Self-adaptive regulation and control method and system of battery thermal management system

Abstract

The invention relates to the technical field of battery thermal regulation and control, and provides a self-adaptive regulation and control method and system for a battery thermal management system, and the method comprises the steps: collecting the real-time temperature data of a battery, analyzing the temperature distribution of the battery after obtaining the current battery temperature information, and inputting the target temperature gradient information into a preset analysis model after obtaining the target temperature gradient information; cooling regulation and control parameters and heating regulation and control parameters are obtained, a battery cooling system and a battery heating system are controlled, a battery temperature regulation and control result and a battery heating regulation and control result are obtained, a battery thermal management control strategy is generated, and self-adaptive regulation and control are conducted on the battery based on the battery thermal management control strategy. By generating a battery thermal management control strategy, self-adaptive regulation and control of the battery are realized, quick response can be realized in a complex working environment and dynamic load change, and the problems of low response speed and insufficient regulation and control precision in the face of the complex working environment and the dynamic load change are solved.

Description

Technical Field

[0001] The present application relates to the technical field of battery thermal regulation, and in particular to an adaptive regulation method and system for a battery thermal management system. Background Art

[0002] With the rapid development of the new energy industry, battery technology is changing with each passing day, and batteries are increasingly used in electric vehicles, energy storage systems, and portable devices. However, batteries generate heat during use. If the battery temperature cannot be effectively managed, its performance, life, and even safety will be affected. Therefore, the research and application of battery thermal management technology has become particularly important.

[0003] Among the related technical means, the battery thermal management system mainly achieves temperature management by combining passive heat dissipation and active regulation. On the one hand, the heat generated in the battery is quickly transferred to the outside through thermal conductive materials and heat dissipation structures to reduce the temperature gradient inside the battery. On the other hand, the battery is kept in the ideal operating temperature range by using thermostats, coolants, heaters and other means to monitor the battery temperature in real time and perform corresponding cooling or heating operations.

[0004] For the above technical solution, although the battery temperature can be controlled by real-time monitoring of the battery temperature and performing corresponding cooling or heating operations to keep the battery in the ideal operating temperature range, it has the problems of slow response speed and insufficient control accuracy when facing complex working environments and dynamic load changes. These shortcomings are particularly obvious in high-power density batteries and extreme environments, thus affecting the overall performance and reliability of the battery. Summary of the invention

[0005] In order to improve the problems of slow response speed and insufficient control accuracy when facing complex working environments and dynamic load changes, the present application provides an adaptive control method and system for a battery thermal management system.

[0006] The present invention provides an adaptive control method for a battery thermal management system, comprising: collecting real-time temperature data of a battery to obtain current battery temperature information, analyzing the temperature distribution of the battery using the current battery temperature information to obtain target temperature gradient information; inputting the target temperature gradient information into a preset analysis model to obtain working state parameters, temperature distribution function and thermal conductivity length; obtaining an environmental heat load coefficient, calculating a battery self-heating coefficient based on the environmental heat load coefficient and the working state parameters, calculating a heat conduction adjustment coefficient through the temperature distribution function and the thermal conductivity length, generating a cooling control parameter based on the environmental heat load coefficient and the battery self-heating coefficient, and generating a heating control parameter based on the environmental heat load coefficient and the heat conduction adjustment coefficient; controlling a battery cooling system according to the cooling control parameter to obtain a battery temperature control result, controlling a battery heating system through the heating control parameter to obtain a battery heating control result; fusing the battery temperature control result with the battery heating control result to generate a battery thermal management control strategy, and adaptively controlling the battery based on the battery thermal management control strategy.

[0007] As a preferred solution, the steps of collecting real-time temperature data of the battery to obtain current battery temperature information, analyzing the temperature distribution of the battery using the current battery temperature information, and obtaining target temperature gradient information include: real-time temperature monitoring of the battery by a thermal sensor to obtain temperature data, noise suppression and filtering of the temperature data by a Kalman filter algorithm to obtain current battery temperature information, modeling the thermal response of the battery by the current battery temperature information to obtain initial temperature gradient information; performing local heat source analysis on the battery based on the initial temperature gradient information to obtain thermal stress distribution in different regions, and comparing the thermal stress distribution with relevant parameters of the battery manufacturing process to calibrate the initial temperature gradient information to obtain target temperature gradient information.

[0008] As a preferred solution, the steps of using a Kalman filter algorithm to suppress noise and filter the temperature data to obtain current battery temperature information, modeling the thermal response of the battery through the current battery temperature information, and obtaining initial temperature gradient information include: using a Kalman filter algorithm to filter the temperature data of each monitoring point to remove environmental noise and measurement errors to obtain current battery temperature information; applying a heat conduction equation to establish a thermal response model of the battery based on the current battery temperature information, calculating the heat distribution at different positions inside the battery, and using a finite difference method to discretize the battery based on the heat distribution to obtain discretized temperature gradient information; performing heat source analysis on the discretized temperature gradient information through finite element analysis to identify the area where heat is generated and quantify the temperature distribution function of each area to obtain initial temperature gradient information.

