A lithium ion battery pulse heating control method, device, equipment and medium

Abstract

The application discloses a lithium ion battery pulse heating control method, device, equipment and medium, and belongs to the field of power battery thermal management. The method comprises the following steps: establishing a lithium ion battery electro-thermal coupling model; the electro-thermal coupling model comprises an electric model and a thermal model; in the process of battery pulse heating, the heating process of the battery from an initial temperature to a target temperature is discretized into multiple continuous temperature stages, and a multi-objective function with the optimization objectives of minimizing total heating time, minimizing total energy loss and minimizing the net change of SOC is constructed; a multi-objective particle swarm optimization algorithm is used to solve the multi-objective function, and a Pareto optimal solution set is obtained; a matching target solution is selected from the Pareto optimal solution set according to the current heating demand, and the battery pulse heating is controlled according to the pulse parameter sequence corresponding to the target solution. The application can flexibly select a heating strategy according to a low-temperature working condition, and significantly improves the adaptability and intelligent level of a power battery low-temperature thermal management system.

Description

Technical Field

[0001] This invention belongs to the field of power battery thermal management technology, specifically relating to a method, device, equipment, and medium for controlling pulse heating of lithium-ion batteries. Background Technology

[0002] Lithium-ion batteries suffer severe performance degradation at low temperatures, with a sharp increase in internal resistance and a reduction in usable capacity, seriously affecting the starting, range, and safety of electric vehicles in cold regions. Pulse self-heating technology, by applying bidirectional pulsed current to the battery and utilizing its internal resistance to generate heat, offers advantages such as rapid heating, good temperature uniformity, and high system integration, making it an effective internal heating solution for addressing battery performance issues at low temperatures.

[0003] Existing pulse heating optimization strategies primarily target single objectives, such as minimizing heating time or energy consumption. These strategies struggle to comprehensively address multiple requirements in practical applications, including heating speed, energy economy, and battery state of charge (SOC) stability. While some studies attempt multi-objective optimization, they generally employ a fixed-weight method, summing multiple objectives into a single solution. This approach heavily relies on the designer's subjective experience in setting weighting coefficients, and each optimization yields only a solution with a specific tendency. It fails to systematically and objectively reveal all possible optimal trade-offs between the three key objectives: heating time, energy loss, and SOC change. This results in poor universality of the optimization strategies and an inability to provide flexible decision-making options for different operating conditions (e.g., prioritizing speed when power is abundant, and prioritizing energy saving when power is scarce). Summary of the Invention

[0004] The present invention aims to at least solve the above-mentioned technical problems existing in the prior art. To this end, the present invention provides a method, apparatus, device and medium for controlling pulse heating of lithium-ion batteries.

[0005] The first aspect of this invention provides a method for controlling pulse heating of a lithium-ion battery, the method comprising: An electro-thermal coupling model for a lithium-ion battery is established; the electro-thermal coupling model includes an electrical model and a thermal model; the electrical model is a second-order RC equivalent circuit model; the thermal model is a lumped parameter thermal model of the battery constructed based on the law of conservation of energy. During the battery pulse heating process, the heating process from the initial temperature to the target temperature is discretized into multiple continuous temperature stages, and a multi-objective function is constructed with the optimization objectives of minimizing the total heating time, minimizing the total energy loss, and minimizing the net change in SOC. The optimization variables of the multi-objective function are the pulse discharge current amplitude, the pulse charging current amplitude, and the pulse duty cycle. The multi-objective function is solved using a multi-objective particle swarm optimization algorithm to obtain the Pareto optimal solution set. Based on the current heating requirement, a matching target solution is selected from the Pareto optimal solution set, and the battery pulse heating is controlled according to the pulse parameter sequence corresponding to the target solution; the current heating requirement includes the current remaining battery power and a preset heating target priority; the heating target priority is at least one of temperature rise priority, energy consumption reduction priority, or stable SOC priority.

