or simulation tools such as MATLAB ^TM, where the program may systematically detect the data with the predetermined types of error and may delete/discard the data from being further processed by other units/modules of processor 204. In some embodiments, because operating data 102 may be in different formats as mentioned above, data cleaning unit 210 may also be configured to harmonize/normalize the data (e.g., transforming the data with varying file formats into one cohesive data set) .

In some embodiments, data cleaning unit 210 may identify data segments of operating data 102 acquired from vehicle 110 and further filter operating data 102 based on the data segments. In some embodiments, data segments may include data pairs of operating voltages and corresponding operating currents. For example, FIG. 5 illustrates an exemplary relationship between operating voltages and corresponding operating currents that characterize the charging of the battery system, according to embodiments of the disclosure. The relationship illustrated in FIG. 5 includes the relationship between the operating voltages and their corresponding operating currents under both a charging status and an uncharged status. For example, S ₀ represents the uncharged state of the battery system (i.e., operating voltages and its corresponding operating currents from 0-t ₀) where the operating voltage U ₀ is relatively low and the corresponding operating current is substantially zero. When the battery system starts to charge, it enters a charging status S ₁ (e.g., during the time period t ₀-t ₁) , where the operating voltage U ₁may rapidly increase accompanied by a dramatic increase of the corresponding operating current from substantially zero to a substantially constant value I ₀.

In some embodiments, data cleaning unit 210 may identify the data segments of operating data 102 that include data corresponding to both the uncharged status and the charging status. Data cleaning unit 210 may discard data segments that do not encompass both statuses. For example, data segments that contain operating data in both status S ₀ (i.e., during time period 0-t ₀) , and status S ₁ (i.e., during time period from t _0-t ₁) may be preserved. Otherwise, data segments missing data from either the uncharged S ₀ status, or the charged S ₁ status may be discarded/filtered. This pre-processing may ensure that each operating data 102 cleaned/processed by data cleaning unit 210 is usable for DCR determination/calculation performed by other units of processor 204.

In some embodiments, DCR determination unit 212 may determine a trend of the DCR of the battery system over a predetermined time period. In some embodiments, the DCR is determined at different time points during the predetermined period of time. For example, FIG. 6 illustrates an exemplary trend of DCR during a predetermined time period, according to embodiments of the disclosure. For instance, as shown in FIG. 6, the predetermined time period may be three months, and DCR determination unit 212 may calculate the DCR 35 times during the period. Although FIG. 6 shows the 35 different time points may distribute evenly over the period of three months, it is contemplated that time points could also be selected unevenly in the period.

In some embodiments, at each time point, DCR determination unit 212 may determine/calculate multiple DCRs corresponding to different charging lengths starting from the charging start time (e.g., t ₀) . For example, as shown in FIG. 5, a DCR corresponding to a charging length of t may be calculated according to equation (1) :

where U ₀ is the operating voltage of the battery system at charging start time point t ₀ (i.e., the uncharged status) , and U ₁ is the operating voltage of the battery system at time point t ₀+t after the batter y system (i.e., the charging status) is being charged for time length t. I ₀is the operating current corresponding to the operating voltage U ₁. In most cases, the operating current will instantaneously rise from substantially zero to a substantially constant value I ₀ when starting to charge the battery system.

Consistent with the embodiments disclosed herein, in one specific example, the different charging lengths may be chosen as 10 seconds (10s hereafter) , 30s and 60s. DCR determination unit 212 may calculate multiple DCRs corresponding to a charging length of 10s (i.e., DCR-10) , multiple DCRs corresponding to a charging length of 30s (i.e., DCR-30) and multiple DCRs corresponding to a charging length of 60s (i.e., DCR-60) starting from different charging start time (i.e., different t ₀s) . DCR determination unit 212 may then determine the DCR trend over the predetermined time period by comparing the relative magnitudes of the multiple DCR-10s, DCR-30s and DCR-60s.

In some embodiments, health evaluation unit 214 may determine a health evaluation result of the battery system based on the DCR trend. Back to the specific example mentioned above, if the DCR-10s are constantly smaller than the DCR-30s which are constantly smaller than DCR-60 during the predetermined time period, the battery system may be considered healthy during the time period.

