CN118894004A - Battery charging control method, storage medium, electronic device and battery replacement station - Google Patents

Abstract

The application relates to the technical field of power exchange stations, in particular to a battery charging control method, a storage medium, electronic equipment and a power exchange station. The application aims to solve the problems of influence on user experience and insufficient optimization degree of the conventional battery charging planning algorithm. To this end, the present application provides a battery charge control method, a storage medium, an electronic device, and a battery exchange station, the battery charge control method including: acquiring probability distribution of arrival time periods of orders; determining the charging power of the battery in each period based on the probability distribution of the arrival period and a preset charging power planning model; the battery charge is controlled based on the charge power for each period. Under the condition of adopting the technical scheme, the probability that the vehicle arrives at the battery replacement station can be comprehensively considered, so that a battery charging planning algorithm is optimized, an optimal charging planning result in a desired sense is obtained, and then battery charging is controlled.

Description

Battery charging control method, storage medium, electronic device and battery replacement station

Technical Field

The present invention relates to the technical field of battery swap stations, and in particular to a battery charging control method, a storage medium, an electronic device and a battery swap station.

Background Art

Reasonable planning of battery charging in battery swap stations can help reduce charging costs, increase revenue, improve charging efficiency, and maintain battery health. To this end, we can continuously train and learn based on historical order data to obtain order prediction results, such as the time when the next order will occur, and then reasonably plan battery charging in battery swap stations based on order prediction results.

Traditional battery charging planning algorithms usually estimate the time when a battery swap order will occur in the future, thereby planning the battery charging behavior. However, this battery charging planning algorithm has two problems. On the one hand, if the actual arrival time of the order is earlier than the estimated arrival time, the user will have to wait for the battery to be fully charged, which will affect the user experience and the evaluation of the battery swap station; on the other hand, if the actual arrival time of the order is later than the estimated arrival time, it will waste optimization space.

Summary of the invention

In order to solve at least one of the above problems in the prior art, that is, to solve the problems of the existing battery charging planning algorithm affecting user experience and insufficient optimization, the present application provides a battery charging control method, which includes:

Get the probability distribution of the order's arrival time period;

Determining the charging power of the battery in each time period based on the probability distribution of the arrival time period and a preset charging power planning model;

Based on the charging power in each time period, controlling the charging of the battery;

The charging power planning model is used to characterize the corresponding relationship between the time period probability distribution and the charging power in each time period.

When the above technical solution is adopted, the probability of the vehicle arriving at the battery swap station can be fully considered, and then the battery charging planning algorithm can be optimized to obtain the optimal charging planning result in the desired sense, and then the battery charging can be controlled to ensure the user experience.

In the preferred technical solution of the above-mentioned battery charging control method, the determining of the charging power of the battery in each time period based on the arrival time period probability distribution and the preset charging power planning model further includes:

Based on the arrival time period probability distribution and a mixed integer linear programming model, the charging power of the battery in each time period is determined.

When the above technical solution is adopted, solving the integer charging power through a mixed integer linear programming model is conducive to more accurately controlling factors such as current and voltage during the charging process.

In the preferred technical solution of the above battery charging control method, the mixed integer linear programming model includes a charging cost minimization objective function and constraints, and the control method further includes:

The charging power of the battery in each time period is determined based on the arrival time period probability distribution, the minimization charging cost objective function and the constraint condition.

When the above technical solution is adopted, the revenue of the battery swap station can be guaranteed by minimizing the charging cost, and the user experience and charging cost expectations can be optimized.

In the preferred technical solution of the above-mentioned battery charging control method, the objective function of minimizing the charging cost takes the charging gear as the optimization variable, and the control method further includes:

Determining the charging gear of the battery in each time period based on the arrival time period probability distribution, the minimization charging cost objective function and the constraint condition;

Based on the corresponding relationship between the charging gear and the charging power, the charging power of the battery in each time period is determined.

In the preferred technical solution of the above-mentioned battery charging control method, the objective function of minimizing charging cost includes order rejection cost, order electricity cost, full-charge retention cost, charging start-stop cost and fast-then-slow auxiliary cost.

When adopting the above technical scheme, while ensuring the revenue of the battery swap station, by considering the order rejection cost, it is possible to ensure that there are replaceable batteries in the battery swap station when the vehicle actually arrives. By considering the full-charge retention cost, the storage time of fully charged batteries in the battery swap station can be reduced, thereby improving the safety in the battery swap station. By considering the charging start and stop cost and the auxiliary cost of first fast and then slow, the charging process can be guaranteed to be continuous as much as possible, preventing battery overcharging, thereby ensuring battery performance and delaying battery degradation.

In the preferred technical solution of the above battery charging control method, the order rejection cost is determined based on the following method:

An order rejection cost is determined based on the arrival time period probability distribution.

In the preferred technical solution of the above battery charging control method, the determining of the order rejection cost based on the arrival time period probability distribution further includes:

Determine order waiting penalty based on the number of order waiting periods;

The order rejection cost is determined based on the arrival period probability distribution and the order waiting penalty.