[0006] Optionally, the multi-objective function includes a function to minimize the total heating time; constructing the objective function to minimize the total heating time includes: Based on the electrical model, a heat generation power model is obtained for each temperature stage with respect to the amplitude of the pulse discharge current, the amplitude of the pulse charging current, and the pulse duty cycle. Substitute the heat generation power model of each temperature stage into the heat balance equation of the thermal model and solve it to obtain the heating time sub-model of each temperature stage. Summing the heating time sub-models for all temperature stages yields the total heating time model; The objective function for minimizing the total heating time is determined based on the total heating time model.

[0007] Optionally, the heat generation power model is constructed based on the following expression:

[0008] In the formula, Let J be the heat generation power of the j-th stage. Let be the ohmic internal resistance of the battery at the starting temperature of the j-th stage. Let J be the amplitude of the pulse discharge current in the j-th stage. Let J be the amplitude of the pulse charging current in the j-th stage. Let be the pulse duty cycle of the j-th stage.

[0009] Optionally, the heating time sub-model is constructed based on the following expression: ; In the formula, For the j-th stage, the battery temperature will be reduced from... Rise to Required heating time, The ambient temperature, Let J be the final temperature of the j-th stage. The starting temperature of the j-th stage The corresponding capacity decay correction function value, Let J be the heat generation power of the j-th stage. For the quality of the battery, This refers to the specific heat capacity of the battery. The equivalent heat transfer coefficient, This refers to the surface area of ​​the battery.

[0010] Optionally, the multi-objective function further includes a total energy loss minimization objective function; constructing the total energy loss minimization objective function includes: Multiply the heat generation power model and the heating time sub-model for each temperature stage to obtain the energy loss for each temperature stage; The objective function for minimizing total energy loss is obtained based on the energy loss at all temperature stages.

[0011] Optionally, the multi-objective function includes a function for minimizing the net change in SOC; constructing the objective function for minimizing the net change in SOC includes: Based on the battery rated capacity and capacity decay correction function, and using the ampere-hour integration method, the first and second instantaneous changes in SOC with respect to the optimized variables are obtained for each temperature stage; the first instantaneous change in SOC is the instantaneous change in SOC caused by the discharge pulse; the second instantaneous change in SOC is the instantaneous change in SOC caused by the charging pulse. The instantaneous change in SOC and the instantaneous change in SOC at each temperature stage are added together to obtain the total instantaneous change in SOC at each temperature stage. The final SOC value at the end of heating is obtained by superimposing the sum of the instantaneous changes in SOC at all temperature stages on the initial SOC value. The objective function for minimizing the net change in SOC is determined based on the initial SOC value and the final SOC value.

[0012] Optionally, the process of pulse heating the battery further includes: The input current, real-time battery temperature, and battery SOC are input into the electrical model to obtain the heat generation power; the input current includes pulse discharge current and pulse charging current. The heat generation power is input into the heat balance equation of the thermal model to obtain the current battery temperature; the current battery temperature is the updated real-time battery temperature. The electrical parameters of the electrical model and the OCV-SOC curve of the battery at the current temperature are updated according to the current battery temperature; the electrical parameters include ohmic internal resistance, polarization resistance and polarization capacitance.

[0013] A second aspect of the present invention provides a lithium-ion battery pulse heating control device, the device comprising: The model building unit is used to establish an electro-thermal coupling model of a lithium-ion battery; the electro-thermal coupling model includes an electrical model and a thermal model; the electrical model is a second-order RC equivalent circuit model; the thermal model is a lumped parameter thermal model of the battery based on the law of conservation of energy. A multi-objective function construction unit is used to discretize the heating process of the battery from the initial temperature to the target temperature into multiple continuous temperature stages during the battery pulse heating process, and construct a multi-objective function with the optimization objectives of minimizing the total heating time, minimizing the total energy loss, and minimizing the net change in SOC; the optimization variables of the multi-objective function are the pulse discharge current amplitude, the pulse charging current amplitude, and the pulse duty cycle; The multi-objective function solving unit is used to solve the multi-objective function using a multi-objective particle swarm optimization algorithm to obtain a Pareto optimal solution set. The battery heating control unit is used to select a matching target solution from the Pareto optimal solution set according to the current heating demand, and control the battery pulse heating according to the pulse parameter sequence corresponding to the target solution; the current heating demand includes the current remaining battery power and a preset heating target priority; the heating target priority is at least one of temperature rise priority, energy consumption reduction priority, or stable SOC priority.

