A method and system for generating a fast charging strategy for a lithium-ion battery

Abstract

The present invention relates to a method and system for generating a fast-charging strategy for a lithium-ion battery. The method comprises: determining the lithium-ion battery model and obtaining corresponding electrochemical parameters; performing simulation modeling on the battery and initializing its state; constructing a fast-charging problem with the goal of improving the battery's charging speed while satisfying constraints; the constraints include limiting the charging current range, the battery's maximum temperature, and limiting the side reaction overpotential to a positive value; solving the fast-charging problem using a DDPG algorithm; training the DDPG algorithm using a battery simulation model as a simulation environment; generating each small charging current segment during a complete charging process through a cyclic execution of the algorithm; cyclically executing the complete charging process a set number of times, completing the algorithm training; and outputting an optimal charging strategy for the battery model. The method and system are advantageous for improving battery charging speed and shortening charging time while ensuring battery safety, and are computationally simple and have a wide range of applications.

Description

Technical Field

[0001] The present invention relates to the technical field of battery charging, and in particular to a method and system for generating a fast charging strategy for a lithium-ion battery. Background Art

[0002] With the development of the electric vehicle industry, lithium-ion batteries, as the power plant of electric vehicles, and their related research are receiving increasing attention. However, long charging times and battery aging remain major challenges facing lithium-ion batteries. Due to the limitations of their inherent electrochemical properties, lithium-ion batteries have long charging times, which easily cause users to have range anxiety and seriously restrict the development of the electric vehicle industry. Rapid charging often leads to battery degradation such as solid electrolyte interface (SEI) growth and lithium deposition, and charging control with excessive current can also pose safety risks and, in severe cases, cause fires, explosions and other accidents. Therefore, how to strike a balance between fast charging and battery life in lithium-ion batteries is an urgent problem that needs to be solved.

[0003] Traditional battery charging management strategies are primarily categorized into model-based and model-free approaches. Model-based approaches utilize mathematical models to optimize input trajectories, but suffer from computational complexity, poor observability, and model uncertainty, limiting their application. Model-free approaches, such as the rule-based adaptive CC-CV method, interact directly with the battery. While widely used due to their simplicity, these approaches are often overly conservative and lack optimality, resulting in prolonged charging times. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for generating a fast charging strategy for a lithium-ion battery. The method and system are conducive to improving the battery charging speed and shortening the battery charging time while ensuring battery safety, and the calculation is simple and has a wide range of applications.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: a method for generating a fast charging strategy for a lithium-ion battery, comprising the following steps:

[0006] Step 1: Determine the lithium-ion battery model for which a fast charging strategy needs to be generated, and obtain the corresponding electrochemical parameters of the battery model;

[0007] Step 2: Simulate and model the battery based on the obtained electrochemical parameters, establish a battery simulation model and initialize the state;

[0008] Step 3: Construct a fast charging problem with the goal of increasing the battery charging speed while satisfying constraints, including limiting the charging current range, limiting the maximum battery temperature, and limiting the side reaction overpotential to a positive value.

[0009] Step 4: Use the DDPG algorithm to solve the fast charging problem. Use the battery simulation model established in Step 2 as a simulation environment to train the DDPG algorithm in the simulation environment. During a complete charging process, the DDPG algorithm is executed cyclically to generate each small charging current, thereby completing a complete charging process. After the complete charging process is executed a set number of times, the algorithm training is completed.

[0010] Step 5: Output the optimal charging strategy for this battery model.

[0011] Furthermore, in step 2, the Doyle–Fuller–Newman model is used to simulate the battery model; at the same time, the battery aging process is simulated by increasing the internal resistance of the anode film once every set number of complete charging processes.

[0012] Furthermore, in step 2, after the battery simulation model is established, the initial state of the battery is randomly set within a certain range. The initial state includes: the initial temperature of the battery T and battery initialization SoC , the subsequent charging process will simulate the change of battery state based on this initial state.