[0009] As a preferred solution, the step of inputting the target temperature gradient information into a preset analysis model to obtain working state parameters, temperature distribution function and thermal conductivity length includes: inputting the target temperature gradient information into an analysis model trained based on historical data to obtain a temperature change trend, extracting the frequency spectrum characteristics of the temperature change trend using a fast Fourier transform, and identifying the periodic changes of temperature fluctuations; and estimating the amplitude and frequency of the periodic changes using a sliding window method to obtain corresponding working state parameters, temperature distribution function and thermal conductivity length.

[0010] As a preferred scheme, the steps of obtaining the environmental heat load coefficient, calculating the battery's own heating coefficient based on the environmental heat load coefficient and the working state parameters, calculating the heat conduction regulation coefficient through the temperature distribution function and the thermal conductivity length, generating cooling control parameters based on the environmental heat load coefficient and the battery's own heating coefficient, and generating heating control parameters according to the environmental heat load coefficient and the heat conduction regulation coefficient include: using an environmental sensor to monitor the environmental information of the battery's environment in real time, calculating the environmental heat load coefficient based on the environmental information, calculating the battery's own heating coefficient according to the environmental heat load coefficient and the working state parameters, and calculating the heat conduction regulation coefficient based on the temperature distribution function and the thermal conductivity length; calculating the required cooling capacity using the environmental heat load coefficient and the battery's own heating coefficient to obtain the cooling control parameters; calculating the heating demand of the battery through the environmental heat load coefficient and the heat conduction regulation coefficient to obtain the heating control parameters.

[0011] As a preferred solution, the calculation formula for calculating the battery's own heating coefficient based on the environmental heat load coefficient and the corresponding battery working state parameters is as follows: in, Indicates the battery's own heating coefficient, Indicates The environmental heat load factor, Indicates The working state parameters of each battery, and For the An empirical coefficient, and is the time-dependent adjustment coefficient, is the environmental heat load coefficient Over time The rate of change, Battery working status parameter Over time The rate of change.

[0012] As a preferred solution, the calculation formula for calculating the heat conduction adjustment coefficient based on the temperature distribution function and the thermal conductivity length is as follows: in, represents the heat transfer regulation coefficient, represents the temperature distribution function inside the battery, represents the thermal conductivity length of the material, and It is The adjustment coefficient of the region, Indicates The second derivative of the temperature gradient in the region, Indicates The Laplace operator of the temperature distribution in the region.

[0013] As a preferred scheme, the steps of controlling the battery cooling system according to the cooling control parameters to obtain a battery temperature control result, and controlling the battery heating system by the heating control parameters to obtain a battery heating control result include: adjusting the coolant flow rate of the cooling system by using the cooling control parameters to obtain a coolant flow rate setting value, and adjusting the cooling pump rate in the cooling system according to the coolant flow rate setting value to obtain a battery temperature control result; adjusting the electric heater power of the heating system based on the heating control parameters to obtain a heater power setting value, and adjusting the heat source output in the heating system according to the heater power setting value to obtain a battery heating control result.

[0014] As a preferred scheme, the step of fusing the battery temperature control result and the battery heating control result to generate a battery thermal management control strategy, and adaptively controlling the battery based on the battery thermal management control strategy includes: fusing the battery temperature control result and the battery heating control result to obtain a comprehensive temperature control state of the battery, calculating the dynamic control thresholds of the cooling system and the heating system according to the comprehensive temperature control state to obtain control threshold parameters; performing closed-loop control of the cooling system and the heating system using the control threshold parameters to obtain a battery thermal management control strategy for cooling and heating, and adaptively controlling the battery according to the battery thermal management control strategy.

[0015] The present application also provides an adaptive control system for a battery thermal management system, comprising: a collection unit, used to collect real-time temperature data of a battery, obtain current battery temperature information, analyze the temperature distribution of the battery using the current battery temperature information, and obtain target temperature gradient information; an analysis unit, used to input the target temperature gradient information into a preset analysis model, and obtain working state parameters, a temperature distribution function, and a thermal conductivity length; a calculation unit, used to obtain an environmental heat load coefficient, calculate a battery self-heating coefficient based on the environmental heat load coefficient and the working state parameters, calculate a thermal conduction adjustment coefficient through the temperature distribution function and the thermal conductivity length, generate a cooling control parameter based on the environmental heat load coefficient and the battery self-heating coefficient, and generate a heating control parameter based on the environmental heat load coefficient and the thermal conduction adjustment coefficient; a control unit, used to control a battery cooling system according to the cooling control parameter to obtain a battery temperature control result, and control a battery heating system through the heating control parameter to obtain a battery heating control result; a generation unit, used to merge the battery temperature control result and the battery heating control result to generate a battery thermal management control strategy, and adaptively control the battery based on the battery thermal management control strategy.

