Battery thermal management system optimization method

Abstract

The invention relates to the technical field of optimization control of a battery thermal management system, in particular to a battery thermal management system optimization method. Comprising the following steps: S1, acquiring a running state parameter and a battery aging state parameter of a battery pack; constructing a thermal characteristic prediction model based on the operation state parameters to generate a predicted maximum temperature and a predicted maximum temperature difference; s2, setting an energy consumption optimization target and an initial security constraint; combining the predicted maximum temperature and the predicted maximum temperature difference to construct a Lagrangian optimization function considering the initial safety constraint; s3, calculating a battery aging factor based on the battery aging state parameters; dynamically correcting the initial safety constraint according to the battery aging factor to generate a corrected dynamic safety limit value; and updating a Lagrange optimization function by using the corrected dynamic safety limit value, and solving the updated Lagrange optimization function to output an optimal cooling system power instruction. According to the method, dynamic self-adaptive adjustment of the thermal management security boundary is realized.

Description

Technical Field

[0001] This invention relates to the field of smart city and intelligent transportation system technology, specifically to an optimization method for a battery thermal management system. Background Technology

[0002] A battery thermal management system is a critical system used to maintain the battery in a suitable temperature range to ensure its performance, safety and lifespan. Its basic principle is to use a cooling system, such as a water pump and a fan, to precisely regulate the heat generation and heat dissipation balance of the battery pack, and control the battery's maximum temperature and internal temperature difference within the design limits. To ensure the safe and efficient operation of a battery throughout its entire lifecycle, the thermal management system needs to acquire real-time operating parameters such as current, temperature, and state of charge, as well as aging parameters reflecting its long-term health level, such as state of health (SOH) and equivalent cycle count. However, as an electrochemical system, the battery's internal heat generation characteristics and its tolerance to high temperatures change with use and aging. Existing thermal management technologies mostly use fixed safety thresholds for control, meaning that the upper limit of the maximum temperature and maximum temperature difference remains constant throughout the battery's entire lifecycle. This static control strategy does not fully consider the characteristic deterioration caused by battery aging. When the battery is in an aging state, the fixed safety boundaries may no longer be applicable, resulting in an inability to effectively delay battery life degradation and even potential safety hazards under extreme operating conditions.

[0003] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention discloses an optimization method for a battery thermal management system. Specifically, the technical solution of this invention is as follows: A method for optimizing a battery thermal management system, comprising: S1. Obtain the battery pack's operating status parameters and battery aging status parameters; based on the operating status parameters, construct a thermal characteristic prediction model to generate the predicted maximum temperature and the predicted maximum temperature difference; S2. Set the energy consumption optimization target and initial safety constraints; combine the predicted maximum temperature and the predicted maximum temperature difference to construct a Lagrange optimization function that takes into account the initial safety constraints; S3. Calculate the battery aging factor based on the battery aging state parameters; dynamically correct the initial safety constraints according to the battery aging factor to generate the corrected dynamic safety limit; update the Lagrange optimization function using the corrected dynamic safety limit, and solve the updated Lagrange optimization function to output the optimal cooling system power command.

[0005] Preferably, S1 includes: Real-time acquisition of battery pack charging and discharging current and cell temperature; and real-time acquisition of external ambient temperature and coolant inlet temperature. Based on the collected charge and discharge current and cell temperature, the battery heat generation rate is calculated using a modified Bernardi thermal model. Based on the collected cell temperature, coolant inlet temperature, and external ambient temperature, the battery heat dissipation rate is calculated through the heat dissipation model determined by the system identification experiment.

[0006] Preferably, S1 further includes: Based on the calculated battery heat generation rate and battery heat dissipation rate, a dynamic evolution prediction model for battery temperature state is established. The dynamic evolution prediction model uses energy conservation to derive the predicted maximum temperature and uses a simplified linear state-space model to generate the predicted maximum temperature difference.

[0007] Preferably, S2 includes: The energy consumption optimization objective is set as minimizing the sum of the power of the cooling system's water pumps and fans; The initial safety constraints are set as follows: the maximum temperature of the battery pack does not exceed the preset maximum temperature safety limit, and the maximum temperature difference of the battery pack does not exceed the preset maximum temperature difference consistency limit.