[0013] Furthermore, in step 3, the fast charging problem is constructed in the form of a Markov decision process, which specifically includes the following steps:

[0014] 301) Define the state and action space: the action is the continuous charging current, and the state includes the battery voltage, temperature and charge level;

[0015]

[0016]

[0017] in, s t is the state space, a t is the action space, SoC is the charge level of the battery, V is the battery voltage, T is the battery temperature, I is the battery current;

[0018] 302) Construct constraints, including limiting the charging current to a set range, limiting the maximum battery temperature, and limiting the side reaction overpotential to a positive value;

[0019]

[0020]

[0021]

[0022] in, t is the current time point, I ( t ) is the current moment t Charging current; I max It is the maximum limit of charging current, negative value represents charging; T ( t ) is the current moment t The battery temperature, T max is the maximum limit of battery temperature; η sr ( t ) is the current moment t The side reaction overpotential;

[0023] 303) Constructing a reward function: After selecting the current action, i.e., charging current, the reward function is used to calculate the immediate reward of the action, which is used to judge the quality of the current action;

[0024]

[0025] in, r t+1 For the reward of the action, r fast For quick items, r safety ( s t , a t ) is a safety term; the safety term includes the side reaction overpotential term and the temperature term:

[0026]

[0027]

[0028]

[0029] in, is the side reaction overpotential term, r temp ( s t , a t ) is the temperature term, λ sr is the side reaction overpotential coefficient, η sr ( t ) is the battery overpotential at the current moment, λ tempis the temperature term coefficient;

[0030] According to the above formula, when the side reaction overpotential is less than 0 or the battery temperature exceeds the maximum limit, the action is penalized to ensure that the battery is in a safe state during the charging process and to increase the battery charging speed.

[0031] Furthermore, in step 4, the DDPG algorithm is trained in a simulation environment, specifically including the following steps:

[0032] 401) Initialization, including initializing the actor network , Critics Network , experience replay buffer and create target networks, including actor target networks μ' and critics target network Q' The structures of the actor-target network and the critic-target network are the same as those of the actor network and the critic network, respectively. The parameters of the actor-target network and the critic-target network are copied from the actor network and the critic network, respectively.

[0033] 402) interacts with the simulation environment formed by the battery simulation model, and obtains the current battery status from the environment s t , selects a charging current for the battery through the actor target network ;in, is the noise used for exploration;

[0034] 403) execute the action with the current charging current I Charge for one time step; observe the reward given by the environment r t and the new state s t+1 ;Will Store into the experience replay buffer;

[0035] 404) Update critics network;

[0036] 405) Update actor network parameters ;

[0037] 406) Soft update target network parameters;

[0038] 407) During a complete charging process, steps 402)-406) are executed cyclically to generate each small portion of charging current, thereby achieving a complete charging process; after the complete charging process is executed cyclically a set number of times, the algorithm training is completed.

[0039] Furthermore, in step 404), the critic network is updated, specifically including the following steps:

[0040] 1) First, randomly sample a small batch of experience from the experience replay buffer ; For each sample, use the target network to calculate the target value y i ;

[0041]

[0042] in, is the discount factor, μ' and Q' For the target network;

[0043] 2) Calculate the predicted value using the current critic network ;

[0044] 3) Calculate mean square error loss L :

[0045]

[0046] in, N is the number of mini-batch samples;

[0047] 4) Use gradient descent to minimize the loss function and update the critic network parameters , as shown below:

[0048]

[0049] in, Indicates assigning the right part of the formula to the left part. β is the learning rate; Denotes the critic network's parameters The gradient of s i and actions a i The rate of change of the critic network output relative to the change of its parameters under

[0050] Furthermore, in step 405), the actor network parameters are updated , specifically including the following steps:

[0051] 1) Randomly sample a small batch of experiences from the experience replay buffer ;

[0052] 2) For each state s t , there is action , action value ; Calculate the actor policy gradient:

[0053]

[0054] in, is the gradient of the critic network with respect to the action, is the gradient of the actor network with respect to the parameters;

[0055] 3) Update actor network parameters ;

[0056]

[0057] in, α is the learning rate.

[0058] Furthermore, in step 406), the target network parameters are soft-updated, and the formula is as follows:

[0059]

[0060]

[0061] in, τ is the set small update coefficient.

[0062] The present invention also provides a lithium-ion battery fast charging strategy generation system, which includes a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the above method can be implemented.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1) The present invention takes into account both charging speed and battery aging issues when generating a fast-charging strategy for lithium-ion batteries. By constructing an optimization problem with charging speed as the target and battery aging as the constraint, the battery charging time is minimized while ensuring battery safety.

[0065] 2) This invention introduces reinforcement learning (RL) to implement a model-free reinforcement learning method that does not rely on complex electrochemical models and can learn optimal strategies in interaction with the environment, reducing computational complexity and alleviating the impact of model uncertainty through a data-driven approach.

