Battery low-temperature alternating-current heating method and device and vehicle

Abstract

The invention relates to a battery low-temperature alternating current heating method and device and a vehicle, and aims to provide a battery low-temperature heating method without generating a lithium precipitation phenomenon, and the method comprises the following steps: obtaining battery temperature data and battery residual capacity; identifying parameters of circuit elements in the equivalent circuit model of the battery by adopting a grey wolf-particle swarm GWO-PSO algorithm; under the condition that it is determined that the battery needs to be heated, the optimal alternating current parameters are calculated based on the battery temperature data, the battery residual capacity and the parameters of the circuit elements; the optimal alternating current parameter is the alternating current parameter corresponding to the maximum heat production rate without generating the lithium precipitation phenomenon; and heating the battery based on the optimal alternating current parameter. According to the method, the efficiency and the accuracy of distinguishing the circuit element parameters are improved through the grey wolf-particle swarm GWO-PSO algorithm, then the optimal alternating current parameters are determined based on the circuit element parameters, the use performance of the battery can be quickly recovered in a low-temperature environment, lithium precipitation is avoided, and the health of the battery is not influenced.

Description

Technical Field

[0001] The present application relates to the technical field of battery thermal management, and in particular to a method, device, and vehicle for low-temperature AC heating of a battery. Background Art

[0002] In low-temperature environments, the battery performance of new energy vehicles is severely affected, which greatly restricts the promotion and popularization of new energy vehicles in cold regions. Therefore, improving the performance of batteries in low-temperature environments and improving the adaptability of new energy vehicles in low-temperature environments have become important research topics.

[0003] At present, the method to improve the low-temperature performance of lithium-ion batteries is mainly to heat the batteries through the battery thermal management system. The AC heating methods currently used are mostly fixed frequency and fixed amplitude, fixed frequency and variable amplitude, or fixed amplitude and variable frequency.

[0004] However, this process carries the risk of lithium plating, which can damage the internal structure of the battery and affect its lifespan. Summary of the Invention

[0005] The present application provides a method, device and vehicle for low-temperature AC heating of a battery to solve the problem of lithium deposition caused by AC heating of a battery in a low-temperature environment.

[0006] In order to achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0007] In a first aspect, a method for low-temperature AC heating of a battery is provided, the method comprising: obtaining battery temperature data and the remaining battery capacity; using a Grey Wolf-Particle Swarm Optimization (GWO-PSO) algorithm to identify parameters of circuit elements in an equivalent circuit model of the battery; when it is determined that the battery needs to be heated, calculating optimal AC parameters based on the battery temperature data, the remaining battery capacity, and the parameters of the circuit elements; the optimal AC parameters are the AC parameters corresponding to the maximum heat production rate without causing lithium plating; and heating the battery based on the optimal AC parameters.

[0008] According to the above technical means, by obtaining battery temperature data and battery remaining capacity, a basic basis is provided for subsequent heating, ensuring that the heating strategy adapts to the actual state of the battery. The Gray Wolf-Particle Swarm (GWO-PSO) algorithm is used to identify the parameters of the circuit components in the battery's equivalent circuit model. This combines the global search capability of the Gray Wolf algorithm with the local search advantage of the Particle Swarm algorithm. Compared with the identification methods using a single Gray Wolf algorithm and a Particle Swarm algorithm, it can obtain the parameters of the circuit components more accurately and quickly. The AC parameters calculated based on these parameters will be reliable. When it is determined that the battery needs to be heated, the optimal AC parameters are calculated based on the battery temperature data, the remaining battery capacity, and the parameters of the circuit components. The AC parameters are the parameters corresponding to the maximum heat generation rate without lithium plating, which can avoid the risk of lithium plating, ensure battery safety, improve heating efficiency, and improve the performance of the battery in low temperature environments.

[0009] In one possible implementation, a battery low-temperature AC heating method uses a Grey Wolf-Particle Swarm (GWO-PSO) algorithm to identify parameters of circuit elements in an equivalent circuit model of a battery, including: obtaining initial parameters of circuit elements in the equivalent circuit model; optimizing the initial parameters of the circuit elements using GWO-PSO to obtain optimized parameters of the circuit elements; and determining parameters of the circuit elements based on the optimized parameters of the circuit elements.

[0010] In this implementation, first, the initial parameters of the circuit elements in the equivalent circuit model are obtained to provide an initial reference for the algorithm optimization and avoid blind search of the algorithm. Then, the GWO-PSO algorithm is used to optimize the initial parameters of the circuit elements to obtain the optimized parameters. Finally, the parameters of the circuit elements are determined based on the optimized parameters. In this way, the optimization of the initial parameters by the GWO-PSO algorithm improves the accuracy of circuit element parameter identification, thereby making the subsequently calculated AC parameters more accurate.

[0011] In one possible implementation, a battery low-temperature AC heating method uses a GWO-PSO algorithm to optimize initial parameters of circuit elements to obtain optimized parameters of the circuit elements, including: using the GWO algorithm to optimize the initial parameters of the circuit elements to obtain first optimized parameters of the circuit elements; and using the PSO algorithm to optimize the first optimized parameters of the circuit elements to obtain second optimized parameters of the circuit elements.

[0012] In this implementation, the GWO algorithm is used to optimize the initial parameters of the circuit components to obtain the first optimized parameters. The PSO algorithm is then used to further optimize these first optimized parameters to obtain the second optimized parameters. This optimization method leverages the GWO algorithm's global search advantages, quickly locking in the optimal parameter range, while also leveraging the PSO algorithm's strengths in localized, detailed search to achieve more precise parameters. This ensures that the calculated AC parameters are more closely aligned with the actual battery state.

[0013] In one possible implementation, a battery low-temperature AC heating method uses a PSO algorithm to optimize the first optimization parameter of the circuit element to obtain a second optimization parameter of the circuit element, including: optimizing the speed of each particle corresponding to the first optimization parameter based on the inertia weight to obtain the optimized speed of each particle; wherein the inertia weight is determined based on the following parameters: the maximum value of the inertia weight, the minimum value of the inertia weight, and the maximum number of iterations; optimizing the position of each particle corresponding to the first optimization parameter based on the optimized speed of each particle to obtain the optimized position of each particle; and determining the second optimization parameter of the circuit element based on the optimized speed of each particle and the optimized position of each particle.

[0014] In this implementation, based on the particle velocity determined by the first optimization parameter, an inertia weight is introduced when determining the particle velocity, including: the maximum value of the inertia weight, the minimum value of the inertia weight, and the maximum number of iterations. This allows the particles to maintain a larger inertia weight in the early stage to expand the search range and explore more possible parameters, and to reduce the inertia weight in the later stage to search more finely in a better area, thereby improving the accuracy of parameter optimization. The second optimization parameter is determined based on the optimized particle velocity and position, further improving the accuracy of identifying circuit component parameters.

