A battery charging optimization method, device, medium and BMS system

Abstract

The present application relates to the field of power battery charging, and particularly relates to a battery charging optimization method, device, medium and BMS system. The method comprises: dividing battery charging into a plurality of continuous charging stages according to SOC; enumerating combinations of different charging currents of the charging stages to generate a plurality of charging schemes; and for each charging scheme, selecting a scheme with the shortest overall charging time as the final charging scheme. The present application uses a binary tree to store charging strategies and information of each SOC interval, requires less computing resources, and is easy to deploy and implement at the end of an electric vehicle controller. The present application segments the battery SOC to search for the optimal charging strategy combination, and combines the PID temperature control of the thermal balance feedforward current to fully utilize the charging performance of the battery within the safe operating boundary. Compared with the existing multi-stage constant current charging scheme or other charging strategy optimization schemes based on genetic algorithm and particle swarm optimization, the present application can balance the rapidity of charging and the safety of the battery.

Description

TECHNICAL FIELD

[0001] The application relates to the field of power battery charging, in particular to a battery charging optimization method, device, medium and BMS system. BACKGROUND

[0002] In recent years, the market share of new energy electric vehicles has been increasing. As a key core component of electric vehicles, the charging performance of lithium batteries directly affects the user experience. The optimal charging strategy for user fast charging demand, while optimizing battery life, charging speed and battery temperature rise, is crucial for the further development and popularization of electric vehicles.

[0003] Multi-stage constant current charging is a common charging strategy for electric vehicles at present. Under this strategy framework, each stage is charged with a preset current for a certain time until the battery SOC reaches the next step switching point. According to the polarization characteristics of lithium batteries, the preset current is large at low SOC; as the SOC increases, the preset current level decreases step by step. However, because the selection of preset current lacks theoretical guidance, it is easy to cause the battery to overheat in a certain stage due to the overlarge charging current, thereby causing the intensification of side reactions, reducing the safety of the battery, and affecting the service life of the battery. In this case, the battery management system (BMS) usually limits the reduction of the charging current at extreme temperatures, which may result in longer charging time, which is contrary to the demand for fast charging. In addition, this charging strategy usually uses constant current in each stage in practical application, without adjusting to the changes in battery system characteristics, resulting in low charging efficiency and inability to adapt to complex scenarios. SUMMARY

[0004] The application discloses a battery charging optimization method, device, medium and BMS system, which can optimize the charging efficiency of the battery.

[0005] To achieve the above-mentioned purpose, on the one hand, a battery charging optimization method is provided, and the specific method is as follows:

[0006] The battery charging is divided into a plurality of continuous charging stages according to the SOC;

[0007] In each charging stage, preset current charging or safe current charging to the next charging stage can be selected;

[0008] Exhaustive charging stage different charging current combinations are generated to generate a plurality of charging schemes;

[0009] For each charging scheme, the scheme with the shortest overall charging time is selected as the final charging scheme.

[0010] Further, the battery charging is divided into a plurality of continuous charging stages, and the specific method is as follows:

[0011] obtain the current SOC of the battery and the target SOC of charging;

[0012] divide the continuous charging stage into equal intervals, with the current SOC of the battery as the starting point and the target SOC of charging as the ending point.

[0013] Further, before selecting the scheme with the shortest overall charging time for each charging scheme, the overall charging time of each charging scheme is obtained by constructing a binary tree, and the specific method is as follows:

[0014] The starting point and ending point of the charging stage correspond to the SOC of the binary tree node, and the same SOC node is located at the same layer of the binary tree, and the SOC is sequentially increased from top to bottom;

[0015] The two child nodes of each SOC node are respectively charging to the next SOC node by the preset current and charging to the next SOC node by the safety current;

[0016] Each SOC node records the predicted charging time and the predicted charging temperature required for the previous SOC node to charge to the current SOC node;

[0017] The predicted charging time recorded by the SOC nodes included in the charging scheme is added as the overall charging time of the charging scheme.

[0018] Further, if the predicted charging temperature is higher than the high temperature boundary temperature when the current SOC node is charged to the next SOC child node, the charging current is adjusted by the PID closed-loop control algorithm until the next charging stage.

[0019] Further, when the SOC node is charged to the next SOC node by the preset current or the safety current, the predicted charging time recorded by the SOC node is the predicted charging time of charging to the next SOC node by the preset current or the safety current.