[0066] 3) The present invention has strong adaptability and can automatically adapt to parameter changes caused by battery aging, maintain charging efficiency, and can adjust strategies under different usage conditions to extend battery life. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a flowchart of a method implementation of an embodiment of the present invention;

[0068] Figure 2 4 is a training flow chart of the DDPG algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0070] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present application belongs.

[0071] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0072] like Figure 1 As shown, this embodiment provides a method for generating a fast charging strategy for a lithium-ion battery, comprising the following steps:

[0073] Step 1: Determine the lithium-ion battery model for which a fast charging strategy needs to be generated, and obtain the corresponding electrochemical parameters of the battery model.

[0074] Step 2: Simulate and model the battery based on the obtained electrochemical parameters, establish a battery simulation model and initialize the state.

[0075] In this example, the Doyle–Fuller–Newman (DFN) model is used to simulate the battery. This model describes the internal electrochemical processes of lithium-ion batteries. By setting the battery's charging current, the model outputs various variables, including the state of charge (SoC), voltage, and side reaction overpotentials.

[0076] In simulating the aging of the battery, this embodiment adopts the method of increasing the internal resistance of the anode film once after each complete charging process of a set number of times. way to simulate the battery aging process.

[0077] After the battery simulation model is established, the initial state of the battery is randomly set within a certain range. The initial state includes: the initial temperature of the battery T and battery initialization SoC In this embodiment, the initial battery temperature T Random value in the range [25, 32]; battery initialization SoCA random value is chosen in the range [0.2, 0.4] to enhance the algorithm's exploration. The subsequent charging process will simulate the battery state changes based on this initial state.

[0078] Step 3: Construct a fast charging problem with the goal of increasing the battery charging speed while satisfying constraints. These constraints include limiting the charging current range, limiting the maximum battery temperature, and limiting the side reaction overpotential to positive values.

[0079] In this embodiment, the fast charging problem is constructed in the form of a Markov decision process, which specifically includes the following steps:

[0080] 301) Define the state and action space: the action is the continuous charging current, and the state includes the battery voltage, temperature and charge level;

[0081]

[0082]

[0083] in, s t is the state space, a t is the action space, SoC is the charge level of the battery, V is the battery voltage, T is the battery temperature, I is the battery current.

[0084] 302) Construct constraints, including:

[0085] i. Excessive charging current will damage the battery, so limit the charging current to the set range;

[0086] ii. Excessively high temperatures can also damage the battery, so the maximum battery temperature is limited;

[0087] iii. The continued presence of lithium deposition can lead to battery damage and aging. Therefore, limiting the side reaction overpotential to a positive value can better avoid this phenomenon.

[0088]

[0089]

[0090]

[0091] in, t is the current time point, I ( t ) is the current moment t Charging current;I max It is the maximum limit of charging current, negative value represents charging; T ( t ) is the current moment t The battery temperature, T max is the maximum limit of battery temperature; η sr ( t ) is the current moment t The side reaction overpotential.

[0092] 303) Constructing a reward function: After selecting the current action, i.e., charging current, the reward function is used to calculate the immediate reward of the action, which is used to judge the quality of the current action;

[0093]

[0094] in, r t+1 For the reward of the action, r fast For quick items, r safety ( s t , a t ) is a safety term; the safety term includes the side reaction overpotential term and the temperature term:

[0095]

[0096]

[0097]

[0098] in, is the side reaction overpotential term, r temp ( s t , a t ) is the temperature term, λ sr is the side reaction overpotential coefficient, η sr ( t ) is the battery overpotential at the current moment, λ temp is the temperature term coefficient.

[0099] According to the above formula, when the side reaction overpotential is less than 0 or the battery temperature exceeds the maximum limit, the action is penalized. This design can minimize the battery overpotential from being less than 0 or the temperature from exceeding the maximum limit, thereby ensuring that the battery is in a safe state during the charging process and improving the battery charging speed.

[0100] Step 4: Apply the DDPG algorithm to solve the fast charging problem. The DDPG algorithm is a policy gradient-based actor-critic method suitable for continuous state and action spaces. It uses a deep neural network to parameterize the actor and critic, and stabilizes the learning process through an experience replay buffer.

[0101] Using the battery simulation model established in step 2 as the simulation environment, train the DDPG algorithm within this environment. During a complete charging process, the DDPG algorithm executes in a loop, generating each small portion of the charging current, thus completing a complete charging process. Algorithm training concludes after the set number of complete charging cycles.

