Multi-stage constant-current charging optimization method for lithium ion battery
By constructing an electrochemical-thermal coupling simulation model and optimizing multi-stage constant current charging using the whale optimization algorithm, the problem of balancing safety and speed during the fast charging process of lithium-ion batteries was solved, generating an easy-to-deploy safe and efficient charging strategy that achieves the best balance between battery health and charging time.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-24
- Publication Date
- 2026-04-10
AI Technical Summary
Existing lithium-ion battery charging technologies struggle to achieve the optimal balance between safety and charging speed during fast charging. Traditional methods suffer from computational complexity, model uncertainty, and individual variability, leading to safety hazards and excessively long charging times.
An electrochemical-thermal coupling simulation model was constructed, and the whale optimization algorithm was used with multi-stage constant current charging as the optimization variable. Combined with the overpotential and temperature constraints of the negative electrode side reaction, the charging strategy was optimized through the fitness function to generate a clear constant current sequence to ensure safety and shorten the charging time.
While ensuring battery safety and lifespan, it significantly shortens charging time, generates an easy-to-deploy fast charging strategy, resolves the contradiction between fast charging and battery health aging, and reduces computational complexity and hardware requirements.
Smart Images
Figure CN121835524A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of data processing-driven energy management optimization technology, specifically relating to a multi-stage constant current charging optimization method for lithium-ion batteries. Background Technology
[0002] With the rapid development of the electric vehicle industry, optimizing the charging performance of lithium-ion batteries, as the core power source, has become a key technological challenge. Shortening charging time is a direct way to alleviate range anxiety and improve the user experience; however, the inherent electrochemical characteristics of batteries pose a significant challenge to fast charging. Excessive charging current can exacerbate internal side reactions, particularly on the negative electrode surface, easily leading to irreversible lithium metal deposition. This not only accelerates capacity decay but also significantly increases the risk of short circuits and thermal runaway. Therefore, how to finely coordinate the multi-dimensional conflicts between charging speed, battery health aging, and operational safety under high-rate charging conditions is the fundamental challenge for achieving technological breakthroughs.
[0003] To address this challenge, the industry has primarily explored two technical approaches. One is model-based open-loop optimization. These methods rely on mathematical models of the battery (such as equivalent circuit models or electrochemical models) to predict its internal state and plan the optimal charging current curve accordingly. While electrochemical mechanism-based models can more accurately describe the dynamic processes of key internal variables such as lithium-ion concentration and potential, thus providing a theoretical possibility for suppressing lithium deposition mechanistically, these models are typically complex, involving numerous partial differential equations, resulting in a heavy computational burden and making them difficult to directly apply to real-time online optimization of vehicle battery management systems. Furthermore, the accuracy of the model heavily depends on the precise calibration of battery parameters, and the time-varying and uncertain parameters during battery production consistency and aging directly affect the safety and optimality of the planned charging strategy.
[0004] Another category is model-free or rule-based empirical methods, such as the widely used segmented constant current and constant voltage charging and its improved strategies. These methods are typically based on extensive experimentation, setting fixed current switching voltages or state-of-charge thresholds. Their advantages lie in their simple logic, strong robustness, and ease of online implementation. However, their "one-size-fits-all" pre-defined rules are difficult to adapt to individual differences in different batteries, different initial states (such as temperature and aging levels), and dynamically changing external environments. More importantly, such strategies are usually based on conservative designs at safety boundaries to ensure that safety hazards are not triggered under worst-case conditions, but this often sacrifices the potential charging speed under most mild conditions, failing to achieve optimal performance under dynamic conditions.
