A method and system for simulating temperature adjustment of a battery based on a battery simulator

Abstract

The present invention discloses a method and system for simulating the temperature adjustment of a battery based on a battery simulator, belonging to the technical field of battery simulators, including an environmental simulation module, a battery simulation module, a data acquisition module, a temperature adjustment module, an equivalent conversion prediction module, and a compensation verification module. The environmental simulation module controls the environmental temperature of the simulated battery and records the environmental temperature data. The method and system for simulating the temperature adjustment of a battery based on a battery simulator according to the present invention can achieve environmental temperature simulation, battery operation simulation, temperature adjustment control, data acquisition, file recording, equivalent conversion and prediction, and verification and correction. By simulating the operation of different batteries in different temperature environments, heating and heat dissipation data are obtained, and an equivalent conversion is combined with relevant usage equipment to obtain a corrected prediction model, which is convenient for allocating heat dissipation and heating equipment during the actual use of the battery and provides data support for engineers.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery simulators, and specifically relates to a method and system for adjusting the temperature of a simulated battery based on a battery simulator. Background Art

[0002] A battery simulator is a device used to simulate the performance and behavior of a battery under actual use conditions. By simulating the voltage, current, and capacity changes of a battery under specific loads, environmental conditions, and usage patterns, important parameters such as the battery's lifespan, charge-discharge characteristics, and temperature response can be predicted.

[0003] When existing battery simulators perform battery simulation work, they mostly use real-time monitoring methods to monitor the temperature of the battery during the simulation process, and control relevant heat dissipation or heating devices to cool or heat up in order to ensure that the battery temperature is within the normal operating temperature range. However, they cannot perform equivalent conversion on the relevant heat dissipation or heating devices based on usage during the battery simulation process, and cannot provide data support to facilitate users to adjust the battery temperature according to the existing heat dissipation or heating devices around them during actual battery use, resulting in low practicality and applicability.

[0004] In view of the above, this case proposes a method and system for adjusting the temperature of a simulated battery based on a battery simulator to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art. For this purpose, the present invention proposes a method and system for adjusting the temperature of a simulated battery based on a battery simulator, and solves the above technical problems by improving the detection method and processing method.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A temperature adjustment system for simulating a battery based on a battery simulator includes an environmental simulation module, a battery simulation module, a data acquisition module, a temperature adjustment module, an equivalent conversion prediction module, and a compensation verification module.

[0008] The environmental simulation module regulates the environmental temperature of the simulated battery, records the environmental temperature data, and transmits the environmental temperature data into the battery simulation module.

[0009] The battery simulation module simulates the working process of the battery, and establishes a simulated battery file in combination with the environmental temperature data of the environmental simulation module.

[0010] The data acquisition module collects relevant data of the battery during the simulation, including voltage, current, and real-time temperature, associates with the simulated battery file, and transmits the relevant data into the temperature regulation module;

[0011] The temperature regulation module includes a heat dissipation device and a heat increase device. It calculates and determines by combining the optimal temperature range of the relevant battery in the simulated battery file and the real-time temperature, and controls the relevant devices to adjust the temperature of the battery during the simulation;

[0012] The equivalent conversion prediction module, based on the adjustment result of the temperature regulation module, combines the relevant parameters of different devices, obtains an equivalent conversion formula based on an algorithm formula, performs equivalent conversion on the heat dissipation or heat increase amount, generates equivalent heat dissipation curves or equivalent heat increase curves of the relevant battery under different ambient temperatures and different working conditions, and obtains a usage prediction model;

[0013] The compensation verification module, based on the usage prediction model predicted by the equivalent conversion prediction module, conducts control simulations on the temperature regulation module, battery simulation module, and environment simulation module, verifies the usage prediction model, generates correction parameters, and corrects the usage prediction model.

[0014] Furthermore, the environment simulation module includes a temperature control box. According to the actual usage environment of the battery, based on the temperature control box, the ambient temperature of the simulated battery is set, and the ambient temperature data is recorded and transmitted into the battery simulation module;

[0015] The battery simulation module includes a battery simulator. Based on the actual usage conditions of different batteries, the working process of the battery is simulated, including charge and discharge simulation and dynamic load simulation. Combining the ambient temperature data of the environment simulation module, a simulated battery file is established, including battery type, simulation type, duration, ambient temperature, number of series cells, number of parallel cells, and the optimal temperature range of the battery;

[0016] The data acquisition module includes a voltage sensor, a current sensor, and a temperature sensor, collects relevant data of the battery during the simulation, including voltage, current, and real-time temperature, associates with the simulated battery file, and transmits the relevant data into the temperature regulation module.

