Battery thermal management control method, device, equipment and medium

Abstract

This application discloses a battery thermal management control method, apparatus, device, and medium, relating to the field of thermal management control technology. The method comprises: obtaining vehicle driving information, determining whether navigation is enabled based on the vehicle driving information, obtaining a judgment result, and predicting the battery temperature based on the judgment result and the vehicle driving information. When the predicted battery temperature meets a preset condition, a thermal management control strategy is executed. The battery temperature is predicted based on the driving information input model, and thermal management is dynamically adjusted based on the predicted temperature, thereby improving the accuracy of the predicted temperature, reducing energy consumption, and ensuring safety.

Description

Technical Field

[0001] The present invention relates to the field of thermal management control technology, and in particular to a battery thermal management control method, device, equipment and medium. Background Art

[0002] With the rapid development of new energy vehicles, power battery thermal management has become a critical component in ensuring stable performance, extending battery life, and improving safety. Effective thermal management ensures that batteries operate within the optimal temperature range, thereby improving battery efficiency, extending battery life, and ensuring safe vehicle operation.

[0003] The battery temperature control currently available on the market predicts the battery temperature rise during driving by analyzing navigation data or big data analysis of typical operating conditions, and controls the closing and opening of the cooling relay based on the prediction results to timely adjust the battery cooling status.

[0004] Despite leveraging navigation data and big data analytics, predictions of driving conditions are incomplete and inaccurate, failing to fully account for more dynamic factors such as real-time traffic conditions and changes in driving habits. This limits the adaptability of thermal management strategies. The system lacks a mechanism for dynamically adjusting thermal management thresholds, making it unable to optimize thermal management strategies based on real-time battery status, environmental conditions, and driving patterns. This can lead to overcooling or inappropriate intervention, impacting battery performance and efficiency. Therefore, developing a method that can accurately predict battery temperature and intelligently adjust thermal management strategies based on the predicted results is crucial. Summary of the Invention

[0005] The main purpose of this application is to provide a battery thermal management control method, device, equipment and medium, aiming to solve the technical problem of how to improve the accuracy of predicting battery temperature and dynamically adjust the temperature.

[0006] To achieve the above objectives, the present application proposes a battery thermal management control method, the method comprising:

[0007] Obtain vehicle driving information;

[0008] Determine whether navigation is turned on for the vehicle based on the vehicle driving information, and obtain a determination result;

[0009] Obtaining a predicted battery temperature based on the judgment result and the vehicle driving information;

[0010] When the predicted battery temperature meets a preset condition, a thermal management control strategy is executed for processing.

[0011] Optionally, before the step of obtaining the predicted battery temperature based on the judgment result and the vehicle driving information, the step includes:

[0012] Obtain vehicle driving sample information and establish an initial temperature prediction model;

[0013] The initial temperature prediction model is trained according to the vehicle driving sample information to obtain a preset temperature prediction model.

[0014] Optionally, the step of training the initial temperature prediction model according to the vehicle driving sample information to obtain a preset temperature prediction model includes:

[0015] Initializing model parameters of the initial temperature prediction model;

[0016] Normalizing the vehicle driving sample information to obtain processed information;

[0017] Inputting the processed information into the initial temperature prediction model to obtain a predicted temperature;

[0018] According to the loss function calculation, the error value between the predicted temperature and the actual temperature is obtained;

[0019] Get the learning rate;

[0020] The model parameters are iteratively updated through an optimization algorithm according to the learning rate until a maximum number of iterations is reached or the error value calculated by the loss function converges to a preset threshold, thereby obtaining a preset temperature prediction model.

[0021] Optionally, after the step of training the initial temperature prediction model according to the vehicle driving sample information to obtain a preset temperature prediction model, the method further includes:

[0022] Evaluating the preset temperature prediction model to obtain an evaluation result;

[0023] If the evaluation result does not meet the prediction requirement, retraining the preset temperature prediction model until the control requirement is met;

[0024] If the evaluation result meets the prediction requirements, the preset temperature prediction model is used as the final temperature prediction model;

[0025] Obtaining a predicted battery temperature based on the judgment result and the vehicle driving information, including:

[0026] The vehicle driving information is passed through the temperature prediction model according to the judgment result to obtain a predicted battery temperature.

[0027] Optionally, the step of applying the vehicle driving information to the temperature prediction model to obtain a predicted battery temperature according to the judgment result includes:

[0028] When the result of the determination is that the vehicle is navigating, inputting vehicle driving information into the initial temperature prediction model to construct a first preset temperature prediction model, wherein the vehicle driving information includes at least destination information, real-time updates of road conditions, estimated driving distance, estimated arrival time, and weather forecast data;

[0029] The vehicle driving information is passed through a first preset temperature prediction model to obtain a predicted battery temperature.