When the above technical solution is adopted, in order to minimize the charging cost, the number of order waiting periods can be reduced as much as possible, ensuring that the waiting time required for the vehicle to actually arrive at the battery swap station for battery swapping is reduced, thereby improving the user's battery swapping experience.

In the preferred technical solution of the above battery charging control method, the order electricity cost is determined based on the following method:

The electricity cost of the order is determined based on the electricity price in each time period.

When the above technical solution is adopted, the charging cost can be reduced by considering the peak and valley of electricity prices.

In the preferred technical solution of the above battery charging control method, the determining of the order electricity cost based on the electricity price in each time period further includes:

Based on the corresponding relationship between the charging power and the charging side degree in a single period, determine the charging side degree in each period;

The order electricity cost is determined based on the electricity price in each time period and the charging-side power consumption in each time period.

When the above technical solution is adopted, due to the existence of charging efficiency, the actual power consumption can be determined by calculating the electricity fee through the charging degree, thereby accurately determining the electricity cost of the order.

In the preferred technical solution of the above battery charging control method, the full-charge retention cost is determined based on the following method:

Determining the full-charge retention cost based on the number of battery full-charge time periods;

and/or

The charging start-stop cost is determined based on the following method:

The charging start and stop cost is determined based on the number of charging state changes.

When the above technical solution is adopted, in order to minimize the charging cost, the storage time of fully charged batteries in the battery swap station and the number of starts and stops during the battery charging process can be reduced, thereby ensuring the safety in the battery swap station and delaying battery degradation.

In the preferred technical solution of the above battery charging control method, the first fast then slow auxiliary cost is determined based on the following method:

The fast-then-slow assistance cost is determined based on each time period and the number of battery energy level increases within each time period.

In the preferred technical solution of the above-mentioned battery charging control method, the determination of the charging power of the battery in each time period based on the probability distribution of the arrival time period, the minimization of the charging cost objective function and the constraint conditions further includes:

Determining the number of battery energy level increases of the battery in each time period based on the arrival time period probability distribution, the minimization charging cost objective function and the constraint conditions;

The charging power of the battery in each time period is determined based on the number of increases in the battery energy level in each time period.

When the above technical solution is adopted, there is a corresponding relationship between the number of battery energy level increases in a single time period and the charging power in the time period. In order to minimize the charging cost, the corresponding charging power decreases when the number of time periods increases, thereby ensuring that a large amount of electricity can be charged into the battery in a short time. When it is close to full charge, low-power charging can be used to protect the battery and delay battery degradation.

In the preferred technical solution of the above battery charging control method, the constraint condition includes a battery charge level constraint, and the control method further includes:

Determine the number of battery energy levels based on battery type and minimum charging power;

The battery charge level constraint is determined based on the number of battery energy levels.

When the above technical solution is adopted, the battery charge level can be accurately modeled by dividing the battery energy levels, so as to accurately determine the battery status, providing the premise and basis for optimizing the battery charging plan.

In the preferred technical solution of the above battery charging control method, the control method further includes:

Based on the sequence of the order and the charge levels of all batteries, the battery corresponding to the order is determined.

When the above technical solution is adopted, the most recent order can be matched with the battery with the highest current power, thereby improving the battery replacement efficiency in the battery replacement station.

The present application also provides a computer-readable storage medium, in which a plurality of program codes are stored, and the program codes are suitable for being loaded and run by a processor to execute any of the above-mentioned battery charging control methods.

The present application also provides an electronic device, comprising 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 battery charging control methods.

The present application also provides a battery swap station, which includes the electronic device described above.

When the above technical solution is adopted, the battery swap station can control the battery charging by optimizing the battery charging planning algorithm after fully considering the probability of the vehicle arriving at the battery swap station.

Solution 1. A battery charging control method, comprising:

Get the probability distribution of the order's arrival time period;

Determining the charging power of the battery in each time period based on the probability distribution of the arrival time period and a preset charging power planning model;

Based on the charging power in each time period, controlling the charging of the battery;

The charging power planning model is used to characterize the corresponding relationship between the time period probability distribution and the charging power in each time period.

Solution 2. According to the battery charging control method of Solution 1, the determining of the charging power of the battery in each time period based on the probability distribution of the arrival time period and the preset charging power planning model further comprises:

Based on the arrival time period probability distribution and a mixed integer linear programming model, the charging power of the battery in each time period is determined.

Solution 3. According to the battery charging control method of Solution 2, the mixed integer linear programming model includes a charging cost minimization objective function and constraints, and the control method further includes:

The charging power of the battery in each time period is determined based on the arrival time period probability distribution, the minimization charging cost objective function and the constraint condition.

Solution 4. According to the battery charging control method of Solution 3, the charging cost minimization objective function uses the charging gear as the optimization variable, and the control method further includes:

Determining the charging gear of the battery in each time period based on the arrival time period probability distribution, the minimization charging cost objective function and the constraint condition;

Based on the corresponding relationship between the charging gear and the charging power, the charging power of the battery in each time period is determined.