For example, FIG. 7 illustrates an exemplary trend of the DCR (e.g., the relative relationship among DCR ₁₀s, the DCR ₃₀s and the DCR ₆₀s) of a healthy battery system, according to embodiments of the disclosure. As illustrated in FIG. 7, magnitudes of the DCR ₁₀, the DCR ₃₀ and the DCR ₆₀ of each time point are in a relative order, e.g., DCR _10 ＜ DCR _30 ＜ DCR ₆₀. Accordingly, health evaluation unit 214 may generate an evaluation result indicating that no malfunction happens to the battery system within the predetermined time period. Otherwise, health evaluation unit 214 may generate an evaluation result indicating that the battery system is malfunctioned. Health evaluation unit 214 may transmit the evaluation result to display device 130 through interface 202.

In some embodiments, server 150 may further include memory 206 and storage 208. Memory 206 and storage 208 may include any appropriate type of mass storage provided to store any type of information that processor 204 may need to process. Memory 206 and storage 208 may be a volatile or non-volatile, magnetic, semiconductor, tape, optical, removable, non-removable, or other type of storage device or tangible (i.e., non-transitory) computer-readable medium including, but not limited to, a ROM, a flash memory, a dynamic RAM, and a static RAM. Memory 206 and/or storage 208 may be configured to store one or more computer programs that may be executed by processor 204 to perform a health evaluation of the battery system herein. For example, memory 206 and/or storage 208 may be configured to store program (s) that may be executed by processor 204 to clean the operating data, to determine a trend of the DCR and/or to evaluate the health of the battery system based on the determined trend.

Memory 206 and/or storage 208 may be further configured to store information and data used by processor 204. For instance, memory 206 and/or storage 208 may be configured to store the various types of data (e.g., the original operating data received from database 140, evaluation result 103 etc. ) . Memory 206 and/or storage 208 may also store intermediate data such as the cleaned/filtered operating data, multiple DCRs, trend of the DCR, etc. The various types of data may be stored permanently, removed periodically, or disregarded immediately after certain data segments are processed.

Because all the data (e.g., operating data 102) of the battery system may be acquired and obtained remotely from vehicle 110 (e.g., vehicle 110 may transmit the operating data to database 140 through the network mentioned above and server 150 may download the operating data from database 140 for the health evaluation disclosed herein) , the health evaluation of the battery system of the vehicles can be more efficient and practical comparing to conventional testing methods.

FIG. 3 illustrates a flowchart of an exemplary method for performing a health evaluation of a battery system, according to embodiments of the disclosure. In some embodiments, method 300 may be implemented by battery health evaluation system 100. Method 300 may include steps S302-S320 as described below. It is to be appreciated that some of the steps may be optional to perform the disclosure provided herein. Further, some of the steps may be performed simultaneously, or in a different order than shown in FIG. 3.

In step S302, database 140 may receive operating data (e.g., operating data102) of the battery systems of one or more vehicles 110 from vehicles 110. For example, database 140 may receive operating data 102 from vehicle 110 in real-time (e.g., by streaming from the sensor (s) to database 140) , or collectively after a period of time (e.g., every few seconds, every few minutes, etc. ) through a network (not shown) . In another example, database 140 may receive operating data 102 directly from vehicle 110 via a direct link (e.g., through a communication cable) . For example, a user of vehicle 110 (i.e., the driver/operator) may periodically drive/direct vehicle 110 to a terminal where database 140 is located for uploading operating data 102. In step S304, database 140 may store the received operating data 102.

In some embodiments, in step S306, server 150 may download the operating data from database 140. For example, server 150 may download/receive the operating data through communication cable or a network (e.g., the same and/or a different network through which operating data 102 is uploaded to database 140) in real-time or collectively (e.g., every few seconds, every few minutes, etc. ) .

In step S308, server 150 may pre-process (e.g., clean/filter) the data (e.g., operating data 102) . In some embodiments, server 150 may discard/filter the corrupt, incorrect and/or inaccurate data (i.e., data with errors) . For example, data that has predetermined types of error such as having typographical errors and/or formality errors (e.g., repetitions of one or more packets, misplacement of one or more packets, wrong payload format, payloads with empty values, etc. ) may be cleaned by server 150. For example, server 150 may execute programs/applications programed using programing languages such as C or C ⁺⁺, or simulation tools such as MATLAB ^TM, where the programs may systematically detect the data with the predetermined types of error and may delete/discard the data from being further processed by server 150. In some embodiments, server 150 may further harmonize/normalize the data (e.g., transforming the data with varying file formats into one cohesive data set) .