[0030] Optionally, before the step of applying the vehicle driving information to the temperature prediction model to obtain a predicted battery temperature according to the judgment result, the step further includes:

[0031] When it is determined that navigation is not enabled on the vehicle, a date category, a first duration, a second duration, and a third duration are obtained based on the vehicle driving information, wherein the date category includes weekdays and weekends, the first duration is a time during which the speed continuously exceeds a preset speed, the second duration is a time during which the vehicle is continuously driven on a highway, and the third duration is a time during which the vehicle is driven uphill;

[0032] Performing classification statistics according to the date categories to obtain the frequencies and distribution patterns of the first duration, the second duration, and the third duration under different date categories;

[0033] Constructing a second preset temperature prediction model according to the frequency and distribution law;

[0034] The vehicle driving information is passed through a second preset temperature prediction model to obtain a predicted battery temperature.

[0035] Optionally, after the step of obtaining the predicted battery temperature based on the judgment result and the vehicle driving information, the following steps are included:

[0036] evaluating the temperature prediction model to obtain a predicted battery temperature;

[0037] Comparing the predicted battery temperatures to obtain a maximum predicted battery temperature and a minimum predicted battery temperature;

[0038] If the error between the maximum predicted battery temperature and the actual maximum temperature does not exceed a preset value and the error between the minimum predicted battery temperature and the actual minimum temperature does not exceed a preset value, constructing a predicted temperature curve according to the temperature prediction model and performing thermal management control according to the predicted temperature curve;

[0039] If the error between the maximum predicted battery temperature and the actual maximum temperature exceeds a preset value or the error between the minimum predicted battery temperature and the actual minimum temperature exceeds a preset value, the temperature prediction model parameters are compensated and updated to obtain an updated temperature prediction model;

[0040] Re-predicting the temperature and constructing a predicted temperature curve based on the updated temperature prediction model;

[0041] Thermal management control is performed according to the predicted temperature curve and the actual temperature.

[0042] In addition, to achieve the above objectives, the present application also proposes a battery thermal management control device, the battery thermal management control device comprising:

[0043] An acquisition module is used to obtain vehicle driving information;

[0044] A judgment module, configured to judge whether the vehicle is turned on for navigation according to the vehicle driving information, and obtain a judgment result;

[0045] an acquisition module, configured to obtain a predicted battery temperature based on the judgment result and the vehicle driving information;

[0046] The control module is configured to execute a thermal management control strategy for processing when the predicted battery temperature meets a preset condition.

[0047] In addition, to achieve the above-mentioned purpose, the present application also proposes a medium, which is a computer-readable medium and stores a computer program. When the computer program is executed by a processor, the steps of the battery thermal management control method as described above are implemented.

[0048] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the battery thermal management control method as described above are implemented.

[0049] This application obtains vehicle driving information, determines whether navigation is enabled based on that information, and then predicts the battery temperature based on that information. When the predicted battery temperature meets pre-set conditions, the thermal management control strategy is executed. The driving information is input into a model to predict the battery temperature, and thermal management is dynamically adjusted based on the predicted temperature, improving the accuracy of the predicted temperature, reducing energy consumption, and ensuring safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0051] Figure 1 This is a flow chart of the first embodiment of the battery thermal management control method of the present application;

[0052] Figure 2 This is a temperature error boundary diagram of the first embodiment of the battery thermal management control method of this application;

[0053] Figure 3 This is a schematic diagram of the module structure of the battery thermal management control device according to an embodiment of the present application;

[0054] Figure 4 Schematic diagram of the device structure of the hardware operating environment involved in the battery thermal management control method in the embodiment of the present application.

[0055] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0056] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0057] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0058] The main solution of the embodiment of the present application is: by obtaining vehicle driving information, judging whether the vehicle has turned on navigation based on the vehicle driving information, obtaining a judgment result, and obtaining a predicted battery temperature based on the judgment result and the vehicle driving information. When the predicted battery temperature exceeds the safe temperature threshold, the battery temperature control strategy is executed to perform cooling processing.

[0059] Based on this, the embodiment of the present application provides a battery thermal management control method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the battery thermal management control method of the present application.

[0060] In this embodiment, the battery thermal management control method includes steps S10 to S40:

[0061] Step S10: Acquire vehicle driving information.