[0008] Preferably, constructing the Lagrangian optimization function includes: Based on the predicted maximum temperature and the maximum temperature safety limit, a temperature constraint function is constructed; Based on the upper limit of the consistency between the predicted maximum temperature difference and the maximum temperature difference, a temperature difference constraint function is constructed; By combining the energy consumption optimization objective, temperature constraint function, and temperature difference constraint function, a Lagrange optimization function is constructed.

[0009] Preferably, the battery aging status parameters include the battery health status and equivalent cycle number estimated by the battery management system; Calculating the battery aging factor includes: Based on the battery health status and equivalent cycle count, the battery aging factor is calculated using a preset aging factor model.

[0010] Preferably, generating the modified dynamic security limits includes: Based on the calculated battery aging factor, a proportional adjustment method is used to correct the maximum temperature safety limit preset in the initial safety constraints, so as to generate the corrected maximum temperature limit. Based on the calculated battery aging factor, a proportional adjustment method is used to correct the maximum temperature difference consistency limit preset in the initial safety constraints, so as to generate the corrected maximum temperature difference limit.

[0011] Preferably, the solution includes: Within each control cycle, the Carlow-Kun-Tucker optimality conditions corresponding to the updated Lagrange optimization function are solved to obtain the optimal power allocation; The optimal power allocation is used as the optimal cooling system power command and output to the water pump and fan drivers of the cooling system.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. This method achieves dynamic adaptive adjustment of thermal management safety boundaries. Based on aging parameters such as battery health status and equivalent cycle count estimated by the battery management system, this method calculates the aging factor in real time and dynamically adjusts the safety limits for maximum temperature and maximum temperature difference accordingly. Compared to traditional methods using fixed thresholds, this proactive strategy of tightening operating boundaries effectively slows down battery lifespan degradation while ensuring battery safety.

[0013] 2. This method achieves full lifecycle optimization of cooling system energy consumption. By constructing an optimization function with the objective of minimizing the total power of the cooling water pump and fan, and solving for the optimality conditions in each control cycle, it ensures that the cooling system always operates at the lowest power consumption while satisfying dynamic safety constraints. This enables the system to continuously and dynamically find the point of lowest energy consumption, thereby achieving significant energy savings throughout the battery's entire lifecycle.

[0014] 3. This method improves the control accuracy and response speed of the thermal management system. Based on real-time operating parameters, this method constructs a thermal characteristic prediction model, which can accurately predict the battery's maximum temperature and maximum temperature difference within a specific future control cycle. This predictive capability transforms the control strategy from traditional responsive control to forward-looking predictive control, avoiding control lag and improving the system's accuracy and robustness in responding to complex operating conditions.

[0015] 4. This method achieves synergy and optimal balance among multiple objectives. It places the battery's real-time operating conditions, long-term health status, system energy consumption, and operational safety within a unified Lagrangian optimization framework for solution. Through this systematic mathematical modeling approach, the complex boundary-constrained problem is transformed into an unconstrained function extremum problem, achieving synergistic optimization and dynamic balance of the two core indicators: operational safety and system energy consumption. Attached Figure Description

