Power battery fast charging time optimization method and device, electronic equipment and storage medium
By simulating the temperature changes of the power battery and optimizing the charging path, the impact of the temperature rise rate on the charging time was resolved, thus optimizing the fast charging time of the power battery and improving charging efficiency and safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-16
- Publication Date
- 2026-03-13
AI Technical Summary
Existing technologies do not consider the impact of the battery temperature rise rate on charging time, resulting in excessively rapid temperature rise during high-rate fast charging at low SOC, which affects battery safety and lifespan, increases the energy consumption of vehicle thermal management, and fails to optimize charging time.
By acquiring the basic parameters and initial state information of the power battery, simulating temperature changes using a second-order equivalent circuit model and a thermal simulation model, and combining optimization algorithms to optimize the charging map, the optimal charging path and time are determined, and the charging current is dynamically adjusted to optimize the fast charging process.
It significantly improves fast charging efficiency, shortens charging time, enhances battery safety and vehicle thermal management efficiency, and reduces user charging costs.
Smart Images
Figure CN121663758A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power battery technology, and in particular to a method, apparatus, electronic device and storage medium for optimizing the fast charging time of a power battery. Background Technology
[0002] The design of fast charging paths for power batteries is usually based on cell testing. Under the current temperature and SOC (State of Charge), the key boundary condition is not to exceed the charging cutoff voltage, which serves as an important guarantee for battery safety and lifespan. However, the impact of temperature rise rate on fast charging time is ignored. Especially in high-rate fast charging scenarios, when the SOC is low, charging according to the existing fast charging current parameters may cause the cell temperature to rise rapidly, thereby triggering current limiting.
[0003] In related technologies, the fast charging process of power batteries is divided into several segments according to the State of Charge (SOC). The internal resistance and maximum charging rate are calibrated through experiments. Guided by the charging time requirements, the charging rate is reduced as much as possible while meeting the charging time requirements, thereby reducing battery degradation.
[0004] However, the methods in the relevant technologies are not optimized for charging time, and the rapid rise in power battery temperature increases the heat exchange with the power battery thermal management system, thereby affecting the overall vehicle thermal management energy consumption and adding extra charging costs for users. Using high-rate fast charging at low SOC has a certain impact on the safety and lifespan of the power battery, but it does not shorten the fast charging time. Moreover, the overall charging time may be longer due to the excessively rapid temperature rise rate of high-rate fast charging at low SOC. This issue urgently needs to be addressed. Summary of the Invention
[0005] This application provides a method, apparatus, electronic device, and storage medium for optimizing the fast charging time of a power battery, thereby solving problems such as the failure to consider the impact of temperature rise rate on charging time in related technologies and improving fast charging efficiency.
[0006] The first aspect of this application provides a method for optimizing the fast charging time of a power battery, including the following steps: Obtain the basic parameters and initial state information of the power battery; The basic parameters and the initial state information are input into a preset second-order equivalent circuit model to obtain the dynamic characteristics of the power battery. The dynamic characteristics of the power battery, the basic parameters, and the initial state information are then input into a preset thermal simulation model to obtain the simulation results of the temperature change of the power battery during the fast charging process. Based on the basic parameters, the initial state information, the dynamic characteristics of the power battery, and the temperature change simulation results, with the goal of minimizing charging time, a preset optimization algorithm is used to optimize the preset charging map to obtain the optimal charging map. The optimal charging time of the power battery is then obtained based on the optimal charging path corresponding to the optimal charging map.
[0007] Optionally, obtaining the optimal charging time for the power battery based on the optimal charging path corresponding to the optimal charging map includes: Based on the preset fast charging optimization strategy and / or the preset fast charging rate correction function, the allowable charging current of the power battery is obtained according to the optimal charging map; The optimal charging path is determined based on the allowable charging current of the power battery, and the optimal charging time is determined based on the optimal charging path.