[0062] It's important to note that acquiring vehicle driving information is fundamental to implementing intelligent thermal management strategies. This process involves the integrated utilization of the vehicle's sensor network, onboard electronic systems, and external data sources. First, a variety of sensors throughout the vehicle, such as GPS, speed, ambient temperature, and battery temperature sensors, collect key data such as the vehicle's location, speed, ambient temperature, and current battery temperature in real time. The GPS module not only provides precise geographic location information but also, when combined with map data, predicts upcoming road conditions, such as whether the vehicle is about to enter a mountainous area or enter a highway. This information is crucial for predicting changes in battery load. Vehicle driving information also includes the selected driving mode (e.g., Economy or Sport). The differences in motor power output in different modes directly affect battery heat generation. Furthermore, the vehicle's internal intelligent system records the driver's operating habits, such as the frequency and intensity of acceleration and braking. These habits also affect the battery's operating state and heat generation.

[0063] Furthermore, according to the vehicle longitudinal kinematic equation, the vehicle drive recovery power P is calculated. drv :

[0064]

[0065] Among them, coefficient A represents the slope resistance correction coefficient, B represents the shareholder resistance coefficient, C represents the wind resistance coefficient, D represents the acceleration resistance coefficient, E represents the transmission system resistance coefficient, α represents the actual road slope, V represents the vehicle speed, and γ represents the driver characteristic correction coefficient.

[0066] Calculate the high voltage accessory power for the battery thermal management system:

[0067] P1=f1(T1-T2)+f2(T3-T4)+P2

[0068]

[0069] P3=P drv *β*ω+P1

[0070] Among them, f1 and f2 represent the high-voltage accessory power (excluding DCDC). The accessory power of the passenger compartment air conditioning system and the battery thermal management system accounts for a large proportion, and there are many ways to calculate the high-voltage accessory power (excluding DCDC), such as PI control adjustment based on the target water temperature and actual water temperature of the power battery, or calculating the heat exchange power requirements at different battery cell temperatures based on lookup tables and rules. The temperature control method of the passenger compartment is similar. T1 is the temperature threshold for starting heating or cooling of the battery, and T2 is the battery temperature; Pdcdc power accounts for a small proportion, usually taking an average value based on normal temperature, high temperature and low temperature. The power consumption of DCDC is about 0.3kW to 1kW, and the impact on the final charge and discharge power of the power battery can be ignored. P3 is the output power of the battery. When P drv If it is positive, it represents driving, otherwise it represents deceleration to recover energy, ω is the motor efficiency, and β is the energy recovery ratio.

[0071] Step S20: determining whether navigation is enabled on the vehicle based on the vehicle driving information, and obtaining a determination result.

[0072] It should be noted that while the vehicle is in motion, the system monitors various vehicle status information in real time through the integrated connected vehicle platform, including whether the vehicle's navigation function is enabled. This determination process involves the interaction record of the vehicle's central control system and the coordinated operation of the data communication module. If the driver activates the in-vehicle navigation system and sets a destination, the system receives a corresponding signal, indicating not only the activation of route planning but also a clear direction and purpose for the upcoming journey. Before determining whether the vehicle's navigation function is enabled, driver information is first obtained; based on this driver information and vehicle driving information, a safety temperature threshold is determined.

[0073] Specifically, once navigation is enabled, the system uses detailed route information provided by the navigation system, such as destination type, road complexity, estimated driving distance, driving pattern, and altitude changes, to comprehensively analyze historical data on battery temperature rise under similar driving conditions and predict the battery temperature trend for the current trip. This prediction model relies on big data analysis and machine learning algorithms, and can be continuously optimized as more driving data accumulates, improving prediction accuracy. When navigation is disabled, the system relies on learning driving patterns on weekdays and non-weekdays, using pattern recognition algorithms to analyze the match between current driving behavior and historical typical driving conditions, indirectly inferring driving conditions and adjusting thermal management strategies. In either case, the present invention aims to use an intelligent prediction mechanism to pre-schedule the front-end cooling module, effectively controlling battery temperature in a low-energy manner and avoiding frequent compressor activation. This improves the energy efficiency and range of electric vehicles while also ensuring battery safety and lifespan.

[0074] Step S30: Obtain the predicted battery temperature based on the judgment result and the vehicle driving information.

[0075] It should be understood that before obtaining the predicted battery temperature, vehicle driving sample information is obtained and an initial temperature prediction model is established. The initial temperature prediction model is trained based on the vehicle driving sample information to obtain a preset temperature prediction model. Based on the judgment result, the vehicle driving information is passed through the preset temperature prediction model to obtain the predicted battery temperature.