[0017] Furthermore, the temperature regulation module includes a heat dissipation device and a heat increase device. It calculates and determines by combining the optimal temperature range of the relevant battery in the simulated battery file and the real-time temperature, and controls the relevant devices to adjust the temperature of the battery during the simulation. The steps are as follows:

[0018] Through the battery simulation module, obtain the optimal temperature range of the relevant battery , combine with the real-time temperature T of the relevant battery obtained by the data acquisition module, and conduct data determination. The specific steps and algorithm formula are as follows:

[0019] Among them, the optimal temperature range of the relevant battery , where A represents the minimum value of the optimal temperature of the relevant battery, B represents the maximum value of the optimal temperature of the relevant battery, and it is determined by combining the real-time temperature T of the relevant battery:

[0020] When , it means that the temperature of the relevant battery is too low. A regulation instruction is transmitted to the heating device to perform battery heating operation, and after t time, a secondary determination is made based on the newly obtained real-time temperature . When , record and maintain the relevant parameters of the heating device, and continue heating. When or , a regulation signal is transmitted to the heating device to adjust the relevant parameters, and after t time, a determination is made based on the real-time temperature obtained again, repeating the above adjustment operation until the real-time temperature is within the interval;

[0021] When , it means that the temperature of the relevant battery is normal and no adjustment is made;

[0022] When , it means that the temperature of the relevant battery is too high. A regulation instruction is transmitted to the cooling device to perform battery cooling operation, and after t time, a secondary determination is made based on the newly obtained real-time temperature . When , record and maintain the relevant parameters of the cooling device, and continue cooling. When or , a regulation signal is transmitted to the cooling device to adjust the relevant parameters, and after t time, a determination is made based on the real-time temperature obtained again, repeating the above adjustment operation until the real-time temperature is within the interval.

[0023] Furthermore, based on the adjustment result of the temperature regulation module, the equivalent conversion prediction module combines the relevant parameters of different devices and obtains an equivalent conversion formula based on an algorithm formula to perform equivalent conversion on the heat dissipation or heat gain, generate relevant battery equivalent heat dissipation curves or equivalent heat gain curves under different ambient temperatures and different working conditions, and obtain a usage prediction model. The specific steps are as follows:

[0024] Based on the adjustment result of the temperature regulation module, at the same ambient temperature, based on different working conditions, obtain the relevant parameters of the cooling device or heating device, including wattage, output efficiency, output duration, time node, and based on the equivalent conversion formula, obtain the heat dissipation or heat gain at different nodes within the total working time;

[0025] Based on the heat dissipation or heat increment of different nodes within the total working time at the same ambient temperature, obtain the relevant battery equivalent heat dissipation curve or equivalent heat increment curve;

[0026] Combined with the heat dissipation or heat increment of different nodes within the total working time at different ambient temperatures, through machine learning methods, obtain usage prediction models, including a heat dissipation prediction model and a heat increment prediction model.

[0027] Furthermore, based on the adjustment result of the temperature regulation module, at the same ambient temperature, obtain the relevant parameters of the heat dissipation device or heat increment device, including wattage, output efficiency, output duration, time node. Based on the equivalent conversion formula, obtain the heat dissipation or heat increment of different nodes within the total working time. The specific steps and algorithm formulas are as follows:

[0028] For heat dissipation, based on different ambient temperatures , respectively collect the relevant heat dissipation device parameters corresponding to the ambient temperature, including wattage , output efficiency , output duration , calculate to obtain the heat dissipation , the specific algorithm formula is:

[0029] ;

[0030] Among them, represents the total working time, respectively represent the output durations after each parameter change within the total working time, represents the relevant battery output duration at the same ambient temperature within the heat dissipation ;

[0031] For heat increment, based on different ambient temperatures , respectively collect the relevant heat increment device parameters corresponding to the ambient temperature, including wattage , output efficiency , output duration , calculate to obtain the heat increment , the specific algorithm formula is:

[0032] ;

[0033] Among them, represents the total working time, respectively represent the output durations after each parameter change within the total working time, represents the relevant battery output duration Related battery output duration below The heat increase within . Based on the above formula, the heat increases at different output durations, different wattages, and output efficiencies within the total working time are obtained .

[0034] Furthermore, by combining the heat dissipation or heat increase at different nodes within the total working time at different ambient temperatures, a prediction model is obtained through a machine learning method, including a heat dissipation prediction model and a heat increase prediction model. The specific steps are as follows

[0035] Collect the and corresponding to the time nodes at different ambient temperatures. Based on the neural network model, the prediction model is trained. The steps are as follows

[0036] Collect relevant data as the training dataset. For the data related to the heat dissipation prediction model, it includes heat dissipation data and the corresponding ambient temperature and time nodes. For the data related to the heat increase prediction model, it includes heat dissipation data and the corresponding ambient temperature and time nodes

[0037] Based on different battery models, select the neural network structure, the number of hidden layers, and the number of neurons in each layer, and use the ReLU function as the activation function

[0038] Divide the training dataset into a training set and a validation set. Train the neural network model with the training set, validate the neural network model with the validation set, and adjust the hyperparameters of the neural network model according to the performance of the validation set

[0039] Select the mean square error as the loss function to measure the difference between the predicted heat dissipation value, the predicted heat increase value, and the actual heat dissipation value and the actual heat increase value in the validation set, and obtain the heat dissipation prediction model and the heat increase prediction model. The specific algorithm formula is

[0040] ;

[0041] Among them represents the predicted heat dissipation value represents the weight matrix from the input layer to the hidden layer the weight from the hidden layer to the output layer represents the non-linear activation function is the bias vector of the hidden layer is the bias term of the output layer represents the transpose of the input feature vector is the time node

[0042] ;

[0043] Among them, represents the predicted value of heat increment, represents the weight matrix from the input layer to the hidden layer, the weight from the hidden layer to the output layer, represents the non-linear activation function, is the bias vector of the hidden layer, is the bias term of the output layer, represents the transpose of the input feature vector, is the time node.