[0005] In recent years, offline optimization-online application frameworks combining intelligent optimization algorithms with battery models have attracted attention, aiming to find globally optimal charging trajectories. However, the practical effectiveness of this framework is still limited by several deep-seated contradictions: First, the quality of the solution to the optimization problem highly depends on the global search capability and convergence efficiency of the selected algorithm. Many traditional optimization algorithms are prone to getting trapped in local optima or experiencing slow convergence when dealing with such high-dimensional, nonlinear, and complex optimization problems. Second, the setting of the optimization objective requires the simultaneous quantification of charging time and multiple safety constraints (such as lithium plating and overheating). Since these two have different dimensions and importance, how to reasonably construct an evaluation function to accurately reflect the trade-off between fast charging and safety is itself a design challenge. Finally, the theoretical current curves obtained from optimization are often complex in shape, placing excessive demands on the tracking and control capabilities of the battery management system, affecting the practicality of engineering applications. Therefore, the industry urgently needs a systematic solution that can efficiently and reliably generate fast charging strategies that are both safe and optimal and easy to implement in engineering. Summary of the Invention
[0006] To address the shortcomings and deficiencies of existing technologies, this invention provides a multi-stage constant current charging optimization method and system for lithium-ion batteries. The core of this approach lies in first constructing an electrochemical-thermal coupled simulation model capable of precisely simulating key internal states of the battery during charging (including negative electrode side reaction overpotential, battery temperature, and state of charge). Based on this, the current sequence of multi-stage constant current charging is defined as the optimization variable, with the objective of minimizing the total charging time. Innovatively, a negative electrode side reaction overpotential greater than zero to suppress lithium deposition and a battery temperature not exceeding a safety limit to prevent thermal runaway are both set as mandatory safety constraints. To solve this constrained optimization problem, this invention provides a specific fitness function, which is the sum of the total charging time and a penalty term weighted by the degree of violation of the two aforementioned safety constraints. Subsequently, the whale optimization algorithm is used to globally solve the problem. During the iteration process, candidate current sequences are simulated and evaluated using the high-fidelity model, their fitness values are calculated, and the search direction is updated using mechanisms such as prey encirclement and bubble net attacks. Finally, the current sequence with the optimal fitness is output as the charging strategy. This invention directly transforms the internal safety state into optimization constraints and uses intelligent algorithms for offline global optimization in a high-fidelity simulation environment. This achieves the goal of effectively shortening charging time while strictly ensuring battery safety and lifespan. The generated strategy can be directly deployed in existing battery management systems.
[0007] The specific technical solution adopted by this invention to solve its technical problem is as follows:
[0008] A multi-stage constant current charging optimization method for lithium-ion batteries includes:
[0009] Construct an electrochemical-thermal coupled simulation model based on the physical and electrochemical parameters of the target lithium-ion battery, capable of simulating the overpotential of the negative electrode side reaction, battery temperature, and state of charge during the charging process;
[0010] A charging optimization problem is established, with the current sequence of multi-stage constant current charging as the optimization variable and minimizing the total charging time as the optimization objective. Hard safety constraints are set as follows: the negative electrode side reaction overpotential should be greater than zero to suppress lithium deposition and the battery temperature should not exceed the safety limit to prevent thermal runaway. A fitness function is defined for solving the problem. The fitness function is a weighted sum of the total charging time and the constraint violation penalty term. The constraint violation penalty term is composed of the weighted sum of the degree of constraint violation by the negative electrode side reaction overpotential and the degree of constraint violation by the battery temperature.
[0011] The whale optimization algorithm is used to solve the charging optimization problem globally. The candidate current sequence is updated iteratively through a cooperative mechanism of surrounding prey, bubble net attack and random search. During the solution process, the total charging time, negative electrode side reaction overpotential and battery temperature corresponding to each candidate current sequence are obtained through an electrochemical-thermal coupling simulation model. The value of the fitness function is calculated based on the obtained results.
[0012] When the preset termination condition is met, the current sequence with the optimal fitness value is output as the multi-stage constant current charging strategy.
[0013] Furthermore, the electrochemical-thermal coupling simulation model includes the Doyle–Fuller–Newman electrochemical model and the lumped parameter thermal model. The lumped parameter thermal model is used to calculate the dynamic changes in battery temperature caused by ohmic heat and reaction heat during the charging process.
[0014] Furthermore, the number of stages in the multi-stage constant current charging is N, where N≥2. The switching of each stage is triggered by the battery's state of charge threshold, which is preset according to the battery's electrochemical characteristics, until the battery's state of charge reaches the target value to complete the charging process.
[0015] Furthermore, each current value in the candidate current sequence is limited to the safe current range allowed by the battery. The safe current range is preset according to the battery type. When the updated candidate current value exceeds the safe current range, boundary processing is performed.
[0016] Furthermore, the weighting coefficients used to calculate the fitness function value include the ratio coefficient of the charging time term and the constraint violation penalty term, as well as the weight of the negative electrode side reaction overpotential violation and the weight of the temperature violation in the constraint violation penalty term, wherein the penalty weight of the negative electrode side reaction overpotential violation constraint is greater than the penalty weight of the battery temperature violation constraint. The weighting coefficients are preset according to the priority requirements of charging speed and safety.