[0015] In one possible implementation, a low-temperature AC heating method for a battery determines the parameters of the circuit element based on the optimization parameters of the circuit element, including: calculating the entropy value of the PSO algorithm based on the second optimization parameter of the circuit element, the entropy value being used to reflect the distribution of particles corresponding to the second optimization parameter in the search space; and when the entropy value is within a preset range, using the second optimization parameter of the circuit element as the parameter of the circuit element.

[0016] In this implementation, the entropy value of the PSO algorithm is calculated based on the second optimization parameter to determine the distribution of particles in the search space. If the entropy value is within the preset range, it means that the particles are relatively clustered, indicating that the second optimization parameter is more effective at this time, and the second optimization parameter can be used as a circuit element parameter.

[0017] In a possible implementation, the battery low-temperature AC heating method further includes: when the entropy value is not within a preset range, re-adopting the GWO-PSO algorithm to optimize the initial parameters of the circuit elements.

[0018] In this implementation, if the entropy value is not within the preset range, it means that the distribution of particles is relatively dispersed, and the second optimization parameter may have deviations and cannot be used as the circuit element parameter. It is necessary to re-use the GWO-PSO algorithm to optimize the initial parameters of the circuit element so that the entropy value is within the preset range and the accuracy of the circuit element parameters is improved.

[0019] In one possible implementation, a battery low-temperature AC heating method calculates optimal AC parameters based on battery temperature data, battery remaining capacity, and circuit component parameters, including: updating the parameters of the circuit components based on the battery temperature data and the battery remaining capacity to obtain updated circuit component parameters; and calculating the optimal AC parameters based on the updated circuit component parameters.

[0020] In this implementation, circuit component parameters are updated based on battery temperature data and remaining battery capacity, ensuring they always match the actual battery conditions and preventing inaccuracies caused by changes in battery status. This allows the AC parameters calculated based on the updated circuit component parameters to better adapt to the battery's current state. This prevents the risk of lithium plating and enables efficient heating.

[0021] In one possible implementation, a low-temperature AC heating method for a battery calculates optimal AC parameters based on updated parameters of circuit elements, including: calculating optimal AC parameters based on updated parameters of circuit elements and preset heating conditions; wherein the preset heating conditions include at least one of the following: the difference between the battery's charge cut-off voltage and the battery's open-circuit voltage is less than or equal to a first preset voltage value; the first preset voltage value is determined based on the battery's total impedance and the AC amplitude; the equilibrium potential of the battery's graphite negative electrode is less than or equal to a second preset voltage value; the second preset voltage value is determined based on the battery's charge transfer impedance and the AC amplitude; the voltage across the charge transfer resistor in the equivalent circuit model is less than the equilibrium potential of the negative electrode.

[0022] In this implementation, the optimal AC parameters are calculated by comprehensively considering the updated circuit component parameters and the preset heating conditions. The preset heating conditions are introduced to avoid lithium deposition that may occur during AC heating, thereby effectively extending the battery life.

[0023] In a second aspect, a battery low-temperature AC heating device is provided, comprising: an acquisition module, an identification module and a processing module; wherein the acquisition module is used to acquire battery temperature data and the remaining battery capacity; the identification module is used to identify the parameters of circuit elements in the equivalent circuit model of the battery using a Grey Wolf-Particle Swarm (GWO-PSO) algorithm; the processing module is used to calculate the optimal AC parameters based on the battery temperature data, the remaining battery capacity and the parameters of the circuit elements when it is determined that the battery needs to be heated; the optimal AC parameters are the AC parameters corresponding to the maximum heat production rate without causing lithium plating; the processing module is further used to heat the battery based on the optimal AC parameters.

[0024] In some embodiments, the identification module is specifically used to obtain initial parameters of circuit elements in the equivalent circuit model;

[0025] GWO-PSO optimizes the initial parameters of the circuit elements to obtain the optimized parameters of the circuit elements; and determines the parameters of the circuit elements based on the optimized parameters of the circuit elements.

[0026] In some embodiments, the identification module is specifically configured to optimize the initial parameters of the circuit element using a GWO algorithm to obtain first optimized parameters of the circuit element; and optimize the first optimized parameters of the circuit element using a PSO algorithm to obtain second optimized parameters of the circuit element.

[0027] In some embodiments, the identification module is specifically used to optimize the speed of each particle corresponding to the first optimization parameter based on the inertia weight to obtain the optimized speed of each particle; wherein the inertia weight is determined based on the following parameters: the maximum value of the inertia weight, the minimum value of the inertia weight, and the maximum number of iterations; based on the optimized speed of each particle, the position of each particle corresponding to the first optimization parameter is optimized to obtain the optimized position of each particle; based on the optimized speed of each particle and the optimized position of each particle, the second optimization parameter of the circuit element is determined.

[0028] In some embodiments, the processing module is specifically used to calculate the entropy value of the PSO algorithm based on the second optimization parameter of the circuit element, and the entropy value is used to reflect the distribution of particles corresponding to the second optimization parameter in the search space; when the entropy value is within a preset range, the second optimization parameter of the circuit element is used as the parameter of the circuit element.

[0029] In some embodiments, the processing module is specifically configured to re-adopt the GWO-PSO algorithm to optimize the initial parameters of the circuit elements when the entropy value is not within a preset range.

[0030] In some embodiments, the processing module is specifically used to update the parameters of the circuit elements based on the battery temperature data and the remaining battery capacity to obtain the updated parameters of the circuit elements; and calculate the optimal AC parameters based on the updated parameters of the circuit elements.

[0031] In some embodiments, the processing module is specifically used to calculate the optimal AC parameters based on the updated parameters of the circuit elements and the preset heating conditions; wherein the preset heating conditions include at least one of the following: the difference between the battery's charge cut-off voltage and the battery's open circuit voltage is less than or equal to a first preset voltage value; the first preset voltage value is determined based on the battery's total impedance and the AC amplitude; the equilibrium potential of the battery's graphite negative electrode is less than or equal to a second preset voltage value; the second preset voltage value is determined based on the battery's charge transfer impedance and the AC amplitude.

[0032] In a third aspect, an electronic device is provided, comprising: a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to implement the battery low-temperature AC heating method of the first aspect.