[0044] Furthermore, the compensation verification module, based on the usage prediction model predicted by the equivalent conversion prediction module, conducts control simulations on the temperature regulation module, battery simulation module, and environment simulation module, verifies the usage prediction model, generates correction parameters, and corrects the usage prediction model. The specific steps are as follows:

[0045] Based on the heat increment prediction model and heat dissipation prediction model obtained from the relevant simulated battery equivalent conversion prediction module, transmit signals to control the temperature regulation module, battery simulation module, and environment simulation module to conduct control simulations, and obtain the predicted values and actual values at different time nodes under the same environmental temperature;

[0046] Based on the obtained predicted values and actual values, verify the usage prediction model, generate correction parameters, and correct the predicted values obtained from the usage prediction model.

[0047] Furthermore, based on the heat increment prediction model and heat dissipation prediction model obtained from the relevant simulated battery equivalent conversion prediction module, transmit signals to control the temperature regulation module, battery simulation module, and environment simulation module to conduct control simulations, and obtain the predicted values and actual values at different time nodes under the same environmental temperature. The specific steps are as follows:

[0048] Based on the normal usage environmental temperature and extreme usage environmental temperature of the simulated battery, extract N groups of environmental temperature data through simple non-repetitive random sampling;

[0049] Transmit control signals into the environment simulation module, battery simulation module, and temperature regulation module. Based on the extracted N groups of environmental temperature data, set the environmental temperature value of the environment simulation module, conduct working simulations through the battery simulation module, and obtain the heat dissipation values or heat increment values at n time nodes through the temperature regulation module;

[0050] Based on the N groups of environmental temperature data and n time nodes, through the heat dissipation prediction model and heat increment prediction model, respectively obtain the heat dissipation predicted values or heat increment predicted values, and correspond to the heat dissipation values or heat increment values of the temperature regulation module.

[0051] Further, based on the obtained predicted values and actual values, the prediction model is verified, correction parameters are generated, and the predicted values obtained using the prediction model are corrected. The specific steps are as follows:

[0052] The predicted values are corrected by the residual method, and its algorithm formula is:

[0053] ;

[0054] Among them, represents the residual of the i-th data point, represents the actual value obtained based on the temperature adjustment module, represents the predicted value obtained using the prediction model. Based on the correction value is calculated. The specific algorithm formula is:

[0055] ;

[0056] Among them, E represents the correction value, n is the number of data points, and the corrected predicted value is obtained. The specific algorithm formula is:

[0057] .

[0058] A method for adjusting the temperature of a battery simulated by a battery simulator includes the following steps:

[0059] S1. Based on the battery simulator, different working conditions of the battery are simulated, and the external environmental temperature of the battery in the simulation is controlled by a temperature control box to adapt to different simulated battery usage conditions;

[0060] S2. Through a voltage sensor, a current sensor, and a temperature sensor, relevant data of the battery during the simulation are collected, including voltage, current, and real-time temperature. Based on the optimal temperature range of the simulated battery and the collected real-time temperature, temperature adjustment is performed through heating or cooling devices;

[0061] S3. Based on the temperature adjustment data, combined with the relevant parameters of different devices, an equivalent conversion formula is obtained based on the algorithm formula, and the heat dissipation or heat gain is equivalently converted to generate relevant battery equivalent heat dissipation curves or equivalent heat gain curves under different environmental temperatures and different working conditions;

[0062] S4. Based on the heat dissipation or heat gain at different nodes during the total working time under different environmental temperatures, a prediction model is obtained through machine learning methods, including a heat dissipation prediction model and a heat gain prediction model;

[0063] S5. Based on the heat dissipation prediction model and the heat increase prediction model, N groups of ambient temperature data are extracted by simple non-repetitive random sampling, and work simulation is carried out through a battery simulator and a temperature control box to obtain actual temperature adjustment data. Based on the actual temperature adjustment data and the prediction data, a correction value is obtained to correct the prediction data.

[0064] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0065] In the present invention, by setting an equivalent conversion prediction module, the temperature adjustment data in the process of simulating the battery operation is equivalently converted, the heat dissipation amount or the heat increase amount is obtained based on the relevant equipment used in the simulation process, and the relevant battery equivalent heat dissipation curve or equivalent heat increase curve under different ambient temperatures and different working conditions is generated, providing data support for the heat dissipation design of the relevant battery by engineers and enhancing the applicability of the obtained data;

[0066] In the present invention, the ambient simulation module adjusts different ambient temperatures, and the temperature adjustment module obtains adjustment data. Based on the relevant data, a heat dissipation prediction model and a heat increase prediction model of the relevant battery are obtained through neural network simulation. By learning a large amount of data, the heat dissipation and heat increase conditions of the battery at different ambient temperatures are predicted, the actual use strategy of the battery is optimized, and the configuration of the heat dissipation device is adjusted according to the simulation results to ensure that the battery can maintain an appropriate working temperature under various environmental conditions;

[0067] In the present invention, by setting a compensation verification module in cooperation with the equivalent conversion prediction module, based on the obtained heat increase prediction model and heat dissipation prediction model, the compensation verification module transmits an instruction signal, adjusts relevant parameters based on the ambient temperature values extracted by simple random non-repetitive sampling, and the temperature adjustment module obtains actual values. Based on the comparison between the predicted values using the prediction model and the actual values, a correction value is generated to further correct the predicted values and further correct the output of the prediction model to ensure that the simulation results are closer to the actual situation;