[0008] Optionally, the preset fast charging rate correction function is: ; in, The initial battery level for charging. The initial cell temperature at the start of charging. The current cell temperature, This is the current battery level. This represents the current ambient temperature.
[0009] Optionally, the allowable charging current of the power battery is: ; in, The preset fast charging rate correction function is... To find the allowable charging rate corresponding to the initial fast charging MAP by using the current cell temperature and battery SOC, This refers to the capacity of the power battery.
[0010] Optionally, the dynamic characteristics of the power battery include at least one of resistance characteristics, inductance characteristics, and cell heat generation.
[0011] A second aspect of this application provides a device for optimizing the fast charging time of a power battery, comprising: The acquisition module is used to acquire the basic parameters and initial state information of the power battery; The simulation module is used to input the basic parameters and the initial state information into a preset second-order equivalent circuit model to obtain the dynamic characteristics of the power battery, and to input the dynamic characteristics of the power battery, the basic parameters and the initial state information into a preset thermal simulation model to obtain the temperature change simulation results of the power battery during the fast charging process. The determination module is used to optimize a preset charging map using a preset optimization algorithm based on the basic parameters, the initial state information, the dynamic characteristics of the power battery, and the temperature change simulation results, with the goal of minimizing the charging time, to obtain the optimal charging map, and to obtain the optimal charging time of the power battery according to the optimal charging path corresponding to the optimal charging map.
[0012] Optionally, the determining module is specifically used for: Based on the preset fast charging optimization strategy and / or the preset fast charging rate correction function, the allowable charging current of the power battery is obtained according to the optimal charging map; The optimal charging path is determined based on the allowable charging current of the power battery, and the optimal charging time is determined based on the optimal charging path.
[0013] Optionally, the preset fast charging rate correction function is: ; in, The initial battery level for charging. The initial cell temperature at the start of charging. The current cell temperature, This is the current battery level. This represents the current ambient temperature.
[0014] Optionally, the allowable charging current of the power battery is: ; in, The preset fast charging rate correction function is... To find the allowable charging rate corresponding to the initial fast charging MAP by using the current cell temperature and battery SOC, This refers to the capacity of the power battery.
[0015] Optionally, the dynamic characteristics of the power battery include at least one of resistance characteristics, inductance characteristics, and cell heat generation.
[0016] A third aspect of this application provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform the power battery fast charging time optimization method as described in the above embodiments.
[0017] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the power battery fast charging time optimization method as described in the above embodiments.
[0018] A fifth aspect of this application provides a computer program product storing a computer program that, when executed by a processor, implements the power battery fast charging time optimization method as described in the above embodiments.
[0019] Therefore, this embodiment of the application obtains the basic parameters and initial state information of the power battery and inputs them into a preset second-order equivalent circuit model to obtain the dynamic characteristics of the power battery. The dynamic characteristics, basic parameters, and initial state information of the power battery are then input into a preset thermal simulation model to obtain the temperature change simulation results of the power battery during fast charging. Based on the basic parameters, initial state information, dynamic characteristics of the power battery, and temperature change simulation results, with the shortest charging time as the objective, a preset optimization algorithm is used to optimize a preset charging map to obtain the optimal charging map. The optimal charging time of the power battery is then obtained based on the optimal charging path corresponding to the optimal charging map. This solves the problem in related technologies that do not consider the impact of temperature rise rate on charging time, thus improving fast charging efficiency.
[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for optimizing the fast charging time of a power battery according to an embodiment of this application; Figure 2 This is a schematic diagram of the power battery charging time simulation model of a power battery fast charging time optimization method according to an embodiment of this application; Figure 3 This is a schematic diagram of a fast charging MAP of a power battery according to an embodiment of the present application for a method to optimize the fast charging time of a power battery; Figure 4 The flowchart below shows a charging path optimization method based on a genetic algorithm for optimizing the fast charging time of a power battery according to an embodiment of this application. Figure 5 This is a schematic diagram of a power battery fast charging time optimization device provided according to an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation
[0022] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.