[0033] In a fourth aspect, a vehicle is provided, the vehicle including the electronic device according to the second aspect; or the vehicle is used to execute the battery low-temperature AC heating method according to the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A diagram of the system architecture involved in a low-temperature AC heating method for a battery provided by the present invention;

[0035] Figure 2 A flow chart of a low-temperature AC heating method for a battery provided by the present invention;

[0036] Figure 3 A schematic diagram of a simulation provided by the present invention;

[0037] Figure 4 A flow chart of another battery low-temperature AC heating method provided by the present invention;

[0038] Figure 5 A schematic diagram of the electrothermal coupling model provided by the present invention;

[0039] Figure 6 A schematic diagram of a bidirectional feedback control mechanism of the GWO-PSO algorithm provided by the present invention;

[0040] Figure 7 A flow chart of another method for low-temperature AC heating of a battery provided by the present invention;

[0041] Figure 8 This is an overall flow chart of a battery low-temperature AC heating method provided by the present invention;

[0042] Figure 9 This is a structural diagram of a battery low-temperature AC heating device provided by the present invention;

[0043] Figure 10 A schematic diagram of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0044] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0045] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0046] like Figure 1 As shown, the system involved in the low-temperature AC heating method for a battery provided in an embodiment of the present application includes: a temperature sensor 10 , a display device 20 , a battery 30 , a power supply 40 and a controller 50 .

[0047] Among them, the battery 30 is respectively communicated with the temperature sensor 10, the display device 20, the power supply 40 and the controller 50; the controller 50 is respectively communicated with the temperature sensor 10, the display device 20, the battery 30 and the power supply 40.

[0048] In some embodiments, the temperature sensor 10 is used to detect the temperature of the environment or object in real time. In this application, the temperature sensor 10 is used to collect the temperature of the lithium-ion battery 30 and the temperature of the environment, so as to subsequently determine whether the battery needs AC heating based on the detected temperature data, thereby ensuring that the battery can operate normally.

[0049] For example, the temperature sensor 10 may be a contact temperature sensor or a non-contact temperature sensor; or the temperature sensor 10 may be a thermal resistor sensor, a thermistor sensor, or a semiconductor temperature sensor, etc. The embodiment of the present application does not limit the specific form of the temperature sensor 10.

[0050] In some embodiments, the display device 20 converts electronic signals into visual information in the form of text, graphics, images, or video, intuitively displaying system status, operating parameters, and an operating interface. This includes dynamic monitoring of real-time data (such as temperature, voltage, and other numerical display) and visual analysis of historical trends (such as curve charts). In this application, the display device 20 is used to visualize the temperature data and remaining battery capacity (State of Charge, SOC) of the battery 30.

[0051] Exemplarily, the display device 20 may be a light emitting diode (LED) display screen, a mechanical dial, a touch control screen, etc., which is not limited in the embodiments of the present application.

[0052] In some embodiments, the battery 30 is a device that converts chemical energy directly into electrical energy, and its core function is to provide a portable and stable power supply for electronic devices. Through internal redox reactions (such as lithium-ion batteries), the battery 30 forms a potential difference between the positive and negative electrodes, driving electrons to move in a directional manner in the external circuit, thereby powering mobile phones, new energy vehicles, energy storage systems and other devices. In this application, the battery 30 realizes the mutual conversion of electrical energy and chemical energy through the intercalation / deintercalation reaction of lithium ions between the positive and negative electrodes, and can serve as the core energy carrier of new energy vehicles.

[0053] In some embodiments, the power supply 40 is a device or system that converts other forms of energy (such as chemical energy, mechanical energy, solar energy, etc.) into electrical energy. Its core function is to provide a stable and controllable supply of electricity for electrical equipment. According to the conversion principle, it can be divided into chemical power sources (such as batteries), physical power sources (such as generators, solar cells) and electronic power sources (such as switching power supplies, voltage stabilizers). It has functions such as voltage conversion, power regulation, and electrical energy storage. It is a key basic equipment to ensure the operation of electronic equipment, the stability of the power system and the utilization of renewable energy. In this application, the alternating current used by the power supply 40 performs AC heating on the lithium-ion battery 30 in a low temperature environment to ensure that the battery 30 can work normally.

[0054] In some embodiments, the controller 50 is configured to obtain battery 30 temperature data and the remaining battery capacity (SOC) and employ a Grey Wolf-Particle Swarm Optimization (GWO-PSO) algorithm to identify the parameters of circuit components in the equivalent circuit model of the battery 30. The controller 50 then calculates optimal AC power parameters based on the battery 30 temperature data, the remaining battery capacity (SOC), and the parameters of the circuit components in the equivalent circuit model. Furthermore, based on the optimal AC power parameters, the AC power source 40 is controlled to heat the battery 30, thereby preventing lithium deposition and improving battery performance and safety in low-temperature environments.

[0055] For example, the controller 50 may be an onboard computing device or a vehicle including an onboard computing device; alternatively, the controller 50 may be a processor (e.g., a central processing unit) in the onboard computing device or vehicle; or alternatively, the controller 50 may be a functional module or functional unit in the onboard computing device or vehicle for executing the battery low-temperature AC heating method. The embodiments of the present application do not limit the specific form of the controller 50.

[0056] The application scenarios of the embodiments of the present disclosure are not limited. The system architecture and business scenarios described in the embodiments of the present disclosure are intended to more clearly illustrate the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art will appreciate that with the evolution of the architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present disclosure are equally applicable to similar technical problems.

[0057] For ease of understanding, the battery low-temperature AC method provided in this application is specifically introduced below with reference to the accompanying drawings.

[0058] In some embodiments, the present application provides a battery low-temperature AC heating method, such as Figure 2 The heating method shown comprises the following steps:

[0059] S201: Obtain battery temperature data and battery remaining capacity.

[0060] In some embodiments, battery temperature data refers to the temperature of the battery itself (such as the surface or internal temperature of the battery cell), typically measured in degrees Celsius (°C). This data reflects the battery's current thermal state and is a key indicator for determining whether heating is necessary (e.g., battery performance degrades when the battery temperature falls below 5°C in a low-temperature environment). In this application, the battery temperature can be monitored in real time using a temperature sensor, and the temperature data can be directly read on a display device (such as an LED display).

[0061] In some embodiments, the remaining battery capacity refers to the percentage of the battery's current available power to the total capacity (e.g., 30% remaining), which is obtained by integrating the voltage and current or by algorithmic estimation. In this application, the remaining battery capacity can be directly read on a display device (e.g., an LED display).

[0062] S202. Use a Grey Wolf-Particle Swarm Optimization (GWO-PSO) algorithm to identify parameters of circuit elements in an equivalent circuit model of a battery.

[0063] The Grey Wolf-Particle Swarm (GWO-PSO) algorithm is a hybrid intelligent optimization algorithm that integrates the Grey Wolf Optimization (GWO) algorithm and the Particle Swarm Optimization (PSO). This algorithm combines the global search capabilities of the GWO algorithm based on the swarm hierarchy with the rapid convergence characteristics of the PSO algorithm based on the individual-swarm optimal memory. The GWO algorithm uses the guidance mechanism of multiple wolves (α, β, and δ) to conduct a global search, quickly finding a potential optimal solution. This solution is then used as a particle in the PSO algorithm's initialization population, allowing the PSO algorithm to optimize based on the high-quality initial solution. This significantly reduces the randomness of the PSO algorithm, allowing it to quickly approach the optimal solution and improve the efficiency and accuracy of parameter identification.