[0016] Compared with the prior art, the present application has the following beneficial effects: fast response speed and sufficient control accuracy. By collecting and analyzing the temperature data of the battery in real time, the target temperature gradient information is obtained, and then the information is input into the preset analysis model to obtain the working state parameters, temperature distribution function and thermal conductivity length, and then the environmental heat load coefficient is obtained, the battery's own heating coefficient and thermal conduction regulation coefficient are calculated, and the cooling control parameters and heating control parameters are generated. According to these parameters, the battery cooling system and heating system are controlled to obtain the battery temperature control results and the battery heating control results, and they are integrated to generate a battery thermal management control strategy, thereby realizing adaptive control of the battery, being able to respond quickly in complex working environments and dynamic load changes, and improving the performance and safety of the battery through precise temperature control, improving the working efficiency and reliability of the battery in high power density and extreme environments, and improving the problems of slow response speed and insufficient control accuracy when facing complex working environments and dynamic load changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0019] Figure 1 It is a flow chart of an adaptive control method of a battery thermal management system provided by an embodiment of the present invention; Figure 2 It is a schematic block diagram of the structure of the adaptive control system of the battery thermal management system provided by an embodiment of the present invention.

[0020] Description of reference numerals: 10. Adaptive control system of battery thermal management system; 11. Collection unit; 12. Analysis unit; 13. Calculation unit; 14. Control unit; 15. Generation unit. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0023] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0024] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0025] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0026] Embodiment 1: like Figure 1 As shown, the present application provides an adaptive control method for a battery thermal management system, including steps S100 to S500.

[0027] Step S100: collect real-time temperature data of the battery to obtain current battery temperature information, analyze the temperature distribution of the battery using the current battery temperature information, and obtain target temperature gradient information.

[0028] In this step, the real-time temperature data of the battery is collected through the sensor system. These sensors are distributed in different positions of the battery to ensure that comprehensive temperature data is collected. Specifically, the collected temperature data is preliminarily analyzed by the data processing module, abnormal data is filtered and data smoothing is performed to obtain the current battery temperature information.

[0029] For example, by installing temperature sensors at various key nodes of the battery, temperature data inside and on the surface of the battery can be collected to ensure the accuracy of the temperature information.

[0030] Step S200: input the target temperature gradient information into a preset analysis model to obtain working state parameters, temperature distribution function and thermal conductivity length.

[0031] In this step, the target temperature gradient information is processed by the analysis model constructed using the machine learning algorithm to predict the temperature change trend and heat distribution characteristics of the battery. Specifically, the analysis model is trained using historical temperature data and current battery temperature information, and the model parameters are continuously optimized to obtain the working state parameters, temperature distribution function and thermal conductivity length.

[0032] For example, the temperature distribution of a battery can be predicted more accurately using an artificial neural network-based analytical model, which generates an accurate temperature distribution function and thermal conductivity length.

[0033] Step S300, obtain the environmental heat load coefficient, calculate the battery's own heating coefficient based on the environmental heat load coefficient and the working state parameters, calculate the heat conduction adjustment coefficient through the temperature distribution function and the thermal conductivity length, generate cooling control parameters based on the environmental heat load coefficient and the battery's own heating coefficient, and generate heating control parameters based on the environmental heat load coefficient and the heat conduction adjustment coefficient.

[0034] In this step, the current ambient temperature, humidity and other environmental heat load coefficients are obtained through the environmental monitoring system, and the heat generation of the battery under the current environmental conditions is calculated in combination with the working state parameters; specifically, the thermal conduction regulation coefficient is calculated by analyzing the temperature distribution function and thermal conductivity length of the battery under different working states.

[0035] For example, using the ambient temperature data acquired in real time, the heat generated by the battery under different environmental conditions can be accurately calculated, thereby generating corresponding cooling control parameters and heating control parameters.

[0036] Step S400: Control the battery cooling system according to the cooling control parameters to obtain the battery temperature control result, and control the battery heating system according to the heating control parameters to obtain the battery heating control result.

[0037] In this step, the cooling system and the heating system work according to the cooling control parameters and the heating control parameters respectively, and the battery temperature is precisely controlled by adjusting the flow of the coolant and the power of the heater in real time. Specifically, the control module is used to coordinate and control the various components of the cooling system and the heating system to ensure that the battery temperature is maintained within the ideal range.

[0038] For example, by adjusting the operating speed of the coolant pump and the heating power of the heater, it is possible to quickly respond to changes in battery temperature and achieve efficient temperature control.

[0039] Step S500: The battery temperature control result and the battery heating control result are integrated to generate a battery thermal management control strategy, and the battery is adaptively controlled based on the battery thermal management control strategy.

[0040] In this step, a comprehensive battery thermal management control strategy is generated by integrating the control results of the cooling system and the heating system. This strategy not only takes into account the current temperature state, but also predicts future temperature change trends. Specifically, the battery is monitored and predicted in real time based on the temperature control model, enabling the system to make control decisions in advance.

[0041] For example, by comprehensively analyzing the current temperature and working status of the battery, short-term and long-term temperature control strategies can be generated to ensure that the battery can maintain the optimal temperature state under different working conditions.