[0016] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0018] Example 1: Please see Figure 1 A method for optimizing a battery thermal management system, comprising: S1. Obtain the battery pack's operating status parameters and battery aging status parameters; based on the operating status parameters, construct a thermal characteristic prediction model to generate the predicted maximum temperature and the predicted maximum temperature difference; S2. Set the energy consumption optimization target and initial safety constraints; combine the predicted maximum temperature and the predicted maximum temperature difference to construct a Lagrange optimization function that takes into account the initial safety constraints; S3. Calculate the battery aging factor based on battery aging state parameters; dynamically correct the initial safety constraints according to the battery aging factor to generate corrected dynamic safety limits; update the Lagrangian optimization function using the corrected dynamic safety limits, and solve the updated Lagrangian optimization function to output the optimal cooling system power command; This invention provides a battery thermal management system optimization method, aiming to achieve a dynamic optimal balance between operational safety and system energy consumption throughout the battery's entire life cycle. This method constitutes a complete and self-consistent technical closed loop, and its specific steps include: S1. Acquire the battery pack's operating state parameters and battery aging state parameters, and construct a thermal characteristic prediction model. This step is the foundation of the entire optimization control strategy. Operating state parameters refer to real-time physical quantities reflecting the battery's current operating condition, such as current, state of charge, and temperature. Their function is to provide immediate input for the prediction model. Battery aging state parameters refer to quantitative indicators characterizing the battery's long-term health level, such as state of health (SOH) and equivalent cycle count. Their function is to provide a basis for subsequent dynamic adjustment of the safety boundary. Based on the real-time acquired operating state parameters, this embodiment constructs a thermal characteristic prediction model. Its core purpose is to establish a precise mathematical relationship between the control variable, i.e., the cooling system power, and the future temperature state of the battery, i.e., the predicted maximum temperature and the predicted maximum temperature difference. The differentiability of this model is a prerequisite for subsequent solution using gradient-based optimization algorithms. S2, set the energy consumption optimization objective and initial safety constraints, and construct a Lagrange optimization function that takes into account the initial safety constraints; this step aims to formalize the actual engineering requirements into a standard mathematical optimization problem; the energy consumption optimization objective refers to minimizing the total power of the cooling system actuators, such as water pumps and fans, which is the core requirement for system energy saving; the initial safety constraints refer to the maximum temperature and maximum temperature difference limits that must be observed in the early stages of the battery's lifespan, as set according to battery design specifications and safety standards; in order to systematically solve this optimization problem with inequality constraints, this embodiment constructs a Lagrange optimization function; the application logic of this function is that it transforms the complex boundary constraint problem into an unconstrained function extremum problem by introducing Lagrange multipliers, which greatly simplifies the solution process and unifies the energy consumption objective and safety constraints under a single mathematical framework; S3: Calculate the battery aging factor based on battery aging state parameters, dynamically correct the initial safety constraints, and solve the updated Lagrange optimization function to output the optimal cooling system power command. This step embodies the core adaptive and forward-looking capabilities of this invention. First, based on the acquired battery aging state parameters, a comprehensive battery aging factor is calculated, which aims to quantify the evolution of the battery from healthy to degraded. Second, this method uses this aging factor to dynamically correct the initial safety constraints set in step S2, generating corrected dynamic safety limits. The technical motivation for this design is that as the battery ages, its heat generation characteristics and tolerance to high temperatures deteriorate, and fixed safety boundaries are no longer applicable. By actively tightening the operating boundaries, battery life degradation can be effectively delayed while ensuring safety. Finally, the Lagrange optimization function is updated using this corrected limit, and the optimal cooling system power command output is the optimal solution under the current operating conditions and the current battery health state. This command is sent to the cooling system actuator to complete one closed-loop control cycle. This embodiment establishes a closed-loop control method integrating prediction, optimization, and adaptation by constructing a predictive model, setting optimization targets, and dynamically correcting safety constraints under aging conditions. Compared with traditional thermal management strategies that use fixed thresholds or responsive control, this invention can proactively and dynamically find the lowest point of system energy consumption based on the real-time operating conditions and long-term health status of the battery, while ensuring absolute battery safety. This results in significant energy-saving effects throughout the battery's entire life cycle and effectively extends the battery's service life.