[0014] A third aspect of the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by the processor to implement the lithium-ion battery pulse heating control method as described in the first aspect.

[0015] A fourth aspect of the present invention provides a computer-readable storage medium storing at least one instruction or at least one program, wherein the at least one instruction or at least one program is loaded and executed by a processor to implement the lithium-ion battery pulse heating control method as described in the first aspect.

[0016] The present invention has at least the following beneficial effects: 1. This invention explicitly constructs the pulse heating of lithium-ion batteries as a three-objective optimization problem of "heating time", "energy loss" and "net change in SOC", and uses a multi-objective optimization algorithm to directly solve the Pareto front. It systematically and objectively reveals the inherent competition and trade-off relationship between the three performance indicators, and overcomes the limitations of traditional single-objective optimization and weighted multi-objective optimization methods that rely on subjective experience and cannot fully reflect the trade-off between objectives. 2. This invention provides decision-makers with a strategy library encompassing all globally optimal compromise solutions by obtaining a Pareto optimal solution set. Users can flexibly select the most suitable heating strategy based on real-time and specific engineering needs (such as remaining battery power, ambient temperature, urgency level, etc.), achieving a leap from "single fixed strategy" to "multi-objective optimal decision-making," significantly enhancing the adaptability and intelligence level of the battery thermal management system. Attached Figure Description

[0017] Figure 1This is a flowchart of a lithium-ion battery pulse heating control method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the battery electro-thermal coupling model in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the second-order RC equivalent circuit model in an embodiment of the present invention; Figure 4 This is a flowchart illustrating the use of a multi-objective particle swarm optimization algorithm to solve a multi-objective function in an embodiment of the present invention; Figure 5 This is a schematic diagram of the three-dimensional Pareto front in an embodiment of the present invention; Figure 6 The example solution in this embodiment of the invention corresponds to the pulse current control sequence. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0019] This invention provides a method for controlling pulse heating of a lithium-ion battery, such as... Figure 1 As shown, it includes the following steps: Step 101: Establish an electro-thermal coupling model for a lithium-ion battery; the electro-thermal coupling model includes an electrical model and a thermal model; the electrical model is a second-order RC equivalent circuit model; the thermal model is a lumped parameter thermal model of the battery constructed based on the law of conservation of energy.

[0020] Specifically, the embodiments of the present invention first establish as follows: Figure 2 The battery electro-thermal coupling model shown is a high-precision battery model used to accurately simulate pulse heating processes, including electrical and thermal models, such as... Figure 3 As shown, the electrical model adopts a second-order RC equivalent circuit model. Its model parameters (ohmic internal resistance R0, polarization resistances R1 and R2, polarization capacitances C1 and C2) were identified through hybrid pulse power characteristic experiments. These model parameters were established as functions of battery temperature and state of charge to accurately characterize the battery's low-temperature dynamic characteristics. The thermal model is based on the law of conservation of energy and is used to calculate the battery temperature rise. Figure 2 As shown, the electrical model calculates the heat generation power based on the input current, real-time battery temperature, and battery SOC, and inputs it into the thermal model. After the thermal model updates the battery temperature, it feeds it back to the electrical model to correct the electrical parameters online, forming a closed-loop simulation system to ensure the accuracy of the heating process prediction.

[0021] In this embodiment of the invention, the recursive least squares method with forgetting factor FFRLS is used to identify all electrical parameters of the second-order RC model online. The identified ohmic internal resistance, polarization resistance, and polarization capacitance are fitted as correlation functions of temperature and SOC, thereby achieving accurate characterization of the battery's electrical characteristics at low temperatures.