[0020] Further, when the SOC node is charged to the next SOC node by adjusting the charging current by the PID closed-loop control algorithm;

[0021] The predicted charging time recorded by the SOC node is the predicted charging time of charging by the PID closed-loop control algorithm.

[0022] Further, the predicted charging temperature is obtained according to the charging current, the environmental temperature and the battery temperature.

[0023] Further, the predicted charging time of the previous SOC node to the current SOC node is determined by the ampere-hour integral model prediction;

[0024] The predicted charging temperature of the previous SOC node to the current SOC node is determined by the battery heating model prediction.

[0025] Further, the scheme with the shortest overall charging time is selected as the final charging scheme, and the specific method is as follows:

[0026] An arbitrary charging scheme is selected as a local optimal charging scheme, and the overall charging time of the local optimal charging scheme is calculated;

[0027] The overall charging time of the remaining schemes is calculated in turn;

[0028] If the overall charging time of the current charging scheme is less than the charging time of the current local optimal charging scheme, the current charging scheme replaces the local optimal charging scheme;

[0029] All charging schemes are traversed, and the overall optimal charging scheme is selected as the final charging scheme.

[0030] Further, when calculating the overall charging time of the charging scheme, if the charging time of the charging scheme is greater than the overall charging time of the current local optimal scheme, the subsequent calculation of the charging scheme is stopped.

[0031] Further, the charging current is adjusted by a PID closed-loop control algorithm, and the specific method is as follows:

[0032] The difference between the predicted charging temperature and the high-temperature boundary temperature is used as the input of the PID controller, and the battery thermal balance current is used as the feedforward;

[0033] The output of the PID controller is the adjusted charging current.

[0034] Further, the battery thermal balance current is obtained as follows: the mapping relationship between the battery thermal balance current and the battery SOC and the battery temperature is calibrated offline, and the battery thermal balance current is obtained by table lookup.

[0035] To achieve the above purpose, on the other hand, a battery charging optimization device is provided, comprising: a charging scheme generation module, a charging time prediction module, and a charging scheme determination module;

[0036] The charging scheme generation module divides the battery charging into a plurality of continuous charging stages according to the SOC, exhaustively combines different charging currents of the charging stages, and generates a plurality of charging schemes;

[0037] The charging time prediction module predicts the overall charging time for each charging scheme;

[0038] The charging scheme determination module selects the scheme with the shortest overall charging time as the final charging scheme.

[0039] To achieve the above purpose, on the other hand, a storage medium is provided, which stores a plurality of instructions, and a processor loads the plurality of instructions to execute the above battery charging optimization method.

[0040] To achieve the above object, in another aspect, a BMS system is provided, comprising the above battery charge optimization device, and / or the above storage medium.

[0041] Due to the adoption of the above technical solutions, the present application has the following beneficial effects:

[0042] 1. Compared with the existing multi-stage constant current charging scheme or other charging strategy optimization schemes based on genetic algorithm and particle swarm optimization, the present application can take into account the rapidity of charging and the safety of the battery.

[0043] 2. The present application segments the battery SOC to search for the optimal charging strategy combination, and combines the PID temperature control of the thermal balance feedforward current to fully utilize the charging performance of the battery within the safe operating boundary.

[0044] 3. The present application uses a binary tree to store the charging strategy and information of each SOC interval, which requires less computing resources and is easy to deploy and implement at the electric vehicle controller end.

[0045] It should be noted that the terms "first", "second", and similar terms used in this document are only used to describe the components in the technical solutions, and do not constitute a limitation on the technical solutions, nor can they be understood as an indication or suggestion of the importance of the corresponding elements; elements with "first", "second", and similar terms indicate that at least one of the elements is included in the corresponding technical solution. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the present application, and to facilitate further understanding of the technical effects, technical features and purposes of the present application, the present application will be described in detail below in conjunction with the drawings, which constitute an essential part of the specification and are used to illustrate the technical solutions of the present application together with the embodiments of the present application, but do not constitute a limitation on the present application.

[0047] The same reference numerals in the drawings represent the same components, specifically:

[0048] Figure 1 Binary tree diagram of segmented combination charging strategy in Example 1.

[0049] Figure 2 Principle diagram of PID control charging current in Example 1.