[0017] Furthermore, the initial state parameters set when constructing the electrochemical-thermal coupling simulation model include ambient temperature, initial battery temperature, initial state of charge, and target state of charge. These initial state parameters are preset according to the actual application scenario.
[0018] Furthermore, the optimal current sequence output is directly written into the charging control program of the battery management system for deployment.
[0019] And, a multi-stage constant current charging optimization system for lithium-ion batteries, comprising:
[0020] The model building module is used to obtain the physical and electrochemical parameters of the target lithium-ion battery and build an electrochemical-thermal coupled simulation model. The model can simulate the negative electrode side reaction overpotential, battery temperature and state of charge during the charging process.
[0021] The optimization problem establishment module is used to establish a charging optimization problem. The current sequence of multi-stage constant current charging is used as the optimization variable, and the optimization objective is to minimize the total charging time. Hard safety constraints are set as the negative electrode side reaction overpotential being greater than zero to suppress lithium deposition and the battery temperature not exceeding the safety limit to prevent thermal runaway. A fitness function is defined for solving the problem. The fitness function is a weighted sum of the total charging time and the constraint violation penalty term. The constraint violation penalty term is composed of the weighted sum of the degree of constraint violation by the negative electrode side reaction overpotential and the degree of constraint violation by the battery temperature.
[0022] The optimization solution module is used to globally solve the charging optimization problem using the whale optimization algorithm. It iteratively updates the candidate current sequence through a cooperative mechanism of surrounding prey, bubble net attack and random search. During the solution process, the electrochemical-thermal coupling simulation model is called to obtain the total charging time, negative electrode side reaction overpotential and battery temperature corresponding to each candidate current sequence, and the value of the fitness function is calculated based on the obtained results.
[0023] The strategy output module is used to output the current sequence with the optimal fitness value as a multi-stage constant current charging strategy when the preset termination condition is met.
[0024] And a computer device including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method described above.
[0025] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.
[0026] Compared with the prior art, the present invention and its preferred embodiments have at least the following beneficial effects:
[0027] First, by deeply integrating a high-fidelity electrochemical-thermal coupling model with intelligent optimization algorithms, the inherent contradiction between charging speed and battery health aging during fast charging is systematically resolved. The constructed model can accurately simulate the dynamic changes of key internal state variables of the battery (such as the overpotential of the negative electrode side reaction), thus providing a reliable basis for avoiding lithium deposition risks at the mechanistic level. On this basis, the safety constraint optimization problem constructed with the core of suppressing lithium deposition and preventing thermal runaway ensures that the optimization process is always carried out within the battery safety boundary, fundamentally eliminating the safety hazards caused by overcharging.
[0028] Secondly, by coupling the whale optimization algorithm to the solution of the specific technical problem in this approach, and leveraging its unique swarm intelligence search mechanism (including the synergy of prey encirclement, bubble net attack, and random search), it can efficiently perform global exploration and local fine-grained search in the complex multi-stage current sequence solution space. Its convergence speed and solution efficiency are superior to traditional optimization algorithms, significantly reducing the computational burden of obtaining the optimal strategy. This method generates a clear multi-stage constant current sequence, rather than a complex continuous current curve, greatly facilitating engineering implementation. The optimized strategy can be directly written into the control program of existing battery management systems without hardware modification, providing a highly practical technical path for developing safe and efficient fast-charging products.
[0029] In summary, this invention achieves a significant improvement in charging speed while ensuring long battery life and high safety, providing a systematic, reliable, and easy-to-deploy solution for the practical application of fast charging technology for lithium-ion batteries. Attached Figure Description
[0030] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:
[0031] Figure 1 This is a schematic diagram of the charging strategy construction and generation process according to an embodiment of the present invention;
[0032] Figure 2 This is a schematic diagram of the algorithm operation flow in an embodiment of the present invention. Detailed Implementation
[0033] To make the features and advantages of the present invention more apparent and understandable, specific embodiments are described below in detail:
[0034] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0035] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.