[0076] Furthermore, it is necessary to understand that to train the initial temperature prediction model, the model parameters are first initialized, the input data is obtained, and then the input data is normalized to obtain the processed input data, and the processed input data is input into the initial temperature prediction model to obtain the predicted output data, and then the error value between the predicted output data and the real data is obtained according to the loss function calculation, and the learning rate is obtained. Finally, the model parameters are iteratively updated through the optimization algorithm according to the learning rate until the maximum number of iterations is reached or the error value calculated by the loss function converges to the preset threshold, and the preset temperature prediction model is obtained. Specifically, first, vehicle driving information covering multiple dimensions such as indoor and outdoor temperature, humidity, ambient light, time, etc. is collected, cleaned and normalized, noise and outliers are eliminated, and a high-quality training data set is formed. The specific normalization is according to the formula:

[0077]

[0078] Normalize the input data to between 0 and 1 to facilitate neural network processing.

[0079] The dataset is divided into training, validation, and test sets in a certain proportion, generally using a 70%, 15%, and 15% distribution ratio to ensure that the model training, tuning, and generalization ability evaluation are scientific and reasonable. Then, these datasets are used to train the initial model. The model adopts a multi-layer perceptron structure, including a carefully designed input layer, two hidden layers, and an output layer. During the training process, the model predicts the output through forward propagation calculation, and the input data of the input layer is calculated through linear transformation to obtain the input data of the hidden layer. Specifically, the input data of the first hidden layer is obtained by the following formula:

[0080] Z (1) =W (1) x+b (1)

[0081] where Z (1) is the input value of the first hidden layer, W (1) is the weight matrix of the first hidden layer, which represents the connection strength between the input layer and the hidden layer. x is the input data of the input layer, and b (1)It is the bias vector used to adjust the activation value of the hidden layer. The superscript indicates the data of the hidden layer. Each hidden layer has a different weight matrix and bias vector.

[0082] The input data of the hidden layer is calculated through the nonlinear ReLU activation function to obtain the output data of the first hidden layer. Specifically, it is obtained by the following formula:

[0083] A (1) =ReLU(z (1) )

[0084] Among them, A (1) is the output data of the first hidden layer. This means that for the input data, the output of the ReLU function is a larger value between 0 and 1. If the input data is greater than 0, the output is 1; if the input data is less than or equal to 0, the output is 0. According to the above two formulas, the output data of the hidden layer is first calculated by linear change and then by nonlinear ReLU activation function until the output data Z of the last hidden layer is obtained. (2) and A (2) , the output data of the last hidden layer is used as the input data of the output layer, and is calculated according to the nonlinear Sigmoid activation function. Thus, the predicted output data is obtained. Specifically, it is obtained according to the following formula:

[0085] Z (3) =W (3) A (2) +b (3)

[0086] Y=Sigmoid(Z( 3 ))

[0087] Among them, Z (3) is the input value of the output layer, W (3) is the weight matrix of the output layer, which represents the connection strength between the output layer and the second hidden layer. (2) is the input data of the second hidden layer, b (3) is the bias vector, and Y is the output value of the output layer.

[0088] In this embodiment, the mean square error (MSE) is used as the loss function to obtain the error between the predicted result and the actual result. Specifically:

[0089]

[0090] Among them, Y i It's real data. is the predicted data, and m is the number of samples. A stochastic gradient descent optimization strategy is then used to determine the learning rate and gradually adjust the model parameters until the loss function converges or the predetermined number of iterations is reached. This process is repeated to gradually approach the global optimal solution. Throughout the training phase, close attention should be paid to model overfitting, and techniques such as regularization and dropout should be employed to mitigate it as appropriate. Model hyperparameters, such as the learning rate and the number of hidden layer nodes, are continuously adjusted through cross-validation and other methods to optimize model performance. After obtaining the preset temperature prediction model, the preset temperature prediction model is evaluated to obtain an evaluation result. If the evaluation result does not meet the prediction requirements, the preset temperature prediction model is retrained until it meets the control requirements. If the evaluation result meets the prediction requirements, the preset temperature prediction model is used as the final temperature prediction model, and vehicle driving information is analyzed.