[0019] Example 2: S1 includes: Real-time acquisition of battery pack charging and discharging current and cell temperature; and real-time acquisition of external ambient temperature and coolant inlet temperature. Based on the collected charge and discharge current and cell temperature, the battery heat generation rate is calculated using a modified Bernardi thermal model. Based on the collected cell temperature, coolant inlet temperature and external ambient temperature, the battery heat dissipation rate is calculated by identifying the heat dissipation model determined by the system identification experiment. S1 also includes: Based on the calculated battery heat generation rate and battery heat dissipation rate, a dynamic evolution prediction model for battery temperature state is established. The dynamic evolution prediction model uses energy conservation to derive the predicted maximum temperature and uses a simplified linear state-space model to generate the predicted maximum temperature difference. Based on Example 1, this embodiment specifies and optimizes the process of constructing the thermal characteristic prediction model in step S1, thereby establishing a high-precision prediction model that can be used for online optimization. This embodiment uses sensors to collect real-time data on the battery pack's charging and discharging current and cell temperature, as well as the external ambient temperature and coolant inlet temperature; these are the fundamental physical quantities input to the model; among them, the charging and discharging current... With cell temperature It is the core basis for calculating heat generation; while the external ambient temperature With coolant inlet temperature These are the key boundary conditions for calculating heat dissipation; Based on the collected charge / discharge current and cell temperature, this embodiment uses a modified Bernardi thermal model to calculate the battery heat generation rate; the purpose of this step is to accurately quantify the intensity of the internal heat source of the battery; the mathematical expression of the model is as follows: in, Total heat production rate, in watts It is calculated using this formula; Real-time current, in amperes. This is the real-time value collected by the vehicle current sensor; Battery equivalent internal resistance, in ohms Its value is related to the state of charge. and temperature The function is calibrated by conducting thermal characteristic experiments on specific battery cells, such as hybrid pulse power characteristic testing, HPPC, and using data fitting methods. Finally, it is stored in the controller in the form of a lookup table or fitting function for real-time calling. Battery temperature, calculated from real-time readings by internal temperature sensors; equivalent internal resistance is also calculated. At that time, Celsius can be used. or Kelvin In calculating the entropy change heat term To ensure physical accuracy, Kelvin must be used. As a unit; Entropy coefficient, measured in volts per Kelvin. Similar to the equivalent internal resistance, it is obtained through experimental calibration and varies with temperature; Based on the collected cell temperature, coolant inlet temperature, and external ambient temperature, the battery heat dissipation rate is calculated using a heat dissipation model determined through system identification experiments. This step aims to establish a direct quantitative relationship between the cooling system's operating power and the actual heat dissipation effect; its mathematical expression is as follows: in, Total heat dissipation rate, in watts It is calculated using this formula; Pump power and fan power, in watts. , as a control variable, serves as the output of the subsequent optimization algorithm; Average battery temperature, coolant inlet temperature, and ambient temperature, all in degrees Celsius. ,in, and These are the real-time values ​​collected by the corresponding position sensors; It is an arithmetic average calculated in real time based on the readings of multiple temperature sensors in the battery pack, used to characterize the overall thermal state of the battery pack; : Model coefficients, where It is a dimensionless nonlinear power index. The equivalent heat transfer coefficient is given by the following units: and To ensure consistency of the formula dimensions; these four parameters are not derived from physics, but determined through system identification experiments on the entire thermal management system; specifically, during the calibration phase, a series of different power combination commands are applied. ,in, This is the rated power instruction for the water pump, in watts (W). This refers to the fan's rated power command, measured in watts (W); it also monitors the temperature difference data when the system reaches steady state. Finally, based on these calibrated datasets, identification algorithms such as the least squares method are used to fit the data. Based on the calculated battery heat generation rate and battery heat dissipation rate, a dynamic evolution prediction model for battery temperature state is established; this model is used to predict the temperature state in the next control cycle. Predicted highest temperature The evolution is derived using the law of conservation of energy, and its model is as follows: in, This is the equivalent heat capacity of the battery pack, measured in joules per Kelvin (J / K). This parameter was calibrated experimentally. This is the control cycle, which sets the parameters for the controller; the other parameters have been defined or calculated in the previous steps. Predicting the maximum temperature difference The evolution is carried out using a simplified linear state-space model, which is as follows: In this formula, The coefficient of thermal decay is a dimensionless temperature difference. and These are the heat production imbalance coefficient and the heat dissipation imbalance coefficient, respectively, both in Kelvin per watt. To ensure dimensional consistency; these three coefficients are obtained through computational fluid dynamics (CFD) simulation or extensive bench test data calibration and identification; during the calibration process, by analyzing temperature field distribution data under different heat flow input conditions, these three empirical parameters characterizing the inherent thermal properties of the system can be fitted. Through the specific modeling methods described above, this embodiment constructs a high-fidelity thermal characteristic prediction model. This model can not only accurately predict the future maximum temperature and maximum temperature difference caused by arbitrary power commands, but also has a clear structure and high computational efficiency, fully meeting the needs of real-time optimization of the vehicle controller. This accurate prediction capability is the fundamental guarantee that the entire optimization method can achieve precise and energy-saving control.