[0042] In this embodiment, the current battery temperature information is obtained by collecting the real-time temperature data of the battery, and the temperature distribution of the battery is analyzed using the current battery temperature information, so as to obtain the target temperature gradient information. Then, the target temperature gradient information is input into the preset analysis model to obtain the working state parameters, temperature distribution function and thermal conductivity length. Then, the environmental heat load coefficient is obtained, the battery self-heating coefficient is calculated based on the environmental heat load coefficient and the working state parameters, and the heat conduction adjustment coefficient is calculated through the temperature distribution function and the thermal conductivity length. Next, the cooling control parameters are generated based on the environmental heat load coefficient and the battery self-heating coefficient, and the heating control parameters are generated according to the environmental heat load coefficient and the heat conduction adjustment coefficient. Finally, the battery cooling system is controlled according to the cooling control parameters to obtain the battery temperature control result, and the battery heating system is controlled by the heating control parameters to obtain the battery heating control result. The battery temperature control result and the battery heating control result are integrated to generate a battery thermal management control strategy, and finally realize the adaptive control of the battery. Accurate management of battery temperature is achieved to improve battery performance and safety. It can quickly respond to dynamic load changes, reduce the impact of temperature gradients on battery performance, perform well in dealing with complex working environments and extreme load conditions, significantly improve the working efficiency and reliability of the battery, and improve the problems of slow response speed and insufficient control accuracy when facing complex working environments and dynamic load changes.

[0043] Embodiment 2: In step S100, the battery is monitored in real time by a thermal sensor to obtain temperature data, and the temperature data is subjected to noise suppression and filtering by a Kalman filter algorithm to obtain current battery temperature information. The thermal response of the battery is modeled by the current battery temperature information to obtain initial temperature gradient information.

[0044] The battery temperature is monitored in real time by thermal sensors installed inside and on the surface of the battery, and temperature data at different locations are collected. Specifically, the collected temperature data is processed using the Kalman filter algorithm to remove environmental noise and measurement errors, thereby obtaining accurate current battery temperature information. On this basis, the thermal response of the battery is modeled in detail using the current battery temperature information to obtain the initial temperature gradient information inside the battery.

[0045] For example, the battery system in an electric vehicle can monitor the temperature changes of the battery in real time through thermal sensors arranged at various locations on the battery, and process the data through the Kalman filter algorithm to ensure the accuracy and reliability of the temperature information, thereby providing basic data for subsequent temperature management.

[0046] Based on the initial temperature gradient information, the local heat source analysis of the battery is performed to obtain the thermal stress distribution in different areas. The thermal stress distribution is compared with the relevant parameters of the battery manufacturing process to calibrate the initial temperature gradient information and obtain the target temperature gradient information.

[0047] By using computer simulation technology to analyze the initial temperature gradient information, the heat source location and thermal stress distribution in different areas of the battery are identified; specifically, by comparing and correcting the thermal stress distribution with relevant parameters in the battery manufacturing process, the accuracy of the temperature gradient analysis is ensured, and ultimately the target temperature gradient information is obtained.

[0048] For example, in the production process of lithium-ion batteries, by simulating and analyzing the impact of different battery manufacturing parameters on thermal stress distribution, the battery's temperature gradient information can be accurately calibrated, providing more accurate data support for battery thermal management.

[0049] Among them, the steps of using the Kalman filter algorithm to suppress noise and filter the temperature data to obtain the current battery temperature information, modeling the thermal response of the battery through the current battery temperature information, and obtaining the initial temperature gradient information include: using the Kalman filter algorithm to filter the temperature data of each monitoring point to remove environmental noise and measurement errors to obtain the current battery temperature information.

[0050] By using the Kalman filter algorithm to process the real-time monitored temperature data, environmental noise and measurement errors are filtered out, making the temperature information more accurate and reliable; specifically, through the recursive algorithm of the Kalman filter, the temperature data of each monitoring point is updated and corrected to obtain accurate current battery temperature information, thereby providing a reliable data basis for subsequent thermal response modeling.

[0051] For example, in the temperature monitoring of smartphone batteries, the Kalman filter algorithm is used to process the temperature sensor data at different locations in real time to remove measurement errors and obtain accurate battery temperature information, thereby adjusting the battery management strategy in a timely manner and improving the safety and service life of the battery.

[0052] The thermal response model of the battery is established based on the current battery temperature information using the heat conduction equation, the heat distribution at different locations inside the battery is calculated, and the battery is discretized based on the heat distribution using the finite difference method to obtain the discretized temperature gradient information.

[0053] The thermal response model of the battery is established by analyzing the current battery temperature information using the heat conduction equation. Specifically, the model is discretized using the finite difference method to calculate the temperature distribution at various locations inside the battery and generate discretized temperature gradient information.

[0054] For example, by simulating and analyzing the temperature distribution of electric vehicle batteries through the heat conduction equation and the finite difference method, the temperature gradient information of the battery under different working conditions can be obtained, thus providing a scientific basis for optimizing the battery thermal management system.

[0055] The heat source analysis of the discretized temperature gradient information is performed through finite element analysis, the heat generation area is identified and the temperature distribution function of each area is quantified to obtain the initial temperature gradient information.

[0056] The finite element analysis method is used to perform a detailed heat source analysis on the discretized temperature gradient information; specifically, the key areas where heat is generated inside the battery are identified, and the temperature distribution function of each area is quantified through mathematical methods to obtain accurate initial temperature gradient information.