[0091] Furthermore, after obtaining the temperature prediction model, the identification parameter model is constructed and trained. The initial identification parameter model is constructed and the battery output current is calculated using the following formula:

[0092]

[0093] Among them U OCV is the open-circuit voltage, R is the equivalent resistance, and I represents the driving or recycling current. The battery charge is updated according to the following formula:

[0094]

[0095] Where dt is the sampling accuracy, Q is the rated capacity of the power battery, which changes with the battery cell temperature and SOH (battery health state); therefore, Q is a fixed value at a specific temperature and time. The battery cell temperature is updated according to the following formula:

[0096]

[0097] Q diss =h env *A env *(T2-T env )+δ*h cool *A cool *(T2-T cool )

[0098]

[0099] where Q Gen is the heat generation power, Q diss is the heat dissipation power, h env is the heat transfer coefficient between the battery and the environment, T emv is the ambient temperature, h cool is the water cooling plate exchange coefficient, Tcool Indicates the coolant temperature, δ represents whether thermal management is turned on. If the battery has no heating, cooling, or heat distribution requirements, the coefficient is 0. C represents the battery specific heat capacity, M represents battery cooling, and k diss1 is the ambient heat dissipation coefficient, k diss2 is the cooling fluid heat dissipation coefficient, g(v,T env ) represents the model compensation coefficient, ρ represents the model compensation start-up permission coefficient, 1 represents permission, and 0 represents non-permission. The battery temperature and power are updated according to the above formula to obtain the trained identification parameter model.

[0100] Furthermore, after obtaining the trained identification parameter model, turn off the thermal management, obtain the maximum cell temperature, minimum cell temperature, voltage, current, SOC, ambient temperature, and vehicle speed data at low temperature, normal temperature, and high temperature, obtain the heat generation correction coefficient of the temperature prediction model parameters and the ambient heat dissipation coefficient through static system identification, then turn on the thermal management, obtain the maximum cell temperature, minimum cell temperature, voltage, current, SOC, ambient temperature, vehicle speed, and battery water inlet temperature data at low temperature, normal temperature, and high temperature, obtain the coolant heat dissipation coefficient through static system identification, obtain the maximum cell temperature, minimum cell temperature, voltage, current, SOC, ambient temperature, vehicle speed, and battery water inlet temperature data at the previous moment, and predict the maximum cell temperature and minimum cell temperature at the current moment based on the trained identification parameter model. If the maximum cell temperature and minimum cell temperature predicted by the model at the current moment are compared with the actual collected maximum cell temperature and minimum cell temperature, as shown in FIG. Figure 2 As shown in the temperature error boundary diagram, if it is within the range of curve 2 and curve 3, then in order to prevent the temperature model from being adjusted too frequently, and within the allowable error range (such as ±2°C), the temperature model prediction value is valid; if the absolute value of the deviation is large, for example, the model prediction value is between curve 1 and curve 2, or between curve 3 and curve 4, the model compensation value is updated according to the current working conditions, and the compensation temperature function is the relationship between vehicle speed and ambient temperature; for example, if the model prediction value is outside curve 1 or curve 4, it means that the cumulative error of the model prediction value is large, and the thermal management state of the whole vehicle or the state of the power battery pack has changed significantly. It is necessary to re-learn the identification parameters for a second time and send an identification request to the cloud. The cloud re-accumulates data for the most recent period (30 days) to re-identify the heat generation correction coefficient, heat dissipation correction coefficient and power battery rated capacity. If the identification parameter model does not meet the requirements, the identification parameter model is adaptively updated according to the compensation value.

[0101] If the vehicle's navigation is enabled, the system inputs sample vehicle driving information into an initial temperature prediction model to construct a first preset temperature prediction model. The sample vehicle driving information includes at least destination information, real-time road condition updates, estimated driving distance, estimated arrival time, and weather forecast data. Based on the judgment result, the vehicle driving information is passed through the first preset temperature prediction model to obtain a predicted battery temperature. Specifically, after determining whether the vehicle has navigation enabled, the system combines this information with other vehicle driving data for in-depth analysis and processing to predict the battery temperature changes during the upcoming trip. If navigation is enabled, the system carefully considers key factors in the navigation data, such as the characteristics of the destination (such as a hospital or shopping mall), the road conditions along the route (highway, national highway, bumpy roads), expected traffic congestion, driving distance, driving mode (such as economy or sport mode), and altitude changes. This information is input into the temperature prediction model, which, based on historical data learning, can identify typical battery temperature rise patterns under different operating conditions, thereby predicting the expected battery temperature changes during the trip.