[0036] Considering the challenges of long charging times and battery aging in lithium-ion batteries, fast charging often leads to battery degradation, such as solid-state electrolyte interphase (SEI) growth and lithium deposition. Therefore, a balance needs to be struck between fast charging and battery life. Traditional charging strategies, such as constant current constant voltage (CC-CV) methods, are widely used due to their simplicity, but are often too conservative, resulting in long charging times. Furthermore, model-driven methods are limited by computational complexity and model uncertainty, while output-based strategies often require trial and error and are difficult to adapt to changes in battery parameters.
[0037] Against this backdrop, this invention proposes a multi-stage constant current charging strategy based on the Whale Optimization Algorithm (WOA), aiming to achieve an optimal balance between charging time and battery health degradation. This method establishes a comprehensive model incorporating battery electrical and aging characteristics, constructs a fitness function adapted to the specific problem, and utilizes the Whale Optimization Algorithm to globally optimize the charging current at each stage. This algorithm simulates the foraging behavior of a whale group, efficiently locating the globally optimal or near-optimal current sequence in a multi-dimensional solution space. This strategy not only overcomes the dependence on model accuracy and computational complexity of traditional methods but also allows for flexible adjustment of optimization weights according to actual needs, adapting to charging requirements in different scenarios.
[0038] Example 1
[0039] like Figure 1 As shown, the specific construction and implementation process of the solution is as follows:
[0040] The type of battery to be studied was determined. This embodiment focuses on a lithium battery with a graphite anode and a LiNiMnCoO2 cathode, but the present invention should not be considered as being limited by this type of lithium battery.
[0041] High-fidelity modeling and simulation of the battery system were performed. This invention uses the Doyle-Fuller-Newman (DFN) electrochemical model as the simulation basis, which can accurately describe the core processes such as diffusion, migration, and reaction kinetics of lithium ions in the internal electrodes and electrolyte of the battery. The physical and electrochemical parameters required for modeling are all derived from the manufacturer's datasheet of the target battery, ensuring the accuracy and representativeness of the model.
[0042] To simulate the thermal behavior of the battery, a lumped-parameter thermal model will be established to calculate the dynamic changes in battery temperature caused by ohmic heat, reaction heat, etc. during the charging process.
[0043] To achieve efficient algorithm integration and simulation calculations, the aforementioned electrochemical-thermal coupling model will be implemented based on the open-source battery modeling tool Pybamm. During simulation initialization, the initial states of the battery, including the ambient temperature, need to be set. The model dynamically outputs key state variables, including SOC, temperature, and side reaction overpotentials, by inputting a specific multi-stage charging current sequence during simulation. This provides an evaluation basis for the optimization algorithm.
[0044] Defining the charging method. Considering both algorithm complexity and engineering practicality, this invention adopts a multi-stage constant current (MCC) method as the optimized charging method. This method divides the entire charging process into N consecutive stages, applying a constant current in each stage.
[0045] Specifically, an N-stage charging strategy can be uniquely defined by a current sequence:
[0046]
[0047] in, This represents the current value during the k-th charging stage.
[0048] In this invention, the number of stages is defined as N=5. The switching between stages is triggered by the battery's State of Charge (SOC). Specifically, the charging interval (e.g., from the initial SOC to the target SOC) is divided into equal intervals based on SOC percentage, and the trigger condition for each stage is that the battery's SOC reaches a preset threshold. In a specific example, the switching points are set at 20%, 40%, 60%, and 80% SOC. The specific charging process is as follows:
[0049] At the start of charging, a constant current is applied in the first stage. Charge the battery.
[0050] As charging progresses, the battery's state of charge (SOC) gradually increases. When the SOC reaches 20%, the charging current switches to the next stage. .
[0051] Subsequently, the system continuously monitors the State of Charge (SOC), and when the SOC reaches 40%, 60%, and 80% respectively, the charging current switches accordingly. , , .
[0052] This process continues until all five stages of constant current charging are completed and the charging termination condition SOC=100% is reached.
[0053] This invention addresses the fast charging problem. To overcome the shortcomings of existing charging strategies in effectively balancing safety and speed, this invention constructs a safety-aware fast charging optimization problem based on an electrochemical model. The core of this problem is to minimize the charging time while strictly satisfying the safety constraints of key internal state variables of the battery.
[0054] First, define the two core security constraints that the optimization process must follow:
[0055] Temperature constraint: During the entire charging process, the battery temperature T(t) must not exceed the safe upper limit T. max This is to prevent the risk of thermal runaway.