[0057] For example, by using finite element analysis technology to simulate the temperature distribution of the battery under high-load working conditions, it is possible to identify areas with higher temperatures and optimize the battery's thermal management strategy by adjusting design parameters.

[0058] In step S200, the target temperature gradient information is input into the analysis model trained based on historical data to obtain the temperature change trend, and the frequency spectrum characteristics of the temperature change trend are extracted using fast Fourier transform to identify the periodic changes of temperature fluctuations.

[0059] By inputting the target temperature gradient information into an analysis model trained based on a large amount of historical temperature data, the trend of battery temperature change over time is obtained; specifically, the fast Fourier transform technology is used to perform spectral analysis on the temperature change trend, extract the periodic change characteristics, and identify the regularity of battery temperature fluctuations.

[0060] For example, by performing fast Fourier transform analysis on the temperature data of electric vehicle batteries in different charging and discharging cycles, the periodic characteristics of temperature changes can be identified, thereby optimizing the battery thermal management control strategy and improving battery performance.

[0061] The sliding window method is used to estimate the amplitude and frequency of periodic changes, and the corresponding working state parameters, temperature distribution function and thermal conductivity length are obtained.

[0062] By applying the sliding window method, the periodic characteristics of temperature changes are estimated in detail, and the amplitude and frequency of temperature fluctuations are calculated; specifically, through the movement and calculation of the sliding window, a series of working state parameters, temperature distribution function and thermal conductivity length are obtained, providing a scientific basis for battery thermal management.

[0063] For example, by analyzing the temperature fluctuations of electric vehicle batteries under different operating modes through the sliding window method, the temperature distribution function and thermal conductivity length under each working state can be accurately estimated, thereby optimizing the battery's thermal management system.

[0064] In step S300, environmental information of the battery environment is monitored in real time using environmental sensors, and the environmental heat load coefficient is calculated based on the environmental information. The battery's own heating coefficient is calculated based on the environmental heat load coefficient and the working state parameters. The heat conduction regulation coefficient is calculated based on the temperature distribution function and the thermal conductivity length.

[0065] By installing environmental sensors around the battery, the temperature, humidity and other information of the battery environment are monitored in real time, and the environmental heat load coefficient is calculated. Specifically, based on the environmental heat load coefficient and the battery operating status parameters, the battery's own heating coefficient is calculated using a mathematical model, and the battery's thermal conduction regulation coefficient is calculated based on the temperature distribution function and thermal conductivity length.

[0066] For example, environmental sensors can be used to monitor the ambient temperature, humidity and other parameters around electric vehicle batteries in real time, calculate the environmental heat load coefficient, and combine the battery's working status parameters to calculate the heat generation of the battery in different environments, thereby providing accurate data for the battery thermal management system.

[0067] The required cooling capacity is calculated using the environmental heat load coefficient and the battery's own heating coefficient to obtain the cooling control parameters.

[0068] By combining the calculated environmental heat load coefficient with the battery's own heating coefficient, the cooling capacity required for the battery under the current environment and working conditions is calculated; specifically, the cooling capacity is quantified using a mathematical model to generate cooling control parameters to provide guidance for the battery cooling system.

[0069] For example, by calculating the heat coefficient of electric vehicle batteries in high temperature environments, the cooling capacity parameters required for cooling can be obtained, thereby guiding the operation of the cooling system and ensuring the safe and stable operation of the battery at high temperatures.

[0070] The heating demand of the battery is calculated through the environmental heat load coefficient and the heat conduction regulation coefficient, and the heating control parameters are obtained.

[0071] By combining the ambient heat load coefficient and the heat conduction regulation coefficient, the heating demand of the battery in a low-temperature environment is calculated; specifically, the heating demand is quantified using a mathematical model to generate heating regulation parameters to provide guidance for the battery heating system.

[0072] For example, by calculating the heating demand parameters of electric vehicle batteries in low temperature environments, the operation of the heating system can be guided to ensure the normal operation of the battery at low temperatures and improve battery performance and life.

[0073] Among them, according to the environmental heat load coefficient and the corresponding battery working state parameters, the calculation formula for calculating the battery's own heating coefficient is as follows: in, Indicates the battery's own heating coefficient, Indicates The environmental heat load factor, Indicates The working state parameters of each battery, and For the An empirical coefficient, and is the time-dependent adjustment coefficient, is the environmental heat load coefficient Over time The rate of change, Battery working status parameter Over time The rate of change.

[0074] The calculated battery's own heating coefficient can accurately describe the heat generation of the battery in different working environments and states, providing a scientific basis for battery thermal management.

[0075] For example, by calculating the heat generation of electric vehicle batteries under different working conditions through the above formula, the heat generation of batteries under high power output and different ambient temperatures can be accurately quantified, providing precise parameters for the regulation of cooling and heating systems.

[0076] Among them, based on the temperature distribution function and thermal conductivity length, the calculation formula for calculating the thermal conduction adjustment coefficient is as follows: in, represents the heat transfer regulation coefficient, represents the temperature distribution function inside the battery, represents the thermal conductivity length of the material, and It is The adjustment coefficient of the region, Indicates The second derivative of the temperature gradient in the region, Indicates The Laplace operator of the temperature distribution in the region.