[0068] In the present invention, the compensation verification module adjusts the simulation conditions in real time based on the ambient temperature values extracted by simple random non-repetitive sampling and records the actual temperature adjustment effect. These data can not only be used to verify the accuracy of the prediction model, but also provide experimental data support for subsequent model optimization and product design;

[0069] The entire temperature adjustment method and system for simulating a battery based on a battery simulator can achieve environmental temperature simulation, battery operation simulation, temperature regulation control, data acquisition, file recording, equivalent conversion and prediction, and verification and correction. By simulating the operation of different batteries in different temperature environments, heating and heat dissipation data are obtained, and equivalent conversion is combined with relevant usage devices to obtain a correction prediction model, facilitating the allocation of heat dissipation and heating devices during the actual use of the battery, and providing data support for engineers. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 FIG. is a block diagram of a temperature adjustment method and system for simulating a battery based on a battery simulator according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0071] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0072] As Figure 1 shown, a temperature adjustment system for simulating a battery based on a battery simulator includes an environment simulation module, a battery simulation module, a data acquisition module, a temperature adjustment module, an equivalent conversion prediction module, and a compensation verification module;

[0073] The environment simulation module regulates the environmental temperature of the simulated battery, records the environmental temperature data, and transmits the environmental temperature data into the battery simulation module;

[0074] The environment simulation module includes a temperature control box. According to the actual use environment of the battery, based on the temperature control box, the environmental temperature of the simulated battery is set, and the environmental temperature data is recorded, and the environmental temperature data is transmitted into the battery simulation module.

[0075] It should be noted that through the temperature control box, it can be ensured that the battery simulator accurately reflects the environmental conditions of the battery during the simulation process, obtain more reliable and realistic simulation results, be more in line with the environmental temperature conditions that may occur during the actual use process, provide more useful analysis and decision-making basis for engineers and researchers, and help optimize the battery management strategy and improve the product performance.

[0076] The battery simulation module includes a battery simulator that simulates the working process of the battery based on the actual usage of different batteries, including charge and discharge simulation and dynamic load simulation. Combining the ambient temperature data of the environment simulation module, a simulated battery file is established, including battery type, simulation type, duration, ambient temperature, number of series-connected cells, number of parallel-connected cells, and the optimal temperature range of the battery.

[0077] The data acquisition module includes a voltage sensor, a current sensor, and a temperature sensor, which collect relevant data of the battery during the simulation process, including voltage, current, and real-time temperature, and associate with the simulated battery file, and transmit the relevant data to the temperature regulation module.

[0078] It should be noted that based on the battery simulator, by setting parameters for the relevant battery, the charge and discharge simulation and dynamic load simulation of the battery are carried out, and combined with the ambient temperature set by the temperature control box, it is used to truly simulate the usage of the battery, and relevant data is collected through the voltage sensor, current sensor, and temperature sensor to provide data support for the subsequent module operation.

[0079] The temperature regulation module includes a heat dissipation device and a heating device. It calculates and determines by combining the optimal temperature range of the relevant battery in the simulated battery file and the real-time temperature, and controls the relevant devices to adjust the temperature of the battery during the simulation process. The specific steps are as follows:

[0080] Through the battery simulation module, obtain the optimal temperature range of the relevant battery. , combine the real-time temperature T of the relevant battery obtained by the data acquisition module for data determination. The specific steps and algorithm formulas are as follows:

[0081] Among them, the optimal temperature range of the relevant battery , A represents the minimum value of the optimal temperature of the relevant battery, B represents the maximum value of the optimal temperature of the relevant battery, and combine with the real-time temperature T of the relevant battery for determination:

[0082] When , it means that the temperature of the relevant battery is too low. Transmit the adjustment instruction to the heating device to perform the battery heating operation, and after t time, based on the newly obtained real-time temperature Perform a secondary determination. When , record and maintain the relevant parameters of the heating device, and continue to heat. When Or , transmit the adjustment signal to the heating device to adjust the relevant parameters, and after t time, make a determination based on the real-time temperature obtained again, and repeat the above adjustment operation until the real-time temperature is within the interval;

[0083] When , it means that the temperature of the relevant battery is normal and no adjustment is made.

[0084] When happens, it means that the temperature of the relevant battery is too high. A transmission adjustment instruction is sent to the heat dissipation device to perform battery heat dissipation operation, and after t time, based on the newly obtained real-time temperature a secondary determination is made. When happens, the relevant parameters of the heat dissipation device are recorded and maintained, and heat dissipation continues. When or happens, a transmission adjustment signal enters the heat dissipation device to adjust the relevant parameters, and after t time, a determination is made based on the real-time temperature obtained again, and the above adjustment operation is repeated until the real-time temperature is within the interval.

[0085] It should be noted that the heating device includes heating wires, heating plates, etc., which are used to heat the battery with a temperature lower than the normal working range during the simulation process. The heat dissipation device includes a cooling fan, a semiconductor refrigeration chip, etc., which are used to dissipate heat from the battery with a temperature higher than the normal working range during the simulation process. The time interval t can be flexibly set according to different types of batteries and working modes, so as to obtain the required data and provide data support for subsequent modules.

