Mobile power supply load adaptive power supply control method and device

Abstract

The invention provides a mobile power supply load adaptive power supply control method and device, is applied to the technical field of mobile batteries, realizes effective management of battery pack temperature through a heat recovery module, and realizes accurate and adaptive adjustment of heat recovery intensity and output voltage by using fuzzy control. The temperature deviation reflects the difference between the current temperature and the ideal temperature of the battery pack, the power demand change rate predicts the change trend of the load power, and the control system comprehensively considers the temperature state and the load demand of the battery pack, so that the heat recovery intensity and the output voltage are adjusted more reasonably; finally, efficient and stable operation of the mobile power supply under different working conditions is realized. Therefore, the power supply problems of low low-temperature charging efficiency, easy loss of charging heat, insufficient load dynamic adaptability and the like can be effectively solved, so that the method has the advantages of guaranteeing long-term stable operation of precision detection equipment in an extreme environment and efficient utilization of energy through self-adaptive power supply.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method and device for adaptively controlling power supply of a mobile power supply load. Background Art

[0002] In severe cold rescue or polar exploration, the stable operation of precision detection equipment is highly dependent on reliable power supply. However, the extremely cold characteristics of severe cold rescue or polar exploration environments pose a severe challenge to power supply equipment. Mobile power supplies are indispensable in field operations and equipment movement scenarios in severe cold rescue or polar exploration due to their portability. However, existing mobile power supply technology has exposed many limitations under severe cold conditions.

[0003] First, the extremely low temperature environment significantly reduces battery performance. Under severe cold conditions, the viscosity of the electrolyte inside the battery increases, and the ion migration rate slows down, resulting in a significant decrease in the battery's charging and discharging efficiency and capacity. Especially during the charging process, low temperatures can seriously hinder electrochemical reactions, prolong charging time, and even cause charging failure. At the same time, the heat generated during the charging process is easily dissipated in the polar environment, further exacerbating the drop in battery temperature, forming a vicious cycle and seriously affecting energy efficiency.

[0004] Secondly, the power consumption requirements of precision detection equipment used for detection, such as instruments for meteorological observation, ice core analysis, geological exploration, etc., are dynamically changing. The power consumption levels of different devices vary significantly, and the power consumption of the same device in different working stages will also fluctuate with task requirements. This dynamic nature of load power consumption requires that mobile power supplies must have fast and accurate power adaptive adjustment capabilities to ensure the stable operation of various types of equipment under different working conditions and the continuous collection of detection data.

[0005] Furthermore, in the field rescue or exploration environment, resources are extremely scarce. The harsh natural environment and the limitations of logistical support make the maintenance cost of precision detection equipment extremely high and difficult. Therefore, the reliability of mobile power is crucial. Any failure in the power supply system may cause the interruption of the rescue mission or exploration mission, and even cause inestimable losses. Therefore, the mobile power supply must have extremely high reliability and environmental adaptability, and be able to operate stably for a long time under extremely cold conditions, unmanned or under few people.

[0006] In summary, the existing mobile power supply technology is difficult to effectively meet the special application requirements in low-temperature environments. Under the harsh conditions of extremely low temperature, variable load, and high reliability, there is an urgent need for a mobile power supply technology that can work efficiently and stably to solve the problems of low charging efficiency at low temperatures, easy loss of charging heat, and insufficient dynamic adaptability of loads, so as to ensure the long-term stable operation of precision detection equipment and efficient use of energy. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present application provides a mobile power supply load adaptive power supply control method and device, which is applied to the field of mobile power supply technology and has the advantages of ensuring long-term stable operation of precision detection equipment in extreme environments and efficient use of energy through adaptive power supply.

[0008] In a first aspect, a method for controlling a mobile power supply load adaptively is provided. The mobile power supply comprises at least a battery pack and a heat recovery module connected to the battery pack. The heat recovery module is a heat conduction element for transferring heat generated by a load during charging or discharging to the battery pack for thermal compensation. The control method comprises the following steps: S1: Obtain the temperature data of the battery pack and the power demand data of the load; S2: Calculating the temperature deviation of the battery pack according to the temperature data, and calculating the load power demand change rate according to the power demand data; S3: taking the temperature deviation and the load power demand change rate as inputs of a fuzzy controller, and calculating the heat recovery intensity and the output voltage adjustment amount through a preset fuzzy control rule; S4: adjusting the working intensity of the heat recovery module according to the heat recovery intensity so that the battery pack maintains a suitable working temperature; adjusting the output voltage of the battery pack according to the output voltage adjustment amount so as to achieve adaptive power supply to the load.

[0009] The present application provides a method for adaptive power supply control of a mobile power supply load, which realizes effective management of the battery pack temperature through a heat recovery module, and realizes accurate and adaptive adjustment of the heat recovery intensity and output voltage by using fuzzy control. The temperature deviation reflects the gap between the current temperature of the battery pack and the ideal temperature, and the power demand change rate indicates the changing trend of the load power. The combination of the two as the input of the fuzzy controller enables the control system to comprehensively consider the temperature state and load demand of the battery pack, so as to more reasonably adjust the heat recovery intensity and output voltage, and finally realize the efficient and stable operation of the mobile power supply under different working conditions. The application of the heat recovery module reduces energy waste and improves energy utilization efficiency. The application of fuzzy control enhances the intelligence and adaptability of the system, so that the mobile power supply can better meet the needs of complex environments and dynamic loads.

[0010] Further, step S3 includes: S31: acquiring the membership functions of the temperature deviation, the load power demand change rate, the output voltage adjustment amount, and the heat recovery intensity respectively according to historical parameters, and configuring a fuzzy controller according to the membership functions; S32: using a Z-Score standardization method to map the temperature deviation and the load power demand change rate to a [-1, 1] interval; S33: inputting the mapped temperature deviation and the load power demand change rate into the fuzzy controller, and obtaining a heat recovery intensity fuzzy value and an output voltage adjustment fuzzy value according to a preset fuzzy control rule; S34: performing defuzzification processing on the fuzzy amount of the heat recovery intensity and the fuzzy amount of the output voltage adjustment amount, and calculating the output voltage adjustment amount and the heat recovery intensity by using the centroid method.

[0011] The present application provides a mobile power load adaptive power supply control method, which makes the implementation of the fuzzy controller clearer and more complete by specifying the configuration of the fuzzy controller, the processing of input variables and the defuzzification method of the output variables, thereby improving the effectiveness and reliability of the mobile power load adaptive power supply control method.

[0012] Further, step S31 includes: S311: Obtain historical parameters, perform data preprocessing on the historical parameters by removing abnormal values, supplementing missing values, and performing data smoothing operations to obtain a historical operation data set; S312: Based on the historical operation data set, optimizing the membership function using an adaptive differential evolution algorithm; S313: Calculate the mean and variance of each parameter of the optimized membership function, write the mean and variance into the membership function definition file of the initial fuzzy controller, and reload the initial fuzzy controller to obtain the fuzzy controller; wherein the various parameters include the temperature deviation, the load power demand change rate, the output voltage adjustment amount and the heat recovery intensity.