[0023] The following describes a method, apparatus, electronic device, and storage medium for optimizing the fast charging time of a power battery according to embodiments of this application, with reference to the accompanying drawings. Addressing the problem mentioned in the background art that the influence of temperature rise rate on charging time is not considered, this application provides a method for optimizing the fast charging time of a power battery. In this method, the basic parameters and initial state information of the power battery are obtained and input into a preset second-order equivalent circuit model to obtain the dynamic characteristics of the power battery. The dynamic characteristics, basic parameters, and initial state information of the power battery are then input into a preset thermal simulation model to obtain the temperature change simulation results of the power battery during the fast charging process. Based on the basic parameters, initial state information, dynamic characteristics of the power battery, and temperature change simulation results, with the shortest charging time as the objective, a preset optimization algorithm is used to optimize a preset charging map to obtain an optimal charging map. The optimal charging time of the power battery is then obtained based on the optimal charging path corresponding to the optimal charging map. This solves the problem of not considering the influence of temperature rise rate on charging time in related technologies and improves fast charging efficiency.
[0024] Specifically, Figure 1 This is a flowchart illustrating a method for optimizing the fast charging time of a power battery, as provided in an embodiment of this application.
[0025] like Figure 1 As shown, the method for optimizing the fast charging time of a power battery includes the following steps: In step S101, the basic parameters and initial state information of the power battery are obtained. Specifically, it obtains basic parameters of the power battery, such as cell capacity, specific heat capacity, thermal conductivity, internal resistance, thermal parameters of thermally conductive adhesive, and parameters of cold plate; it also obtains initial state information of the power battery, such as SOC, initial cell temperature, fast charging MAP, and ambient temperature.
[0026] In step S102, the basic parameters and initial state information are input into a preset second-order equivalent circuit model to obtain the dynamic characteristics of the power battery. The dynamic characteristics, basic parameters and initial state information of the power battery are then input into a preset thermal simulation model to obtain the simulation results of the temperature change of the power battery during the fast charging process.
[0027] Among them, the preset second-order equivalent circuit model is a pre-established second-order circuit mathematical model used to simulate the dynamic electrical characteristics of the power battery; the preset thermal simulation model refers to a pre-established and configured mathematical model or calculation tool used to simulate the temperature change of the power battery during charging and discharging.
[0028] Specifically, the pre-defined second-order equivalent circuit model primarily functions to describe the dynamic characteristics of the battery through an equivalent circuit, including the relationship between current and voltage changes, as well as the calculation of cell heat generation and charging time. Then, these dynamic characteristic data (such as real-time current, voltage, and internal resistance changes) along with the battery's basic parameters and initial state information are input into the pre-defined thermal simulation model. Figure 2 As shown, Figure 2 This is a schematic diagram of the power battery charging time simulation model for a power battery fast charging time optimization method according to an embodiment of this application. The preset thermal simulation model mainly includes a heat transfer module for the battery cell / thermal conductive adhesive / cold plate / cooling medium, a cooling circuit module, and a thermal management strategy module. The heat transfer module simulates the heat transfer process between different materials, including heat conduction and heat convection. For example, the heat conduction module simulates the heat transfer inside the battery cell, and the convection module simulates the cooling effect of the cooling medium (such as air or liquid cooling plate). The cooling circuit module is used to simulate the working state of the cooling circuit during charging, simulating the flow and pressure changes of the cooling medium, thereby affecting the heat dissipation performance of the battery. The thermal management strategy module is used to simulate the impact of different thermal management strategies on the heat dissipation performance of the battery. For example, strategies such as fan-driven, semiconductor cooling, or natural convection cooling can be selected, and different strategies have a significant impact on the heat dissipation performance of the battery. Through the preset thermal simulation model, the charging time under different ambient temperatures, different initial SOC and initial battery cell temperatures can be accurately calculated, thereby evaluating the thermal safety of the battery under fast charging conditions and obtaining the simulation results of the temperature change of the power battery during the fast charging process.