[0022] Step 102: During the battery pulse heating process, the heating process of the battery from the initial temperature to the target temperature is discretized into multiple continuous temperature stages, and a multi-objective function is constructed with the optimization objectives of minimizing the total heating time, minimizing the total energy loss, and minimizing the net change in SOC. The optimization variables of the multi-objective function are the pulse discharge current amplitude, the pulse charging current amplitude, and the pulse duty cycle.

[0023] Specifically, a multi-objective optimization problem of pulse heating is constructed, and the optimization variable is defined as the amplitude of the pulse discharge current I. d Pulse charging current amplitude I c The pulse duty cycle D discretizes the process of heating the battery from the initial temperature to the target temperature into N consecutive temperature stages, and constructs three competing optimization objectives, including minimizing the total heating time, minimizing the total energy loss, and minimizing the net change in SOC.

[0024] In one possible implementation, the multi-objective function includes a function to minimize the total heating time; constructing the objective function to minimize the total heating time includes: Step 201: Based on the electrical model, obtain the heat generation power model for each temperature stage with respect to the amplitude of the pulse discharge current, the amplitude of the pulse charging current, and the pulse duty cycle.

[0025] Step 202: Substitute the heat generation power model of each temperature stage into the heat balance equation of the thermal model and solve it to obtain the heating time sub-model of each temperature stage.

[0026] Step 203: Sum the heating time sub-models for all temperature stages to obtain the total heating time model.

[0027] Step 204: Determine the objective function for minimizing the total heating time based on the total heating time model.

[0028] Specifically, heating time is the core evaluation index for low-temperature preheating of lithium batteries. To construct an objective function with the goal of minimizing the total heating time, the process of heating the battery from the initial temperature to the target temperature is discretized into N consecutive temperature stages, and three pulse parameters are simultaneously optimized in the j-th stage (j=1,2,3,...,N). The pulse parameters are the optimized variables in this embodiment of the invention, namely the pulse discharge current amplitude, the pulse charging current amplitude, and the pulse duty cycle. The heat generation power model for the j-th stage with respect to the pulse parameters is determined based on the electrical model.

[0029] In one possible implementation, the heat generation power model is constructed based on the following expression:

[0030] In the formula, Let J be the heat generation power of the j-th stage. Let be the ohmic internal resistance of the battery at the starting temperature of the j-th stage. Let J be the amplitude of the pulse discharge current in the j-th stage. Let J be the amplitude of the pulse charging current in the j-th stage. Let be the pulse duty cycle of the j-th stage.

[0031] The heat balance equation for the j-th stage is:

[0032] In the formula, T is the battery temperature and t is time. Where is the ambient temperature, h is the equivalent heat transfer coefficient in the battery thermal model, s is the surface area of ​​the battery, m is the mass of the battery, and c is the specific heat capacity of the battery.

[0033] Solving the heat balance equation yields the battery temperature in the j-th stage from... Rise to Time required This refers to the heating time sub-model.

[0034] In one possible implementation, the heating time sub-model is constructed based on the following expression: ; In the formula, For the j-th stage, the battery temperature will be reduced from... Rise to Required heating time, The ambient temperature, Let J be the final temperature of the j-th stage. The starting temperature of the j-th stage The corresponding capacity decay correction function value, here is the low-temperature capacity decay correction function, used to characterize the reduction ratio of the actual usable capacity of the battery to the rated capacity under low-temperature conditions. It can be obtained through a discrete interpolation table or a polynomial fitting expression by constant temperature capacity calibration test. Let m be the heat generation power in the j-th stage, m be the mass of the battery, c be the specific heat capacity of the battery, h be the equivalent heat transfer coefficient, and s be the surface area of ​​the battery.