Solution 5. According to the battery charging control method described in Solution 3, the objective function for minimizing charging cost includes order rejection cost, order electricity cost, full-charge retention cost, charging start-stop cost, and fast-then-slow auxiliary cost.

Solution 6. According to the battery charging control method of Solution 5, the order rejection cost is determined based on the following method:

An order rejection cost is determined based on the arrival time period probability distribution.

Solution 7. According to the battery charging control method of Solution 6, determining the order rejection cost based on the arrival time period probability distribution further comprises:

Determine order waiting penalty based on the number of order waiting periods;

The order rejection cost is determined based on the arrival period probability distribution and the order waiting penalty.

Solution 8. According to the battery charging control method of Solution 5, the order electricity cost is determined based on the following method:

The electricity cost of the order is determined based on the electricity price in each time period.

Solution 9. According to the battery charging control method of Solution 8, determining the order electricity cost based on the electricity price of each time period further includes:

Based on the corresponding relationship between the charging power and the charging side degree in a single period, determine the charging side degree in each period;

The order electricity cost is determined based on the electricity price in each time period and the charging-side power consumption in each time period.

Solution 10. According to the battery charging control method of Solution 5, the full-charge retention cost is determined based on the following method:

Determining the full-charge retention cost based on the number of battery full-charge time periods;

and/or

The charging start-stop cost is determined based on the following method:

The charging start and stop cost is determined based on the number of charging state changes.

Solution 11. According to the battery charging control method of Solution 5, the first fast then slow auxiliary cost is determined based on the following method:

The fast-then-slow assistance cost is determined based on each time period and the number of battery energy level increases within each time period.

Solution 12. According to the battery charging control method of Solution 11, determining the charging power of the battery in each time period based on the probability distribution of the arrival time period, the minimization of the charging cost objective function and the constraint conditions further includes:

Determining the number of battery energy level increases of the battery in each time period based on the arrival time period probability distribution, the minimization charging cost objective function and the constraint conditions;

The charging power of the battery in each time period is determined based on the number of increases in the battery energy level in each time period.

Solution 13. According to the battery charging control method of Solution 3, the constraint condition includes a battery charge level constraint, and the control method further includes:

Determine the number of battery energy levels based on battery type and minimum charging power;

The battery charge level constraint is determined based on the number of battery energy levels.

Solution 14. The battery charging control method according to Solution 1, further comprising:

Based on the sequence of the order and the charge levels of all batteries, the battery corresponding to the order is determined.

Solution 15. A computer-readable storage medium storing a plurality of program codes, wherein the program codes are suitable for being loaded and executed by a processor to execute the battery charging control method according to any one of Solutions 1 to 14.

Solution 16. An electronic device comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes.

The program code is suitable for being loaded and executed by the processor to execute the battery charging control method described in any one of Schemes 1 to 14.

Option 17. A battery swap station, comprising the electronic device described in Option 16.

BRIEF DESCRIPTION OF THE DRAWINGS

The battery charging control method of the present application is described below with reference to the accompanying drawings. In the accompanying drawings:

FIG1 is a flowchart of the main steps of a battery charging control method according to an embodiment of the present application;

FIG2 is a flowchart of the steps of determining the charging power of a battery in each time period based on the probability distribution of the arrival time period, the minimization of the charging cost objective function and the constraint conditions according to one embodiment of the present application;

FIG3 is a schematic diagram of the main structure of an electronic device according to an embodiment of the present application.

Reference numerals list

301. Processor; 302. Storage device.

DETAILED DESCRIPTION

The preferred embodiments of the present application 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 application and are not intended to limit the scope of protection of the present application. For example, although the present embodiment determines the charging power of the battery in each time period in combination with the number of battery energy level increases, this is not intended to limit the scope of protection of the present application. Without departing from the principles of the present application, those skilled in the art can directly determine the corresponding charging power by the charging gear of the battery in each time period.

In the description of this application, "processor" may include hardware, software or a combination of the two. 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. Computer-readable storage media include any suitable medium 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.

As described in the background technology, reasonable planning of battery charging in battery swap stations helps reduce charging costs, increase battery swap station revenue, improve charging efficiency, and maintain battery health. To this end, we can continuously train and learn based on historical order data to obtain order prediction results such as the time when the next order will occur, and then reasonably plan battery charging in battery swap stations based on order prediction results.

Traditional battery charging planning algorithms usually estimate the time when a battery swap order will occur in the future, thereby planning the battery charging behavior. However, this battery charging planning algorithm has two problems. On the one hand, if the actual arrival time of the order is earlier than the estimated arrival time, the user will have to wait for the battery to be fully charged, which will affect the user experience and the evaluation of the battery swap station; on the other hand, if the actual arrival time of the order is later than the estimated arrival time, it will waste optimization space.

The battery charging control method of the present application is described below with reference to FIG1 , which is a flow chart of the main steps of the battery charging control method of one embodiment of the present application.