[0102] like Figure 2 As shown in the figure, training the DDPG algorithm in a simulated environment includes the following steps:

[0103] 401) Initialization, including initializing the actor network , Critics Network , experience replay buffer and create target networks, including actor target networks μ' and critics target network Q' The structures of the actor-target network and the critic-target network are the same as those of the actor network and the critic network, respectively, and the parameters of the actor-target network and the critic-target network are copied from the actor network and the critic network, respectively.

[0104] 402) interacts with the simulation environment formed by the battery simulation model, and obtains the current battery status from the environment s t , selects a charging current for the battery through the actor target network ;in, is the noise used for exploration.

[0105] The status you want to obtain The constraints on the action, i.e. the charging current and temperature, are as follows:

[0106]

[0107]

[0108]

[0109] 403) execute the action with the current charging current I Charge for one time step; observe the reward given by the environment r t and the new state s t+1 ;Will Stored to the experience replay buffer.

[0110] award r t The calculation is done using the following formula. This reward takes both charging speed and battery aging into account.

[0111]

[0112]

[0113]

[0114]

[0115] in, , , .

[0116] 404) Update the critic network; specifically, the following steps:

[0117] 1) First, randomly sample a small batch of experience from the experience replay buffer ; For each sample, use the target network to calculate the target value y i ;

[0118]

[0119] in, is the discount factor, set to 0.99; μ' and Q' For the target network.

[0120] 2) Calculate the predicted value using the current critic network .

[0121] 3) Calculate mean square error loss L :

[0122]

[0123] in, N is the number of mini-batch samples, set to 64.

[0124] 4) Use gradient descent to minimize the loss function and update the critic network parameters , as shown below:

[0125]

[0126] in, Indicates assigning the right part of the formula to the left part. β is the learning rate, which determines the distance moved along the gradient direction at each parameter update, and its size affects the efficiency and stability of learning. In this embodiment, β Set to 0.001; Denotes the critic network's parameters The gradient of s i and actions a i The rate of change of the critic network output relative to the change of its parameters under

[0127] 405) Update actor network parameters ; Specifically including the following steps:

[0128] 1) Randomly sample a small batch of experiences from the experience replay buffer .

[0129] 2) For each state s t , there is action , action value ; Calculate the actor policy gradient:

[0130]

[0131] in, is the gradient of the critic network with respect to the action, is the gradient of the actor network with respect to its parameters.

[0132] 3) Update actor network parameters ;

[0133]

[0134] in, α is the learning rate, set to 0.0001.

[0135] 406) Soft update the target network parameters. The purpose of soft update is to make the target network parameter changes smoother, avoid the adverse effects of frequent target value updates, and prevent large fluctuations and instability in the algorithm during the learning process. The formula for soft update of target network parameters is as follows:

[0136]

[0137]

[0138] in, τ is a small update coefficient set to 0.001.

[0139] 407) During a complete charging process, steps 402)-406) are executed in a loop to generate each small portion of charging current, thereby completing a complete charging process. In this embodiment, each charging process ends when the battery SoC reaches 0.8.

[0140] The algorithm training is completed after the charging process is executed for the set number of times.

[0141] Step 5: Output the optimal charging strategy for this battery model.

[0142] This embodiment also provides a lithium-ion battery fast charging strategy generation system, including a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the above method can be implemented.

[0143] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0144] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0147] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