[0035] Summing up the heating times of all stages yields the total heating time; therefore, the first objective function... The objective function that minimizes the total heating time is expressed as follows:

[0036] In one possible implementation, the multi-objective function further includes a total energy loss minimization objective function; constructing the total energy loss minimization objective function includes: Step 301: Multiply the heat generation power model and the heating time sub-model for each temperature stage to obtain the energy loss for each temperature stage.

[0037] Step 302: Obtain the objective function for minimizing total energy loss based on the energy loss at all temperature stages.

[0038] Specifically, while pursuing rapid heating, reducing energy consumption is key to improving system economy. Energy loss during pulse heating primarily stems from the Joule heating effect of the battery's internal resistance. Minimizing energy loss helps reduce the impact of the heating process on the vehicle's driving range.

[0039] Based on the preceding steps, the heat generation power and the required heating time for the j-th stage have been determined. The work done by this heat generation power during the stage's duration is the energy loss for that stage. Therefore, multiplying the heat generation power and the required heating time for the j-th stage yields the energy loss for that stage. The second objective function aims to minimize the total energy loss. f 2 is defined as the sum of energy losses at each stage, expressed as follows:

[0040] Will and Substituting the calculation formula into the above formula, we can establish... f 2 with all pulse parameters The explicit functional relationship between them, optimizing this objective, will guide the algorithm to seek the parameter combination that minimizes internal resistance heat generation while meeting the temperature rise requirement.

[0041] In one possible implementation, the multi-objective function includes a function for minimizing the net change in SOC; constructing the objective function for minimizing the net change in SOC includes: Step 401: Based on the battery rated capacity and capacity decay correction function, and using the ampere-hour integration method, obtain the first and second instantaneous changes in SOC with respect to the optimized variables at each temperature stage; the first instantaneous change in SOC is the instantaneous change in SOC caused by the discharge pulse; the second instantaneous change in SOC is the instantaneous change in SOC caused by the charging pulse. Step 402: Add the first instantaneous change in SOC and the second instantaneous change in SOC at each temperature stage to obtain the sum of the instantaneous changes in SOC at each temperature stage; Step 403: Based on the initial SOC value, sum the instantaneous changes in SOC at all temperature stages to obtain the final SOC value at the end of heating.

[0042] Step 404: Determine the objective function for minimizing the net change in SOC based on the initial SOC value and the final SOC value.

[0043] Specifically, to maintain the battery's usable energy state, the net change in state of charge (SOC) caused by the heating process needs to be minimized. Using the ampere-hour integral method, the instantaneous changes in SOC caused by the discharge and charge pulses in the j-th stage are respectively:

[0044]

[0045] In the formula, C nom For the battery's rated capacity, For the j-th stage, the battery temperature will be reduced from... Rise to Required heating time, The starting temperature of the j-th stage The corresponding capacity decay correction function value, This refers to the instantaneous change in SOC caused by the discharge pulse, i.e., the first instantaneous change in SOC. This refers to the instantaneous change in SOC caused by the charging pulse, i.e., the second instantaneous change in SOC. Let J be the amplitude of the pulse discharge current in the j-th stage. Let J be the amplitude of the pulse charging current in the j-th stage. Let be the pulse duty cycle of the j-th stage.

[0046] Battery SOC value at the end of heating From initial value Iterative calculation yielded the following results: The expression is as follows:

[0047] In the formula, The battery SOC value at the end of heating. This is the battery SOC value at the start of heating.

[0048] Therefore, the third objective function aims to minimize the net change in battery SOC. f 3 is defined as:

[0049] To ensure the feasibility of optimization and battery safety, constraints are set, including: the charging and discharging current amplitude must not exceed the battery's allowable limit, the battery's operating voltage must be within a safe window, and the battery's SOC must be maintained within a preset range during heating. The preset range of battery SOC is set by the implementer according to the actual working conditions, and a typical safe range can be set to 0.3~0.9.

[0050] Step 103: Solve the multi-objective function using the multi-objective particle swarm optimization algorithm to obtain the Pareto optimal solution set.