[0029] Optionally, in some embodiments, the dynamic characteristics of the power battery include at least one of resistance characteristics, inductance characteristics, and cell heat generation.
[0030] It is understandable that the resistance of a power battery mainly consists of internal resistance and contact resistance. Internal resistance determines the relationship between current and voltage, while contact resistance affects current distribution. A second-order equivalent circuit can more accurately simulate the battery's resistance characteristics, thus allowing for more precise calculation of current and voltage changes. The inductive characteristics of a power battery primarily stem from the cell's self-inductance effect. Inductance affects the rate of current change, thereby influencing voltage changes. A second-order equivalent circuit, by introducing an inductance term, can more comprehensively describe the battery's dynamic characteristics. Through a pre-defined second-order equivalent circuit model, the cell's heat generation process can be simulated. Specifically, heat is generated when current flows through the cell. This heat generation is closely related to the magnitude of the current and the duration of the current flow. By introducing resistance and inductance terms, the second-order equivalent circuit can more accurately calculate the cell's heat generation.
[0031] In step S103, based on basic parameters, initial state information, dynamic characteristics of the power battery, and temperature change simulation results, with the goal of minimizing charging time, a preset optimization algorithm is used to optimize the preset charging map to obtain the optimal charging map, and the optimal charging time of the power battery is obtained according to the optimal charging path corresponding to the optimal charging map.
[0032] The preset charging map is a predefined two-dimensional / three-dimensional control lookup table that reflects the maximum safe charging current or power allowed for the power battery under different states (such as SOC, temperature, etc.).
[0033] Specifically, this invention employs a genetic algorithm or particle swarm optimization method to optimize the fast charging path. Based on a power battery charging time simulation model, it inputs initial SOC, cell temperature, ambient temperature, and charging MAP, taking the shortest charging time as the optimization objective. The optimization objective is to minimize the fast charging time while ensuring battery safety (the power battery charging current does not exceed the initial charging MAP setting during optimization). A genetic algorithm or particle swarm optimization method (a preset intelligent optimization algorithm) is used to iteratively optimize the initial charging MAP. During the optimization process, the algorithm dynamically adjusts the charging current curve while strictly satisfying safety constraints such as voltage, temperature, and polarization, outputting the optimal charging map; for example... Figure 3 As shown, Figure 3 This diagram illustrates a fast-charging map of a power battery according to an embodiment of this application, illustrating a method for optimizing fast-charging time of a power battery. The optimization results are verified by selecting characteristic fast-charging conditions, such as high temperature, normal temperature, low temperature, and extremely low temperature, with initial SOCs of 10%, 30%, and 50%. Based on the segmented constant-current charging strategy determined by the optimal charging map, the charging time for each stage is calculated by time integration and then summed to obtain the optimal charging time of the power battery within the safety boundary, achieving coordinated optimization of fast charging and battery safety.
[0034] For example, optimizing the charging MAP (Modular Map) to find the optimal charging path under given conditions; using a genetic algorithm as an example, the charging model optimizes the MAP path as follows: Figure 4 As shown, Figure 4 This document presents a flowchart of a charging path optimization method for fast charging time of a power battery based on a genetic algorithm, according to one embodiment of this application. First, the initial state parameters of the battery are input, including key parameters such as initial SOC (State of Charge) and cell temperature. Then, the parameters of the genetic algorithm are set, including control parameters such as population size, number of iterations, crossover rate, and mutation rate. An initial population is generated, representing different potential charging path schemes. Mutation operations are used to introduce diversity and prevent the algorithm from getting trapped in local optima. A crossover operation is performed to combine the superior characteristics of different individuals. A charging time simulation model is called to evaluate each individual (charging path scheme), calculate the fitness value, and select the best individuals to enter the next generation based on the fitness value. The termination condition is checked (such as reaching the maximum number of iterations or fitness convergence). If not, iterative optimization continues. Once the condition is met, the optimal charging MAP is decoded, and finally, a charging time report is generated, outputting the optimized charging path.