[0020] Example 3: S2 includes: The energy consumption optimization objective is set as minimizing the sum of the power of the cooling system's water pumps and fans; The initial safety constraints are set as follows: the maximum temperature of the battery pack does not exceed the preset maximum temperature safety limit, and the maximum temperature difference of the battery pack does not exceed the preset maximum temperature difference consistency limit. Constructing the Lagrangian optimization function includes: Based on the predicted maximum temperature and the maximum temperature safety limit, a temperature constraint function is constructed; Based on the upper limit of the consistency between the predicted maximum temperature difference and the maximum temperature difference, a temperature difference constraint function is constructed; By combining the energy consumption optimization objective, temperature constraint function, and temperature difference constraint function, a Lagrange optimization function is constructed. Based on Example 1, this embodiment elaborates on the construction process of the energy consumption and safety constraint optimization model in step S2, transforming the abstract control objective into precise mathematical language; To achieve the aforementioned objectives, this embodiment defines an energy consumption optimization target; this target is set as minimizing the sum of the power of the cooling system's water pumps and fans; this definition intuitively and effectively represents the main electrical energy consumption of the thermal management system; mathematically, the objective function... Defined as: in It is a vector of control variables that includes the power of the water pump and the power of the fan. Meanwhile, this embodiment clarifies the initial safety constraints; these constraints are set as follows: the highest temperature of the battery pack does not exceed a preset maximum temperature safety limit, and the maximum temperature difference of the battery pack does not exceed a preset maximum temperature difference consistency limit; these two limits... and The core boundaries for safe battery operation are the battery design specifications and industry safety regulations; for example, for a certain ternary lithium battery, its initial maximum safe temperature limit... Can be set to Maximum temperature difference consistency limit Can be set to ; Based on the above objectives and constraints, the core step in this embodiment lies in constructing the Lagrange optimization function. This process transforms an optimization problem with inequality constraints into an equivalent unconstrained problem for solution. The specific construction process is as follows: Constructing temperature constraint functions This function is based on the dynamic evolution prediction model established in Example 2 (formula). The predicted maximum temperature output With the maximum safe temperature limit The purpose is to transform temperature safety requirements into standard inequality constraints; its expression is: When this inequality holds true, it means that the temperature is within a safe range; Constructing temperature difference constraint function This function is based on the prediction of the maximum temperature difference. Upper limit of consistency with maximum temperature difference The purpose is to transform the internal temperature uniformity requirements of the battery pack into a standard inequality constraint form; its expression is: When this inequality holds true, it means that the battery pack temperature consistency meets the requirements; Combining the energy consumption optimization objective, temperature constraint function, and temperature difference constraint function, the final Lagrangian optimization function is constructed. This function integrates all the above elements into a unified mathematical framework: in, These are Lagrange multiplier vectors. To ensure dimensional consistency, the multipliers... and The units should be power / temperature, such as W / K; they represent the marginal energy cost the system needs to expend to maintain the boundary of the constraint when the corresponding constraint is activated; their components and These multipliers are not preset parameters, but rather are introduced during the optimization process along with the optimal power. The result variables that are calculated together; This embodiment precisely transforms the engineering objectives of energy saving and safety boundary temperature and temperature difference constraints into mathematical objective functions and inequality constraints, and uses the Lagrange multiplier method to construct a unified optimization function, thus laying a solid foundation for subsequent numerical solutions. This systematic mathematical modeling approach ensures that the optimization problem has a solution and that the solution is optimal, achieving synergistic optimization of the two core indicators of energy consumption and safety.