[0102] If the vehicle's navigation is not enabled, the system determines the date category, first duration, second duration, and third duration based on the vehicle's driving sample information. The date categories include weekdays and weekends. The first duration is the time the vehicle's speed continuously exceeds a preset speed, the second duration is the time spent driving continuously on a highway, and the third duration is the time spent climbing a hill. Classification and statistics are performed based on the date category to determine the frequency and distribution of the first, second, and third durations for each date category. Based on the frequency and distribution patterns, a second preset temperature prediction model is constructed. Based on the determination result, the vehicle's driving information is applied to the second preset temperature prediction model to obtain a predicted battery temperature. Specifically, when navigation is disabled, the system relies on daily learning of the vehicle's driving behavior, particularly focusing on driving habits on weekdays and non-weekdays. For example, the system records and analyzes the vehicle's behavior patterns of continuous highway driving, speeds above 80 km / h, and climbing hills in mountainous areas. The frequency and duration of these behaviors are then input into the temperature prediction model to categorize the battery into corresponding typical operating conditions. By statistically analyzing the distribution patterns of these operating conditions on different dates and then inputting them into the temperature prediction model, we can predict the driving conditions most likely to be encountered on this trip and estimate the battery temperature changes accordingly.

[0103] Step S40 : When the predicted battery temperature meets the preset condition, the thermal management control strategy is executed for processing.

[0104] It should be understood that, under the premise of ensuring user power and safety, by accurately predicting future user power consumption requirements and battery temperature changes, and then intelligently adjusting the power battery thermal management threshold, the goal of saving vehicle energy consumption is achieved. When the vehicle approaches the destination, if the predicted battery temperature is within the safety boundary (below 50°C and above -20°C), the battery thermal management will not be turned on to save overall energy consumption. Conversely, if the battery temperature is predicted to exceed the safety boundary, the battery thermal management will be turned on in advance according to the predicted temperature curve to ensure that the battery temperature is within a safe range when arriving at the destination. Heating can be achieved through heat pump / PTC heating, electric drive waste heat or engine waste heat, while cooling is achieved through air conditioning compressor cooling or front-end module cooling. The timing of turning on or off the thermal management is intelligently adjusted according to the predicted temperature curve to minimize the frequency of thermal management activation, thereby improving the driving range.

[0105] Furthermore, a comparison is performed based on the predicted battery temperatures to obtain a maximum predicted battery temperature and a minimum predicted battery temperature. If the error between the maximum predicted battery temperature and the actual maximum temperature does not exceed a preset value, and the error between the minimum predicted battery temperature and the actual minimum temperature does not exceed a preset value, a predicted temperature curve is constructed based on the temperature prediction model, and thermal management control is performed based on the predicted temperature curve. If the error between the maximum predicted battery temperature and the actual maximum temperature exceeds a preset value, or the error between the minimum predicted battery temperature and the actual minimum temperature exceeds a preset value, the temperature prediction model parameters are compensated and updated to obtain an updated temperature prediction model. Based on the updated temperature prediction model, the temperature is re-predicted and a predicted temperature curve is constructed. Thermal management control is performed based on the predicted temperature curve and the actual temperature. If the accuracy of the prediction model does not meet the preset conditions, a predicted temperature curve from the vehicle to the destination is re-predicted based on the compensated values, and thermal management is intelligently enabled based on the new prediction result. If the model needs to be re-identified, the latest identification parameters are obtained from the cloud platform, and the temperature curve is re-predicted based on these parameters to ensure the accuracy of the thermal management strategy.

[0106] Through such an intelligent temperature control strategy, the present invention can not only effectively prevent battery overheating or overcooling, protect battery health and extend its service life, but also minimize energy consumption during battery thermal management without affecting vehicle performance, thereby improving the overall energy efficiency and driving experience of electric vehicles.

[0107] This embodiment provides a battery thermal management control method. This method obtains vehicle driving information, determines whether navigation is enabled based on the vehicle's driving information, and then predicts the battery temperature based on the vehicle's driving information. When the predicted battery temperature meets preset conditions, a thermal management control strategy is executed. The driving information is input into a model to predict the battery temperature, and thermal management is dynamically adjusted based on the predicted temperature. This improves the accuracy of the predicted temperature, reduces energy consumption, and ensures safety.

[0108] This application also provides a battery thermal management control device, please refer to Figure 3 , the device comprises:

[0109] An acquisition module 10 is used to acquire vehicle driving information;

[0110] A judgment module 20 is used to judge whether the vehicle is turned on for navigation according to the vehicle driving information and obtain a judgment result;

[0111] An acquisition module 10 is used to obtain a predicted battery temperature based on the judgment result and vehicle driving information;

[0112] The control module 30 is configured to execute a thermal management control strategy for processing when the predicted battery temperature meets a preset condition.