[0064] S203 : When it is determined that the battery needs to be heated, calculate optimal AC power parameters based on the battery temperature data, the remaining battery capacity, and parameters of circuit components.

[0065] Among them, the optimal AC parameters are the AC parameters corresponding to the maximum heat generation rate without lithium deposition.

[0066] As an implementable manner, determining whether the battery needs to be heated may include: determining that the battery needs to be heated when the battery temperature data is lower than a preset safe operating temperature (eg, battery temperature <5° C.).

[0067] As another possible implementation method, determining whether the battery needs to be heated may also include: when the remaining battery capacity is less than 20SOC and the battery is in a low temperature environment (such as the battery temperature <10°C), in order to avoid a sudden power drop, determining that the battery needs to be heated.

[0068] As another possible implementation, determining whether the battery needs to be heated may also include: when the battery temperature continues to drop and the rate exceeds a threshold (eg, <-0.5°C / min), predicting in advance that the battery needs to be heated.

[0069] S204 : Heating the battery based on the optimal AC power parameters.

[0070] Among them, the optimal AC parameters are those calculated through an optimization algorithm, which can avoid battery damage (such as lithium plating) and achieve maximum heat generation efficiency.

[0071] In some embodiments, if the battery reaches the target temperature, heating is stopped. If the temperature does not reach the target temperature, a determination is made as to whether the battery has achieved a preset temperature rise (e.g., 5°C). If the temperature reaches the target temperature, the optimal AC parameters are updated to the latest state; if not, heating is continued while maintaining the current AC parameters.

[0072] For example, a sinusoidal AC excitation with the optimal amplitude and frequency shown in Table 2 is applied to a battery with an SOC of 30%, and the temperature data of the battery is recorded in real time, as shown in FIG. Figure 3 In the simulation experiment shown, the vertical axis records the real-time temperature of the battery (°C), and the horizontal axis records the heating time (s). The dotted line represents the experimental value, and the solid line represents the simulation value. The experimental results show that the battery is successfully heated from -20°C to 3.78°C in 272s, and the convection heat transfer coefficient is 16Wm -2 k -1 , the temperature rise rate is as high as 5.25℃ / min.

[0073] Based on the above embodiment, by obtaining battery temperature data and battery remaining capacity, a basic basis is provided for subsequent heating, ensuring that the heating strategy adapts to the actual state of the battery, and using the Gray Wolf-Particle Swarm GWO-PSO algorithm to identify the parameters of the circuit elements in the equivalent circuit model of the battery. This combines the global search capability of the Gray Wolf algorithm and the local search advantage of the Particle Swarm algorithm. Compared with the identification method using a single Gray Wolf algorithm and a Particle Swarm algorithm, the parameters of the circuit elements can be obtained more accurately and quickly, and the AC parameters calculated based on these parameters will be reliable. When it is determined that the battery needs to be heated, the optimal AC parameters are calculated based on the battery temperature data, the remaining battery capacity, and the parameters of the circuit elements. The AC parameters are the parameters corresponding to the maximum heat generation rate without lithium plating, which can avoid the risk of lithium plating, ensure battery safety, improve heating efficiency, and improve the performance of the battery in a low temperature environment.

[0074] In some embodiments, the above step S202 uses the Grey Wolf-Particle Swarm Optimization (GWO-PSO) algorithm to identify the parameters of the circuit elements in the equivalent circuit model of the battery, such as Figure 4 As shown, the steps include:

[0075] S401. Obtain initial parameters of circuit elements in an equivalent circuit model.

[0076] Among them, the parameters corresponding to each circuit element of the equivalent circuit model at each battery state and temperature are identified using the improved GWO-PSO algorithm to obtain the initial parameters of the circuit elements, including: U ocv Indicates the open circuit voltage of the battery, U ct Represents the voltage across the charge transfer resistor, U sei Represents the voltage across the SEI film resistor, U t represents the terminal voltage, L represents the inductance of the battery, R o Represents the ohmic resistance of the battery, R ct represents the charge transfer resistance, R sei represents the resistance of the SEI film, Q sei represents the constant phase component of the SEI film, Q dl represents the double layer constant phase element, and I represents the current.

[0077] like Figure 5 The figure shows a schematic diagram of the electrothermal coupling model. The state equation of the second-order RQ equivalent circuit model is as follows:

[0078]

[0079] Where C sei Indicates the constant phase element Q sei Parameters, n sei Indicates the constant phase element Q sei Index, C dlIndicates the constant phase element Q dl Parameters, n dl Indicates the constant phase element Q dl The index of .

[0080] The heat generation rate Q(t) in the thermal model is expressed as follows:

[0081]

[0082] Where, I ac is the AC amplitude, Z Re is the real part of the battery impedance. The real part of the impedance in the second-order RQ equivalent circuit model is expressed as:

[0083]

[0084] Where f is the frequency of the alternating current.

[0085] Heat dissipation Q in thermal model c The expression is:

[0086] Q c =hA(TT f )

[0087] Where h is the convection heat transfer coefficient between the battery and the outside air, A is the surface area of ​​the battery, and T f is the ambient temperature.

[0088] S402 , using the GWO-PSO algorithm to optimize the initial parameters of the circuit elements to obtain the optimized parameters of the circuit elements.

[0089] In some embodiments, the GWO-PSO algorithm is used to optimize the initial parameters of a circuit element to obtain the optimized parameters of the circuit element. This includes: first, optimizing the initial parameters of the circuit element using the GWO algorithm to obtain first optimized parameters of the circuit element; then, optimizing the first optimized parameters of the circuit element using the PSO algorithm to obtain second optimized parameters of the circuit element.

[0090] First, the GWO algorithm uses the multi-wolf guidance mechanism of α, β, and δ to conduct a global search to quickly find the potential optimal solution and obtain the first optimization parameters of the circuit element. This particle is then used as a particle in the PSO algorithm initialization population, allowing the PSO algorithm to optimize the circuit element based on the first optimization parameters to obtain the second optimization parameters. This can significantly reduce the randomness of the PSO algorithm, quickly approach the optimal solution, and improve the efficiency and accuracy of parameter identification.

[0091] In some embodiments, the GWO algorithm is an optimization algorithm based on improved swarm intelligence, which calculates the position X of each gray wolf in the wolf pack. i=[x i，1 , x i，2 ,...,x i，d ] is a feasible solution in the solution space. Before each iterative calculation, the wolf pack is divided into four categories according to the fitness function values ​​of the individuals, namely the alpha wolves α, β, δ and the remaining gray wolves ω, which represent the current optimal fitness, the second optimal fitness, the third optimal fitness and the remaining individuals. Then, a mathematical model is used to simulate the hunting behavior of the high-ranking wolves α, β, δ and the remaining gray wolves ω, including the processes of encircling, approaching and attacking prey, to obtain the global optimal solution, which is expressed as follows:

[0092] D=|C·X p (t)-X(t)|

[0093] X(t+1)=X p (t)-A·D

[0094] Where D represents the distance vector between the wolf and its prey, t is the current number of iterations, and X p (t) represents the position of the prey, X(t) is the position of the individual in the wolf pack. By updating the position, the distance between the gray wolf and the target is continuously shortened, and the prey is finally captured.