[0077] By calculating the thermal conduction regulation coefficient, the effect of heat conduction inside the battery can be accurately described, providing data support for temperature control.

[0078] For example, by calculating the heat conduction effect of lithium-ion batteries under different working conditions through the above formula, the high-temperature areas inside the battery can be effectively identified, thereby optimizing the design of the thermal management system.

[0079] In step S400, the coolant flow rate of the cooling system is adjusted using the cooling control parameters to obtain a coolant flow rate setting value, and the cooling pump rate in the cooling system is adjusted according to the coolant flow rate setting value to obtain a battery temperature control result.

[0080] By adjusting the cooling control parameters, specifically, adjusting the coolant flow rate setting value and controlling the speed of the coolant pump, the coolant can effectively take away the excess heat of the battery and keep the battery temperature stable.

[0081] For example, by adjusting the coolant pump rate in real time, the battery of an electric vehicle can be cooled quickly during high-speed driving or fast charging, avoiding overheating and improving battery safety.

[0082] The electric heater power of the heating system is adjusted based on the heating control parameter to obtain a heater power setting value. According to the heater power setting value, the heat source output in the heating system is adjusted to obtain a battery heating control result.

[0083] By adjusting the heating control parameters, specifically, adjusting the power setting value of the electric heater and controlling the power output of the heater, the battery can be quickly heated up in a low temperature environment and maintain an ideal operating temperature.

[0084] For example, by adjusting the heater power output, when an electric vehicle is started in a cold environment, the battery can quickly reach the operating temperature, improving operating efficiency and extending battery life.

[0085] In step S500, the battery temperature control result and the battery heating control result are data-fused to obtain a comprehensive temperature control state of the battery, and the dynamic control thresholds of the cooling system and the heating system are calculated according to the comprehensive temperature control state to obtain the control threshold parameters.

[0086] By integrating the regulation results of the cooling system and the heating system, specifically, the control threshold parameters are dynamically calculated based on the comprehensive temperature control state to optimize the operation strategies of the cooling system and the heating system.

[0087] For example, by comprehensively analyzing the temperature control status of the battery and generating dynamic control threshold parameters, the battery management system can accurately regulate the cooling system and heating system under different working conditions to ensure that the battery operates within the optimal temperature range.

[0088] The cooling system and the heating system are closed-loop controlled using control threshold parameters to obtain a battery thermal management control strategy for cooling and heating, and the battery is adaptively regulated according to the battery thermal management control strategy.

[0089] Through a closed-loop control method, specifically, based on the control threshold parameters, the operating status of the cooling system and the heating system are adjusted in real time, a battery thermal management control strategy is generated, and the battery is adaptively regulated to ensure its optimal performance in different environments and working conditions.

[0090] For example, through a closed-loop control system, electric vehicle batteries can adjust the cooling and heating systems in real time during high power output and low-temperature startup to ensure stable battery temperature and improve battery life and safety.

[0091] In this embodiment, the battery is monitored in real time by a thermal sensor, and the temperature data is noise-suppressed and filtered by a Kalman filter algorithm to obtain the current battery temperature information, and a thermal response model of the battery is established based on the information to obtain the initial temperature gradient information. The initial temperature gradient information is analyzed by a local heat source, and the parameters related to the thermal stress distribution and the battery manufacturing process are calibrated to obtain the target temperature gradient information. The target temperature gradient information is input into an analysis model trained based on historical data, and the frequency spectrum characteristics of the temperature change trend are extracted by fast Fourier transform, and the amplitude and frequency of the periodic change are estimated by the sliding window method to obtain the corresponding working state parameters, temperature distribution function and thermal conductivity length. The environmental information is monitored in real time by an environmental sensor, and the environmental heat load coefficient is calculated based on the environmental information, and the battery's own heating coefficient and thermal conduction regulation coefficient are calculated in combination with the working state parameters. Through these parameters, the required cooling capacity and heating requirements are calculated, and the cooling control parameters and heating control parameters are generated. Based on these parameters, the operation of the cooling system and the heating system is adjusted to obtain the battery temperature control results and heating control results. Finally, these control results are data-fused to calculate the dynamic control thresholds of the cooling system and the heating system, generate the battery thermal management control strategy, and adaptively control the battery through a closed-loop control method. This embodiment achieves efficient thermal management of the battery through precise temperature monitoring and control strategies, and improves the performance and safety of the battery in a complex working environment.

[0092] Embodiment 3: like Figure 2 As shown, the present application also proposes an adaptive control system 10 for a battery thermal management system, including a collection unit 11, an analysis unit 12, a calculation unit 13, a control unit 14 and a generation unit 15.

[0093] The acquisition unit 11 is mainly used to acquire real-time temperature data of the battery to obtain current battery temperature information, and to analyze the temperature distribution of the battery using the current battery temperature information to obtain target temperature gradient information.

[0094] The analysis unit 12 is mainly used to input the target temperature gradient information into a preset analysis model to obtain working state parameters, temperature distribution function and thermal conductivity length.