[0050] Figure 3 Flowchart of the process of selecting the overall shortest charging time scheme in Example 1. DETAILED DESCRIPTION

[0051] The application will be further described in detail below with reference to the accompanying drawings and embodiments. Of course, the following specific embodiments described are only to explain the technical solutions of the application, but not to limit the application. In addition, the parts described in the embodiments or drawings are only examples of the relevant parts of the application, but not the whole application.

[0052] Embodiment 1

[0053] A battery charging optimization method, as shown in the figure, the specific steps are as follows: Figure 3

[0054] S1, divide the battery charging into several continuous charging stages according to SOC, in each charging stage, you can choose preset current charging or safe current charging to the next charging stage.

[0055] Specifically, the continuous charging stage is divided into several continuous charging stages, and the specific method is as follows:

[0056] S11, get the current SOC of the battery and the charging target SOC.

[0057] S12, take the current SOC of the battery as the starting SOC start , and take the charging target SOC as the end SOC end , and divide the continuous charging stage at equal intervals.

[0058] In this embodiment, take SOC start as the starting point, and take SOC end as the end point, and take the integer multiple of 5 in the middle to get the SOC list. For example, SOC start is 16%, SOC end is 94%, and the segmented SOC list is [16%, 20%, 25%, …, 90%, 94%]. Define that there are n elements in the SOC list, and the calculation formula of n is as follows:

[0059] n = [(SOC end + 4) div 5] - [(SOC start - 4) div 5]

[0060] The SOC list is represented as: [SOC0, SOC1, SOC2, …, SOC n-2 , SOC n-1 ], (SOC0 = SOC start , SOC n-1 = SOC end )

[0061] S2, exhaust the combination of different charging currents of the charging stage to generate several charging schemes.

[0062] ​S3. For each charging scheme, optimize and select the scheme with the shortest overall charging time as the final charging scheme.

[0063] In step S3, before optimizing and selecting the shortest overall charging time scheme for each charging scheme, the overall charging time of each charging scheme is obtained by constructing a binary tree. The specific method is as follows:

[0064] S311. The SOC corresponding to the start and end points of the charging stage is used as a binary tree node. Nodes with the same SOC are located at the same level of the binary tree, and the SOC increases sequentially from top to bottom.

[0065] S312. Each SOC node has two child nodes, one charged to the next SOC node by a preset current and the other charged to the next SOC node by a safe current.

[0066] When a SOC node charges to the next SOC node with a preset current or a safe current, the predicted charging time recorded by the SOC node is the predicted charging time to the next SOC node with the preset current or the safe current.

[0067] S313. When predicting each charging scheme, if the predicted charging temperature of the current charging stage is higher than the high temperature boundary temperature, the charging current is adjusted through the PID closed-loop control algorithm until the next charging stage.

[0068] When a SOC node uses a PID closed-loop control algorithm to adjust the charging current to charge to the next SOC node, the predicted charging time recorded by the SOC node is the predicted charging time for the PID closed-loop control algorithm to adjust the charging current.

[0069] The predicted charging temperature is obtained based on the charging current, ambient temperature, and battery temperature.

[0070] The predicted charging temperature from the previous SOC node to the current SOC node is determined by the battery electrothermal model.

[0071] The PID closed-loop control algorithm adjusts the charging current, such as... Figure 2 As shown, specifically: the difference between the predicted charging temperature and the high-temperature boundary temperature is used as the input to the PID controller, and the battery thermal balance current I is used as the input. forward As a feedforward, the output of the PID controller is to adjust the charging current I(k) = I forward (k)+I PID (k).

[0072] The formula for the PID closed-loop control algorithm is as follows:

[0073] e(k)=T(k)-T high

[0074] I P(k) = K P • e(k)

[0075] I I (k) = I I (k-1) + K I • e(k)

[0076] I D (k) = K D • [e(k) - e(k-1)]

[0077] I PID (k) = I P (k) + I I (k) + I D (k)

[0078] The battery thermal balance current is obtained in the following manner: offline calibration of the mapping relationship between the battery thermal balance current and the battery SOC and the battery temperature, and table lookup to obtain the battery thermal balance current. The defined battery thermal balance current I forward refers to: the heat dissipation amount of the heat management strategy adopted by the BMS according to the battery temperature and other factors and the heat generation amount of the battery using the thermal balance current for charging are basically balanced, and the battery temperature does not change significantly within a certain time.