[0021] Example 4: Battery aging status parameters include battery health status and equivalent cycle count estimated by the battery management system; Calculating the battery aging factor includes: Based on the battery health status and equivalent cycle count, the battery aging factor is calculated using a preset aging factor model. The generated revised dynamic security limits include: Based on the calculated battery aging factor, a proportional adjustment method is used to correct the maximum temperature safety limit preset in the initial safety constraints, so as to generate the corrected maximum temperature limit. Based on the calculated battery aging factor, a proportional adjustment method is used to correct the maximum temperature difference consistency limit preset in the initial safety constraints, so as to generate the corrected maximum temperature difference limit. Based on Example 1, this embodiment specifically implements the battery aging state monitoring and constraint dynamic correction part in step S3, which is the key to the realization of full life cycle adaptive control in this invention. In this embodiment, the battery aging state parameters are defined as the battery health state (SOH) and equivalent cycle number (Nc) estimated by the battery management system (BMS); battery health state This refers to the percentage of energy a current battery can store and release relative to a new battery, typically estimated by the BMS using online identification algorithms such as extended Kalman filtering; equivalent cycle count. This refers to the standard number of cycles converted from charging and discharging processes at different depths and rates. The BMS calculates this number using the ampere-hour integration method combined with empirical weighting. This embodiment uses a preset aging factor model to calculate the battery aging factor. The purpose of this step is to fuse information from multiple aging dimensions into a single, normalized metric; the expression for this model is: in, :Aging factor, dimensionless, calculated by this formula; Battery health status and equivalent cycle count are both real-time estimates from the BMS. : Design reference cycle life, in cycles, is a design parameter provided by the battery manufacturer; The weighting coefficient, dimensionless, is an adjustable parameter set based on the degradation mechanism of a specific battery and accelerated aging test data. It typically satisfies... ; The most critical step in this embodiment is generating the corrected dynamic safety limit. This embodiment uses a proportional adjustment method to correct the preset limit in the initial safety constraint. This method is clear in principle and easy to implement in engineering. It should be noted that this linear weighted model is an engineering simplification of the complex physical process of battery aging. Its purpose is to obtain a quantitative index that can macroscopically characterize the degree of battery aging with a low computational cost, thereby meeting the needs of the vehicle controller to dynamically adjust the control strategy in real time. This model considers two key aging dimensions, capacity decay (SOH) and cycle wear (Nc), and is suitable for degradation scenarios dominated by cycle life. Revision of the maximum safe temperature limit: Revised maximum temperature limit The generation is based on the initial limit. Subtract one factor related to aging from the base Quantities that are directly proportional; among which, It is a dimensionless correction sensitivity coefficient; this coefficient is determined based on accelerated aging test data, specifically, through different constant ambient temperatures. The following cycle aging test was conducted on the battery to obtain a series of results regarding the relationship between temperature and battery cycle life. data points Then, by using regression analysis and other methods, the degree of influence of temperature on lifespan is determined, thereby calibrating... Reference ambient temperature For example, 25°C is an industry standard or design specification; the linear proportional adjustment method here is an effective engineering approximation, assuming that within a certain range, the reduction in the upper limit of temperature safety is proportional to the aging factor. This relationship is adjusted by the sensitivity coefficient. Precise calibration is used to ensure its effectiveness; calibration is performed using experimental data. The value is usually between 0.1 and 0.5 to reflect the temperature sensitivity of different types of batteries; Correction to the maximum temperature difference consistency limit: Corrected maximum temperature difference limit The generation logic is similar, with the correction amount directly related to the initial limit. and aging factors Proportional; This is the corresponding dimensionless corrected sensitivity coefficient, and its calibration method is the same as... Similarly, by analyzing different constant temperature differences Based on battery pack aging test data, the impact of temperature difference on the inconsistency of battery pack life degradation is quantified, thereby determining its value; similar to temperature limit correction, this linear model simplifies the complex relationship of temperature difference on life, and ensures its engineering practicality through experimental data-driven parameter calibration. This embodiment successfully links the long-term aging state of the battery with the short-term optimization control target by introducing a quantifiable aging factor and establishing a proportional correction mechanism for dynamic safety limits based on it. This feedforward dynamic correction strategy enables the thermal management system to anticipate the higher safety risks of aging batteries and proactively adopt a more conservative operating strategy. Compared with the traditional method of using fixed safety limits throughout the entire life cycle, this invention can significantly delay the aging process of the battery, thereby maximizing the economic value and service life of the battery while ensuring safety. To ensure the robustness of the model, boundary checks need to be performed on the calculation results in practical applications; for example, for aging factors. Set an upper limit for the calculation results, such as To prevent extreme values ​​caused by sensor malfunctions or model extrapolation; at the same time, the corrected dynamic safety limits... and Set a reasonable lower limit, for example, stipulate It must not be lower than a certain preset minimum operating temperature, such as , It must not be lower than a certain minimum allowable temperature difference, such as This is to avoid physically meaningless or unsatisfactory constraints and ensure that the optimization solver can operate stably under all operating conditions. The model proposed in this embodiment is a simplified model built to achieve real-time optimization, effectively balancing accuracy and computational efficiency. In future research, more aging characteristic parameters can be introduced to construct a nonlinear dynamic safety boundary model, further improving the model's adaptability and accuracy throughout its life cycle and under complex operating conditions. It should be noted that the currently used linear proportional adjustment model is an effective engineering approximation within the main health range of the battery, with advantages such as simple calculation, easy calibration, and online implementation. However, this model may have some deviations for the strongly nonlinear degradation behavior at the end of the battery's life. Furthermore, current models primarily focus on aging caused by capacity decay and cycle count. For special aging scenarios dominated by other specific factors, such as long-term low-temperature storage and high-rate lithium plating, it may be necessary to introduce additional state parameters to influence the aging factor. The calculation is used to compensate for the loss, so as to further improve the generalization ability of the model.