[0035] Therefore, this process, by simulating biological evolution mechanisms and considering battery state constraints, automatically finds the optimal charging path with the shortest charging time. The charging time simulation model is crucial for evaluation; it integrates the battery's electrochemical and thermodynamic characteristics to ensure that the optimization results are both efficient and safe.
[0036] Optionally, in some embodiments, obtaining the optimal charging time of the power battery according to the optimal charging path corresponding to the optimal charging map includes: obtaining the allowable charging current of the power battery according to the optimal charging map based on a preset fast charging optimization strategy and / or a preset fast charging rate correction function; determining the optimal charging path according to the allowable charging current of the power battery; and determining the optimal charging time according to the optimal charging path.
[0037] Optionally, in some embodiments, the preset fast charging rate correction function is: ; in, The initial battery level for charging. The initial cell temperature at the start of charging. The current cell temperature, This is the current battery level. This represents the current ambient temperature.
[0038] Optionally, in some embodiments, the allowable charging current of the power battery is: ; in, This is the preset fast charging rate correction function. To find the allowable charging rate corresponding to the initial fast charging MAP by using the current cell temperature and battery SOC, This refers to the capacity of the power battery.
[0039] Understandably, for fast charging path optimization models, the optimal charging path differs depending on the initial temperature and initial SOC, and these paths may intersect, with the values of the intersecting parts affecting the fast charging experience. Therefore, the method of using a single fast charging map to control the fast charging path in related technologies cannot guarantee optimal fast charging time under all operating conditions. This paper optimizes the fast charging path for different initial cell temperatures and initial SOCs to obtain the optimal charging path under those conditions. This charging path is then compared with the initial map to establish a fast charging rate correction function. During vehicle fast charging, based on real-time collected data on the initial fast charging temperature, initial fast charging SOC, current cell temperature, current battery SOC, and current ambient temperature, the fast charging rate is calculated using the preset fast charging rate correction function. This allows for dynamic adjustment of fast charging parameters, optimization of fast charging efficiency, and reduction of charging time.
[0040] Furthermore, to address the issue that using a single fast-charging map to control the fast-charging path cannot guarantee optimal fast-charging time for all operating conditions, a method based on big data statistics can be employed. This involves analyzing user fast-charging behavior and statistically analyzing the probability distribution of initial cell temperature and SOC at the start of fast charging. During fast-charging path optimization, these statistical results are embedded, prioritizing initial charging time and initial cell temperature based on the probability of each user's operating condition from low to high. Low-probability conditions are optimized first, while the optimization results for high-probability conditions cover the overlap with low-probability conditions. However, this method requires the fewest modifications to current fast-charging strategies but sacrifices a small portion of the user's charging experience.
[0041] It should be noted that other optimization algorithms can also be considered in the embodiments of this application, such as simulated annealing, differential evolution, and other optimization algorithms to replace genetic algorithms or particle swarm optimization algorithms, in order to further improve optimization efficiency. Deep learning models can be used to predict battery temperature and SOC changes, combined with dynamic optimization strategies to achieve more accurate fast charging path selection. This invention combines a real-time battery monitoring system to dynamically adjust the fast charging path, further reducing charging time. This invention performs multi-dimensional optimization, considering multiple indicators such as fast charging rate, charging safety, and user experience, and designs a multi-objective optimization algorithm.
[0042] Therefore, this invention, by constructing a charging time calculation model, employing optimization algorithms to optimize the fast charging path, and establishing a fast charging rate function, significantly improves fast charging efficiency by considering factors such as initial SOC, initial temperature, current SOC, current temperature, fast charging rate boundary, and ambient temperature during the fast charging process. This solves the drawback of related technologies that do not consider the impact of temperature rise rate on charging time in the charging path. By dynamically optimizing the fast charging path, shorter charging times can be achieved under different operating conditions. By optimizing the fast charging MAP through genetic algorithms or particle swarm optimization, charging time is reduced, especially for high-rate fast charging, avoiding excessively long charging times due to rapid temperature rise. Combining user behavior big data, the fast charging path priority is optimized to improve user experience and charging efficiency.