[0079] In step S313, when the battery temperature rises to the opening temperature T on , the PID control mode is switched on, and the charging request current is set by the PID control mode, which will not cause the battery temperature to rise to exceed the target temperature. After the PID control mode is turned on, when the battery temperature drops to the PID closing temperature T off , the PID control is turned off, and the normal charging mode is switched on. T off = T on -0.2. The normal charging mode refers to: according to the selected charging strategy corresponding to the SOC interval, using the preset current or the safety current for constant current charging. The target temperature refers to the high temperature boundary T high of the battery set by the BMS. When this temperature is reached, the BMS will actively reduce the charging current to prevent the battery from overheating, so that the battery temperature drops as soon as possible to T high below. A multi-stage constant current basic charging strategy is deployed as a benchmark strategy for global optimization of the charging strategy. The multi-stage constant current basic charging strategy refers to: dividing the SOC from 0% to 100% into multiple stages, and setting the preset current in each stage according to a certain decreasing ratio. I base represents the preset current.

[0080] S313, each SOC node records the predicted charging time and the predicted charging temperature required for the last SOC node to charge to the current SOC node.

[0081] In step S313, the predicted charging time from the previous SOC node to the current SOC node is determined by the ampere-hour integral model.

[0082] In step S31, a full binary tree is built to store the relevant charging information of the segmented combined charging strategy at the segment points, such as... Figure 1 As shown. Define the binary tree node as... Where: i corresponds to the index of the element in the SOC list; j represents the index of the node at level i in the binary tree. Node The information included is: the corresponding State of Charge (SOC) value, the estimated cumulative charging time to reach that SOC, and the estimated battery temperature to reach that SOC. All this information is included in the node. Therefore, i = 0 indicates the level where the root node of the binary tree is located, and this level has 1 node. T0 represents the initial temperature of the battery at the start of charging.

[0083] i = 1 to (n-2) represents the level of the middle node of the binary tree, and each level has 2 nodes. i 1 node

[0084] i = (n-1) represents the level of the leaf nodes in the binary tree, and this level has 2 nodes. n-1 1 node

[0085] The parent node to the left child node indicates the use of a preset current I. base Charging, the purpose is fast charging; the parent node to the right child node indicates that safe current charging is used, using I. small To represent a small current, take I. small =0.5C, because a 0.5C charging current strikes a good balance between lithium battery charging time and charging safety.

[0086] Parent node To the left child node The connection between them represents the SOC interval [SOC i SOC i+1 Using I base Charging; Parent node To the right child node The connection between them represents the SOC interval [SOC i SOC i+1 Using I small Charge.

[0087] Ultimately stored in each leaf node This indicates the estimated cumulative charging time to reach the target SOC after segmented charging current combinations. Converting the superscript j to binary, each bit from high to low corresponds to the charging current used in the corresponding SOC range; 0 indicates the preset current I.base Charging, 1 represents the safe current I small Charging. As represents the charging mode in the SOC interval [SOC0, SOC1] and [SOC1, SOC2] using I small Charging, using I in other SOC intervals base Charging.

[0088] In step S3, the scheme with the shortest overall charging time is selected as the final charging scheme, and the specific steps are as follows:

[0089] S321, randomly select a charging scheme as a local optimal charging scheme, and calculate the overall charging time of the local optimal charging scheme.

[0090] S322, the overall charging time of the remaining scheme is calculated in turn.

[0091] In step S322, when calculating the overall charging time of the charging scheme, if the charging time of the charging scheme is greater than the overall charging time of the current local optimal scheme, stop the subsequent calculation of the charging scheme.

[0092] S323, if the overall charging time of the current charging scheme is less than the charging time of the current local optimal charging scheme, replace the local optimal charging scheme with the current charging scheme.

[0093] S324, traverse all charging schemes, and select the overall optimal charging scheme as the final charging scheme.

[0094] S325, add the predicted charging time recorded by the SOC node included in the charging scheme as the overall charging time of the charging scheme.

[0095] In step S32, the calculation time step of the global optimization combination strategy search algorithm is Δt = 1s, and J represents the serial number of the leaf node to be searched, and the flow is as shown in Figure 3 , and the specific operation is as follows:

[0096] P1, initialization, set k = 0, J = 0, i = 0, T(0) = T0, t(0) = 0, SOC(0) = SOC0.

[0097] P2, convert J to binary, and get the charging mode of the corresponding SOC interval by taking the corresponding bit.