[0013] The present application provides a mobile power load adaptive power supply control method, which ensures the quality of data by preprocessing historical operation data, laying the foundation for subsequent optimization of membership function; adopts adaptive differential evolution algorithm, uses historical data to automatically optimize membership function, so that membership function can better adapt to the actual situation of system operation and improve the control accuracy of fuzzy controller; calculates the mean and variance of optimized membership function parameters, and writes these statistics into fuzzy controller, realizes the determination of membership function, and completes the configuration of fuzzy controller. Through this scheme, the problem of difficulty in reasonable configuration of membership function in fuzzy controller is solved, and the performance and adaptive ability of fuzzy control system are improved.

[0014] Further, step S312 includes: S3121: Initializing a population of an adaptive differential evolution algorithm based on the historical operation data set, wherein each individual in the population represents a set of the membership functions; S3122: Calculate the output error between the individuals in the population and the current actual operation data; S3123: updating the population according to the output error by using mutation, crossover and selection operations to obtain an updated next generation population; S3124: Iteratively execute the operations from step S3122 to step S3123 based on the next generation population to gradually optimize the individuals in the population until the number of iterations reaches a preset maximum value or the output error is less than or equal to a preset error threshold, and output the optimized membership function.

[0015] The present application provides a method for adaptive power supply control of a mobile power supply load, which ensures the operability and effectiveness of the optimization process by clarifying the specific optimization process of the membership function, solves the problem that the optimization method is not specific, and enables the optimization of the membership function to be carried out more effectively and accurately, thereby improving the control accuracy and adaptability of the fuzzy controller.

[0016] Further, step S33 includes: S331: Establishing a preset fuzzy control rule, wherein the preset fuzzy control rule includes a plurality of fuzzy control rules, each of which is composed of a condition part and a conclusion part, wherein the condition part includes a temperature deviation fuzzy set and a load power demand change rate set, and the conclusion part includes a heat recovery intensity fuzzy set and an output voltage adjustment amount fuzzy set; S332: Inputting the mapped temperature deviation and the load power demand change rate into the condition part of each fuzzy control rule, and calculating the activation strength of each fuzzy control rule, wherein the activation strength is the product of the temperature deviation membership value and the load power demand change membership value; S333: Determine the membership function of the heat recovery intensity fuzziness set and the output voltage adjustment amount fuzziness set of each fuzzy control degree rule according to the activation intensity by taking the minimum operation; S334: performing maximum calculation on the membership functions of the heat recovery intensity fuzzy degree set and the output voltage adjustment amount fuzzy degree set of all the fuzzy control degree rules to obtain the aggregated heat recovery intensity fuzzy amount and the output voltage adjustment amount fuzzy amount.

[0017] Further, step S34 includes: S341: performing defuzzification processing on the fuzzy value of the heat recovery intensity and the fuzzy value of the output voltage adjustment, and calculating the initial value of the heat recovery intensity and the initial value of the output voltage adjustment by using the centroid method; S342: constructing a first-order low-pass filter to filter the initial value of the heat recovery intensity and the initial value of the output voltage adjustment amount; S343: performing a limiting process on the filtered initial value of the heat recovery intensity and the initial value of the output voltage adjustment amount to obtain the heat recovery intensity and the output voltage adjustment amount.

[0018] Further, step S4 includes: S41: collecting the actual temperature transmitted to the battery pack by the heat recovery module according to the heat recovery intensity and the actual value of the output voltage output by the battery pack according to the output voltage adjustment amount, and calculating the temperature difference between the actual temperature and the target temperature, and the voltage deviation between the actual value of the output voltage and the target adjustment amount; S42: Calculate the heat recovery intensity compensation amount according to the temperature difference, and calculate the voltage compensation amount according to the voltage deviation; S43: Add the heat recovery intensity compensation amount to the heat recovery intensity of the heat recovery module to control the operation of the heat recovery module so that the battery pack maintains an appropriate operating temperature; add the voltage compensation amount to the actual value of the output voltage of the mobile power supply to obtain the final output voltage, so as to accurately control the output voltage of the battery pack and realize adaptive power supply to the load.

[0019] Further, step S42 includes: S421: According to the temperature difference, the heat recovery intensity compensation amount is calculated by proportional integral differential PID control algorithm, and the calculation formula is: ,in, for The heat recovery intensity compensation amount at the moment, for The temperature difference at time, is the proportional gain, is the integral gain, is the differential gain, Indicates from time To current time Temperature difference The integral of S422: According to the voltage deviation, the voltage compensation amount is calculated by a proportional-integral-differential (PID) control algorithm, and the calculation formula is: ,in, for The voltage compensation amount at the moment, for The voltage deviation at the moment, is the proportional gain, is the integral gain, is the differential gain, Indicates from time To current time Voltage deviation 's points.

[0020] Further, step S2 includes: S21: performing Kalman filtering on the temperature data to obtain filtered temperature data; S22: Compare the filtered temperature data with preset optimal operating temperature data of the battery pack to obtain a temperature deviation; S23: performing sliding average filtering on the power demand data to obtain filtered load power demand data; S24: Calculate the difference between the filtered load power demand data at the current moment and the filtered load power demand data at the previous moment based on the filtered load power demand data, and then divide the difference by the time interval to obtain the load power demand change rate.

[0021] In a second aspect, a mobile power load adaptive power supply control device is applied to the steps of any one of the above-mentioned mobile power load adaptive power supply control methods, characterized in that the device comprises: Data acquisition module: used to obtain the temperature data of the battery pack and the power demand data of the load; A data calculation module: used to calculate the temperature deviation of the battery pack according to the temperature data, and calculate the load power demand change rate according to the power demand data; Fuzzy control module: used to take the temperature deviation and the load power demand change rate as inputs of the fuzzy controller, and calculate the heat recovery intensity and the output voltage adjustment amount through preset fuzzy control rules; Power supply control module: used to adjust the working intensity of the heat recovery module according to the heat recovery intensity so that the battery pack maintains a suitable working temperature; adjust the output voltage of the battery pack according to the output voltage adjustment amount to achieve adaptive power supply to the load.