As shown in FIG1 , in order to solve the problems of the existing battery charging planning algorithm affecting user experience and insufficient optimization, the battery charging control method in the embodiment of the present application mainly includes the following steps:

S101, obtaining the probability distribution of the arrival time of the order;

S102, determining the charging power of the battery in each time period based on the probability distribution of the arrival time period and a preset charging power planning model;

S103, controlling battery charging based on the charging power in each time period;

Among them, the charging power planning model is used to characterize the corresponding relationship between the time period probability distribution and the charging power in each time period.

In this embodiment, the probability of a certain order occurring at various times can be predicted first through historical order data, that is, the probability of a vehicle arriving at a battery swap station for battery swapping at a certain time can be predicted, thereby obtaining the probability distribution of the arrival time period of the order. Then, the probability distribution of the arrival time period can be input into the charging power planning model to obtain the charging power of a single battery in each time period, and then the charging module can control the charging of the corresponding battery with the obtained charging power.

Through the method described in steps S101 to S103 above, the probability of the vehicle arriving at the battery swap station can be fully considered, and the battery charging planning algorithm can be optimized to obtain the optimal charging planning result in the desired sense, and then the battery charging can be controlled to ensure user experience.

The specific implementation methods of the present application are described in detail below.

In some implementations, the battery corresponding to each order is determined based on:

Based on the order sequence and the charge levels of all batteries, the battery corresponding to the order is determined.

In this embodiment, the next predicted order can be allocated to the corresponding battery according to the order of order arrival and the charge level of the battery, so as to obtain the charging power for each time period of the battery based on the battery and the probability distribution of the arrival time period and the preset charging power planning model, and then control the battery to charge. For example, for an order that is about to arrive, the order is matched to the battery with the highest current charge level, that is, the highest power, and then the charging power for the next time period is determined according to the charge state of the battery. Of course, this kind of order and battery matching method is not static. For example, if there are differences in battery models, the order and the battery need to be matched by comprehensively considering the battery model and the charge level of the battery; for another example, if the battery swap station has set a minimum value, that is, how many fully charged batteries are required in the battery swap station, when the minimum value is not reached, the battery with the highest current charge level can also be controlled to be fully charged as soon as possible to meet the demand. At this time, the order is matched to the battery with the lower current charge level, and then the charging power for the next time period is determined according to the state of the battery.

In some embodiments, step S102 “determining the charging power of the battery in each time period based on the probability distribution of the arrival time period and a preset charging power planning model” further includes:

Based on the probability distribution of arrival time periods and the mixed integer linear programming model, the charging power of the battery in each time period is determined.

In this embodiment, the mixed integer linear programming model can be used to solve the integer charging power. Controlling the battery charging by the integer charging power is conducive to more accurately controlling factors such as the current and voltage during the charging process. Of course, the setting of the mixed integer linear programming model is not necessary. Those skilled in the art can select a target model such as a mixed integer nonlinear programming model, a multi-objective optimization model, etc. according to needs, as long as the arrival probability distribution of the order can be comprehensively considered.

In one possible implementation, the mixed integer linear programming model includes a charging cost minimization objective function and constraints, and the step of "determining the charging power of the battery in each time period based on the arrival time period probability distribution and the mixed integer linear programming model" further includes:

Based on the probability distribution of arrival time, the objective function of minimizing charging cost and constraints, the charging power of the battery in each time period is determined. The following is an introduction to each of the above parts.

1. Minimize the charging cost objective function

The following first introduces the objective function of minimizing the charging cost of the present application. In a possible implementation, the objective function of minimizing the charging cost is:

in, is the order rejection cost, is the order electricity cost, λ ₂ ∑ _t L _t is the full-charge retention cost, λ ₃ ∑ _t U _t is the charging start-stop cost, is the auxiliary cost of fast first and slow later, t is the time period, and k is the charging gear.

The five types of costs are introduced below:

1.1 Order rejection cost

is the order rejection cost, where p _t (real constant) is the probability of an order occurring in period t, and represents the probability corresponding to period t in the probability distribution of the order’s arrival period. There exists ∑ _t p _t = 1, (real variable) is the order waiting penalty, which means the penalty for the battery not being fully charged when the order arrives if the order occurs in period t.

In this embodiment, the order rejection cost is determined based on the following method:

Determine order waiting penalty based on the number of order waiting periods;

Determine the order rejection cost based on the arrival time probability distribution and order waiting penalty.

It should be explained that the order rejection cost is the penalty cost set when the battery is not fully charged when the order arrives. Since the battery is not fully charged, the order transaction may fail, or the user's waiting time is too long, which affects the evaluation of the battery swap station. Therefore, the setting of the order rejection cost in the objective function of minimizing the charging cost can minimize the user's waiting time. In addition, the single time period can be set to 5min, 10min, etc. For example, the business hours are 10h and the single time period is 10min, and the total number of time periods is 60.