[0086] The equivalent conversion prediction module, based on the adjustment result of the temperature adjustment module, combines the relevant parameters of different devices, obtains the equivalent conversion formula based on the algorithm formula, performs equivalent conversion on the heat dissipation amount or heat gain amount, generates the relevant battery equivalent heat dissipation curve or equivalent heat gain curve under different environmental temperatures and different working conditions, and obtains the usage prediction model. The specific steps are as follows:

[0087] Based on the adjustment result of the temperature adjustment module, under the same environmental temperature, based on different working conditions, obtain the relevant parameters of the heat dissipation device or heating device, including wattage, output efficiency, output duration, time node. Based on the equivalent conversion formula, obtain the heat dissipation amount or heat gain amount at different nodes within the total working time. The specific steps and algorithm formula are as follows:

[0088] For the heat dissipation amount, based on different environmental temperatures , respectively collect the relevant heat dissipation device parameters corresponding to the environmental temperature, including wattage , output efficiency , output duration , calculate the heat dissipation amount , and the specific algorithm formula is:

[0089] ;

[0090] Among them, represents the total working time, respectively represent the output duration after each parameter change within the total working time, represent the relevant battery output duration under the same ambient temperature The heat dissipation within, based on the above formula, obtain the heat dissipation at different output durations, different wattages, and output efficiencies within the total working time ;

[0091] For the heat increase, based on different ambient temperatures , respectively collect the relevant heat increase equipment parameters corresponding to the ambient temperature, including wattage , output efficiency , output duration , calculate to obtain the heat increase , the specific algorithm formula is:

[0092] ;

[0093] Among them, represents the total working time, respectively represent the output duration after each parameter change within the total working time, represent the relevant battery output duration under the same ambient temperature The heat increase within, based on the above formula, obtain the heat increase at different output durations, different wattages, and output efficiencies within the total working time .

[0094] Based on the heat dissipation or heat increase at different nodes within the total working time under the same ambient temperature, obtain the relevant battery equivalent heat dissipation curve or equivalent heat increase curve

[0095] It should be noted that according to the obtained , according to the at different nodes and , based on MATLAB By using the curve fitting toolbox, an equivalent heat dissipation curve or equivalent heat increase curve can be plotted based on the existing data points

[0096] Combined with the heat dissipation or heat increase at different nodes within the total working time under different ambient temperatures, through machine learning methods, obtain usage prediction models, including heat dissipation prediction models and heat increase prediction models. The specific steps are as follows:

[0097] Collect the corresponding to the time nodes under different ambient temperatures and , based on the neural network model, train the usage prediction model. The steps are as follows:

[0098] Collect relevant data as the training dataset. For the heat dissipation prediction model, the relevant data includes heat dissipation data and the corresponding ambient temperature and time nodes. For the heat increase prediction model, the relevant data includes heat dissipation data and the corresponding ambient temperature and time nodes;

[0099] Based on different battery models, select the neural network structure, the number of hidden layers, and the number of neurons in each layer, and use ReLU function as the activation function;

[0100] Divide the training dataset into a training set and a validation set. Train the neural network model using the training set and validate the neural network model based on the validation set. Adjust the hyperparameters of the neural network model according to the performance of the validation set;

[0101] It should be noted that the division ratio of the validation set to the training set is usually 2:8, that is, 80% of the dataset is the training set and 20% is the validation set. The training set is used to train the neural network model, and the parameters of the model, including weights and biases, are adjusted by optimizing the loss function. The validation set is used to adjust the hyperparameters and structure of the model to optimize the performance and generalization ability of the model.

[0102] Select the mean squared error as the loss function to measure the difference between the predicted heat dissipation value, the predicted heat increase value, and the actual heat dissipation value and the actual heat increase value in the validation set, and obtain the heat dissipation prediction model and the heat increase prediction model. The specific algorithm formula is:

[0103] ;

[0104] Among them, represents the predicted heat dissipation value, represents the weight matrix from the input layer to the hidden layer, the weight from the hidden layer to the output layer, represents the non-linear activation function, is the bias vector of the hidden layer, is the bias term of the output layer, represents the transpose of the input feature vector, is the time node;

[0105] ;

[0106] Among them, represents the predicted heat increase value, represents the weight matrix from the input layer to the hidden layer, the weight from the hidden layer to the output layer, represents the non-linear activation function, is the bias vector of the hidden layer, is the bias term of the output layer, represents the transpose of the input feature vector, is the time node.

[0107] It should be noted that the predicted heat gain value and the predicted heat dissipation value are the outputs predicted by the model. The weight matrix from the input layer to the hidden layer contains the connection weights between the hidden layer neurons and the input features. Each hidden layer neuron has a bias term to adjust the activation threshold of the neuron. By adjusting the weights and biases and selecting an appropriate activation function, the neural network can learn and predict the heat dissipation or heat gain under the given input environmental temperature and time node.

[0108] The compensation verification module, based on the usage prediction model predicted by the equivalent conversion prediction module, conducts control simulation for the temperature regulation module, battery simulation module, and environmental simulation module, verifies the usage prediction model, generates correction parameters, and corrects the usage prediction model. The specific steps are as follows:

[0109] Based on the heat gain prediction model and heat dissipation prediction model obtained from the relevant simulated battery equivalent conversion prediction module, transmit signal to control the temperature regulation module, battery simulation module, and environmental simulation module for control simulation, and obtain the predicted values and actual values at different time nodes under the same environmental temperature. The specific steps are as follows:

[0110] Based on the normal usage environmental temperature and extreme usage environmental temperature of the simulated battery, extract N groups of environmental temperature data by simple non-repetitive random sampling method;

[0111] Transmit control signals into the environmental simulation module, battery simulation module, and temperature regulation module. Based on the extracted N groups of environmental temperature data, set the environmental temperature value of the environmental simulation module, conduct working simulation through the battery simulation module, and obtain the heat dissipation values or heat gain values at n time nodes through the temperature regulation module;

[0112] Based on the N groups of environmental temperature data and n time nodes, respectively obtain the predicted heat dissipation values or heat gain values through the heat dissipation prediction model and heat gain prediction model, and correspond to the heat dissipation values or heat gain values of the temperature regulation module.