[0022] Beneficial effects: The present application proposes a method and device for adaptive power supply control of a mobile power supply load, which realizes effective management of the battery pack temperature through a heat recovery module, and realizes accurate and adaptive adjustment of the heat recovery intensity and output voltage by using fuzzy control. The temperature deviation reflects the gap between the current temperature of the battery pack and the ideal temperature, and the power demand change rate indicates the change trend of the load power. The combination of the two as the input of the fuzzy controller enables the control system to comprehensively consider the temperature state and load demand of the battery pack, so as to more reasonably adjust the heat recovery intensity and output voltage, and finally realize the efficient and stable operation of the mobile power supply under different working conditions. The application of the heat recovery module reduces energy waste and improves energy utilization efficiency. The application of fuzzy control improves the intelligence and adaptability of the system, so that the mobile power supply can better meet the needs of complex environments and dynamic loads. Therefore, the present application can effectively solve power supply problems such as low charging efficiency at low temperature, easy loss of charging heat, and insufficient dynamic adaptability of the load, so as to have the advantages of ensuring the long-term stable operation of precision detection equipment in extreme environments and efficient utilization of energy through adaptive power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a mobile power load adaptive power supply control method proposed in this application.

[0024] Figure 2 This is a structural diagram of a mobile power load adaptive power supply control device proposed in this application.

[0025] Description of reference numerals: 201, data acquisition module; 202, data calculation module; 203, fuzzy control module; 204, power supply control module. DETAILED DESCRIPTION

[0026] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application usually described and marked in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first, second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0028] Existing mobile power supply technology is difficult to effectively respond to special application requirements in low-temperature environments. Under the harsh conditions of extremely low temperature, variable load, and high reliability, there is an urgent need for a mobile power supply technology that can work efficiently and stably to solve the problems of low charging efficiency at low temperatures, easy loss of charging heat, and insufficient dynamic adaptability of loads, so as to ensure the long-term stable operation of precision detection equipment and efficient use of energy. In order to solve this problem, this application proposes a mobile power load adaptive power supply control method and device, as follows: Please refer to Figure 1 In the first aspect, a mobile power supply load adaptive power supply control method is provided, wherein the mobile power supply at least comprises a battery pack and a heat recovery module connected to the battery pack, wherein the heat recovery module is a heat conduction element, and is used to transfer the heat generated by the load during charging or discharging to the battery pack for thermal compensation, and the control method comprises the steps of: S1: Obtain the temperature data of the battery pack and the power demand data of the load; S2: Calculate the temperature deviation of the battery pack according to the temperature data, and calculate the load power demand change rate according to the power demand data; S3: Taking the temperature deviation and the load power demand change rate as the input of the fuzzy controller, the heat recovery intensity and the output voltage adjustment amount are calculated by preset fuzzy control rules; S4: adjusting the working intensity of the heat recovery module according to the heat recovery intensity so that the battery pack maintains a suitable working temperature; adjusting the output voltage of the battery pack according to the output voltage adjustment amount so as to achieve adaptive power supply to the load.

[0029] Among them, in step S1, temperature data is obtained by directly measuring the surface temperature of the battery pack through a temperature sensor, and power demand data is obtained by real-time collection and calculation through a current and voltage sensor installed at the load end.

[0030] In step S2, the temperature deviation is the difference between the actual measured battery pack temperature and the preset optimal operating temperature. For example, the optimal operating temperature is preset to 16 degrees Celsius. If the current temperature is 7 degrees Celsius, the temperature deviation is 9 degrees Celsius. The load power demand change rate is obtained by calculating the difference between the power demands at two adjacent sampling moments and dividing it by the time interval, reflecting the speed of load power change.

[0031] In step S3, the fuzzy controller can be implemented by a single chip microcomputer or a PLC or other controller, and the fuzzy control rules are preset. For example, when the temperature deviation is negative and the load power demand change rate is positive, the heat recovery intensity is increased and the output voltage adjustment amount is appropriately reduced.

[0032] In step S4, the working intensity of the heat recovery module is adjusted by controlling the thermal conductivity of the heat conduction element, for example, using a thyristor to control the heating power of the heating plate, or controlling the current of the Peltier element to adjust the heat conduction efficiency. The output voltage is adjusted by a DC-DC converter, and the microcontroller outputs a PWM signal to control the duty cycle of the DC-DC converter, thereby accurately adjusting the output voltage.

[0033] Specifically, the technical solution first obtains the temperature data of the battery pack and the power demand data of the load through step S1 to provide a data basis for subsequent control decisions. Step S2 calculates the temperature deviation and the load power demand change rate based on the acquired data. These two parameters can accurately reflect the current working state of the battery pack and the change trend of the load. Step S3 is the control core. The fuzzy controller receives the temperature deviation and the load power demand change rate as input, and uses the preset fuzzy control rules for reasoning and calculation to obtain the heat recovery intensity and the output voltage adjustment. The introduction of fuzzy control enables the system to effectively handle nonlinear and uncertain problems and adapt to complex and changeable working conditions. Step S4 adjusts the working intensity of the heat recovery module and the output voltage of the battery pack according to the heat recovery intensity and output voltage adjustment calculated in step S3. By adjusting the heat recovery module, the heat generated by the load can be recycled to the battery pack, maintaining the battery pack working within a suitable temperature range, and improving the battery performance and service life. At the same time, by adjusting the output voltage, the mobile power supply can adaptively supply power according to the actual power demand of the load, improving the power supply efficiency and stability. The synergistic effect of heat recovery and fuzzy control realizes the efficient and stable operation of the mobile power supply under different working conditions, and solves the technical problems that the battery pack temperature is difficult to maintain and the output voltage cannot be adaptively adjusted according to load demand.

[0034] Further, step S3 includes: S31: Obtaining membership functions of temperature deviation, load power demand change rate, output voltage adjustment amount, and heat recovery intensity respectively according to historical parameters, and configuring a fuzzy controller according to the membership function; S32: Use the Z-Score normalization method to map the temperature deviation and the load power demand change rate to the [-1,1] interval; S33: inputting the mapped temperature deviation and load power demand change rate into the fuzzy controller, and obtaining the heat recovery intensity fuzzy quantity and the output voltage adjustment fuzzy quantity according to the preset fuzzy control rule; S34: performing defuzzification processing on the fuzzy quantity of heat recovery intensity and the fuzzy quantity of output voltage adjustment, and calculating the output voltage adjustment quantity and the heat recovery intensity by using the centroid method.

[0035] Among them, in step S31, the historical parameters are used to determine the membership function of the fuzzy controller, so that the fuzzy controller can be configured based on the actual operation data. The membership function is the core component of the fuzzy controller, which defines the fuzzy set of input variables (temperature deviation, load power demand change rate) and output variables (output voltage adjustment amount, heat recovery intensity). By analyzing the historical operation data, the fuzzy set of variables such as temperature deviation, load power demand change rate, output voltage adjustment amount and heat recovery intensity can be more accurately defined, thereby improving the control accuracy and adaptability of the fuzzy controller.