In this embodiment, the final order waiting penalty can be determined by adding a unit order waiting penalty for each waiting period, wherein the order waiting penalty is measured in yuan, and the order rejection cost in the desired sense can be obtained by summing the product of the order arrival probability in each period and the order waiting penalty, thereby achieving a comprehensive consideration of the user's waiting time for battery replacement. Of course, the above-mentioned single period and order waiting penalty settings are not static, and those skilled in the art can change them according to needs. In addition, those skilled in the art can change the weight of the order rejection cost in the entire minimization of charging cost objective function by changing the setting of the unit order waiting penalty.

1.2 Order electricity cost

is the electricity cost of the order, where (real constant) is the electricity price in period t, P _k (real constant) is the charging degree in a single period when the battery is in gear k, It is a 0-1 variable. If the battery is in the k position during the t period, it takes 1, otherwise it takes 0.

In this embodiment, the order electricity cost is determined based on the following method:

Determine the electricity cost of the order based on the electricity price in each time period.

Specifically, based on the corresponding relationship between the charging power and the charging-side kilowatt-hour in a single time period, the charging-side kilowatt-hour in each time period is determined; based on the electricity price in each time period and the charging-side kilowatt-hour in each time period, the order electricity cost is determined.

It should be explained that the charging gear k is used to represent the charging power. In this embodiment, there is a corresponding relationship between the charging gear and the charging power. For example, the charging power corresponding to gear 1 is 10kw, gear 2 is 20kw, etc. The order electricity cost is the electricity cost in the power grid generated during the charging process. is the degree of the charging side in the t period, and the meaning of the formula is roughly the degree of the charging side that is increased by the gear position of the battery in the t period. During the battery charging process, the battery is generally charged by an AC/DC module. Due to the existence of charging efficiency, the amount of electricity discharged by the AC/DC module cannot be completely absorbed by the battery. In this embodiment, the degree of the charging side refers to the degree generated on the charging module side, which represents the actual amount of electricity discharged on the charging module side. The charging efficiency can be taken into consideration when determining the electricity fee by the degree of the charging side, so as to accurately determine the electricity fee cost in the power grid generated during the charging process. Among them, the specific values of the charging efficiency and the degree of the charging side can be determined by experiments. After determining the charging power, that is, the charging power on the battery side, the discharge power on the charging module side can be further confirmed by the charging efficiency.

In the case of adopting the above technical solution, the actual electricity cost incurred in the power grid during the battery charging process can be obtained by summing the product of the electricity price and the charging side degree in each time period. The setting of the order electricity cost can be included in the minimization of the charging cost objective function so that the impact of the peak and valley of electricity prices on the charging cost can be comprehensively considered. In order to minimize the charging cost, a charging strategy of charging more in the period of high electricity prices and less in the period of low electricity prices can be implemented. In addition, it is not necessary to confirm the setting of the order electricity cost by the charging side degree. For example, those skilled in the art can regard the discharge power on the charging module side as the charging power on the battery side, but considering the accurate determination of the order electricity cost, confirming the order electricity cost by the charging side degree is a better choice. In addition, the setting of the above charging gear is not fixed, and those skilled in the art can also change the corresponding relationship between the charging gear and the charging power according to needs.

1.3 Fully charged retention cost

λ ₂ Σ _t L _t is the full-charge retention cost, where λ ₂ (real constant, unit: yuan/period) is the battery full-charge penalty, and L _t is a 0-1 variable, which takes the value of 1 if the battery is fully charged within the t period, otherwise it takes the value of 0.

In this embodiment, the full-charge retention cost is determined based on the following method:

Based on the number of battery full-charge periods, the full-charge retention cost is determined.

It should be explained that the full-charge retention cost is the penalty cost set for fully charging the battery in advance before the order arrives, and ∑ _t L _t is the number of battery full-charge time periods. In this embodiment, the full-charge retention cost can be obtained by summing the product of the battery full-charge penalty and the number of battery full-charge time periods. On the one hand, the increase in the number of battery full-charge time periods will affect the battery replacement efficiency and battery replacement revenue of the battery replacement station. On the other hand, the increase in the number of fully charged batteries in the battery replacement station theoretically increases the risk of fire or explosion, which in turn leads to reduced safety in the battery replacement station. Therefore, the setting of the full-charge retention cost in the objective function of minimizing the charging cost includes that the setting of the full-charge retention cost can minimize the storage time of the fully charged battery, improve the battery replacement efficiency, battery replacement revenue and safety of the battery replacement station. Of course, those skilled in the art can change the weight of the order rejection cost in the entire minimization of the charging cost objective function by changing λ ₂ , that is, the value of the battery full-charge penalty.

1.4 Charging start-stop cost

λ ₃ Σ _t U _t is the charging start-stop cost, where λ ₃ (real constant, unit: yuan/time) is the battery charging start-stop penalty, and U _t is a 0-1 variable, which takes the value of 1 if the battery is in a charging state within the t period, otherwise it takes the value of 0.

In this embodiment, the charging start-stop cost is determined based on the following method:

Determine the charging start and stop cost based on the number of charging state changes.