[0095] The calculation unit 13 is mainly used to obtain the environmental heat load coefficient, calculate the battery's own heating coefficient based on the environmental heat load coefficient and the working state parameters, calculate the heat conduction regulation coefficient through the temperature distribution function and the thermal conductivity length, generate cooling control parameters based on the environmental heat load coefficient and the battery's own heating coefficient, and generate heating control parameters according to the environmental heat load coefficient and the heat conduction regulation coefficient.

[0096] The control unit 14 is mainly used to control the battery cooling system according to the cooling control parameters to obtain the battery temperature control result, and to control the battery heating system according to the heating control parameters to obtain the battery heating control result.

[0097] The generation unit 15 is mainly used to integrate the battery temperature control result and the battery heating control result, generate a battery thermal management control strategy, and perform adaptive control on the battery based on the battery thermal management control strategy.

[0098] In this embodiment, the real-time temperature data of the battery is collected by the acquisition unit 11 to obtain the current battery temperature information, and the temperature distribution of the battery is analyzed in detail using this information to obtain the target temperature gradient information. Subsequently, the analysis unit 12 inputs the target temperature gradient information into the preset analysis model, and obtains the working state parameters, temperature distribution function and thermal conductivity length based on the model calculation. After further processing of these parameters, the calculation unit 13 obtains the environmental heat load coefficient, combines the working state parameters, calculates the battery's own heating coefficient through the temperature distribution function and thermal conductivity length, and obtains the thermal conduction regulation coefficient. Based on these results, the calculation unit 13 generates cooling control parameters and heating control parameters. Then, the control unit 14 controls the battery cooling system according to the cooling control parameters to obtain the battery temperature control result, and controls the battery heating system according to the heating control parameters to obtain the battery heating control result. Finally, the generation unit 15 integrates the cooling and heating control results to generate a comprehensive battery thermal management control strategy, and realizes adaptive control of the battery through the strategy. This embodiment achieves accurate monitoring, analysis, calculation and regulation of battery temperature by integrating the coordinated work of multiple units, so that the battery can maintain a stable temperature in various working environments, thereby improving the performance, safety and service life of the battery.

[0099] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each unit described above can refer to the corresponding process in the aforementioned embodiment of the adaptive control method of the battery thermal management system, and will not be repeated here.

[0100] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An adaptive control method for a battery thermal management system, characterized in that: include: Collecting real-time temperature data of the battery to obtain current battery temperature information, and using the current battery temperature information to analyze the temperature distribution of the battery to obtain target temperature gradient information; Inputting the target temperature gradient information into a preset analysis model to obtain working state parameters, temperature distribution function and thermal conductivity length; Obtaining an environmental heat load coefficient, calculating a battery self-heating coefficient based on the environmental heat load coefficient and the working state parameter, calculating a heat conduction adjustment coefficient through the temperature distribution function and the thermal conductivity length, generating a cooling adjustment parameter based on the environmental heat load coefficient and the battery self-heating coefficient, and generating a heating adjustment parameter according to the environmental heat load coefficient and the heat conduction adjustment coefficient; The battery cooling system is controlled according to the cooling control parameters to obtain a battery temperature control result, and the battery heating system is controlled according to the heating control parameters to obtain a battery heating control result; The battery temperature control result and the battery heating control result are integrated to generate a battery thermal management control strategy, and the battery is adaptively controlled based on the battery thermal management control strategy.

2. The adaptive control method of the battery thermal management system according to claim 1, characterized in that: The step of collecting the real-time temperature data of the battery to obtain current battery temperature information, and analyzing the temperature distribution of the battery using the current battery temperature information to obtain target temperature gradient information includes: Real-time temperature monitoring of the battery is performed by a thermal sensor to obtain temperature data, noise suppression and filtering of the temperature data are performed by a Kalman filter algorithm to obtain current battery temperature information, thermal response of the battery is modeled by the current battery temperature information to obtain initial temperature gradient information; Based on the initial temperature gradient information, a local heat source analysis is performed on the battery to obtain thermal stress distribution in different areas, and the thermal stress distribution is compared with relevant parameters of the battery manufacturing process to calibrate the initial temperature gradient information to obtain target temperature gradient information.

3. The adaptive control method of the battery thermal management system according to claim 2, characterized in that: The step of using a Kalman filter algorithm to suppress noise and filter the temperature data to obtain current battery temperature information, and modeling the thermal response of the battery through the current battery temperature information to obtain initial temperature gradient information includes: The temperature data of each monitoring point is filtered using a Kalman filter algorithm to remove environmental noise and measurement errors to obtain current battery temperature information; Applying a heat conduction equation to establish a thermal response model of the battery based on the current battery temperature information, calculating the heat distribution at different positions inside the battery, and discretizing the battery based on the heat distribution using a finite difference method to obtain discretized temperature gradient information; The discretized temperature gradient information is subjected to heat source analysis through finite element analysis, the heat generating area is identified and the temperature distribution function of each area is quantified to obtain the initial temperature gradient information.