[0036] In step S32, the Z-Score standardization method is used to process the input data. Z-Score standardization is a commonly used data preprocessing method, which can uniformly map data of different dimensions and numerical ranges to a standard normal distribution, that is, a distribution with a mean of 0 and a standard deviation of 1. In this solution, the temperature deviation and the load power demand change rate are mapped to the [-1,1] interval after Z-Score standardization, eliminating the differences in dimensions and numerical ranges, which is conducive to the unified processing and rule formulation of the fuzzy controller and improves the robustness of the control system.

[0037] In step S33, the mapped input variables are input into the fuzzy controller, and the output of the fuzzy controller, i.e., the fuzzy quantity of heat recovery intensity and the fuzzy quantity of output voltage adjustment, is obtained through the preset fuzzy control rules. The fuzzy control rule is the control strategy of the fuzzy controller, which describes the fuzzy relationship between the input variables and the output variables. The preset fuzzy control rule can be formulated based on the technician's experience or experimental data. In step S34, defuzzification is performed to obtain accurate heat recovery intensity and output voltage adjustment that can be directly used in the control system. Since the output of the fuzzy controller is a fuzzy quantity and cannot be directly used to control the actuator, defuzzification is required. The centroid method is a commonly used defuzzification method, which obtains an accurate output value by calculating the centroid of the fuzzy set. The use of the centroid method to calculate the output voltage adjustment and heat recovery intensity can ensure the smoothness and continuity of the output.

[0038] In some specific embodiments, step S31 can be specifically implemented as follows: first, collect historical operation data of the mobile power supply under different working conditions, and the historical operation data includes parameters such as temperature deviation, load power demand change rate, output voltage adjustment amount, and heat recovery intensity. Then, preprocess the historical data, including operations such as outlier removal, missing value supplementation, and smoothing, to obtain a high-quality historical operation data set. Next, based on the historical operation data set, cluster analysis, statistical analysis, or machine learning methods are used to analyze the data distribution characteristics of variables such as temperature deviation, load power demand change rate, output voltage adjustment amount, and heat recovery intensity, and determine the fuzzy set and membership function type of each variable, such as triangular membership function, Gaussian membership function, or trapezoidal membership function. Finally, according to the determined membership function, configure the input and output interface and parameters of the fuzzy controller to complete the initialization setting of the fuzzy controller. In step S32, the Z-Score standardization method can be calculated using the following formula: x' = (x - μ) / σ, where x is the original data, μ is the mean of the data, σ is the standard deviation of the data, and x' is the standardized data. In step S34, the centroid method can be calculated using the following formula: y = ∫μ(y) * y dy / ∫μ(y) dy, where y is the precise output value after defuzzification, and μ(y) is the membership function of the output fuzzy set. Through the above specific implementation, the technical solution of the claim can be implemented more specifically and achieve the expected technical effect.

[0039] Further, step S31 includes: S311: Obtain historical parameters, perform data preprocessing on the historical parameters by removing abnormal values, supplementing missing values, and performing data smoothing operations to obtain a historical operation data set; S312: Based on the historical operation data set, an adaptive differential evolution algorithm is used to optimize the membership function; S313: Calculate the mean and variance of each parameter of the optimized membership function, write the mean and variance into the membership function definition file of the initial fuzzy controller, and reload the initial fuzzy controller to obtain the fuzzy controller; wherein the various parameters include temperature deviation, load power demand change rate, output voltage adjustment amount and heat recovery intensity.

[0040] Among them, in step S311, the acquisition of historical parameters can be performed by collecting and recording the temperature data of the battery pack, the power demand data of the load, the output voltage adjustment amount, the heat recovery intensity and other parameters during the actual operation of the mobile power supply. In the data preprocessing process, outlier removal can be performed by, for example, using the 3σ principle to remove data points that exceed three times the standard deviation of the mean, missing value supplementation can be performed by linear interpolation or mean filling, and data smoothing operations can be performed by using methods such as sliding average filtering or Kalman filtering to reduce data noise and improve data quality.

[0041] In step S312, the adaptive differential evolution algorithm is a global optimization algorithm for automatically searching and optimizing the parameters of the membership function based on the historical operation data set. During the optimization process, the shape and parameters of the membership function are adjusted to minimize the output error of the fuzzy controller so that the membership function can better reflect the actual operation characteristics of the system.

[0042] In step S313, the mean and variance of each parameter of the optimized membership function are calculated, and these statistics can reflect the concentration and dispersion of the membership function parameters. By writing the mean and variance into the fuzzy controller, the accurate definition and configuration of the membership function can be achieved.

[0043] In some specific embodiments, the historical operation data set may include the operation data of the mobile power supply under different ambient temperatures and load conditions, for example, it may include operation data at different temperatures such as -20°C, -10°C, 0°C, and under different load conditions such as light load, medium load, and heavy load. The population size of the adaptive differential evolution algorithm can be set to 50, the maximum number of iterations can be set to 100, and the crossover probability and mutation probability can be adjusted according to actual conditions. The membership function can use a Gaussian membership function or a triangular membership function, etc., and its parameters include a center value and a width, etc. By optimizing these parameters through the adaptive differential evolution algorithm, a set of optimal membership function parameters can be obtained, which are used to configure the fuzzy controller to realize adaptive power supply control of the mobile power supply load.

[0044] Further, step S312 includes: S3121: Initialize the population of the adaptive differential evolution algorithm based on the historical running data set, where each individual in the population represents a set of membership functions; S3122: Calculate the output error between individuals in the population and the current actual operation data; S3123: According to the output error, the population is updated by using mutation, crossover and selection operations to obtain an updated next generation population; S3124: Iteratively execute the operations from step S3122 to step S3123 based on the next generation population to gradually optimize the individuals in the population until the number of iterations reaches a preset maximum value or the output error is less than or equal to a preset error threshold, and output the optimized membership function.

[0045] In step S3121, the population initialization may be implemented as follows: First, determine the type of membership function, for example, Gaussian membership function or triangular membership function. Then, for each membership function, determine the parameters that need to be optimized. After that, randomly generate an initial population within a preset parameter range, and each individual in the population represents a set of membership function parameters.

[0046] In step S3122, the output error may be calculated by substituting the membership function parameters represented by each individual in the population into the fuzzy controller, using the historical operation data set as input, running the fuzzy controller, and obtaining the output result of the controller. This output result is compared with the actual output result in the historical operation data set, and the error between the two is calculated. For example, the mean square error may be used as an evaluation index of the output error.

[0047] In step S3123, the population update method can be: using the standard mutation, crossover and selection operations of the differential evolution algorithm. Specifically, the mutation operation can generate new individuals by randomly perturbing the individuals in the population; the crossover operation can generate new individuals by exchanging some parameters of two or more individuals; the selection operation can select individuals with smaller output errors to enter the next generation of population based on the output error calculated in step S3122. In step S3124, the iterative optimization method can be: setting the maximum number of iterations or the preset error threshold as the termination condition of the algorithm. When the number of iterations reaches the maximum value, or the output error is less than or equal to the preset error threshold, the iterative process ends, and the membership function parameters represented by the individual with the smallest output error in the current population are determined as the optimized membership function.