[0095] C=2·r1

[0096] A=2·a·r2-a

[0097] Where C is a vector coefficient, which is used to impose a certain offset on the prey position, thereby increasing the uncertainty of the search and preventing it from falling into the local optimal solution too early. A is a convergence factor vector, which controls the global search and local search. In the early stage of the calculation, when |A|>1, the gray wolf will stay away from the prey and conduct a wide search in the entire search space; in the later stage of the calculation, when |A|<1, the gray wolf will gradually approach the prey position, conduct a detailed search for the potential optimal solution area, and finally obtain the global optimal solution. Where r1 and r2 are random vectors in [0,1], and a is a dynamic control parameter used to adjust the convergence factor A. When a<0.2·(1-t / t max ), the position is [X min ,X max ] randomly jumps, where t max is the maximum number of iterations; when a≥0.2·(1-t / t max ), the ordinary gray wolf will update its own position according to the position of the high-level wolves α, β, and δ, so as to continuously approach the prey. First, the distance to the α, β, and δ wolves is calculated respectively by the following formula.

[0098]

[0099] On this basis, the three possible positions of ordinary wolves are calculated as shown in the following formula:

[0100]

[0101] The position of an ordinary wolf is determined by the weighted average of X1(t), X2(t), and X3(t), as shown in the following formula. This can integrate the information of α, β, and δ, retain a certain degree of diversity, and avoid falling into a local optimal solution.

[0102] X(t+1)=w a X1(t)+w β X2(t)+w δ X3(t)+Levy(λ)

[0103]

[0104] Finally, as the number of iterations t increases, a gradually decreases linearly from 2 to 0, making the convergence factor |A| < 1, and the algorithm gradually converges towards the global optimal solution of the population, and finally obtains the global optimal solution. In this process, the contribution of high-order wolves α, β, and δ is introduced to give dynamic weights w i , and add the Levy flight perturbation, where λ ranges from [1,3].

[0105] In some embodiments, determining the parameters of the circuit element based on the optimization parameters of the circuit element may include: calculating an entropy value of the PSO algorithm based on a second optimization parameter of the circuit element, the entropy value being used to reflect the distribution of particles corresponding to the second optimization parameter in the search space; and using the second optimization parameter of the circuit element as the parameter of the circuit element when the entropy value is within a preset range.

[0106] Among them, the threshold is used to quantify the discrete degree of the particle swarm distribution in the parameter space. The setting of the preset range threshold is to ensure that the particle swarm is in a balanced state of global exploration and local optimization. The PSO algorithm realizes a two-way feedback control mechanism by feeding back the population search entropy value to the GWO algorithm. The expression of the PSO algorithm entropy value H is as follows:

[0107]

[0108] Where, X i Represents the position of each gray wolf in the wolf pack, and the value range of H can be [0,1].

[0109] In some embodiments, H exhibits dynamic changes during algorithm execution. In the initial randomization phase, H is in a high-entropy state, indicating a relatively dispersed population distribution. The algorithm has a strong exploration capability within the solution space and is able to extensively search for possible solutions. As the algorithm progresses to the mid-convergence phase, H gradually decreases, indicating that the population is beginning to converge toward the optimal solution. In the late-convergence phase, H drops to a low-entropy level, indicating that the population has converged, with most individuals concentrated near the optimal solution, and the algorithm's search process is essentially stable.

[0110] As an implementable manner, determining the parameters of the circuit element may include: when the entropy value is within a preset range, H∈[0,1], using the second optimized parameter of the circuit element as the parameter of the circuit element.

[0111] As another possible implementation, the parameters of the circuit elements are determined. This may also include: if the entropy value is not within a preset range, re-optimizing the initial parameters of the circuit elements using the GWO-PSO algorithm. For example, if H>1, it indicates that the particle distribution is dispersed, the search has not converged, and further optimization is required; if H<0, it indicates that the particles are concentrated in a local area and may be trapped in a local optimum. In this case, the initial parameters of the circuit elements need to be re-optimized until the threshold is within the preset range.

[0112] S403 : Determine parameters of the circuit element based on the optimized parameters of the circuit element.

[0113] In some embodiments, a bidirectional feedback control mechanism using an improved GWO-PSO algorithm is used to identify the parameters of circuit elements in an equivalent circuit model. The Nyquist curves obtained from AC impedance tests of the battery at different temperatures are fitted. Within a predefined computational domain, an iterative calculation is performed to obtain a solution that satisfies a cutoff condition. This solution is then introduced into the PSO algorithm as a particle in the initial population. Further iterative calculations are performed through the PSO algorithm until the cutoff condition is met, and the corresponding parameters of the circuit elements in the equivalent circuit model are output.

[0114] Among them, the two-way feedback control mechanism, such as Figure 6 As shown in the figure, the GWO algorithm searches the solution space extensively, screening out elite solutions. The resulting elite solution matrix is ​​then passed to the PSO algorithm as the high-quality particles for its initial population. The GWO algorithm also dynamically adjusts the a parameter and passes it to the PSO algorithm, shrinking the PSO search range in tandem with the GWO algorithm. The PSO algorithm calculates the distribution entropy of the current particle swarm and feeds this entropy back to the GWO algorithm. The entropy reflects the dispersion of the particle distribution. The GWO algorithm leads the global search, leveraging the gray wolf group's hierarchy (α, β, and δ wolves) to extensively explore the parameter space and avoid falling into local optima. The PSO algorithm, on the other hand, fine-tunes the parameters to rapidly converge to a high-precision solution.

[0115] In some embodiments, the bidirectional feedback control mechanism is a dynamic adjustment control strategy that can dynamically switch between the PSO algorithm and the GWO algorithm using two indicators: entropy and fitness f(Xi). For example, when H < ε (ε = 0.2), a switch from the PSO algorithm to the GWO algorithm is triggered, entering the PSO algorithm phase, enabling a more refined search of the currently discovered potential optimal solution area. When the PSO algorithm's fitness f(Xi) < ε, its fitness can also be understood as the error between the algorithm's fitted value and the actual value. To prevent the algorithm from falling into the dilemma of a local optimum, a switch from the PSO algorithm to the GWO algorithm is triggered, re-entering the GWO algorithm phase for a global search. This allows the algorithm to find the optimal global solution within a larger solution space, enhancing its exploration capabilities.