[0113] It should be noted that according to the method of simple random non-repetitive sampling, randomly extract data points from the normal usage environmental temperature and extreme usage environmental temperature as the environmental temperature input in the subsequent simulation process, and utilize the coordinated work of the environmental simulation module, battery simulation module, and temperature regulation module to obtain the actual values. Combining with the predicted values obtained by the usage prediction model provides data support for the acquisition of correction values.

[0114] Based on the obtained predicted values and actual values, for validating the use of the prediction model, correction parameters are generated to correct the predicted values obtained using the prediction model. The specific steps are as follows:

[0115] The correction of the predicted value is performed by the residual method, and its algorithm formula is:

[0116] ;

[0117] Where, represents the residual of the i-th data point, represents the actual value obtained based on the temperature regulation module, represents the predicted value obtained based on the use of the prediction model. Based on the correction value is calculated, and the specific algorithm formula is:

[0118] ;

[0119] Where, E represents the correction value, n is the number of data points, and the corrected predicted value is obtained, and the specific algorithm formula is:

[0120] .

[0121] It should be noted that by comparing the residuals of the predicted value and the actual value, the deviation of using the prediction equation is estimated, and this deviation is applied to future predictions for correction, which can further improve the accuracy of the predicted value, thereby providing theoretical data support for the actual use of the battery.

[0122] A method for adjusting the temperature of a battery simulated by a battery simulator includes the following steps:

[0123] S1. Based on the battery simulator, different working conditions of the battery are simulated, and the external environmental temperature of the battery in the simulation is controlled by a temperature control box to adapt to different simulated battery usage situations;

[0124] S2. Through a voltage sensor, a current sensor, and a temperature sensor, relevant data of the battery during the simulation are collected, including voltage, current, and real-time temperature. Based on the optimal temperature range of the simulated battery and the collected real-time temperature, temperature adjustment is performed through heating or cooling equipment;

[0125] S3. Based on the temperature adjustment data, combined with the relevant parameters of different devices, an equivalent conversion formula is obtained based on the algorithm formula to perform equivalent conversion on the heat dissipation or heat gain, and generate relevant battery equivalent heat dissipation curves or equivalent heat gain curves for different environmental temperatures and different working conditions;

[0126] S4. Based on the heat dissipation or heat gain of different nodes within the total working time at different ambient temperatures, a prediction model is obtained through machine learning methods, including a heat dissipation prediction model and a heat gain prediction model;

[0127] S5. Based on the heat dissipation prediction model and the heat gain prediction model, N groups of ambient temperature data are extracted through simple non-repetitive random sampling, and work simulation is carried out through a battery simulator and a temperature control box to obtain actual temperature adjustment data. Based on the actual temperature adjustment data and the prediction data, a correction value is obtained to correct the prediction data.

[0128] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation; the modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the method of this embodiment.

[0129] The above embodiments are only used to illustrate the technical methods of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical methods of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A temperature adjustment system for simulating a battery based on a battery simulator, characterized in that: It includes an environmental simulation module, a battery simulation module, a data acquisition module, a temperature regulation module, an equivalent conversion prediction module, and a compensation verification module; The environmental simulation module regulates the environmental temperature of the simulated battery, records the environmental temperature data, and transmits the environmental temperature data into the battery simulation module; The battery simulation module simulates the working process of the battery, combines the environmental temperature data of the environmental simulation module, and establishes a simulated battery file; The data acquisition module collects the relevant data of the battery during the simulation process, including voltage, current, and real-time temperature, associates with the simulated battery file, and transmits the relevant data into the temperature regulation module; The temperature regulation module includes a heat dissipation device and a heat increase device. It calculates and determines by combining the relevant battery optimal temperature range and the real-time temperature in the simulated battery file, and controls the relevant devices to regulate the temperature of the battery during the simulation process; The equivalent conversion prediction module, based on the adjustment result of the temperature regulation module, combines the relevant parameters of different devices, obtains an equivalent conversion formula based on the algorithm formula, performs equivalent conversion on the heat dissipation or heat increase amount, generates equivalent heat dissipation curves or equivalent heat increase curves of the relevant battery under different environmental temperatures and different working conditions, and obtains a usage prediction model. The specific steps are as follows: Based on the adjustment result of the temperature adjustment module, under the same ambient temperature and different working conditions, relevant parameters of the heat dissipation device or the heat increase device are obtained, including wattage , output efficiency , output duration , and wattage , output efficiency , output duration , based on the equivalent conversion formula and , the heat dissipation or heat increase at different nodes within the total working time is obtained or heat increase ; Based on the heat dissipation or heat increase amount at different nodes during the total working time under the same environmental temperature, obtain the equivalent heat dissipation curve or equivalent heat increase curve of the relevant battery; Combined with the heat dissipation or heat increase amount at different nodes during the total working time under different environmental temperatures, through machine learning methods, based on different battery models, select a neural network structure to obtain a usage prediction model, including a heat dissipation prediction model and a heat increase prediction model; The compensation verification module, based on the usage prediction model predicted by the equivalent conversion prediction module, conducts a control simulation on the temperature regulation module, the battery simulation module, and the environmental simulation module, verifies the usage prediction model, generates correction parameters, and corrects the usage prediction model.