[0048] In some specific embodiments, for the adaptive power supply control method for mobile power supply load, step S312 can be implemented as follows: First, based on the historical operation data set, a population of 50 individuals is initialized, each individual represents the membership function parameters of temperature deviation, load power demand change rate, output voltage adjustment amount and heat recovery intensity, and the membership function uses a Gaussian function. Then, the output error of each individual is calculated, and the error calculation method is the root mean square error. The output performance of the fuzzy controller represented by each individual is verified using the historical operation data set. After that, the differential evolution algorithm is used to update the population, the mutation operator uses the DE / rand / 1 strategy, the crossover operator uses the binomial crossover, the selection operator uses the greedy selection, the crossover probability is set to 0.8, and the mutation scaling factor is set to 0.5. Finally, the above steps are iteratively executed, the maximum number of iterations is set to 100 times, and the preset error threshold is set to 0.001. When the number of iterations reaches 100 times or the root mean square error is less than 0.001, the iteration is terminated, and the membership function parameters represented by the optimal individual are output to complete the optimization of the membership function.

[0049] Further, step S33 includes: S331: Establishing a preset fuzzy control rule, wherein the preset fuzzy control rule includes a plurality of fuzzy control rules, each of which is composed of a condition part and a conclusion part, wherein the condition part includes a temperature deviation fuzzy set and a load power demand change rate set, and the conclusion part includes a heat recovery intensity fuzzy set and an output voltage adjustment amount fuzzy set; S332: Input the mapped temperature deviation and load power demand change rate into the condition part of each fuzzy control rule, and calculate the activation strength of each fuzzy control rule, where the activation strength is the product of the temperature deviation membership value and the load power demand change membership value; S333: according to the activation intensity, a minimum operation is used to determine the membership function of the heat recovery intensity fuzzy set and the output voltage adjustment amount fuzzy set of each fuzzy control degree rule; S334: Performing maximum calculation on the membership functions of the heat recovery intensity fuzzy degree set and the output voltage adjustment amount fuzzy degree set of all fuzzy control degree rules to obtain aggregated heat recovery intensity fuzzy amounts and output voltage adjustment amount fuzzy amounts.

[0050] Among them, in step S331, the establishment of preset fuzzy control rules is the core link of the control method, and the effectiveness of fuzzy control rules directly affects the control performance. As an implementation method, a method combining the experience of technicians with experimental data can be used to formulate fuzzy control rules. For example, fuzzy subsets of temperature deviation and load power demand change rate can be pre-set, such as "positive large", "positive small", "zero", "negative small", "negative large", etc., as well as fuzzy subsets of heat recovery intensity and output voltage adjustment amount, such as "strong", "medium", "weak", etc. Then, according to the operating characteristics and control objectives of the mobile power supply under different working conditions, the fuzzy control rules are determined. For example, when the temperature deviation is "negative large" and the load power demand change rate is "positive large", the fuzzy control rule can be set as "if the temperature deviation is negative large and the load power demand change rate is positive large, then the heat recovery intensity is strong and the output voltage adjustment amount is positive small". Fuzzy control rules can be organized and managed in the form of a table to facilitate the call and execution of the fuzzy controller.

[0051] In step S332, the calculation of activation intensity is a key step in the fuzzy reasoning process. Specifically, after the temperature deviation and the load power demand change rate are mapped to the [-1,1] interval, the membership value of the input variable needs to be calculated for each fuzzy control rule. The membership value indicates the degree to which the input variable belongs to a fuzzy subset. The activation intensity is obtained by calculating the product of the temperature deviation membership value and the load power demand change rate membership value. This product operation method reflects the characteristics of the "and" operation in fuzzy logic, that is, the rule will only be activated when all conditions in the conditional part of the rule are met to a certain extent, and the degree of activation depends on the minimum value of the degree of satisfaction of all conditions.

[0052] In step S333, the minimum operation is used to determine the contribution of each fuzzy control rule to the output fuzzy set. In specific implementation, for each fuzzy control rule, according to the activation strength calculated in step S332 and the membership function of the output fuzzy set in the conclusion part of the rule, the minimum operation is adopted, that is, the smaller value of the activation strength and the membership function of the output fuzzy set is taken as the contribution of the rule to the membership function of the output fuzzy set. This minimum operation ensures that the influence of each rule on the final output is limited by its activation strength, and avoids irrelevant or weakly related rules from having too much influence on the output.

[0053] In step S334, the maximum operation is used to aggregate the output fuzzy sets of all fuzzy control rules. After completing step S333, each fuzzy control rule has made a certain contribution to the output fuzzy set. In order to obtain the final fuzzy output result, the contributions of all rules need to be aggregated. The maximum operation is a commonly used fuzzy set aggregation method. It compares the membership functions of the same output variables output by all rules and takes the maximum value as the membership function of the aggregated output fuzzy quantity. This maximum operation embodies the characteristics of the "or" operation in fuzzy logic, that is, the final fuzzy output result is the union of the conclusions of all activated rules.

[0054] In some specific embodiments, the preset fuzzy control rules can be designed as a rule table in the following form. Assume that the temperature deviation fuzzy set includes {negative large, negative small, zero, positive small, positive large}, the load power demand change rate fuzzy set includes {negative large, negative small, zero, positive small, positive large}, the heat recovery intensity fuzzy set includes {weak, medium, strong}, and the output voltage adjustment amount fuzzy set includes {negative small, zero, positive small}. The fuzzy control rule table can be a two-dimensional table, the rows and columns represent the temperature deviation fuzzy set and the load power demand change rate fuzzy set respectively, each cell in the table corresponds to a fuzzy control rule, and the conclusion part of the rule is filled in the cell, that is, the heat recovery intensity fuzzy set and the output voltage adjustment amount fuzzy set. For example, when the temperature deviation is "negative large" and the load power demand change rate is "positive large", the conclusion part of the corresponding fuzzy control rule can be set to "heat recovery intensity is strong, output voltage adjustment amount is positive small". By consulting the rule table, the output of the fuzzy controller under different input conditions can be quickly determined. The specific rules in the rule table can be adjusted and optimized according to the actual application scenario and control requirements to achieve the best control effect.

[0055] Through the above fuzzy reasoning process, the fuzzy controller can adaptively adjust the heat recovery intensity and output voltage according to the current temperature deviation and load power demand change rate, thereby realizing load adaptive power supply control of the mobile power supply, solving the effectiveness and rationality problems of the preset fuzzy control rules, and ensuring the performance of the fuzzy controller.