[0113] The battery thermal management control device provided in this application, employing the battery thermal management control method of the aforementioned embodiment, can address the technical problem of improving the accuracy of battery temperature prediction and dynamically adjusting the temperature. Compared to the prior art, the battery thermal management control device provided in this application has the same beneficial effects as the battery thermal management control method provided in the aforementioned embodiment. Other technical features of the battery thermal management control device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0114] In one embodiment, the acquisition module 10 is further used to acquire vehicle driving sample information and establish an initial temperature prediction model; the initial temperature prediction model is trained according to the vehicle driving sample information to obtain a preset temperature prediction model.

[0115] In one embodiment, the acquisition module 10 is also used to initialize the model parameters of the initial temperature prediction model; normalize the vehicle driving sample information to obtain processed information; input the processed information into the initial temperature prediction model to obtain the predicted temperature; calculate according to the loss function to obtain the error value between the predicted temperature and the actual temperature; obtain the learning rate; iteratively update the model parameters through the optimization algorithm according to the learning rate until the maximum number of iterations is reached or the error value calculated by the loss function converges to a preset threshold, thereby obtaining a preset temperature prediction model.

[0116] In one embodiment, the acquisition module 10 is further used to evaluate the preset temperature prediction model to obtain an evaluation result; if the evaluation result does not meet the prediction requirements, the preset temperature prediction model is retrained until the control requirements are met; if the evaluation result meets the prediction requirements, the preset temperature prediction model is used as the final temperature prediction model; based on the judgment result and the vehicle driving information, the predicted battery temperature is obtained, including: based on the judgment result, the vehicle driving information is passed through the temperature prediction model to obtain the predicted battery temperature.

[0117] In one embodiment, the acquisition module 10 is further configured to, when determining that the vehicle is in navigation mode, input vehicle driving information into an initial temperature prediction model to construct a first preset temperature prediction model. The vehicle driving information includes at least destination information, real-time road condition updates, estimated driving distance, estimated arrival time, and weather forecast data. The vehicle driving information is then passed through the first preset temperature prediction model to obtain a predicted battery temperature.

[0118] In one embodiment, the acquisition module 10 is further used to obtain a date category, a first duration, a second duration, and a third duration based on the vehicle driving information when the judgment result is that the navigation is not turned on for the vehicle, wherein the date category includes weekdays and weekends, the first duration is the time when the speed continuously exceeds a preset speed, the second duration is the time when the vehicle is continuously driven on a highway, and the third duration is the time when the vehicle is climbing; perform classification statistics according to the date category to obtain the frequency and distribution pattern of the first duration, the second duration, and the third duration under different date categories; construct a second preset temperature prediction model based on the frequency and distribution pattern; and pass the vehicle driving information through the second preset temperature prediction model to obtain a predicted battery temperature.

[0119] In one embodiment, the control module 30 is further used to evaluate the temperature prediction model to obtain a predicted battery temperature; compare the predicted battery temperatures to obtain a maximum predicted battery temperature and a minimum predicted battery temperature; if the error between the maximum predicted battery temperature and the actual maximum temperature does not exceed a preset value and the error between the minimum predicted battery temperature and the actual minimum temperature does not exceed a preset value, then construct a predicted temperature curve according to the temperature prediction model, and perform thermal management control according to the predicted temperature curve; if the error between the maximum predicted battery temperature and the actual maximum temperature exceeds a preset value or the error between the minimum predicted battery temperature and the actual minimum temperature exceeds a preset value, then compensate and update the temperature prediction model parameters to obtain an updated temperature prediction model; based on the updated temperature prediction model, re-predict the temperature and construct a predicted temperature curve; and perform thermal management control according to the predicted temperature curve and the actual temperature.

[0120] The present application provides a battery thermal management control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the battery thermal management control method in the above-mentioned embodiment 1.

[0121] Reference below Figure 4 , which shows a schematic structural diagram of a battery thermal management control device suitable for implementing an embodiment of the present application. The battery thermal management control device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The battery thermal management control device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0122] like Figure 4As shown, the battery thermal management control device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the battery thermal management control device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 can allow the battery thermal management control device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a battery thermal management control device with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0123] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0124] The battery thermal management control device provided in this application, employing the battery thermal management control method of the aforementioned embodiment, can address the technical problem of improving the accuracy of battery temperature prediction and dynamically adjusting the temperature. Compared to the prior art, the battery thermal management control device provided in this application achieves the same beneficial effects as the battery thermal management control method of the aforementioned embodiment. Other technical features of the battery thermal management control device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0125] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0126] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0127] The present application provides a computer-readable medium having computer-readable program instructions (ie, a computer program) stored thereon, and the computer-readable program instructions are used to execute the battery thermal management control method in the above embodiment.