[0056] Side reaction overpotential constraint: To ensure battery health, lithium metal deposition must be effectively suppressed. This invention addresses the side reaction overpotential constraint at the negative electrode. As an indicator for observing this risk, it is mandatory to meet the following requirements throughout the charging process. The conditions are crucial for preventing irreversible battery aging and ensuring cycle life.
[0057] Based on the above constraints, the optimization objective of this invention can be described as: finding an optimal N-stage constant current charging current sequence. This allows for a reduction in the total charging time during the process of charging from the initial state (initial SOC, initial temperature) to the target SOC in a battery dynamic system described by the DFN model and the thermal model. The goal is to keep the time as short as possible while strictly ensuring that the temperature and side reaction overpotentials do not exceed limits. To transform this multi-objective constrained optimization problem into a mathematical form suitable for solving with intelligent optimization algorithms, this invention employs the following fitness function:
[0058]
[0059]
[0060] in, It is the total charging time, a direct measurement of charging speed. These are the penalty items for violating constraints, which quantify the degree to which overpotential and temperature exceed the safety threshold. and These are the weighting coefficients for the overpotential and temperature terms. A feasible optimal solution should avoid this situation as much as possible. and The weighting coefficients for charging time and safety violation penalties.
[0061] Based on the simulated battery model established above as the simulation environment, the whale optimization algorithm is used to solve this fast charging problem. The specific process is as follows:
[0062] (1) Initialize the whale population: The position of each whale in the population represents a complete N-stage charging current sequence, i.e. The population is randomly initialized within a set current range.
[0063] (2) Simulation and evaluation: For each individual in the population (i.e., each current sequence) The data was then input into a battery electrochemical-thermal coupling model implemented in Pybamm and the simulation was run. After the simulation, the total charging time was recorded. and restrictions on penalties .
[0064] (3) Calculate fitness: Based on the results of step (2), use the fitness function described above. Calculate the fitness value for each individual. The lower the fitness value, the better the overall performance of the charging strategy (shorter charging time and safer).
[0065] (4) WOA Iterative Optimization: The whale population continuously updates its position (i.e., optimizes the charging current sequence) according to the intelligent search mechanism of WOA. In each iteration, the algorithm coordinates three core behaviors through a probabilistic selection strategy: first, surrounding the prey, causing individuals to move closer to the current optimal solution and strengthening local development; second, bubble net attack, causing individuals to walk around the optimal solution along a spiral path to achieve fine search; and third, random search, where individuals randomly follow other individuals under specific conditions to maintain population diversity and expand the global exploration range. Through the coordinated operation of the above mechanisms, the algorithm continuously optimizes the current sequence and repeats steps (2) and (3) for iterative calculation until it converges to the optimal charging strategy.
[0066] (5) Output the optimal strategy: When the preset maximum number of iterations or the fitness value converges, the algorithm terminates and outputs the individual with the best fitness in the current population. That is, under the set safety constraints, a multi-stage constant current charging strategy with the best balance between charging time and safety is achieved.
[0067] Example 2,
[0068] Based on the solution construction and process design of Example 1, the following steps are implemented:
[0069] Step 1: Determine the battery model that needs charging and obtain the electrochemical parameters for building the simulation model from the corresponding battery manufacturer. For example, this study may focus on a lithium battery with a graphite anode and a LiNiMnCoO2 cathode.
[0070] Step 2: Using the collected electrochemical parameters, the program establishes the corresponding battery DFN model, degradation model (referring to the model part used to simulate side reactions (such as lithium deposition), which is already included in the DFN model framework), and thermal model for program simulation.
[0071] In one instance, the battery's initial state is as follows: ambient temperature Battery initial temperature The initial battery SOC is set to 0.0, and the target charging SOC is set to 1.0.
[0072] Step 3: Use the whale optimization algorithm to solve the fast charging problem. The ultimate goal is to find a charging current sequence. This allows it to minimize the comprehensive fitness function J. The flowchart is as follows: Figure 2 As shown. The specific steps are as follows:
[0073] (1) Algorithm initialization
[0074] Each individual location in the whale population is defined as a candidate charging current sequence, i.e. Each current value Limited to the battery's permissible safe current range For example, setting =7.5A, .
[0075] In terms of algorithm parameters, the whale population size is set. Maximum number of iterations In the algorithm, the control parameter 'a' decreases linearly from 2 to 0 with each iteration. During initialization, it is randomly generated within the solution space. Individual whales constitute the initial population.