[0043] According to the fast charging time optimization method for power batteries proposed in this application, the basic parameters and initial state information of the power battery are obtained and input into a preset second-order equivalent circuit model to obtain the dynamic characteristics of the power battery. The dynamic characteristics, basic parameters, and initial state information of the power battery are then input into a preset thermal simulation model to obtain the temperature change simulation results of the power battery during fast charging. Based on the basic parameters, initial state information, dynamic characteristics of the power battery, and temperature change simulation results, with the shortest charging time as the objective, a preset optimization algorithm is used to optimize a preset charging map to obtain the optimal charging map. The optimal charging time of the power battery is then obtained based on the optimal charging path corresponding to the optimal charging map. This solves the problem in related technologies that do not consider the impact of temperature rise rate on charging time, thus improving fast charging efficiency.
[0044] Next, referring to the accompanying drawings, a power battery fast charging time optimization device proposed according to an embodiment of this application is described.
[0045] Figure 5 This is a block diagram of a power battery fast charging time optimization device according to an embodiment of this application.
[0046] like Figure 5 As shown, the power battery fast charging time optimization device 10 includes: an acquisition module 100, a simulation module 200, and a determination module 300.
[0047] Among them, the acquisition module 100 is used to acquire the basic parameters and initial state information of the power battery; The simulation module 200 is used to input basic parameters and initial state information into a preset second-order equivalent circuit model to obtain the dynamic characteristics of the power battery, and to input the dynamic characteristics, basic parameters and initial state information of the power battery into a preset thermal simulation model to obtain the simulation results of the temperature change of the power battery during the fast charging process. The determination module 300 is used to optimize a preset charging map based on basic parameters, initial state information, dynamic characteristics of the power battery and temperature change simulation results, with the goal of minimizing charging time. It uses a preset optimization algorithm to obtain the optimal charging map and obtains the optimal charging time of the power battery based on the optimal charging path corresponding to the optimal charging map.
[0048] Optionally, the determining module 300 is specifically used for: obtaining the allowable charging current of the power battery based on the optimal charging map according to a preset fast charging optimization strategy and / or a preset fast charging rate correction function; determining the optimal charging path according to the allowable charging current of the power battery; and determining the optimal charging time according to the optimal charging path.
[0049] Optionally, the preset fast charging rate correction function is: ; in, The initial battery level for charging. The initial cell temperature at the start of charging. The current cell temperature, This is the current battery level. This represents the current ambient temperature.
[0050] Optionally, the allowable charging current of the power battery: ; in, This is the preset fast charging rate correction function. To find the allowable charging rate corresponding to the initial fast charging MAP by using the current cell temperature and battery SOC, This refers to the capacity of the power battery.
[0051] Optionally, the dynamic characteristics of the power battery include at least one of the following: resistance characteristics, inductance characteristics, and cell heat generation.
[0052] It should be noted that the foregoing explanation of the embodiment of the method for optimizing the fast charging time of a power battery also applies to the device for optimizing the fast charging time of a power battery in this embodiment, and will not be repeated here.
[0053] According to the power battery fast charging time optimization device proposed in this application, the basic parameters and initial state information of the power battery are obtained and input into a preset second-order equivalent circuit model to obtain the dynamic characteristics of the power battery. The dynamic characteristics, basic parameters, and initial state information of the power battery are then input into a preset thermal simulation model to obtain the temperature change simulation results of the power battery during fast charging. Based on the basic parameters, initial state information, dynamic characteristics of the power battery, and temperature change simulation results, with the shortest charging time as the objective, a preset optimization algorithm is used to optimize a preset charging map to obtain the optimal charging map. The optimal charging time of the power battery is then obtained based on the optimal charging path corresponding to the optimal charging map. This solves the problem in related technologies that do not consider the impact of temperature rise rate on charging time, thus improving fast charging efficiency.