[0116] For example, Table 1 shows the identification results of the ohmic internal resistance parameters of a battery at SOC = 30% and different ambient temperatures using the improved GWO-PSO algorithm, and compares them with experimental measurements and the results of traditional identification methods. This comparative analysis shows that the identification results of the improved GWO-PSO algorithm are closest to the experimental values ​​and significantly outperform the traditional identification method. This result verifies the superiority and accuracy of the GWO-PSO algorithm in battery parameter identification.

[0117] Table 1 Comparison of ohm internal resistance parameter identification data

[0118]

[0119] In some embodiments, as Figure 7 As shown, in the above step S402, the PSO algorithm is used to optimize the first optimization parameter of the circuit element to obtain the second optimization parameter of the circuit element, which can be implemented as the following steps:

[0120] S701 : Optimize the speed of each particle corresponding to the first optimization parameter based on the inertia weight to obtain the optimized speed of each particle.

[0121] The inertia weight is determined based on the following parameters: the maximum value of the inertia weight, the minimum value of the inertia weight, and the maximum number of iterations.

[0122] In some embodiments, the inertia weight is a parameter used in the PSO algorithm to balance global and local search. The maximum inertia weight is used for global search, typically in the initial search phase. The minimum inertia weight is used for local search, typically in the later convergence phase. The maximum number of iterations is one of the termination conditions for optimization algorithms (such as PSO and GWO), indicating the upper limit of the number of rounds the algorithm can run.

[0123] For example, the inertia weight satisfies the following formula:

[0124]

[0125] Where ω is the inertia weight, ω max is the maximum value of the inertia weight, ω min is the minimum value of the inertia weight, t is the current number of iterations, t max is the maximum number of iterations.

[0126] Among them, the inertia weight ω can control the global and local search capabilities. A larger ω value can retain the current velocity of the particle, thereby performing a global search, while a smaller ω will slow down the particle speed and enhance the local search capability.

[0127] As a practical approach, the particle velocity v i , satisfying the following formula:

[0128]

[0129] Where r1 and r2 are random numbers in the range [0,1], which are used to increase the randomness of particle motion; p best is the optimal position of the particle at that moment, g best is the historical best position of all particle swarms; c1 is the individual learning factor, c2 is the group learning factor, which respectively control the particle to p best and g best The trend of approaching. A velocity acceleration mechanism is introduced, where γ is the momentum factor.

[0130] S702 : Based on the optimization speed of each particle, optimize the position of each particle corresponding to the first optimization parameter to obtain the optimized position of each particle.

[0131] In the PSO algorithm, the particle moves along the velocity direction in the parameter space, and the change in its position is equal to the velocity value. The position of the particle represents a candidate solution to the problem to be optimized.

[0132] As a practical approach, the particle position x i , satisfying the following formula:

[0133] x i (t+1)=x i (t)+v i (t+1)

[0134] S703 : Determine a second optimization parameter of the circuit element based on the optimization speed of each particle and the optimization position of each particle.

[0135] In some embodiments, the second optimization parameter is determined by the new position of each particle after its optimized speed is updated. For example, if the fitness value of the new position is better than the historical optimal record of the particle, the position is updated to the individual historical optimal solution p of the particle.best , and at the same time select the global optimal solution g from the individual optimal solutions of all particles best .

[0136] Based on the above embodiments, during the search process, the particles can maintain a larger inertia weight in the early stage to expand the search range and explore more possible parameters, and can also reduce the inertia weight in the later stage to search more finely in a better area, thereby improving the accuracy of parameter optimization. The second optimization parameter is determined based on the optimized particle speed and position, further improving the accuracy of identifying circuit component parameters.

[0137] In some embodiments, calculating optimal AC power parameters based on the battery temperature data, the remaining battery capacity, and the parameters of the circuit components in step S303 includes: updating the parameters of the circuit components based on the battery temperature data and the remaining battery capacity to obtain updated parameters of the circuit components; and calculating optimal AC power parameters based on the updated parameters of the circuit components.

[0138] Battery temperature data and remaining battery capacity can be directly read through a display device (such as an LED screen). Based on this real-time battery temperature data and remaining battery capacity, the Grey Wolf-Particle Swarm Optimization (GWO-PSO) algorithm is used to identify the parameters of circuit components in the battery's equivalent circuit model. This ensures that the circuit component parameters always match the actual battery conditions, avoiding parameter inaccuracies caused by changes in battery status. Consequently, the AC parameters calculated based on the updated circuit component parameters better adapt to the current battery status.

[0139] In some implementations, calculating the optimal AC parameters based on the updated parameters of the circuit elements includes calculating the optimal AC parameters based on the updated parameters of the circuit elements and preset heating conditions.

[0140] Among them, the preset heating conditions are constraints set to ensure the safety and electrochemical stability of the battery's low-temperature AC heating process. The threshold limits of key parameters such as voltage and potential can be used to ensure that the heating process does not cause risks such as lithium deposition and overvoltage.

[0141] As an implementable method, the preset heating condition setting may include: the difference between the battery's charge cut-off voltage and the battery's open circuit voltage is less than or equal to a first preset voltage value, and the first preset voltage value is determined based on the battery's total impedance and the AC current amplitude. The expression is:

[0142] I ac |Z total |≤U max -U ocv

[0143] I ac |Zct |≤U n，ocv

[0144] Where, I ac is the charge transfer current, Z total is the total impedance of the battery; U max The battery charging cut-off voltage is 4.2V and 2.5V respectively; Z ct is the charge transfer impedance.

[0145] According to the second-order RQ equivalent circuit model, Z total and Z ct Expressed as:

[0146]

[0147] Therefore, during AC heating, the maximum allowable current amplitude L ac，max The expression is:

[0148]

[0149] For example, as shown in Table 2, the optimal values ​​of the AC parameters when the SOC is 30% are obtained by calculating the maximum allowable current amplitude at which lithium deposition does not occur at different temperatures and frequencies of the battery during low-temperature AC heating. The current amplitude and frequency corresponding to the maximum heat generation rate in each temperature range from -20°C to 5°C can be found.

[0150] Table 2 Optimal values ​​of AC parameters when SOC is 30%

[0151]

[0152] As another possible implementation, the setting of the preset heating conditions may also include: the equilibrium potential of the graphite negative electrode of the battery is less than or equal to a second preset voltage value, and the second preset voltage value is determined based on the charge transfer impedance of the battery and the AC current amplitude. The expression is:

[0153] |η|<U n，ocv

[0154] Where η is the overpotential of the negative electrode, u n，ocv is the equilibrium potential of the graphite negative electrode.

[0155] Among them, the lithium deposition reaction potential is usually 0V (VSLi / Li + ). In the electrochemical reaction of the battery, the overpotential η of the negative electrode is:

[0156] |η|=φ s -φ l -U n，ocv

[0157] Where, φs is the solid phase potential of the graphite negative electrode, φ l is the liquidus potential of the graphite negative electrode.