[0076] (2) Fitness assessment cycle
[0077] For each individual in the population (i.e., each current sequence) ), calculate its fitness value using the following steps:
[0078] a. Simulation Execution and Model Parameters: The current sequence... As a control input, it is loaded into the DFN electrochemical-thermal coupling model built based on Pybamm.
[0079] b. Data Acquisition: During the simulation, the charging status along the entire timeline is monitored and recorded in real time, including: total charging time. Overpotential of negative electrode side reaction and battery temperature .
[0080] c. Calculate the penalty term: Based on the simulation data, calculate the constraint violation penalty term through numerical integration. The penalty weight coefficient can be set to... , This significantly amplifies the cost of violating constraints.
[0081]
[0082] d. Calculate the overall fitness: The obtained... and Substitute into the fitness function The calculation is performed. The weighting coefficients can be set according to the optimization preference; for example, when focusing on charging speed, they can be set to... =0.55、 =0.45.
[0083]
[0084] (3) WOA Iterative Update
[0085] In each generation, the algorithm updates the positions of all individual whales according to its mechanisms (surrounding prey, bubble web attack, random search). After each position update, it is necessary to check whether the new position exceeds the current boundary. And perform necessary boundary processing.
[0086] (4) Termination and Output
[0087] Repeat steps (2) and (3) until the preset maximum number of iterations is reached. When the algorithm terminates, it outputs the minimum fitness value found during the entire optimization process. Individual position . This is the optimal five-stage constant current charging strategy solved by this invention. This strategy can be directly written into the charging control program of the battery management system (BMS) to perform safe and fast charging in practical applications.
[0088] Through the above steps, this invention can propose an optimal charging strategy for any type of lithium-ion battery, taking into account both charging speed and battery aging to a certain extent.
[0089] Compared with the prior art, the above design of the present invention has the following outstanding differences and advantages:
[0090] (1) By constructing a mathematical problem with charging time as the optimization target and the core state of the battery (overpotential and temperature of side reaction) as the hard safety constraint, and using the whale optimization algorithm for global search, it is possible to systematically find the multi-stage constant current strategy with the fastest charging speed under the premise of strictly eliminating the risk of lithium deposition and overheating, which fundamentally solves the inherent contradiction between fast charging and battery health.
[0091] (2) Unlike traditional simplified models or purely data-driven methods, this invention uses a high-precision Doyle–Fuller–Newman (DFN) electrochemical model for simulation, which can more realistically reflect the internal dynamics of the battery. At the same time, the Whale Optimization Algorithm (WOA), which has a simple structure and fast convergence speed, is used for current sequence optimization. Compared with traditional optimization algorithms (such as PSO and GA), this algorithm is more efficient in solving such problems, and significantly reduces the computational complexity and hardware computing power requirements while ensuring the optimality of the strategy.
[0092] (3) The multi-stage constant current (MCCC) charging method and the stage switching logic based on state of charge (SOC) are very easy to implement and deploy on existing battery management systems (BMS). The optimization results in a clear sequence of constant current values, rather than a complex continuous curve, which greatly facilitates engineering applications and provides a direct and reliable technical path for developing safe and efficient fast charging products.
[0093] Based on the same inventive concept, this invention also provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the programs include program instructions, and the processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, used to implement one or more instructions, specifically for loading and executing one or more instructions stored in a computer storage medium to implement the above-described method.
[0094] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium storing a computer program, which, when executed by a processor, performs the above-described method. This storage medium can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0095] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0096] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.
[0097] This invention is not limited to the above-described preferred embodiments. Anyone inspired by this invention can derive other forms of multi-stage constant current charging optimization methods for lithium-ion batteries. All equivalent variations and modifications made within the scope of the claims of this invention should be included in the scope of this invention.