[0056] Further, step S34 includes: S341: performing defuzzification processing on the fuzzy quantity of heat recovery intensity and the fuzzy quantity of output voltage adjustment, and calculating the initial value of heat recovery intensity and the initial value of output voltage adjustment by using the centroid method; S342: constructing a first-order low-pass filter to filter the initial value of the heat recovery intensity and the initial value of the output voltage adjustment amount; S343: performing a limiting process on the filtered initial value of the heat recovery intensity and the initial value of the output voltage adjustment amount to obtain the heat recovery intensity and the output voltage adjustment amount.

[0057] In step S341, the centroid method is used to defuzzify the heat recovery intensity fuzzy quantity and the output voltage adjustment quantity fuzzy quantity to obtain the heat recovery intensity initial value and the output voltage adjustment quantity initial value. The centroid method is a commonly used defuzzification method, which obtains an executable accurate value by calculating the weighted average of the fuzzy set output membership.

[0058] In step S342, a first-order low-pass filter is constructed to filter the initial value of the heat recovery intensity and the initial value of the output voltage adjustment amount calculated in step S341. The first-order low-pass filter allows low-frequency signals to pass through while attenuating high-frequency signals, thereby smoothing the initial value, reducing noise interference, and filtering out sudden changes.

[0059] In step S343, the limiting process is performed on the initial value of the filtered heat recovery intensity and the initial value of the output voltage adjustment to ensure that the final output heat recovery intensity and output voltage adjustment are within a reasonable range and avoid the values ​​exceeding the actual physical system constraints.

[0060] In some specific embodiments, the first-order low-pass filter can be configured as an RC filter with a time constant of 0.1 seconds. The filter is designed to filter out high-frequency noise in the initial value after defuzzification and smooth the control signal. In the limiting processing link, the upper limit value of the heat recovery intensity is set to 1, the lower limit value is set to 0, the upper limit value of the output voltage adjustment amount is set to 0.1V, and the lower limit value is set to -0.1V. These parameter values ​​are set based on the actual operating characteristics and control requirements of the mobile power system. By adopting the above-mentioned specific parameter configuration, it can be ensured that the heat recovery intensity and the output voltage adjustment amount vary within a reasonable range, ensuring the stable operation of the control system.

[0061] Further, step S4 includes: S41: collecting the actual temperature transmitted to the battery pack by the heat recovery module according to the heat recovery intensity and the actual value of the output voltage output by the battery pack according to the output voltage adjustment amount, and calculating the temperature difference between the actual temperature and the target temperature, and the voltage deviation between the actual value of the output voltage and the target adjustment amount; S42: Calculate the heat recovery intensity compensation amount according to the temperature difference, and calculate the voltage compensation amount according to the voltage deviation; S43: The heat recovery intensity compensation amount is added to the heat recovery intensity of the heat recovery module to control the operation of the heat recovery module so that the battery pack maintains a suitable operating temperature; the voltage compensation amount is added to the actual value of the output voltage of the mobile power supply to obtain the final output voltage, so as to accurately control the output voltage of the battery pack and realize adaptive power supply to the load.

[0062] In step S41, the temperature sensor is used to collect the actual temperature transmitted from the heat recovery module to the battery pack, and the voltage sensor is used to collect the actual output voltage of the battery pack. The actual temperature is compared with the preset target temperature to calculate the temperature difference. The actual output voltage is compared with the target voltage adjustment output by the fuzzy controller to calculate the voltage deviation.

[0063] In step S42, the temperature difference and the voltage deviation are used as inputs of the PID controller to calculate the heat recovery intensity compensation and the voltage compensation. The PID controller processes the temperature difference and the voltage deviation according to the three links of proportion, integration and differentiation to obtain the corresponding compensation.

[0064] In step S43, the heat recovery intensity compensation amount is added to the heat recovery intensity output by the fuzzy controller to form a final heat recovery intensity control signal, which is used to adjust the working intensity of the heat recovery module. The voltage compensation amount is added to the voltage adjustment amount output by the fuzzy controller to obtain a final output voltage control signal, which is used to accurately control the output voltage of the battery pack.

[0065] By introducing a feedback correction mechanism, this solution can dynamically adjust the heat recovery intensity and output voltage according to the actual operating conditions, improving the accuracy and robustness of the control system. As a result, the battery pack can maintain a suitable operating temperature and accurate adaptive power supply of the load can be achieved.

[0066] Further, step S42 includes: S421: According to the temperature difference, the heat recovery intensity compensation amount is calculated by proportional integral differential PID control algorithm, and the calculation formula is: ,in, for The heat recovery intensity compensation amount at the moment, for The temperature difference at time, is the proportional gain, is the integral gain, is the differential gain, Indicates from time To current time Temperature difference The integral of S422: According to the voltage deviation, the voltage compensation amount is calculated by a proportional-integral-differential (PID) control algorithm, and the calculation formula is: ,in, for The voltage compensation amount at the moment, for The voltage deviation at the moment, is the proportional gain, is the integral gain, is the differential gain, Indicates from time To current time Voltage deviation 's points.

[0067] Specifically, this solution aims to solve the problem of unclear compensation calculation method, and accurately calculates the heat recovery intensity compensation and voltage compensation by introducing PID control algorithm. When working, first collect the temperature difference between the actual temperature and the target temperature, and the voltage deviation between the actual value of the output voltage and the target adjustment amount. Then, the temperature difference is input into the PID controller, and the PID controller calculates the heat recovery intensity compensation according to the preset control parameters. The compensation is superimposed on the heat recovery intensity of the heat recovery module to adjust the working intensity of the heat recovery module so that the battery pack temperature approaches the target temperature. At the same time, the voltage deviation is also input into another PID controller to calculate the voltage compensation, which is superimposed on the actual value of the output voltage of the mobile power supply to obtain the final output voltage, so as to achieve accurate control of the output voltage. Due to the maturity and effectiveness of the PID control algorithm, the accuracy and effectiveness of the calculation of the heat recovery intensity compensation and the voltage compensation can be guaranteed, thereby improving the performance of the mobile power supply load adaptive power supply control, ensuring that the battery pack operates in a suitable temperature range, and providing a stable and reliable power supply for the load.

[0068] Further, step S2 includes: S21: Perform Kalman filtering on the temperature data to obtain filtered temperature data; S22: Compare the filtered temperature data with preset optimal operating temperature data of the battery pack to obtain a temperature deviation; S23: Perform sliding average filtering on the power demand data to obtain filtered load power demand data; S24: Calculate the difference between the filtered load power demand data at the current moment and the filtered load power demand data at the previous moment based on the filtered load power demand data, and divide the difference by the time interval to obtain the load power demand change rate.