It should be explained that the charging start-stop cost is the penalty cost set for the battery charging state change caused by the battery start-stop, and ∑ _t U _t is the number of charging state changes. In this embodiment, the charging start-stop cost is obtained by summing the product of the battery charging start-stop penalty and the number of charging state changes. Since frequent starts and stops during battery charging may have an adverse effect on the battery life, that is, accelerate battery decay. Therefore, the setting of the charging start-stop cost in the objective function of minimizing the charging cost includes minimizing the number of charging state changes, delaying battery decay, and ensuring battery safety performance. Of course, those skilled in the art can change the weight of the charging start-stop cost in the entire minimizing charging cost objective function by changing λ ₃ , that is, the value of the battery charging start-stop penalty.

1.5 Fast first then slow auxiliary cost

is the auxiliary cost of charging fast first and then slowing down, where λ ₄ (real constant, unit: yuan/time segment) is the penalty of charging the battery fast first and then slowing down, G _k (integer constant) is the number of energy level increases in a single time period when the battery is in gear k, It is a 0-1 variable. If the battery is in position k during time period t, it takes the value 1, otherwise it takes the value 0.

In this embodiment, the first fast then slow auxiliary cost is determined based on the following method:

Based on each time period and the number of battery energy level increases within each time period, the fast-then-slow assistance cost is determined.

It should be explained that the auxiliary cost of charging the battery first quickly and then slowly is the penalty cost set for charging the battery first quickly and then slowly. is the number of battery energy level increases caused by the gear position of the battery in the time period t, where the number of energy level increases in a single time period has a corresponding relationship with the charging power. The first fast and then slow auxiliary cost is obtained by summing the time period and the product of the battery energy level increase in the time period, which is conducive to limiting the charging power in different periods of the charging process. For example, in the later stage of the charging process, the larger the value of t is, the greater the charging cost is, The smaller the value of is, the smaller the charging power corresponding to the t period is. To ensure that the battery is fully charged, the smaller the value of t is required to be. The larger the value of is, the faster the battery is charged first and then the slower the battery is charged, and the slower the battery degradation is achieved. In addition, those skilled in the art can change the weight of the faster-then-slow auxiliary cost in the entire minimization charging cost objective function by changing λ _4, i.e., the value of the battery charging faster-then-slow penalty.

In a possible implementation, the number of battery energy levels is first determined, and then the number of battery energy level increases is determined based on the number of battery energy levels, wherein the number of battery energy levels is determined based on the following method:

Determine the number of battery energy levels based on the battery type and minimum charging power.

In this embodiment, a single time period can be set to 5 minutes, and the minimum charging power is set to 10kw. For a battery with a specification of 150 degrees, when charging at the minimum power in a single time period, the battery charging degree is 5/6 degrees. Taking this charging degree as the charging degree corresponding to a single energy level, for this type of battery, the battery energy level number is 150/(5/6)=180. When the charging power is 10kw, the corresponding battery energy level increase number is 1, and when the charging power is 20kw, the corresponding battery energy level increase number is 2. By analogy, the battery energy level increase number corresponding to all charging powers in a single time period can be obtained.

2. Constraints

The following introduces each constraint condition in combination with the objective function of minimizing charging cost and battery energy level:

2.1 Battery energy level transition constraints

Where _Bt (integer variable) is the battery energy level at the beginning of period t, is the number of battery energy level increases caused by the gear position in the t period, G _k (integer constant) is the number of energy level increases in a single period when the battery is in the k gear position, It is a 0-1 variable. If the battery is in the k position during the t period, it takes 1, otherwise it takes 0.

2.2 Battery initial energy level constraints

Bt _＝1 ＝ _B0 (2)

Wherein, B ₀ (integer constant) is the initial energy level of the battery.

2.3 Battery energy level upper and lower limit constraints

1≤B _t ≤S+1(3)

Wherein, B _t (integer variable) is the battery energy level at the beginning of period t, and S is the number of battery energy levels - 1.

2.4 Battery Fully Charged State Constraints

B _t -S≤L _t ≤B _t /(S+1) (5)

Wherein, _Lt is a 0-1 variable, which takes the value of 1 if the battery is fully charged during the period t, and takes the value of 0 otherwise.

It should be explained that describing the battery charging process by the energy level of the battery and the number of battery energy level increases is conducive to accurately modeling the battery SOC (state of charge), so as to accurately determine the battery charge level, that is, the remaining battery power, and provide the premise and basis for optimizing the battery charging model. In this embodiment, the battery charge level constraints can be determined based on the number of battery energy levels, wherein the battery charge level constraints include the upper and lower limit constraints of the battery energy level and the battery full charge state constraints. When the above technical solution is adopted, it is conducive to judging whether the battery is full from the perspective of the battery energy level. For example, when the number of battery energy levels is 180, the lowest energy level of the battery is 1, and the highest energy level is 180. When the battery is at the highest energy level, it is determined that the battery is in a fully charged state. At this time, _Lt takes 1, otherwise it takes 0.

2.5 Battery charging status constraints

Where _Yt is a 0-1 variable, which takes the value 1 if the battery is charging during the period t, and takes the value 0 otherwise. It is a 0-1 variable. If the battery is in the k position during the t period, it takes 1, otherwise it takes 0.