[0051] A multi-objective optimization algorithm is used to solve the Pareto front. Specifically, a multi-objective particle swarm optimization algorithm is used to solve the above problem, referring to... Figure 4 A sequence of pulse parameters is encoded into a "particle". After initializing the particle swarm, for each particle, the electro-thermal coupling model established in this embodiment is used for simulation to calculate its corresponding three objective function values. Based on the Pareto dominance relationship, the particle quality is compared, and the individual historical optimal and global non-dominated solution sets (external archive) are updated. By iteratively guiding the particle swarm to search for the Pareto front region in the target space, the algorithm finally outputs a complete Pareto optimal solution set. Each solution in this set represents a pulse heating strategy that achieves Pareto optimality on all three objectives. A schematic diagram of the three-dimensional Pareto front is shown below. Figure 5 As shown, the trade-offs between the three objectives are clearly illustrated.

[0052] Step 104: Select a matching target solution from the Pareto optimal solution set according to the current heating requirement, and control the battery pulse heating according to the pulse parameter sequence corresponding to the target solution; the current heating requirement includes the current remaining battery power and the preset heating target priority; the heating target priority is at least one of temperature rise priority, energy consumption reduction priority, or stable SOC priority.

[0053] Specifically, decisions and applications are ultimately made based on the Pareto optimal solution set. The obtained Pareto optimal solution set constitutes an "optimal strategy library." In practical applications, the battery management system can select the most suitable strategy from this solution set based on the current state (such as remaining charge, urgency of heating demand) and the priority of the optimization objective. For example, using the "minimum trade-off" principle, a solution with balanced overall performance can be selected, and its corresponding pulse current control sequence is as follows: Figure 6 As shown, the charging and discharging current amplitude adaptively adjusts with temperature rise. Applying this parameter sequence to practical control enables multi-objective collaborative optimization of pulse heating.

[0054] In one possible implementation, the battery pulse heating process further includes: The input current, real-time battery temperature, and battery SOC are input into the electrical model to obtain the heat generation power; the input current includes pulse discharge current and pulse charging current. The heat generation power is input into the heat balance equation of the thermal model to obtain the current battery temperature; the current battery temperature is the updated real-time battery temperature. The electrical parameters of the electrical model and the OCV-SOC curve of the battery at the current temperature are updated according to the current battery temperature; the electrical parameters include ohmic internal resistance, polarization resistance and polarization capacitance.

[0055] Specifically, in the formula for calculating heat generation power... It is a bivariate function of battery temperature and battery SOC. Therefore, based on the real-time battery temperature and battery SOC, the corresponding ohmic internal resistance of the battery is first obtained, and then the battery heat generation power is further calculated. The OCV-SOC curve is the curve corresponding to the relationship between the battery open-circuit voltage (OCV) and the battery state of charge (SOC). In the entire battery pulse heating process, each iteration simultaneously completes a closed-loop iteration of electrical model calculation of heat generation, thermal model temperature update, and feedback correction of circuit parameters.

[0056] In summary, in this embodiment of the invention, the pulse heating of lithium-ion batteries is explicitly constructed as a three-objective optimization problem of "heating time", "energy loss" and "net change in SOC", and a multi-objective optimization algorithm is used to directly solve the Pareto front. This systematically and objectively reveals the inherent competition and trade-off relationship between the three performance indicators, overcoming the limitations of traditional single-objective optimization and weighted multi-objective optimization methods that rely on subjective experience and cannot fully reflect the trade-offs between objectives.

[0057] The embodiments of this invention provide decision-makers with a strategy library covering all globally optimal compromise solutions by obtaining a Pareto optimal solution set. Users can flexibly select the most suitable heating strategy according to real-time and specific engineering needs (such as remaining battery power, ambient temperature, urgency, etc.), realizing a leap from "single fixed strategy" to "multi-objective optimal decision-making", significantly enhancing the adaptability and intelligence level of the battery thermal management system.