2. The temperature adjustment system for simulating a battery based on a battery simulator according to claim 1, characterized in that: The environmental simulation module includes a temperature control box. According to the actual usage environment of the battery, based on the temperature control box, set the environmental temperature of the simulated battery, record the environmental temperature data, and transmit the environmental temperature data into the battery simulation module; The battery simulation module includes a battery simulator. Based on the actual usage conditions of different batteries, simulate the working process of the battery, including charge and discharge simulation and dynamic load simulation. Combine the environmental temperature data of the environmental simulation module to establish a simulated battery file, including battery type, simulation type, duration, environmental temperature, number of series cells, number of parallel cells, and battery optimal temperature range; The data acquisition module includes a voltage sensor, a current sensor, and a temperature sensor. It collects the relevant data of the battery during the simulation process, including voltage, current, and real-time temperature, associates with the simulated battery file, and transmits the relevant data into the temperature regulation module.

3. The temperature adjustment system for simulating a battery based on a battery simulator according to claim 1, characterized in that: The temperature regulation module includes a heat dissipation device and a heat increase device. It calculates and determines by combining the relevant battery optimal temperature range and the real-time temperature in the simulated battery file, and controls the relevant devices to regulate the temperature of the battery during the simulation process. The specific steps are as follows: Obtain the optimal temperature range of the relevant battery through the battery simulation module , and perform data determination in combination with the real-time temperature T of the relevant battery obtained by the data acquisition module. The specific steps and algorithm formulas are as follows: The optimal temperature range of the relevant battery , where A represents the minimum value of the optimal temperature of the relevant battery, B represents the maximum value of the optimal temperature of the relevant battery, and the determination is made in combination with the real-time temperature T of the relevant battery: When it indicates that the temperature of the relevant battery is too low. A heating adjustment instruction is transmitted to the heating device to perform battery heating operation, and after t time, a secondary determination is made based on the newly obtained real-time temperature When it is the case, record and maintain the relevant parameters of the heating device, and continue heating. When or it is the case, a regulation signal is transmitted to the heating device to adjust the relevant parameters, and after t time, a determination is made based on the real-time temperature obtained again, and the above adjustment operation is repeated until the real-time temperature is within the interval; When it indicates that the relevant battery temperature is normal and no adjustment is required; When it indicates that the relevant battery temperature is too high. Transmit an adjustment instruction to the heat dissipation device to perform battery heat dissipation operation, and based on the newly obtained real-time temperature after t time conduct a secondary determination. When it is the case, record and maintain the relevant parameters of the heat dissipation device, and continue heat dissipation. When or it is the case, transmit an adjustment signal to the heat dissipation device to adjust the relevant parameters, and make a determination based on the real-time temperature obtained again after t time, and repeat the above adjustment operation until the real-time temperature is within the interval.

4. A temperature adjustment system for simulating a battery based on a battery simulator according to claim 1, characterized in that: Based on the adjustment result of the temperature adjustment module, under the same ambient temperature, relevant parameters of the heat dissipation device or heat increase device are obtained, including wattage, output efficiency, output duration, and time node. Based on the equivalent conversion formula, the heat dissipation or heat increase at different nodes within the total working time is obtained. The specific steps and algorithm formulas are as follows: For the heat dissipation amount, based on different ambient temperatures , collect the relevant heat dissipation equipment parameters at the corresponding ambient temperatures respectively, including wattage , output efficiency , output duration , and calculate to obtain the heat dissipation amount . The specific algorithm formula is as follows: ； Wherein, represents the total working time, respectively represent the output duration after each parameter change within the total working time, represents the same ambient temperature and the relevant battery output duration thereunder and the heat dissipation amount within the relevant battery output duration under different output durations, different wattages, and output efficiencies within the total working time is obtained based on the above formula ; For the increased heat quantity, based on different ambient temperatures , relevant parameters of the heat generation equipment corresponding to the ambient temperatures are collected respectively, including wattage , output efficiency , output duration , and the increased heat quantity is calculated . The specific algorithm formula is as follows: ； Among them, represents the total working time, respectively represent the output duration after each parameter change within the total working time, represents the same ambient temperature the relevant battery output duration under the heat increase within, and based on the above formula, the heat increases at different output durations, different wattages, and output efficiencies within the total working time are obtained .