2.6 Battery charging state change constraints

U _t ≥ Y _t -Y _t-1 (6)

U _t ≥ Y _t-1 -Y _t (7)

Wherein, _Ut is a 0-1 variable, which takes the value of 1 if the battery charging state changes from period t-1 to period t, and takes the value of 0 otherwise.

2.7 Order waiting penalty constraints

in, is a 0-1 variable. If the order arrives in time period t and the battery is fully charged, it takes 0, otherwise it takes 1. T (real constant) is the total number of time periods. L _t is a 0-1 variable. If the battery is fully charged in time period t, it takes 1, otherwise it takes 0. M (real constant) takes Tλ ₁ , λ ₁ (real constant, yuan/time period) is the unit order waiting penalty. (real variable) is the order waiting penalty.

It should be explained that the above constraint conditions are only a method for setting constraint conditions of an embodiment. Those skilled in the art can constrain some variables according to other equations or inequalities, for example, For example, by It can also play a role in For example, the minimum value is 0 and the maximum value is Tλ ₁ .

It is understood by those skilled in the art that the setting of the objective function of minimizing the charging cost is not static. In an alternative implementation, the mixed integer linear programming model may include the objective function of maximizing the revenue of the battery swap station and its corresponding constraints. In addition, in a possible implementation, the value of λ ₁ (unit order waiting penalty) is 1000, the value of λ ₂ (battery full charge penalty) and λ ₃ (battery charging start and stop penalty) are both 0.1, and the value of λ ₄ (battery charging fast first and then slow penalty) is 0.0005, but the value setting is not fixed. Those skilled in the art can change it according to the needs to adjust the weight of each item, or remove the influence of this item by setting λ ₂ (battery full charge penalty), λ ₃ (battery charging start and stop penalty) or λ ₄ (battery charging fast first and then slow penalty) to 0. In addition, those skilled in the art can change the specific calculation method of each cost according to the needs, as long as it does not affect the normal realization of the effect of minimizing the charging cost.

In some embodiments, the determined arrival probability distribution of the order is substituted into the objective function of minimizing the charging cost, and is solved with equations (1)-(10) as constraints to determine the charging power of the battery in each time period.

Please refer to Figure 2 below, which is a flowchart of the steps of determining the charging power of the battery in each time period based on the probability distribution of the arrival time period, the minimization of the charging cost objective function and the constraints of an embodiment of the present application.

As shown in FIG2 , in a possible implementation manner, the charging power of the battery in each time period is determined by the following steps:

S201, determining the number of battery energy level increases of the battery in each time period based on the probability distribution of the arrival time period, the objective function of minimizing the charging cost and the constraint conditions;

S202, determining the charging power of the battery in each time period based on the number of battery energy level increases in each time period.

In this embodiment, combined with the above-mentioned charging gear and energy level division method, when the charging gear is gear 1, the charging power is 10kw, and the corresponding battery energy level increase number in a single period is 1; when the charging gear is gear 2, the charging power is 20kw, and the corresponding battery energy level increase number in a single period is 2. Therefore, it can be concluded that the battery energy level increase number in a single period has a corresponding relationship with the gear position, that is, the two are numerically the same. Therefore, when solving the charging power of the battery in each period, the battery energy level increase number in each period can be directly output, and this can be regarded as the charging gear in the corresponding period, and then the battery can be controlled to charge with the charging power corresponding to the charging gear. For example, directly outputting the energy level of each period Its value is equivalent to the charging gear in each time period. The charging power in each time period is determined according to the corresponding relationship between the charging gear and the charging power.

However, the above method of determining the charging power of the battery in each time period is not necessary. In another possible implementation, the charging cost minimization objective function takes the charging gear k as the optimization variable. In this case, the charging power of the battery in each time period can be determined based on the following steps:

Determine the charging level of the battery in each time period based on the probability distribution of the arrival period, the objective function of minimizing the charging cost and the constraints;

Based on the corresponding relationship between the charging gear and the charging power, the charging power of the battery in each time period is determined.

In this embodiment, when solving the charging power of the battery in each time period, the optimal charging gear for each time period can be directly output. For example, when the auxiliary cost of first fast and then slow is removed from the objective function of minimizing the charging cost, the number of battery energy level increases in each time period cannot be directly output. In this case, the charging gear in each time period is directly determined. For example, based on To further determine the charging position of the battery during time period t.

It should be explained that, although the objective function of minimizing charging cost in this embodiment is set for the purpose of fully charging the battery before the order arrives, its setting is not necessary. For example, when the battery is insufficient, due to the pricing strategy of the battery swap station, the user may choose to first swap less batteries during the peak electricity price period to maintain vehicle operation, and then exchange for fully charged batteries during the low electricity price period to reduce the total battery swap price. Therefore, those skilled in the art can determine the state in which the battery can be replaced according to the needs, and this state corresponds to the fully charged state in this embodiment.