[0058] This invention also provides a lithium-ion battery pulse heating control device, which includes: The model building unit is used to establish an electro-thermal coupling model of a lithium-ion battery; the electro-thermal coupling model includes an electrical model and a thermal model; the electrical model is a second-order RC equivalent circuit model; the thermal model is a lumped parameter thermal model of the battery based on the law of conservation of energy. A multi-objective function construction unit is used to discretize the heating process of the battery from the initial temperature to the target temperature into multiple continuous temperature stages during the battery pulse heating process, and construct a multi-objective function with the optimization objectives of minimizing the total heating time, minimizing the total energy loss, and minimizing the net change in SOC; the optimization variables of the multi-objective function are the pulse discharge current amplitude, the pulse charging current amplitude, and the pulse duty cycle; The multi-objective function solving unit is used to solve the multi-objective function using a multi-objective particle swarm optimization algorithm to obtain a Pareto optimal solution set. The battery heating control unit is used to select a matching target solution from the Pareto optimal solution set according to the current heating demand, and control the battery pulse heating according to the pulse parameter sequence corresponding to the target solution; the current heating demand includes the current remaining battery power and a preset heating target priority; the heating target priority is at least one of temperature rise priority, energy consumption reduction priority, or stable SOC priority.

[0059] In another embodiment of the present invention, an electronic device is also provided, the electronic device including a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by the processor to implement the lithium-ion battery pulse heating control method in the embodiment of the present invention.

[0060] In another embodiment of the present invention, a computer-readable storage medium is also provided, wherein at least one instruction or at least one program is stored in the storage medium, and the at least one instruction or at least one program is loaded and executed by a processor to implement the lithium-ion battery pulse heating control method in the embodiment of the present invention.

[0061] 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 one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0062] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for controlling pulse heating of a lithium-ion battery, characterized in that, include: Establish an electro-thermal coupling model for lithium-ion batteries; The electro-thermal coupling model includes an electrical model and a thermal model; The electrical model is a second-order RC equivalent circuit model; the thermal model is a battery lumped parameter thermal model constructed based on the law of conservation of energy. During the battery pulse heating process, the heating process from the initial temperature to the target temperature is discretized into multiple continuous temperature stages, and a multi-objective function is constructed with the optimization objectives of minimizing the total heating time, minimizing the total energy loss, and minimizing the net change in SOC. The optimization variables of the multi-objective function are the pulse discharge current amplitude, the pulse charging current amplitude, and the pulse duty cycle. The multi-objective function is solved using a multi-objective particle swarm optimization algorithm to obtain the Pareto optimal solution set. Based on the current heating requirement, a matching target solution is selected from the Pareto optimal solution set, and the battery pulse heating is controlled according to the pulse parameter sequence corresponding to the target solution; the current heating requirement includes the current remaining battery power and a preset heating target priority; the heating target priority is at least one of temperature rise priority, energy consumption reduction priority, or stable SOC priority.

2. The lithium-ion battery pulse heating control method according to claim 1, characterized in that, The multi-objective function includes a function that minimizes the total heating time. Constructing the objective function that minimizes the total heating time includes: Based on the electrical model, a heat generation power model is obtained for each temperature stage with respect to the amplitude of the pulse discharge current, the amplitude of the pulse charging current, and the pulse duty cycle. Substitute the heat generation power model of each temperature stage into the heat balance equation of the thermal model and solve it to obtain the heating time sub-model of each temperature stage. Summing the heating time sub-models for all temperature stages yields the total heating time model; The objective function for minimizing the total heating time is determined based on the total heating time model.

3. The lithium-ion battery pulse heating control method according to claim 2, characterized in that, The heat generation power model is constructed based on the following expression: In the formula, Let J be the heat generation power of the j-th stage. Let be the ohmic internal resistance of the battery at the starting temperature of the j-th stage. Let J be the amplitude of the pulse discharge current in the j-th stage. Let J be the amplitude of the pulse charging current in the j-th stage. Let be the pulse duty cycle of the j-th stage.