[0022] Example 5: The solution includes: Within each control cycle, the Carlow-Kun-Tucker optimality conditions corresponding to the updated Lagrange optimization function are solved to obtain the optimal power allocation; The optimal power allocation is used as the optimal cooling system power command and output to the water pump and fan drivers of the cooling system. Based on Example 1, this embodiment provides a detailed description of the final solution process in step S3, explaining how to transform the constructed mathematical model into specific control instructions. Within each control cycle, this embodiment solves the Caro-Kuhn-Tucker (KKT) optimality conditions corresponding to the updated Lagrangian optimization function to obtain the optimal power allocation. The KKT optimality conditions are first-order necessary conditions for the optimal solution to a constrained optimization problem and are a standard mathematical method for solving the Lagrangian function. Its core logic is reflected in the following aspects: gradient condition At the optimal solution, the gradient of the Lagrangian function with respect to the control variable, i.e., power, must be zero. Complementary relaxation conditions This is the key to achieving energy conservation; its underlying logic is that if a constraint is relaxed, that is... If the temperature is far below the limit, then the corresponding Lagrange multipliers The value must be zero, in which case the system will have minimizing energy consumption as its sole objective; only when one constraint is activated, i.e. The temperature is about to reach the limit, and the corresponding multiplier Only then is a positive value allowed, thus allowing the system to increase energy consumption to strictly meet the constraint; Original feasibility and duality feasibility Make sure you understand that you are within the safety boundaries; In vehicle controllers, this set of nonlinear equations is usually solved in real time using efficient numerical iterative algorithms such as sequential quadratic programming (SQP) or interior point method (IPM). Solving the KKT conditions yields the optimal power allocation. In this embodiment, the optimal power allocation is used as the optimal cooling system power command and output to the water pump and fan drivers of the cooling system. After receiving the specific power value, the drivers precisely control the motor operation through pulse width modulation (PWM) and other methods to realize the physical execution of the command. This process of state perception, model prediction, constraint correction, optimization solution, and control execution continuously cycles within each control cycle, such as 1 second, forming a complete closed-loop control system. This embodiment successfully transforms complex optimization theory into an engineering-implementable closed-loop control logic. By efficiently solving the KKT conditions in each control cycle, this method ensures that the power output of the cooling system is always the optimal solution under the current operating conditions and battery health status, that is, the lowest energy consumption under the premise of strictly adhering to the dynamic safety boundary. This real-time and continuous optimization capability enables the system to respond instantly to any changes in the external environment and internal state, thereby effectively transforming the theoretical energy-saving potential and life-extending effect into stable benefits in actual operation.