[0069] Among them, Kalman filtering is performed to reduce the noise in the temperature data, thereby obtaining smooth temperature data. Kalman filtering is an algorithm that uses the linear system state equation to optimally estimate the system state through system input and output observation data. Sliding average filtering is applied to power demand data to smooth power fluctuations and obtain stable load power demand data. Sliding average filtering calculates the average value of the data in the window as the filtering result by setting a time window. The temperature deviation is obtained by comparing the filtered temperature data with the preset optimal operating temperature data. The load power demand change rate is obtained by calculating the difference between the filtered load power demand data at the current moment and the previous moment, and dividing it by the time interval, which is used to characterize the speed of load power change.

[0070] Specifically, Kalman filtering uses the dynamic model of the temperature change process of the mobile power battery pack and the measurement model of the temperature sensor to predict and update the temperature data, effectively filter out the random noise and interference in the temperature data, and obtain a more accurate temperature estimate. For example, during the temperature data acquisition process, sensor noise or environmental disturbances may cause random fluctuations in the temperature data. Kalman filtering can suppress these fluctuations, provide a more stable temperature signal, and provide a reliable data basis for the subsequent calculation of temperature deviations. The sliding average filter sets a time window, such as 10 seconds, and averages the power demand data in the window to smooth the rapid fluctuations of the power data. For example, when the load power changes rapidly in a short period of time, the sliding average filter can weaken this rapid change, extract the trend characteristics of the power change, and obtain more stable load power demand data, providing stable data support for the subsequent calculation of the load power demand change rate. Therefore, through the data preprocessing of Kalman filtering and sliding average filtering, the quality of temperature data and power demand data can be improved, noise interference can be reduced, and the accuracy of temperature deviation and load power demand change rate can be guaranteed, laying the foundation for the accuracy of subsequent fuzzy control.

[0071] Please refer to Figure 2 In a second aspect, a mobile power load adaptive power supply control device is applied to any of the steps of the mobile power load adaptive power supply control method mentioned above, characterized in that the device comprises: Data acquisition module 201: used to acquire temperature data of the battery pack and power demand data of the load; Data calculation module 202: used to calculate the temperature deviation of the battery pack according to the temperature data, and calculate the load power demand change rate according to the power demand data; Fuzzy control module 203: used to take the temperature deviation and the load power demand change rate as the input of the fuzzy controller, and calculate the heat recovery intensity and the output voltage adjustment amount through the preset fuzzy control rules; The power supply control module 204 is used to adjust the working intensity of the heat recovery module according to the heat recovery intensity so that the battery pack maintains a suitable working temperature; and adjust the output voltage of the battery pack according to the output voltage adjustment amount to achieve adaptive power supply to the load.

[0072] The data acquisition module 201 may collect temperature sensor data of the battery pack and power measurement device data on the load side. The temperature sensor may be a thermistor or an integrated temperature sensor, and the power measurement device may be a combination of a current sensor and a voltage sensor.

[0073] The data calculation module 202 receives the data from the data acquisition module and obtains the temperature deviation by subtraction operation, specifically the current temperature data minus the preset optimal working temperature. The load power demand change rate is obtained by differential operation, that is, the current power demand data minus the power demand data at the previous moment, and then divided by the time interval.

[0074] The fuzzy control module 203 is the control core, and a fuzzy control rule table is preset inside. The rule table defines the corresponding fuzzy sets of heat recovery intensity and output voltage adjustment amount under different fuzzy set combinations of temperature deviation and load power demand change rate.

[0075] The power supply control module 204 receives the heat recovery intensity and output voltage adjustment amount output by the fuzzy control module. The working intensity of the heat recovery module is achieved by adjusting the thermal conductivity of the heat conduction element, for example, using a thyristor to control the heating power of the heat conduction element. The output voltage of the battery pack is adjusted by controlling the reference voltage of the power management chip through a digital-to-analog converter.

[0076] Specifically, when the device is running, the data acquisition module 201 monitors the battery pack temperature and load power demand in real time. The data calculation module 202 processes the collected data, calculates the temperature deviation and the load power demand change rate, and uses the calculation results as the input of the fuzzy control module. The fuzzy control module 203 queries the heat recovery intensity and output voltage adjustment corresponding to the current temperature deviation and load power demand change rate according to the preset fuzzy control rules. The power supply control module 204 adjusts the working state of the heat recovery module according to the heat recovery intensity calculated by the fuzzy control module 203, transfers the heat generated by the load to the battery pack, realizes thermal compensation, and maintains the battery pack working in a suitable temperature range. At the same time, the power supply control module 204 also adjusts the output voltage of the battery pack according to the output voltage adjustment amount, so that the output voltage of the mobile power supply can quickly adapt to the change of the power demand of the load, and realizes load adaptive power supply. Through the collaborative work of the above modules, the mobile power supply can achieve stable control of the battery pack temperature and precise adjustment of the output voltage under different working conditions, ensuring the stability and reliability of the power supply system.

[0077] In some specific embodiments, the data acquisition module 201 uses a digital temperature sensor of model DS18B20 to collect the battery pack temperature, and uses a current and voltage sensor of model INA219 to collect load power data. The data calculation module 202 is implemented using an STM32 single-chip microcomputer, and the data processing program runs inside the single-chip microcomputer to complete the calculation of temperature deviation and load power demand change rate. The fuzzy control rule table of the fuzzy control module 203 is stored in the Flash memory of the single-chip microcomputer, and the fuzzy reasoning process is implemented by a table lookup method to improve the control response speed. In the power supply control module 204, the heat recovery module uses a semiconductor refrigeration sheet, and the driving voltage of the semiconductor refrigeration sheet is adjusted by a PWM pulse width modulation signal to achieve the adjustment of the heat recovery intensity. The output voltage adjustment is achieved by controlling the feedback pin voltage of the power management chip MP2307, and the feedback pin voltage is output by a DAC digital-to-analog converter controlled by the single-chip microcomputer.

[0078] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0079] The above description is only an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for controlling a mobile power supply load adaptively, wherein the mobile power supply comprises at least a battery pack and a heat recovery module connected to the battery pack, wherein the heat recovery module is a heat conduction element, and is used to transfer the heat generated by the load during charging or discharging to the battery pack for thermal compensation, wherein: The control method comprises the steps of: S1: Obtain the temperature data of the battery pack and the power demand data of the load; S2: Calculating the temperature deviation of the battery pack according to the temperature data, and calculating the load power demand change rate according to the power demand data; S3: taking the temperature deviation and the load power demand change rate as inputs of a fuzzy controller, and calculating the heat recovery intensity and the output voltage adjustment amount through a preset fuzzy control rule; S4: adjusting the working intensity of the heat recovery module according to the heat recovery intensity so that the battery pack maintains a suitable working temperature; adjusting the output voltage of the battery pack according to the output voltage adjustment amount so as to achieve adaptive power supply to the load.