5. A temperature adjustment system for simulating a battery based on a battery simulator according to claim 4, characterized in that: Combining the heat dissipation or heat increase at different nodes within the total working time under different ambient temperatures, through machine learning methods, usage prediction models are obtained, including a heat dissipation prediction model and a heat increase prediction model. The specific steps are as follows: Collect the corresponding ones at time nodes under different ambient temperatures and , based on the neural network model, for training using the prediction model, the steps are as follows: Collect relevant data as the training data set. For the heat dissipation prediction model, the relevant data includes heat dissipation data and the corresponding ambient temperature and time nodes. For the heat increase prediction model, the relevant data includes heat dissipation data and the corresponding ambient temperature and time nodes; Based on different battery models, select the neural network structure, the number of hidden layers, and the number of neurons in each layer, and use ReLU function as the activation function; The training dataset is divided into a training set and a validation set. The neural network model is trained using the training set, and the neural network model is validated based on the validation set. The hyperparameters of the neural network model are adjusted according to the performance of the validation set. The mean squared error is selected as the loss function to measure the difference between the predicted heat dissipation value, predicted heat increase value and the actual heat dissipation value and actual heat increase value in the validation set, and the heat dissipation prediction model and heat increase prediction model are obtained. The specific algorithm formulas are as follows: ； Among them, represents the predicted value of the heat dissipation, represents the weight matrix from the input layer to the hidden layer, the weight from the hidden layer to the output layer, represents the non-linear activation function, is the bias vector of the hidden layer, is the bias term of the output layer, represents the transpose of the input feature vector, is the time node; ； Among them, represents the predicted value of increased heat, represents the weight matrix from the input layer to the hidden layer, the weight from the hidden layer to the output layer, represents the non-linear activation function, is the bias vector of the hidden layer, is the bias term of the output layer, represents the transpose of the input feature vector, is the time node.

6. The temperature adjustment system for simulating a battery based on a battery simulator according to claim 1, characterized in that: The compensation verification module, based on the usage prediction model predicted by the equivalent conversion prediction module, conducts control simulation on the temperature adjustment module, battery simulation module, and environment simulation module, verifies the usage prediction model, generates correction parameters, and corrects the usage prediction model. The specific steps are as follows: Based on the heat increase prediction model and heat dissipation prediction model obtained from the relevant simulated battery equivalent conversion prediction module, signals are transmitted to control the temperature adjustment module, battery simulation module, and environment simulation module for control simulation, and the predicted values and actual values at different time nodes under the same ambient temperature are obtained. Based on the obtained predicted values and actual values, the usage prediction model is verified, correction parameters are generated, and the predicted values obtained from the usage prediction model are corrected.

7. A temperature adjustment system for simulating a battery based on a battery simulator according to claim 6, characterized in that: Based on the heat increase prediction model and heat dissipation prediction model obtained from the relevant simulated battery equivalent conversion prediction module, signals are transmitted to control the temperature adjustment module, battery simulation module, and environment simulation module for control simulation, and the predicted values and actual values at different time nodes under the same ambient temperature are obtained. The specific steps are as follows: Based on the normal use ambient temperature and extreme use ambient temperature of the simulated battery, N groups of ambient temperature data are extracted by simple non-repetitive random sampling. A control signal is transmitted into the environment simulation module, battery simulation module, and temperature adjustment module. Based on the extracted N groups of ambient temperature data, the ambient temperature value of the environment simulation module is set, the battery simulation module conducts work simulation, and the heat dissipation value or heat increase value at n time nodes is obtained through the temperature adjustment module. Based on the N groups of ambient temperature data and n time nodes, the predicted heat dissipation value or predicted heat increase value is obtained respectively through the heat dissipation prediction model and heat increase prediction model, and corresponds to the heat dissipation value or heat increase value of the temperature adjustment module.

8. A temperature adjustment system for simulating a battery based on a battery simulator according to claim 7, characterized in that: Based on the obtained predicted values and actual values, the usage prediction model is verified, correction parameters are generated, and the predicted values obtained from the usage prediction model are corrected. The specific steps are as follows: The predicted value is corrected by the residual method, and the algorithm formula is as follows: ； Among them, represents the residual of the i-th data point, represents the actual value obtained based on the temperature adjustment module, represents the predicted value obtained based on the use of the prediction model. Based on calculate the correction value. The specific algorithm formula is: ； Among them, E represents the correction value, n is the number of data points, and the corrected predicted value is obtained , and the specific algorithm formula is as follows: 。 9. A method for adjusting the temperature of a battery simulated by a battery simulator, characterized in that, This method uses a temperature adjustment system for simulating a battery based on a battery simulator as described in any one of claims 1-8, and includes the following steps: S1. Based on the battery simulator, simulate different working conditions of the battery, and control the external environmental temperature of the battery in the simulation through a temperature control box to adapt to different simulated battery usage conditions; S2. Collect relevant data of the battery during the simulation, including voltage, current, and real-time temperature, through a voltage sensor, a current sensor, and a temperature sensor. Based on the optimal temperature range of the simulated battery and the collected real-time temperature, adjust the temperature through heating or cooling equipment; S3. Based on the data of temperature adjustment, combined with the relevant parameters of different devices, obtain an equivalent conversion formula based on an algorithm formula, perform equivalent conversion on the heat dissipation or heat gain, and generate relevant battery equivalent heat dissipation curves or equivalent heat gain curves under different environmental temperatures and different working conditions; S4. Based on the heat dissipation or heat gain at different nodes during the total working time under different environmental temperatures, obtain a usage prediction model through a machine learning method, including a heat dissipation prediction model and a heat gain prediction model; S5. Based on the heat dissipation prediction model and the heat gain prediction model, extract a set of environmental temperature data through a simple non-repetitive random sampling method, and perform a working simulation through the battery simulator and the temperature control box to obtain actual temperature adjustment data. Based on the actual temperature adjustment data and the prediction data, obtain a correction value to correct the prediction data.

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