4. The lithium-ion battery pulse heating control method according to claim 2, characterized in that, The heating time sub-model is constructed based on the following expression: ； In the formula, For the j-th stage, the battery temperature will be reduced from... Rise to Required heating time, The ambient temperature, Let J be the final temperature of the j-th stage. The starting temperature of the j-th stage The corresponding capacity decay correction function value, Let J be the heat generation power of the j-th stage. For the quality of the battery, This refers to the specific heat capacity of the battery. The equivalent heat transfer coefficient, This refers to the surface area of ​​the battery.

5. The lithium-ion battery pulse heating control method according to claim 2, characterized in that, The multi-objective function further includes a total energy loss minimization objective function; constructing the total energy loss minimization objective function includes: Multiply the heat generation power model and the heating time sub-model for each temperature stage to obtain the energy loss for each temperature stage; The objective function for minimizing total energy loss is obtained based on the energy loss at all temperature stages.

6. The lithium-ion battery pulse heating control method according to claim 1, characterized in that, The multi-objective function includes the objective function of minimizing the net change in SOC; Constructing the objective function that minimizes the net change in SOC includes: Based on the battery rated capacity and capacity decay correction function, and using the ampere-hour integration method, the first and second instantaneous changes in SOC with respect to the optimized variables are obtained for each temperature stage; the first instantaneous change in SOC is the instantaneous change in SOC caused by the discharge pulse; the second instantaneous change in SOC is the instantaneous change in SOC caused by the charging pulse. The instantaneous change in SOC and the instantaneous change in SOC at each temperature stage are added together to obtain the total instantaneous change in SOC at each temperature stage. The final SOC value at the end of heating is obtained by superimposing the sum of the instantaneous changes in SOC at all temperature stages on the initial SOC value. The objective function for minimizing the net change in SOC is determined based on the initial SOC value and the final SOC value.

7. The lithium-ion battery pulse heating control method according to claim 1, characterized in that, The battery pulse heating process also includes: The input current, real-time battery temperature, and battery SOC are input into the electrical model to obtain the heat generation power; the input current includes pulse discharge current and pulse charging current. The heat generation power is input into the heat balance equation of the thermal model to obtain the current battery temperature; the current battery temperature is the updated real-time battery temperature. The electrical parameters of the electrical model and the OCV-SOC curve of the battery at the current temperature are updated according to the current battery temperature; the electrical parameters include ohmic internal resistance, polarization resistance, and polarization capacitance.

8. A lithium-ion battery pulse heating control device, characterized in that, The device includes: The model building unit is used to establish an electro-thermal coupling model of a lithium-ion battery; the electro-thermal coupling model includes an electrical model and a thermal model; the electrical model is a second-order RC equivalent circuit model; the thermal model is a lumped parameter thermal model of the battery based on the law of conservation of energy. A multi-objective function construction unit is used to discretize the heating process of the battery from the initial temperature to the target temperature into multiple continuous temperature stages during the battery pulse heating process, and construct a multi-objective function with the optimization objectives of minimizing the total heating time, minimizing the total energy loss, and minimizing the net change in SOC; the optimization variables of the multi-objective function are the pulse discharge current amplitude, the pulse charging current amplitude, and the pulse duty cycle; The multi-objective function solving unit is used to solve the multi-objective function using a multi-objective particle swarm optimization algorithm to obtain a Pareto optimal solution set. The battery heating control unit is used to select a matching target solution from the Pareto optimal solution set according to the current heating demand, and control the battery pulse heating according to the pulse parameter sequence corresponding to the target solution; the current heating demand includes the current remaining battery power and a preset heating target priority; the heating target priority is at least one of temperature rise priority, energy consumption reduction priority, or stable SOC priority.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the lithium-ion battery pulse heating control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the lithium-ion battery pulse heating control method as described in any one of claims 1-7.

Images