[0023] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention; any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0024] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing a battery thermal management system, characterized in that, The specific steps include: S1. Obtain the battery pack's operating status parameters and battery aging status parameters; based on the operating status parameters, construct a thermal characteristic prediction model to generate the predicted maximum temperature and the predicted maximum temperature difference; S2. Set the energy consumption optimization target and initial safety constraints; combine the predicted maximum temperature and the predicted maximum temperature difference to construct a Lagrange optimization function that takes into account the initial safety constraints; S3. Calculate the battery aging factor based on the battery aging state parameters; dynamically correct the initial safety constraints according to the battery aging factor to generate the corrected dynamic safety limit; update the Lagrange optimization function using the corrected dynamic safety limit, and solve the updated Lagrange optimization function to output the optimal cooling system power command.

2. The battery thermal management system optimization method according to claim 1, characterized in that, S1 includes: Real-time acquisition of battery pack charging and discharging current and cell temperature; and real-time acquisition of external ambient temperature and coolant inlet temperature. Based on the collected charge and discharge current and cell temperature, the battery heat generation rate is calculated using a modified Bernardi thermal model. Based on the collected cell temperature, coolant inlet temperature, and external ambient temperature, the battery heat dissipation rate is calculated through the heat dissipation model determined by the system identification experiment.

3. The battery thermal management system optimization method according to claim 2, characterized in that, S1 also includes: Based on the calculated battery heat generation rate and battery heat dissipation rate, a dynamic evolution prediction model for battery temperature state is established. The dynamic evolution prediction model uses energy conservation to derive the predicted maximum temperature and uses a simplified linear state-space model to generate the predicted maximum temperature difference.

4. The battery thermal management system optimization method according to claim 1, characterized in that, S2 include: The energy consumption optimization objective is set as minimizing the sum of the power of the cooling system's water pumps and fans; The initial safety constraints are set as follows: the maximum temperature of the battery pack does not exceed the preset maximum temperature safety limit, and the maximum temperature difference of the battery pack does not exceed the preset maximum temperature difference consistency limit.

5. The battery thermal management system optimization method according to claim 4, characterized in that, Constructing the Lagrangian optimization function includes: Based on the predicted maximum temperature and the maximum temperature safety limit, a temperature constraint function is constructed; Based on the upper limit of the consistency between the predicted maximum temperature difference and the maximum temperature difference, a temperature difference constraint function is constructed; By combining the energy consumption optimization objective, temperature constraint function, and temperature difference constraint function, a Lagrange optimization function is constructed.

6. The battery thermal management system optimization method according to claim 1, characterized in that, Battery aging status parameters include battery health status and equivalent cycle count estimated by the battery management system; Calculating the battery aging factor includes: Based on the battery health status and equivalent cycle count, the battery aging factor is calculated using a preset aging factor model.

7. The battery thermal management system optimization method according to claim 6, characterized in that, The generated revised dynamic security limits include: Based on the calculated battery aging factor, a proportional adjustment method is used to correct the maximum temperature safety limit preset in the initial safety constraints, so as to generate the corrected maximum temperature limit. Based on the calculated battery aging factor, a proportional adjustment method is used to correct the maximum temperature difference consistency limit preset in the initial safety constraints, so as to generate the corrected maximum temperature difference limit.

8. The battery thermal management system optimization method according to claim 1, characterized in that, The solution includes: Within each control cycle, the Carlow-Kun-Tucker optimality conditions corresponding to the updated Lagrange optimization function are solved to obtain the optimal power allocation; The optimal power allocation is used as the optimal cooling system power command and output to the water pump and fan drivers of the cooling system.

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