[0098] P3, judge whether T(k) reaches the PID start temperature. If the PID start condition is reached, switch to the PID control mode, and get I forward (kv) from T(kv) and T high (kv) by T(kv) and T PID(k), set the charging request current I(k) = I forward (k) + I PID (k). If the PID start condition is not reached, set the charging current I(k) = I base [SOC(k)] or I small [SOC(k)].

[0099] P4, k = k + 1, calculate the increase of SOC by the ampere-hour integral model and the change of temperature by the battery electro-thermal model. The formula for calculating the increase of SOC is: where Q c is the total capacity of the battery. The formula for calculating the change of temperature is: where AQ is the change of heat calculated by the electro-thermal model and C is the heat exchange coefficient of the battery. where AQ is the change of heat calculated by the electro-thermal model and C is the heat exchange coefficient of the battery.

[0100] P5, update SOC, cumulative time and battery temperature: SOC(k) = SOC(k-1) + ASOC, t(k) = k, T(k) = T(k+1) + AT.

[0101] P6, determine whether SOC(k) reaches the upper limit SOC i+1 of the corresponding SOC interval. i+1 Convert J to binary, take the corresponding bit to calculate the superscript j of the corresponding node, and update the node information If SOC(k) < SOC i+1 , return to P3.

[0102] P7, i = i + 1, determine whether the leaf node is reached by i. If the leaf node is not reached, return to P3. If the leaf node is reached, proceed to the next step.

[0103] P8, i = i - 1, i.e. return to the parent node, and then determine whether the right child node has been calculated according to J. If it has been calculated, repeat this step. If it has not been calculated, proceed to the next step.

[0104] P9, J = J + 1. Convert J to binary, take the corresponding bit to calculate the superscript j of the corresponding node, and take the value from the corresponding node .

[0105] P10, judge whether t(k) is less than all calculated leaf nodes, if less than, return to P2. If not less than, it indicates that the arrival of the node is not the optimal solution, no longer based on the node to continue to calculate, return to P9.

[0106] After the above search, take the segment combination strategy corresponding to the leaf node with the minimum cumulative charging time as the global optimization multi-stage charging strategy.

[0107] Embodiment 2:

[0108] A battery charging optimization device, comprising: a charging scheme generation module, a charging time prediction module and a charging scheme determination module;

[0109] The charging scheme generation module divides the battery charging into several continuous charging stages according to the SOC, exhaustively generates several charging schemes for the combination of different charging currents of the charging stages;

[0110] The charging time prediction module predicts the total charging time for each charging scheme;

[0111] The charging scheme determination module selects the scheme with the shortest total charging time as the final charging scheme.

[0112] It should be noted that the above embodiments are only for more clearly illustrating the technical solutions of the present application, and those skilled in the art can understand that the embodiments of the present application are not limited to the above content, and the obvious changes, replacements or substitutions based on the above content do not exceed the scope of the technical solutions of the present application; other embodiments also fall within the scope of the present application without departing from the concept of the present application.

Claims

1. A battery charging optimization method, characterized in that, The specific method is as follows: Based on the SOC, battery charging is divided into several continuous charging stages; In each charging stage, you can choose to charge with a preset current or charge with a safe current until the next charging stage. By exhaustively exploring the combinations of different charging currents during the charging phase, several charging schemes are generated. For each charging scheme, the scheme with the shortest overall charging time is selected as the final charging scheme. Before optimizing and selecting the shortest overall charging time for each charging scheme, the overall charging time for each scheme is obtained by constructing a binary tree. The specific method is as follows: The SOC corresponding to the start and end points of the charging stage is used as the nodes of the binary tree. Nodes with the same SOC are located at the same level of the binary tree, and the SOC increases sequentially from top to bottom. Each SOC node has two child nodes, one charged to the next SOC node by a preset current, and the other charged to the next SOC node by a safe current. Each SOC node records the predicted charging time and predicted charging temperature required for the previous SOC node to charge to the current SOC node. The predicted charging time recorded by all SOC nodes in the additive charging scheme is used as the overall charging time of the charging scheme. The scheme with the shortest overall charging time is selected as the final charging scheme. The specific method is as follows: Choose any charging scheme as the locally optimal charging scheme and calculate the total charging time of the locally optimal charging scheme; Calculate the total charging time for the remaining schemes in sequence; If the overall charging time of the current charging scheme is less than the charging time of the current locally optimal charging scheme, then the local optimal charging scheme is replaced by the current charging scheme. Iterate through all charging schemes and select the overall optimal charging scheme as the final charging scheme.