4. The adaptive control method of the battery thermal management system according to claim 1, characterized in that: The step of inputting the target temperature gradient information into a preset analysis model to obtain working state parameters, temperature distribution function and thermal conductivity length includes: The target temperature gradient information is input into an analysis model trained based on historical data to obtain a temperature change trend, and the frequency spectrum characteristics of the temperature change trend are extracted using a fast Fourier transform to identify periodic changes in temperature fluctuations; The sliding window method is used to estimate the amplitude and frequency of the periodic change, and the corresponding working state parameters, temperature distribution function and thermal conductivity length are obtained.

5. The adaptive control method of the battery thermal management system according to claim 1, characterized in that: The steps of obtaining an environmental heat load coefficient, calculating a battery self-heating coefficient based on the environmental heat load coefficient and the working state parameter, calculating a heat conduction adjustment coefficient through the temperature distribution function and the thermal conductivity length, generating a cooling adjustment parameter based on the environmental heat load coefficient and the battery self-heating coefficient, and generating a heating adjustment parameter according to the environmental heat load coefficient and the heat conduction adjustment coefficient include: Using an environmental sensor to monitor the environmental information of the environment in which the battery is located in real time, calculating the environmental heat load coefficient based on the environmental information, calculating the battery's own heating coefficient according to the environmental heat load coefficient and the working state parameter, and calculating the heat conduction adjustment coefficient based on the temperature distribution function and the thermal conductivity length; Calculate the required cooling capacity using the environmental heat load coefficient and the battery's own heat coefficient to obtain a cooling control parameter; The heating demand of the battery is calculated by the environmental heat load coefficient and the heat conduction adjustment coefficient to obtain the heating control parameter.

6. The adaptive control method of the battery thermal management system according to claim 5, characterized in that: The calculation formula for calculating the battery's own heating coefficient based on the environmental heat load coefficient and the corresponding battery working state parameters is as follows: in, Indicates the battery's own heating coefficient, Indicates The environmental heat load factor, Indicates The working state parameters of each battery, and For the An empirical coefficient, and is the time-dependent adjustment coefficient, is the environmental heat load coefficient Over time The rate of change, Battery working status parameter Over time The rate of change.

7. The adaptive control method of the battery thermal management system according to claim 5, characterized in that: The calculation formula for calculating the heat conduction adjustment coefficient based on the temperature distribution function and the thermal conductivity length is as follows: in, represents the heat transfer regulation coefficient, represents the temperature distribution function inside the battery, represents the thermal conductivity length of the material, and It is The adjustment coefficient of the region, Indicates The second derivative of the temperature gradient in the region, Indicates The Laplace operator of the temperature distribution in the region.

8. The adaptive control method of the battery thermal management system according to claim 1, characterized in that: The step of controlling the battery cooling system according to the cooling control parameter to obtain a battery temperature control result, and controlling the battery heating system according to the heating control parameter to obtain a battery heating control result comprises: The cooling control parameter is used to adjust the coolant flow rate of the cooling system to obtain a coolant flow rate setting value, and the cooling pump rate in the cooling system is adjusted according to the coolant flow rate setting value to obtain a battery temperature control result; The electric heater power of the heating system is adjusted based on the heating control parameter to obtain a heater power setting value, and the heat source output in the heating system is adjusted according to the heater power setting value to obtain a battery heating control result.

9. The adaptive control method of the battery thermal management system according to claim 1, characterized in that: The step of fusing the battery temperature control result and the battery heating control result to generate a battery thermal management control strategy, and adaptively controlling the battery based on the battery thermal management control strategy includes: fusing the battery temperature control result and the battery heating control result to obtain a comprehensive temperature control state of the battery, and calculating dynamic control thresholds of a cooling system and a heating system according to the comprehensive temperature control state to obtain a control threshold parameter; The control threshold parameters are used to perform closed-loop control on the cooling system and the heating system to obtain a battery thermal management control strategy for cooling and heating, and the battery is adaptively regulated according to the battery thermal management control strategy.

10. An adaptive control system for a battery thermal management system, characterized in that: include: A collection unit, used to collect real-time temperature data of the battery to obtain current battery temperature information, and analyze the temperature distribution of the battery using the current battery temperature information to obtain target temperature gradient information; An analysis unit, used to input the target temperature gradient information into a preset analysis model to obtain working state parameters, temperature distribution function and thermal conductivity length; a calculation unit, configured to obtain an environmental heat load coefficient, calculate a battery self-heating coefficient based on the environmental heat load coefficient and the working state parameter, calculate a heat conduction adjustment coefficient through the temperature distribution function and the thermal conductivity length, generate a cooling adjustment parameter based on the environmental heat load coefficient and the battery self-heating coefficient, and generate a heating adjustment parameter according to the environmental heat load coefficient and the heat conduction adjustment coefficient; A control unit, used to control the battery cooling system according to the cooling control parameters to obtain a battery temperature control result, and to control the battery heating system according to the heating control parameters to obtain a battery heating control result; A generating unit is used to merge the battery temperature control result and the battery heating control result to generate a battery thermal management control strategy, and adaptively control the battery based on the battery thermal management control strategy.

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