Claims

1. A method for generating a fast charging strategy for a lithium-ion battery, characterized in that: The following steps are involved: Step 1: Determine the lithium-ion battery model for which a fast charging strategy needs to be generated, and obtain the corresponding electrochemical parameters of the battery model; Step 2: Simulate and model the battery based on the obtained electrochemical parameters, establish a battery simulation model and initialize the state; Step 3: Construct a fast charging problem with the goal of increasing the battery charging speed while satisfying constraints, including limiting the charging current range, limiting the maximum battery temperature, and limiting the side reaction overpotential to a positive value. Step 4: Use the DDPG algorithm to solve the fast charging problem. Use the battery simulation model established in Step 2 as a simulation environment to train the DDPG algorithm in the simulation environment. During a complete charging process, the DDPG algorithm is executed cyclically to generate each small charging current, thereby completing a complete charging process. After the complete charging process is executed a set number of times, the algorithm training is completed. Step 5: Output the optimal charging strategy for this battery model; In step 2, the Doyle–Fuller–Newman model is used to simulate the battery. At the same time, the battery aging process is simulated by increasing the internal resistance of the anode film once every set number of complete charging processes. In step 2, after the battery simulation model is established, the initial state of the battery is randomly set within a certain range. The initial state includes: the initial battery temperature T and the initial battery SoC. The subsequent charging process will simulate the change of the battery state based on the initial state; In step 3, the fast charging problem is formulated in the form of a Markov decision process, which includes the following steps: 301) Define the state and action space: the action is the continuous charging current, and the state includes the battery voltage, temperature and charge level; s t ={SoC,V,T} a t ={I} Among them, s t is the state space, a t is the action space, SoC is the charge level of the battery, V is the voltage of the battery, T is the temperature of the battery, and I is the current of the battery; 302) Constructing constraints, including: limiting the charging current to a set range, limiting the maximum battery temperature, and limiting the side reaction overpotential to a positive value; -I max ≤I(t)≤0 T(t)≤T max or sr (t)≥0 Where t is the current time point, I(t) is the charging current at the current time point t; I max is the maximum limit of the charging current, and a negative value represents charging; T(t) is the battery temperature at the current time t, T max is the maximum limit of battery temperature; η sr (t) is the side reaction overpotential at the current time t; 303) Constructing a reward function: After selecting the current action, i.e., charging current, the immediate reward of the action is calculated through the reward function to judge whether the current action is good or bad; r t+1 =r fast +r safety (s t ,a t ) Among them, r t+1 is the reward of the action, r fast is a fast term, r safety (s t ,a t ) is a safety term; the safety term includes the side reaction overpotential term and the temperature term: in, is the side reaction overpotential term, r temp (s t ,a t ) is the temperature term, λ sr is the side reaction overpotential coefficient, η sr (t) is the battery overpotential at the current moment, λ temp is the temperature term coefficient; According to the above formula, when the side reaction overpotential is less than 0 or the battery temperature exceeds the maximum limit, the action is penalized to ensure that the battery is in a safe state during the charging process and to increase the battery charging speed; In step 4, the DDPG algorithm is trained in a simulated environment, which includes the following steps: 401) Initialization, including initialization of the actor network μ(s t |θ μ ), critic network Q(s t , a t |θ Q ), the experience replay buffer and create a target network, including the actor target network μ' and the critic target network Q'; the structures of the actor target network and the critic target network are the same as those of the actor network and the critic network, respectively, and the parameters of the actor target network and the critic target network are copied from the actor network and the critic network, respectively; 402) interacts with the simulation environment formed by the battery simulation model, and according to the current battery status s obtained from the environment t , selects a charging current for the battery through the actor target network in, is the noise used for exploration; 403) Execute the action, charge for one time step with the current charging current I; observe the reward r given by the environment t and the new state s t+1 ; will (s t , a t , r t , s t+1 ) is stored in the experience replay buffer; 404) Update critic network; 405) Update the actor network parameters θ μ ; 406) soft update target network parameters; 407) During a complete charging process, steps 402)-406) are executed cyclically to generate each small portion of charging current, thereby completing a complete charging process; the algorithm training is completed after the set number of complete charging processes are executed cyclically; In step 404), the critic network is updated, specifically including the following steps: 1) First, randomly sample a small batch of experiences (s) from the experience replay buffer i , a i , r i , s i+1 ); For each sample, use the target network to calculate the target value y i ; y i =r i +γQ′(s i+1 ,μ′(s i+1 |θ μ′ )|θ Q′ ) Where γ is the discount factor, μ' and Q' are the target networks; 2) Calculate the predicted value using the current critic network 3) Calculate the mean square error loss L: Where N is the number of mini-batch samples; 4) Use gradient descent to minimize the loss function and update the critic network parameters θ Q , as shown below: Among them, ← means assigning the right part to the left part, and β is the learning rate; Denotes the critic network's parameter θ Q The gradient of i and action a i Under , the rate of change of the critic network output relative to the change of its parameters; In step 405), update the actor network parameters θ μ , specifically including the following steps: 1) Randomly sample a small batch of experiences (s) from the experience replay buffer i , a i , r i , s i+1 ); 2) For each state s t , there is action a t =μ(s t |θ μ ), action value Q(s) i , a i |θ Q ); Calculate the actor's policy gradient: in, is the gradient of the critic network with respect to the action, is the gradient of the actor network with respect to the parameters; 3) Update the actor network parameters θ μ ; Among them, α is the learning rate; In step 406), the target network parameters are soft-updated, and the formula is as follows: i Q′ ←tth Q +(1-τ)θ Q′ i μ′ ←tth μ +(1-τ)θ μ′ Among them, τ is the set small update coefficient.

2. A lithium-ion battery fast charging strategy generation system, characterized in that: The device comprises a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method according to claim 1 can be implemented.

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