[0158] Among them, the overpotential of the negative electrode can be approximately expressed as:

[0159] |η|≈i ct R n，ct

[0160] Where i ct is the Faraday current, R n，ct is the charge transfer resistance of the negative electrode. Therefore, it can be considered that the key factor determining whether the battery will deposit lithium is the magnitude of the Faradaic current passing through the negative electrode charge transfer resistance. However, since the identification of the negative electrode charge transfer resistance requires a three-electrode battery or electrochemical model, it is simplified and the total charge transfer resistance of the battery is used instead of the negative electrode charge transfer resistance. The above formula is rewritten as:

[0161] η≈i ct R ct

[0162] As another possible implementation, the setting of the preset heating conditions may also include: in the equivalent circuit model, the voltage U across the charge transfer resistor ct =i ct R ct , the voltage across the charge transfer resistor in the equivalent circuit model is less than the equilibrium potential of the negative electrode. Its expression is:

[0163] U ct <U n，ocv

[0164] Where U ct is the voltage across the charge transfer resistor, U n，ocv is the equilibrium potential of the graphite negative electrode.

[0165] The above mainly introduces the solution provided by the embodiment of the present application from the perspective of the method. In order to realize the above functions, the battery low-temperature AC heating device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easy to realize that the technical goals in this field are combined with the units and algorithm steps of each example described in the embodiments disclosed in this article, and the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technical goals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0166] The present application embodiment provides a method for heating a battery at low temperature by alternating current, such as Figure 8The figure shows the overall flow chart of the battery low-temperature AC heating method, which specifically includes the following steps:

[0167] S801: Detect battery temperature and remaining battery capacity.

[0168] S802: Determine whether heating is required.

[0169] For example, if the battery needs to be heated, the process jumps to step S803; if the battery does not need to be heated, the process ends directly.

[0170] S803: Use the GWO-PSO algorithm to identify circuit component parameters.

[0171] S804: Calculate optimal AC parameters.

[0172] Exemplarily, the optimal AC power parameters are calculated based on the detected battery temperature, the remaining battery capacity, and the GWO-PSO algorithm to identify circuit component parameters.

[0173] S805: Apply optimal AC excitation to the battery.

[0174] S806: Determine whether the target temperature is reached.

[0175] Exemplarily, if the target temperature is reached, heating is stopped; if the target temperature is not reached, the process jumps to step S807.

[0176] S807: Determine whether the temperature rises to a preset temperature rise value.

[0177] Exemplarily, if the temperature rises to a preset temperature rise value, the process jumps to step S803; if the temperature does not rise to the preset temperature rise value, the process jumps to step S805.

[0178] The above mainly introduces the solution provided in the embodiment of the present application from the perspective of method.

[0179] like Figure 9 As shown, the battery low temperature AC heating device 900 includes: an acquisition module 901, an identification module 902 and a processing module 903; the acquisition module 901 is used to obtain battery temperature data and battery remaining capacity; the identification module 902 is used to use gray wolf-particle swarm

[0180] The GWO-PSO algorithm identifies the parameters of circuit elements in the equivalent circuit model of the battery; processing module 903 is used to calculate the optimal AC parameters based on battery temperature data, battery remaining capacity and circuit element parameters when it is determined that the battery needs to be heated; the optimal AC parameters are the AC parameters corresponding to the maximum heat generation rate without causing lithium plating; the processing module is also used to heat the battery based on the optimal AC parameters.

[0181] Furthermore, the identification module 902 is specifically used to obtain initial parameters of circuit elements in the equivalent circuit model; optimize the initial parameters of the circuit elements using the GWO-PSO algorithm to obtain optimized parameters of the circuit elements; and determine the parameters of the circuit elements based on the optimized parameters of the circuit elements.

[0182] Furthermore, the identification module 902 is specifically configured to optimize the initial parameters of the circuit element using the GWO algorithm to obtain the first optimized parameters of the circuit element; and optimize the first optimized parameters of the circuit element using the PSO algorithm to obtain the second optimized parameters of the circuit element.

[0183] Furthermore, the identification module 902 is specifically configured to optimize the speed of each particle corresponding to the first optimization parameter based on the inertia weight to obtain the optimized speed of each particle; wherein the inertia weight is determined based on the following parameters: the maximum value of the inertia weight, the minimum value of the inertia weight, and the maximum number of iterations; based on the optimized speed of each particle, optimize the position of each particle corresponding to the first optimization parameter to obtain the optimized position of each particle; and determine the second optimization parameter of the circuit element based on the optimized speed of each particle and the optimized position of each particle.

[0184] Furthermore, the processing module 903 is specifically used to calculate the entropy value of the PSO algorithm based on the second optimization parameter of the circuit element, and the entropy value is used to reflect the distribution of particles corresponding to the second optimization parameter in the search space; when the entropy value is within a preset range, the second optimization parameter of the circuit element is used as the parameter of the circuit element.

[0185] Furthermore, the processing module 903 is specifically configured to re-adopt the GWO-PSO algorithm to optimize the initial parameters of the circuit elements when the entropy value is not within the preset range.

[0186] Furthermore, the processing module 903 is specifically configured to update the parameters of the circuit elements based on the battery temperature data and the remaining battery capacity to obtain updated parameters of the circuit elements; and calculate the optimal AC parameters based on the updated parameters of the circuit elements.

[0187] Furthermore, the processing module 903 is specifically configured to calculate optimal AC parameters based on the updated parameters of the circuit elements and preset heating conditions; wherein the preset heating conditions include at least one of the following: the difference between the battery's charge cut-off voltage and the battery's open-circuit voltage is less than or equal to a first preset voltage value; the first preset voltage value is determined based on the battery's total impedance and the AC amplitude; the equilibrium potential of the battery's graphite negative electrode is less than or equal to a second preset voltage value; the second preset voltage value is determined based on the battery's charge transfer impedance and the AC amplitude;

[0188] like Figure 10As shown, the electronic device 1000 includes but is not limited to: a processor 1001 and a memory 1002 .

[0189] The memory 1002 is used to store executable instructions of the processor 1001. It is understandable that the processor 1001 is configured to execute instructions to implement the low-temperature AC heating method for a battery in the above embodiment.

[0190] It should be noted that those skilled in the art can understand that Figure 10 The electronic device structure shown in the figure does not limit the electronic device 1000. The electronic device 1000 may include Figure 10 More or fewer components may be shown, or certain components may be combined, or the components may be arranged differently.

[0191] The processor 1001 is the control center of the electronic device 1000. It connects the various parts of the entire electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in the memory 1002 and calling data stored in the memory 1002, it performs various functions of the electronic device 1000 and processes data, thereby monitoring the electronic device 1000 as a whole. The processor 1001 may include one or more processing units. Optionally, the processor 1001 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly handles wireless communications. It is understood that the modem processor may not be integrated into the processor 1001.