In step S310, server 150 may identify data segments of the operating data acquired from vehicle 110 and further filter the operating data based on if each of the data segment includes at least one of the from the uncharged status data and the data from the charged status data. In some embodiments, data segments may include data pairs of operating voltages and corresponding operating currents as illustrated in FIG. 5. The relationship illustrated in FIG. 5 includes the relationship between operating voltages and their corresponding operating currents under both a charging status and an uncharged status. For example, S ₀ represents the uncharged state of the battery system (i.e., operating voltages and its corresponding operating currents from 0-t ₀) where the operating voltage U ₀ is relatively low and the corresponding operating current is substantially zero. When the battery system starts to charge, it enters a charging status S ₁ (e.g., during the time period t ₀-t ₁) , where the operating voltage U ₁may rapidly increase accompanied by the dramatic increase of the corresponding operating current from substantially zero to a substantially constant value I ₀.

In some embodiments, server 150 may identify the data segments of the operating data and discard/filter the data segments that do not encompass both statuses. For example, data segments that contain operating data in both status S ₀ (i.e., during time period 0-t ₀) , and status S ₁ (i.e., during time period from t _0-t ₁) may be preserved. Otherwise, in step S312, the operating data segments missing data from either the uncharged S ₀ status, or no data from the charged S ₁ status may be discarded/filtered. This pre-processing may ensure that each of the operating data cleaned/processed by step S310 is usable for DCR determination/calculation performed by later steps.

In step S314, server 150 may determine a trend of the DCR of the battery system over a predetermined time period. For example, FIG. 4 illustrates a flowchart of an exemplary method 400 for determining a trend of the DCR, according to embodiments of the disclosure. In some embodiments, the trend of the DCR may be determined based on determining/calculating the DCR at each time point, corresponding to different charging lengths within the predetermined time period, starting from the charging start time (e.g., t ₀) . For example, as shown in FIG. 5, a DCR corresponding to a charging length of t may be calculated according to equation (1) .

Consistent with the embodiments disclosed herein, in some embodiments, the different charging lengths may be chosen as 10 seconds (10s hereafter) , 30s and 60s. Server 150 may calculate multiple DCRs corresponding to a charging length of 10s (i.e., DCR-10s) in step S402, multiple DCRs corresponding to a charging length of 30s (i.e., DCR-30s) in step S404 and multiple DCRs corresponding to a charging length of 60s (i.e., DCR-60s) in step S406 starting from different charging start time (i.e., different t ₀s) . For instance, as shown in FIG. 6, the predetermined time period may be three months, and server 150 may calculate the DCR-10 35 times during the period in step S402. Although FIG. 6 shows the 35 different time points may distribute evenly over the period of three months, it is contemplated that time points could also be selected unevenly in the period. Similar determination method may be applied to acquiring the multiple DCR-30s in step 404 and acquiring the multiple DCR-60s in step S406. In step S408, server 150 may then determine the DCR trend over the 35-day period by comparing the relative magnitude of the multiple DCR-10s, DCR-30s and DCR-60s.

Back to method 300. In step S316, server 150 may determine a health evaluation result of the battery system based on the DCR trend. Back to the specific example mentioned above, if the DCR-10s are constantly smaller than the DCR-30s which are constantly smaller than the DCR-60s during the predetermined time period, in step S318, a health evaluation result indicating that there is no malfunction of the battery system during the predetermined time period may be generated and transmitted to display device 130. For example, FIG. 7 illustrates an exemplary trend of the DRC (i.e., the relative relationship among DCR ₁₀s, the DCR ₃₀s and the DCR ₆₀s) of a healthy battery system, according to embodiments of the disclosure. As illustrated in FIG. 7, magnitudes of the DCR ₁₀, the DCR ₃₀ and the DCR ₆₀ of each time point during the predetermined time period are in a relative order, e.g., DCR _10 ＜ DCR _30 ＜ DCR ₆₀. Accordingly, server 150 may generate an evaluation result indicating that no malfunction happens to the battery system within the predetermined time period.

Back to step S316, if the DCR trend is not normal, for example, magnitudes of the DCR ₁₀, the DCR ₃₀ and the DCR ₆₀ of each time point are not in a relative magnitude order, e.g., DCR _10 ＜ DCR _30 ＜ DCR ₆₀, in step S320, server 150 may generate an evaluation result indicating that the battery system is malfunctioned. Health evaluation unit 214 may transmit the evaluation result to display device 130 through interface 202.