Claims
1. A method for optimizing multi-stage constant current charging of a lithium-ion battery, the method comprising: The method comprises the steps of: constructing an electro-thermal coupling simulation model based on physical and electrochemical parameters of a target lithium-ion battery, which can simulate the overpotential of negative electrode side reactions, battery temperature and state of charge during charging; establishing a charging optimization problem, taking a multi-stage constant current charging current sequence as an optimization variable, taking the minimum total charging time as an optimization objective, taking the overpotential of negative electrode side reactions greater than zero to inhibit lithium deposition and the battery temperature not exceeding the upper limit of safety to prevent thermal runaway as hard safety constraints, and defining a fitness function for solving, wherein the fitness function is a weighted sum of the total charging time and a constraint violation penalty term, and the constraint violation penalty term is composed of the weighted sum of the degree of violation of the overpotential of negative electrode side reactions and the degree of violation of the battery temperature; solving the charging optimization problem globally by using a whale optimization algorithm, iteratively updating the candidate current sequence through the cooperative mechanism of surrounding prey, bubble net attack and random search, and obtaining the total charging time, the overpotential of negative electrode side reactions and the battery temperature corresponding to each candidate current sequence through the electro-thermal coupling simulation model during the solving process, and calculating the value of the fitness function based on the obtained results; when the preset termination condition is met, outputting the current sequence with the optimal fitness value as the multi-stage constant current charging strategy.
2. The method of claim 1, wherein: The electro-thermal coupling simulation model comprises a Doyle-Fuller-Newman electrochemical model and a lumped parameter thermal model, and the lumped parameter thermal model is used to calculate the dynamic change of the battery temperature caused by ohmic heat and reaction heat during charging.
3. The method of claim 1, wherein: The number of stages of the multi-stage constant current charging is N, and N is greater than or equal to 2. The switching of each stage is triggered by a state of charge threshold of the battery, which is pre-set according to the electrochemical characteristics of the battery. The charging is completed until the state of charge of the battery reaches a target value.
4. The method of claim 1, wherein: Each current value in the candidate current sequence is limited in a safe current range allowed by the battery, and the safe current range is pre-set according to the type of the battery. When the updated candidate current value exceeds the safe current range, boundary processing is performed.
5. The method of claim 1, wherein: The weight coefficients for calculating the value of the fitness function include a matching coefficient of the charging time term and the constraint violation penalty term, and a negative electrode side reaction overpotential violation weight and a temperature violation weight in the constraint violation penalty term, wherein the penalty weight of the negative electrode side reaction overpotential violation constraint is greater than the penalty weight of the battery temperature violation constraint. The weight coefficients are pre-set according to the priority demand of charging speed and safety.
6. The method of claim 1, wherein: The initial state parameters set when constructing the electro-thermal coupling simulation model include an ambient temperature, a battery initial temperature, an initial state of charge and a target state of charge, which are pre-set according to actual application scenarios.
7. The method of claim 1, wherein: The optimal current sequence output is directly written into a charging control program of a battery management system for deployment.
8. A multi-stage constant current charging optimization system for a lithium-ion battery, characterized in that, The method comprises the steps of: a model construction module, configured to obtain physical and electrochemical parameters of a target lithium-ion battery, and construct an electro-thermal coupling simulation model, which can simulate the overpotential of negative electrode side reactions, battery temperature and state of charge during charging; The optimization problem establishing module is configured to establish a charging optimization problem, take a current sequence of multi-stage constant current charging as an optimization variable, minimize total charging time as an optimization objective, take a negative electrode side reaction overpotential greater than zero to inhibit lithium deposition and a battery temperature not exceeding a safety upper limit to prevent thermal runaway as hard safety constraints, and define a fitness function for solving, the fitness function being a weighted sum of the total charging time and a constraint violation penalty term, the constraint violation penalty term being composed of a degree of negative electrode side reaction overpotential constraint violation and a degree of battery temperature constraint violation. The optimization solving module is configured to globally solve the charging optimization problem by using a whale optimization algorithm, iteratively update a candidate current sequence through a cooperative mechanism of surrounding prey, bubble net attack and random search, and in the solving process, call the electrochemical-thermal coupling simulation model to obtain a total charging time, a negative electrode side reaction overpotential and a battery temperature corresponding to each candidate current sequence, and calculate a value of the fitness function based on the obtained results. The strategy output module is configured to output a current sequence with an optimal fitness value as a multi-stage constant current charging strategy when a preset termination condition is met.
9. A computer device, comprising: The computer program is stored in the storage medium and is executed by the processor to implement the multi-stage constant current charging optimization method of the lithium ion battery.
10. A non-transitory computer-readable storage medium, comprising: The computer program is stored in the storage medium and is executed by the processor to implement the multi-stage constant current charging optimization method of the lithium ion battery.