[0192] The memory 1002 can be used to store software programs and various data. The memory 1002 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and application programs required by at least one functional module (such as a determination unit, a processing unit, etc.). Furthermore, the memory 1002 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0193] This embodiment further provides a vehicle, which includes the electronic device 1000; or, the vehicle is used to execute the battery low-temperature AC heating method of any embodiment.

[0194] In an exemplary embodiment, the present application also provides a computer program product including one or more instructions, which can be executed by the processor 1001 of the electronic device 1000 to implement the battery low-temperature AC heating method in the above embodiment.

[0195] It should be noted that when the instructions in the above-mentioned computer-readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the various processes of the above-mentioned method embodiment are implemented and the same technical effect as the above-mentioned method can be achieved. To avoid repetition, they will not be repeated here.

[0196] Through the description of the above implementation methods, technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete the full classification or partial functions described above.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0198] The units described as separate components may or may not be physically separate, and the components shown as units may be one physical unit or multiple physical units, that is, they may be located in one place or distributed in multiple places. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0199] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0200] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the full classification part or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor (processor) to execute the full classification part or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks or optical disks.

[0201] The above embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Any equivalent substitution or modification made by those skilled in the art based on the present invention is within the protection scope of the present invention.

Claims

1. A battery low-temperature AC heating method, characterized in that: The method comprises: Get battery temperature data and remaining battery capacity; Using a Grey Wolf-Particle Swarm Optimization (GWO-PSO) algorithm to identify parameters of circuit elements in an equivalent circuit model of the battery; When it is determined that the battery needs to be heated, calculating optimal AC parameters based on the battery temperature data, the remaining battery capacity, and the parameters of the circuit components; the optimal AC parameters are AC parameters corresponding to a maximum heat generation rate without causing lithium deposition; The battery is heated based on the optimal AC power parameters.

2. The battery low-temperature AC heating method according to claim 1, characterized in that: The method of using the Grey Wolf-Particle Swarm Optimization (GWO-PSO) algorithm to identify parameters of circuit elements in the equivalent circuit model of the battery includes: Obtaining initial parameters of circuit elements in the equivalent circuit model; Optimizing the initial parameters of the circuit elements using the GWO-PSO algorithm to obtain optimized parameters of the circuit elements; Based on the optimized parameters of the circuit element, parameters of the circuit element are determined.

3. The battery low-temperature AC heating method according to claim 2, characterized in that: The adopting of the GWO-PSO algorithm to optimize the initial parameters of the circuit elements to obtain the optimized parameters of the circuit elements includes: Optimizing the initial parameters of the circuit element using the GWO algorithm to obtain first optimized parameters of the circuit element; The PSO algorithm is used to optimize the first optimization parameter of the circuit element to obtain the second optimization parameter of the circuit element.

4. The battery low-temperature AC heating method according to claim 3, characterized in that: The step of optimizing the first optimization parameter of the circuit element by using the PSO algorithm to obtain the second optimization parameter of the circuit element includes: Based on the inertia weight, optimizing the speed of each particle corresponding to the first optimization parameter to obtain the optimized speed of each particle; wherein the inertia weight is determined based on the following parameters: the maximum value of the inertia weight, the minimum value of the inertia weight, and the maximum number of iterations; Optimizing the position of each particle corresponding to the first optimization parameter based on the optimization speed of each particle to obtain the optimized position of each particle; A second optimization parameter of the circuit element is determined based on the optimized speed of each particle and the optimized position of each particle.

5. The battery low-temperature AC heating method according to claim 3, characterized in that: The determining of the parameters of the circuit element based on the optimized parameters of the circuit element comprises: Calculating an entropy value of the PSO algorithm based on a second optimization parameter of the circuit element, where the entropy value is used to reflect the distribution of particles corresponding to the second optimization parameter in the search space; When the entropy value is within a preset range, the second optimized parameter of the circuit element is used as a parameter of the circuit element.

6. The battery low-temperature AC heating method according to claim 5, characterized in that: The method further comprises: When the entropy value is not within the preset range, the GWO-PSO algorithm is re-adopted to optimize the initial parameters of the circuit elements.

7. The battery low-temperature AC heating method according to claim 1, characterized in that: The calculating of optimal AC power parameters based on the battery temperature data, the battery remaining capacity, and the parameters of the circuit components includes: updating the parameters of the circuit element based on the battery temperature data and the remaining battery capacity to obtain updated parameters of the circuit element; Based on the updated parameters of the circuit elements, optimal AC parameters are calculated.

8. The battery low-temperature AC heating method according to claim 7, characterized in that: The calculating of the optimal AC parameters based on the updated parameters of the circuit elements includes: Calculating optimal AC parameters based on the updated parameters of the circuit elements and preset heating conditions; wherein the preset heating conditions include at least one of the following: The difference between the battery's charge cut-off voltage and the battery's open-circuit voltage is less than or equal to a first preset voltage value; the first preset voltage value is determined based on the battery's total impedance and the AC current amplitude; The equilibrium potential of the graphite negative electrode of the battery is less than or equal to a second preset voltage value; the second preset voltage value is determined based on the charge transfer impedance and the alternating current amplitude of the battery; The voltage across the charge transfer resistor in the equivalent circuit model is less than the equilibrium potential of the negative electrode.

9. A battery low-temperature AC heating device, characterized in that: include: Acquisition module, recognition module and processing module; The acquisition module is used to obtain battery temperature data and battery remaining capacity; The identification module is used to identify parameters of circuit elements in the equivalent circuit model of the battery using a Grey Wolf-Particle Swarm Optimization (GWO-PSO) algorithm; The processing module is configured to calculate optimal AC power parameters based on the battery temperature data, the remaining battery capacity, and the parameters of the circuit components when determining that the battery needs to be heated; the optimal AC power parameters are AC power parameters corresponding to the maximum heat generation rate without causing lithium deposition; The processing module is further configured to heat the battery based on the optimal AC power parameters.

10. The battery low-temperature AC heating device according to claim 9, characterized in that: The identification module is specifically used to obtain initial parameters of circuit elements in the equivalent circuit model; optimize the initial parameters of the circuit elements using the GWO-PSO algorithm to obtain optimized parameters of the circuit elements; Based on the optimized parameters of the circuit element, parameters of the circuit element are determined.

11. An electronic device, characterized in that: include: A memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program to perform the battery low-temperature AC heating method according to any one of claims 1 to 8.

12. A vehicle, characterized in that: The vehicle includes the electronic device according to claim 11; or, the vehicle includes the battery low-temperature AC heating device according to any one of claims 9 to 10; or, the vehicle is used to perform the battery low-temperature AC heating method according to any one of claims 1 to 8.

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