Another aspect of the disclosure is directed to a non-transitory computer-readable medium storing instructions which, when executed, cause one or more processors to perform the methods, as discussed above. The computer-readable medium may include volatile or non-volatile, magnetic, semiconductor, tape, optical, removable, non-removable, or other types of computer-readable medium or computer-readable storage devices. For example, the computer-readable medium may be the storage device or the memory module having the computer instructions stored thereon, as disclosed. In some embodiments, the computer-readable medium may be a disc or a flash drive having the computer instructions stored thereon.

It will be apparent to those skilled in the art that various modifications and variations can be made to the disclosed system and related methods. Other embodiments will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed system and related methods.

It is intended that the specification and examples be considered as exemplary only, with a true scope being indicated by the following claims and their equivalents.

Claims

A system for performing a health evaluation of a battery system, comprising:

a communication interface configured to receive operating data of the battery system acquired from one or more vehicles; and

at least one processor coupled to the communication interface and configured to:

determine a parameter indicative of a charging characteristic of the battery system based on the operating data;

determine a trend of the parameter over a predetermined time period; and

evaluate a health of the battery system based on the trend of the parameter.
The system of claim 1, wherein to determine the trend of the parameter, the at least one processor is further configured to determine the parameter at different time points within the predetermined time period.
The system of claim 2, wherein to determine the trend of the parameter, the at least one processor is further configured to, for each time point:

determine a first value of the parameter corresponding to a first charging length since a charging start time; and

determine a second value of the parameter corresponding to a second charging length since the charging start time, the second charging length being longer than the first charging time length.
The system of claim 3, wherein the trend is indicative of a relative magnitude of the first value compared to the second value over the predetermined time period.
The system of claim 4, wherein the at least one process is configured to determine that the battery system is healthy when the first value is consistently smaller than the second value over the predetermined time period.
The system of claim 3, the at least one processor is further configured to, for each time point, determine a third value of the parameter corresponding to a third charging length since the charging start time, the third charging length being longer than the second charging length,

wherein the trend is indicative of a relative magnitude among the first value, the second value, and the third value, over the predetermined time period.
The system of claim 1, wherein the at least one processor is further configured to identify data segments in the operating data that characterize charging of the battery system, and determine the parameter based on the identified data segments.
The system of claim 7, wherein each data segment comprises a first data point corresponding to an uncharged status and a second data point corresponding to a charging status.
The system of claim 1, wherein the operating data comprises data pairs each comprising at least one of an operating voltage and a corresponding operating current of the battery system.
The system of claim 1, wherein the parameter is determined based on dividing an increment in an operating voltage of the battery system over a charging period by an operating current used for charging the battery system.
A method for performing a health evaluation of a battery system, comprising:

receiving operating data of the battery system acquired from one or more vehicles;

determining a parameter indicative of a charging characteristic of the battery system based on the operating data;

determining a trend of the parameter over a predetermined time period; and

evaluating a health of the battery system based on the trend of the parameter.
The method of claim 11, further comprising determining the parameter at different time points within the predetermined time period.
The method of claim 12, further comprising:

determining a first value of the parameter corresponding to a first charging length since a charging start time; and

determining a second value of the parameter corresponding to a second charging length since the charging start time, the second charging length being longer than the first charging time length.
The method of claim 13, wherein the trend is indicative of a relative magnitude of the first value compared to the second value over the predetermined time period.
The method of claim 14, further comprising determining that the battery system is healthy when the first value is consistently smaller than the second value over the predetermined time period.
The method of claim 13, further comprising:

for each time point, determine a third value of the parameter corresponding to a third charging length since the charging start time, the third charging length being longer than the second charging length, wherein the trend is indicative of a relative magnitude among the first value, the second value, and the third value, over the predetermined time period.
The method of claim 11 further comprising:

identifying data segments in the operating data that characterize charging of the battery system; and

determining the parameter based on the identified data segments.
The method of claim 17, wherein each data segment comprises a first data point corresponding to an uncharged status and a second data point corresponding to a charging status.
The method of claim 11, wherein the operating data comprises data pairs each comprising at least one of an operating voltage and a corresponding operating current of the battery system.
A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method for health evaluation of a battery system, the method comprising:

determining a parameter indicative of a charging characteristic of the battery system based on operating data of the battery system acquired from one or more vehicles;

determining a trend of the parameter over a predetermined time period; and

evaluating a health of the battery system based on the trend of the parameter.