2. The battery charging optimization method as described in claim 1, characterized in that, The process is divided into several continuous charging stages, as detailed below: Obtain the current SOC of the battery and the target SOC for charging; Starting from the current SOC of the battery and ending at the target SOC, the continuous charging phases are divided into equal intervals.

3. The battery charging optimization method as described in claim 1, characterized in that, If the predicted charging temperature is higher than the high-temperature boundary temperature when the current SOC node is charging to the next SOC child node, the charging current will be adjusted through the PID closed-loop control algorithm until the next charging stage.

4. The battery charging optimization method as described in claim 1, characterized in that, When a SOC node charges to the next SOC node with a preset current or a safe current, the predicted charging time recorded by the SOC node is the predicted charging time to the next SOC node with the preset current or the safe current.

5. The battery charging optimization method as described in claim 3, characterized in that, When the SOC node uses a PID closed-loop control algorithm to adjust the charging current to charge to the next SOC node; The predicted charging time recorded by the SOC node is the predicted charging time for adjusting the charging current using the PID closed-loop control algorithm.

6. The battery charging optimization method as described in claim 3, characterized in that, The predicted charging temperature is obtained based on the charging current, ambient temperature, and battery temperature.

7. The battery charging optimization method as described in claim 6, characterized in that, The predicted charging time from the previous SOC node to the current SOC node is determined by the ampere-hour integral model. The predicted charging temperature from the previous SOC node to the current SOC node is determined by the battery electrothermal model.

8. The battery charging optimization method as described in claim 1, characterized in that, When calculating the overall charging time of a charging scheme, if the charging time of the current local optimal scheme is greater than the overall charging time of the current local optimal scheme, the subsequent calculation of that charging scheme is stopped.

9. The battery charging optimization method as described in claim 3, characterized in that, The charging current is adjusted using a PID closed-loop control algorithm, as follows: The difference between the predicted charging temperature and the high-temperature boundary temperature is used as the input to the PID controller, and the battery thermal balance current is used as the feedforward. The output of the PID controller is used to adjust the charging current.

10. The battery charging optimization method as described in claim 9, characterized in that, The battery thermal balance current is obtained as follows: the mapping relationship between the battery thermal balance current and the battery SOC and battery temperature is calibrated offline, and the battery thermal balance current is obtained by looking up the table.

11. A battery charging optimization device, characterized in that, include: The module includes a charging scheme generation module, a charging time prediction module, and a charging scheme determination module. The charging scheme generation module divides battery charging into several continuous charging stages according to SOC, exhaustively enumerates the combinations of different charging currents in the charging stages, and generates several charging schemes. The charging time prediction module predicts the total charging time for each charging scheme. The charging scheme determination module selects the scheme with the shortest overall charging time as the final charging scheme; Before optimizing and selecting the shortest overall charging time for each charging scheme, the overall charging time for each scheme is obtained by constructing a binary tree. The specific method is as follows: The SOC corresponding to the start and end points of the charging stage is used as the nodes of the binary tree. Nodes with the same SOC are located at the same level of the binary tree, and the SOC increases sequentially from top to bottom. Each SOC node has two child nodes, one charged to the next SOC node by a preset current, and the other charged to the next SOC node by a safe current. Each SOC node records the predicted charging time and predicted charging temperature required for the previous SOC node to charge to the current SOC node. The predicted charging time recorded by all SOC nodes in the additive charging scheme is used as the overall charging time of the charging scheme. The scheme with the shortest overall charging time is selected as the final charging scheme. The specific method is as follows: Choose any charging scheme as the locally optimal charging scheme and calculate the total charging time of the locally optimal charging scheme; Calculate the total charging time for the remaining schemes in sequence; If the overall charging time of the current charging scheme is less than the charging time of the current locally optimal charging scheme, then the local optimal charging scheme is replaced by the current charging scheme. Iterate through all charging schemes and select the overall optimal charging scheme as the final charging scheme.

12. A storage medium, characterized in that, The processor stores several instructions, and loads several instructions to execute the battery charging optimization method according to any one of claims 1 to 10.

13. A BMS system, characterized in that, Includes the battery charging optimization device of claim 11, and / or the storage medium of claim 12.

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