[0054] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.
[0055] When the processor 602 executes the program, it implements the power battery fast charging time optimization method provided in the above embodiments.
[0056] Furthermore, electronic devices also include: Communication interface 603 is used for communication between memory 601 and processor 602.
[0057] The memory 601 is used to store computer programs that can run on the processor 602.
[0058] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.
[0059] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.
[0060] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.
[0061] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.
[0062] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for optimizing the fast charging time of a power battery.
[0063] This application also provides a computer program product, which stores a computer program that, when executed by a processor, implements the above-mentioned method for optimizing the fast charging time of a power battery.
[0064] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0065] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0066] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.
[0067] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.
[0068] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.
Claims
1. A method for optimizing the fast charging time of a power battery, characterized in that, Includes the following steps: Obtain the basic parameters and initial state information of the power battery; The basic parameters and the initial state information are input into a preset second-order equivalent circuit model to obtain the dynamic characteristics of the power battery. The dynamic characteristics of the power battery, the basic parameters, and the initial state information are then input into a preset thermal simulation model to obtain the simulation results of the temperature change of the power battery during the fast charging process. Based on the basic parameters, the initial state information, the dynamic characteristics of the power battery, and the temperature change simulation results, with the goal of minimizing charging time, a preset optimization algorithm is used to optimize the preset charging map to obtain the optimal charging map. The optimal charging time of the power battery is then obtained based on the optimal charging path corresponding to the optimal charging map.
2. The method according to claim 1, characterized in that, The step of obtaining the optimal charging time for the power battery based on the optimal charging path corresponding to the optimal charging map includes: Based on the preset fast charging optimization strategy and / or the preset fast charging rate correction function, the allowable charging current of the power battery is obtained according to the optimal charging map; The optimal charging path is determined based on the allowable charging current of the power battery, and the optimal charging time is determined based on the optimal charging path.
3. The method according to claim 2, characterized in that, The preset fast charging rate correction function is: ； in, The initial battery level for charging. The initial cell temperature at the start of charging. The current cell temperature, This is the current battery level. This represents the current ambient temperature.
4. The method according to claim 2, characterized in that, The allowable charging current of the power battery is: ； in, The preset fast charging rate correction function is... To find the allowable charging rate corresponding to the initial fast charging MAP by using the current cell temperature and battery SOC, This refers to the capacity of the power battery.
5. The method according to claim 1, characterized in that, The dynamic characteristics of the power battery include at least one of the following: resistance characteristics, inductance characteristics, and cell heat generation.
6. A device for optimizing the fast charging time of a power battery, characterized in that, include: The acquisition module is used to acquire the basic parameters and initial state information of the power battery; The simulation module is used to input the basic parameters and the initial state information into a preset second-order equivalent circuit model to obtain the dynamic characteristics of the power battery, and to input the dynamic characteristics of the power battery, the basic parameters and the initial state information into a preset thermal simulation model to obtain the temperature change simulation results of the power battery during the fast charging process. The determination module is used to optimize a preset charging map using a preset optimization algorithm based on the basic parameters, the initial state information, the dynamic characteristics of the power battery, and the temperature change simulation results, with the goal of minimizing the charging time, to obtain the optimal charging map, and to obtain the optimal charging time of the power battery according to the optimal charging path corresponding to the optimal charging map.
7. The apparatus according to claim 6, characterized in that, The determining module is specifically used for: Based on the preset fast charging optimization strategy and / or the preset fast charging rate correction function, the allowable charging current of the power battery is obtained according to the optimal charging map; The optimal charging path is determined based on the allowable charging current of the power battery, and the optimal charging time is determined based on the optimal charging path.
8. An electronic device, characterized in that, include: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the power battery fast charging time optimization method as described in any one of claims 1-5.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the power battery fast charging time optimization method as described in any one of claims 1-5.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the power battery fast charging time optimization method as described in any one of claims 1-5.