It is understood by those skilled in the art that the present invention implements all or part of the processes in the method of the above embodiment, and can also be completed by instructing the relevant hardware through a computer program, and the above computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above method embodiments can be implemented. Among them, the above computer program includes computer program code, and the above computer program code can be in source code form, object code form, executable file or some intermediate form. The above computer-readable storage medium can include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc. that can carry the above computer program code.

Furthermore, the present application also provides an electronic device. Refer to Figure 3, which is a schematic diagram of the main structure of an electronic device according to an embodiment of the present application. As shown in Figure 3, the electronic device in the embodiment of the present application mainly includes a processor 301 and a storage device 302. The storage device 302 can be configured to store a program for executing the battery charging control method of the above method embodiment, and the processor 301 can be configured to execute the program in the storage device 302, which includes but is not limited to the program for executing the battery charging control method of the above method embodiment. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application.

In some possible implementations of the present application, the electronic device may include multiple processors 301 and multiple storage devices 302. The program for executing the program-initiated control method of the above-mentioned method embodiment may be divided into multiple subprograms, and each subprogram may be loaded and run by the processor 301 to execute different steps of the program-initiated control method of the above-mentioned method embodiment. Specifically, each subprogram may be stored in different storage devices 302, and each processor 301 may be configured to execute the programs in one or more storage devices 302 to jointly implement the battery charging control method of the above-mentioned method embodiment, that is, each processor 301 executes different steps of the program-initiated control method of the above-mentioned method embodiment to jointly implement the battery charging control method of the above-mentioned method embodiment.

The above-mentioned multiple processors 301 may be processors deployed on the same device. For example, the above-mentioned electronic device may be a high-performance device composed of multiple processors, and the above-mentioned multiple processors 301 may be processors configured on the high-performance device. In addition, the above-mentioned multiple processors 301 may also be processors deployed on different devices. For example, the above-mentioned electronic device may be a server cluster, and the above-mentioned multiple processors 301 may be processors on different servers in the server cluster.

Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the battery charging control method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned battery charging control method. For ease 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 application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

Furthermore, the present invention also provides a battery swap station. In an embodiment of a battery swap station according to the present invention, the battery swap station may include the electronic device in the above electronic device embodiment.

When the above technical solution is adopted, the battery swap station can control the battery charging by optimizing the battery charging planning algorithm after fully considering the probability of the vehicle arriving at the battery swap station.

Those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims of the present application, any one of the claimed embodiments may be used in any combination.

So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those 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 application.

Claims (10)

1. A battery charge control method, characterized by comprising:

Acquiring probability distribution of arrival time periods of orders;

determining the charging power of the battery in each period based on the arrival period probability distribution and a preset charging power planning model;

Controlling the battery to charge based on the charging power of each period;

The charging power planning model is used for representing the corresponding relation between the time interval probability distribution and the charging power of each time interval.

2. The battery charge control method of claim 1, wherein determining the charge power of the battery at each time interval based on the arrival time interval probability distribution and a preset charge power planning model further comprises:

and determining the charging power of the battery in each period based on the arrival period probability distribution and a mixed integer linear programming model.

3. The battery charge control method of claim 2, wherein the mixed integer linear programming model includes a minimization of a charge cost objective function and constraints, the control method further comprising:

determining a charging power of the battery at each time period based on the arrival time period probability distribution, the minimized charging cost objective function, and the constraint condition.

4. The battery charge control method according to claim 3, wherein the minimized charge cost objective function uses a charge gear as an optimization variable, the control method further comprising:

determining a charging gear of the battery in each period based on the arrival period probability distribution, the minimized charging cost objective function and the constraint condition;

And determining the charging power of the battery in each period based on the corresponding relation between the charging gear and the charging power.

5. The battery charge control method of claim 3, wherein the minimized charge cost objective function comprises an order rejection cost, an order electric charge cost, a full charge retention cost, a charge start-stop cost, and a fast-then-slow auxiliary cost.

6. The battery charge control method according to claim 5, wherein the order rejection cost is determined based on:

And determining order rejection cost based on the arrival period probability distribution.

7. The battery charge control method of claim 6, wherein said determining an order rejection cost based on said arrival period probability distribution further comprises:

determining an order wait penalty based on the number of order wait periods;

The order rejection cost is determined based on the arrival period probability distribution and the order wait penalty.

8. The battery charge control method according to claim 5, wherein the order electric charge cost is determined based on:

And determining the electricity fee cost of the order based on the electricity prices of the time periods.

9. The battery charge control method according to claim 8, wherein said determining the order charge cost based on the electricity prices for each period further comprises:

Determining the charging side degree of each period based on the corresponding relation between the charging power and the charging side degree in the single period;

and determining the electricity charge cost of the order based on the electricity price of each period and the charging side degree of each period.

10. The battery charge control method according to claim 5, wherein the full-power retention cost is determined based on:

Determining the full-power retention cost based on the number of full-power periods of the battery;

And/or

The charging start-stop cost is determined based on the following mode:

and determining the charge start-stop cost based on the charge state change times.