2. A mobile power source load adaptive power supply control method according to claim 1, characterized in that: Step S3 includes: S31: acquiring the membership functions of the temperature deviation, the load power demand change rate, the output voltage adjustment amount and the heat recovery intensity respectively according to the historical parameters, and configuring a fuzzy controller according to the membership functions; S32: using a Z-Score standardization method to map the temperature deviation and the load power demand change rate to a [-1, 1] interval; S33: inputting the mapped temperature deviation and the load power demand change rate into the fuzzy controller, and obtaining a heat recovery intensity fuzzy value and an output voltage adjustment fuzzy value according to a preset fuzzy control rule; S34: performing defuzzification processing on the fuzzy amount of the heat recovery intensity and the fuzzy amount of the output voltage adjustment amount, and calculating the output voltage adjustment amount and the heat recovery intensity by using the centroid method.

3. A mobile power source load adaptive power supply control method according to claim 2, characterized in that: Step S31 includes: S311: Obtain historical parameters, perform data preprocessing on the historical parameters by removing abnormal values, supplementing missing values, and performing data smoothing operations to obtain a historical operation data set; S312: Based on the historical operation data set, optimizing the membership function using an adaptive differential evolution algorithm; S313: Calculate the mean and variance of each parameter of the optimized membership function, write the mean and variance into the membership function definition file of the initial fuzzy controller, and reload the initial fuzzy controller to obtain the fuzzy controller; wherein the various parameters include the temperature deviation, the load power demand change rate, the output voltage adjustment amount and the heat recovery intensity.

4. A mobile power source load adaptive power supply control method according to claim 3, characterized in that: Step S312 includes: S3121: Initializing a population of an adaptive differential evolution algorithm based on the historical operation data set, wherein each individual in the population represents a set of the membership functions; S3122: Calculate the output error between the individuals in the population and the current actual operation data; S3123: updating the population according to the output error by using mutation, crossover and selection operations to obtain an updated next generation population; S3124: Iteratively execute the operations of step S3122 to step S3123 based on the next generation population to gradually optimize the individuals in the population until the number of iterations reaches a preset maximum value or the output error is less than or equal to a preset error threshold, and output the optimized membership function.

5. A mobile power source load adaptive power supply control method according to claim 2, characterized in that: Step S33 includes: S331: Establishing a preset fuzzy control rule, wherein the preset fuzzy control rule includes a plurality of fuzzy control rules, each of which is composed of a condition part and a conclusion part, wherein the condition part includes a temperature deviation fuzzy set and a load power demand change rate set, and the conclusion part includes a heat recovery intensity fuzzy set and an output voltage adjustment amount fuzzy set; S332: Inputting the mapped temperature deviation and the load power demand change rate into the condition part of each fuzzy control rule, and calculating the activation strength of each fuzzy control rule, wherein the activation strength is the product of the temperature deviation membership value and the load power demand change membership value; S333: Determine the membership function of the heat recovery intensity fuzziness set and the output voltage adjustment amount fuzziness set of each fuzzy control degree rule according to the activation intensity by taking the minimum operation; S334: performing maximum calculation on the membership functions of the heat recovery intensity fuzzy degree set and the output voltage adjustment amount fuzzy degree set of all the fuzzy control degree rules to obtain the aggregated heat recovery intensity fuzzy amount and the output voltage adjustment amount fuzzy amount.

6. A mobile power source load adaptive power supply control method according to claim 5, characterized in that: Step S34 includes: S341: performing defuzzification processing on the fuzzy value of the heat recovery intensity and the fuzzy value of the output voltage adjustment, and calculating the initial value of the heat recovery intensity and the initial value of the output voltage adjustment by using the centroid method; S342: constructing a first-order low-pass filter to filter the initial value of the heat recovery intensity and the initial value of the output voltage adjustment amount; S343: performing a limiting process on the filtered initial value of the heat recovery intensity and the initial value of the output voltage adjustment amount to obtain the heat recovery intensity and the output voltage adjustment amount.

7. A mobile power source load adaptive power supply control method according to claim 1, characterized in that: Step S4 includes: S41: collecting the actual temperature transmitted to the battery pack by the heat recovery module according to the heat recovery intensity and the actual value of the output voltage output by the battery pack according to the output voltage adjustment amount, and calculating the temperature difference between the actual temperature and the target temperature, and the voltage deviation between the actual value of the output voltage and the target adjustment amount; S42: Calculate the heat recovery intensity compensation amount according to the temperature difference, and calculate the voltage compensation amount according to the voltage deviation; S43: Add the heat recovery intensity compensation amount to the heat recovery intensity of the heat recovery module to control the operation of the heat recovery module so that the battery pack maintains an appropriate operating temperature; add the voltage compensation amount to the actual value of the output voltage of the mobile power supply to obtain the final output voltage, so as to accurately control the output voltage of the battery pack and realize adaptive power supply to the load.

8. A mobile power source load adaptive power supply control method according to claim 7, characterized in that: Step S42 includes: S421: According to the temperature difference, the heat recovery intensity compensation amount is calculated by proportional integral differential PID control algorithm, and the calculation formula is: ,in, for The heat recovery intensity compensation amount at the moment, for The temperature difference at time, is the proportional gain, is the integral gain, is the differential gain, Indicates from time To current time Temperature difference The integral of S422: According to the voltage deviation, the voltage compensation amount is calculated by a proportional-integral-differential (PID) control algorithm, and the calculation formula is: ,in, for The voltage compensation amount at the moment, for The voltage deviation at the moment, is the proportional gain, is the integral gain, is the differential gain, Indicates from time To current time Voltage deviation 's points.

9. A mobile power source load adaptive power supply control method according to claim 1, characterized in that: Step S2 includes: S21: performing Kalman filtering on the temperature data to obtain filtered temperature data; S22: Compare the filtered temperature data with preset optimal operating temperature data of the battery pack to obtain a temperature deviation; S23: performing sliding average filtering on the power demand data to obtain filtered load power demand data; S24: Calculate the difference between the filtered load power demand data at the current moment and the filtered load power demand data at the previous moment based on the filtered load power demand data, and then divide the difference by the time interval to obtain the load power demand change rate.

10. A mobile power load adaptive power supply control device, applied to the steps of a mobile power load adaptive power supply control method as described in any one of claims 1 to 9, characterized in that: The device comprises: Data acquisition module: used to obtain the temperature data of the battery pack and the power demand data of the load; A data calculation module: used to calculate the temperature deviation of the battery pack according to the temperature data, and calculate the load power demand change rate according to the power demand data; Fuzzy control module: used to take the temperature deviation and the load power demand change rate as inputs of the fuzzy controller, and calculate the heat recovery intensity and the output voltage adjustment amount through preset fuzzy control rules; Power supply control module: used to adjust the working intensity of the heat recovery module according to the heat recovery intensity so that the battery pack maintains a suitable working temperature; adjust the output voltage of the battery pack according to the output voltage adjustment amount to achieve adaptive power supply to the load.

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