[0128] The computer-readable medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination thereof. More specific examples of computer-readable media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0129] The computer-readable medium may be included in the battery thermal management control device; or may exist independently without being incorporated into the battery thermal management control device.

[0130] The computer-readable medium carries one or more programs. When the one or more programs are executed by the battery thermal management control device, the battery thermal management control device can write computer program codes for performing the operations of the present application in one or more programming languages ​​or a combination thereof. The programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, and also conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0131] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0132] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0133] The computer-readable medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned battery thermal management control method. This computer-readable medium addresses the technical problem of improving the accuracy of battery temperature prediction and dynamically adjusting the temperature. Compared to the prior art, the beneficial effects of the computer-readable medium provided in this application are the same as those of the battery thermal management control method provided in the aforementioned embodiment, and are not further elaborated here.

[0134] The present application also provides a computer program product, including a computer program, which implements the steps of the battery thermal management control method as described above when the computer program is executed by a processor.

[0135] The computer program product provided in this application can solve the technical problem of improving the accuracy of battery temperature prediction and dynamically adjusting the temperature. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the battery thermal management control method provided in the above embodiment, and will not be elaborated here.

[0136] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A battery thermal management control method, characterized in that: The method comprises: obtaining vehicle driving information; Determine whether navigation is turned on for the vehicle based on the vehicle driving information, and obtain a determination result; Obtaining a predicted battery temperature based on the judgment result and the vehicle driving information; When the predicted battery temperature meets a preset condition, executing a thermal management control strategy for processing; Evaluate the preset temperature prediction model and obtain evaluation results; If the evaluation result does not meet the prediction requirements, retrain the preset temperature prediction model until the control requirements are met; If the evaluation result meets the prediction requirements, the preset temperature prediction model is used as the final temperature prediction model; Obtaining a predicted battery temperature based on the judgment result and the vehicle driving information, comprising: applying the vehicle driving information to the temperature prediction model based on the judgment result to obtain a predicted battery temperature; Before the step of applying the vehicle driving information to the temperature prediction model to obtain a predicted battery temperature according to the judgment result, the method further includes: When it is determined that navigation is not enabled on the vehicle, a date category, a first duration, a second duration, and a third duration are obtained based on the vehicle driving information, wherein the date category includes weekdays and weekends, the first duration is a time during which the speed continuously exceeds a preset speed, the second duration is a time during which the vehicle is continuously driven on a highway, and the third duration is a time during which the vehicle is driven uphill; Performing classification statistics according to the date categories to obtain the frequencies and distribution patterns of the first duration, the second duration, and the third duration under different date categories; Constructing a second preset temperature prediction model according to the frequency and distribution law; The vehicle driving information is passed through a second preset temperature prediction model to obtain a predicted battery temperature.

2. The method according to claim 1, wherein Before the step of obtaining a predicted battery temperature based on the judgment result and the vehicle driving information, the method includes: Obtain vehicle driving sample information and establish an initial temperature prediction model; The initial temperature prediction model is trained according to the vehicle driving sample information to obtain a preset temperature prediction model.

3. The method according to claim 2, wherein The step of training the initial temperature prediction model according to the vehicle driving sample information to obtain a preset temperature prediction model includes: Initializing model parameters of the initial temperature prediction model; Normalizing the vehicle driving sample information to obtain processed information; Inputting the processed information into the initial temperature prediction model to obtain a predicted temperature; According to the loss function calculation, the error value between the predicted temperature and the actual temperature is obtained; Get the learning rate; The model parameters are iteratively updated through an optimization algorithm according to the learning rate until a maximum number of iterations is reached or the error value calculated by the loss function converges to a preset threshold, thereby obtaining a preset temperature prediction model.

4. The method according to claim 3, wherein The step of applying the vehicle driving information to the temperature prediction model to obtain a predicted battery temperature according to the judgment result includes: When the result of the determination is that the vehicle is navigating, inputting vehicle driving information into the initial temperature prediction model to construct a first preset temperature prediction model, wherein the vehicle driving information includes at least destination information, real-time updates of road conditions, estimated driving distance, estimated arrival time, and weather forecast data; The vehicle driving information is passed through a first preset temperature prediction model to obtain a predicted battery temperature.

5. The method according to any one of claims 1 to 4, characterized in that After the step of obtaining the predicted battery temperature based on the judgment result and the vehicle driving information, the method further includes: evaluating the temperature prediction model to obtain a predicted battery temperature; Comparing the predicted battery temperatures to obtain a maximum predicted battery temperature and a minimum predicted battery temperature; If the error between the maximum predicted battery temperature and the actual maximum temperature does not exceed a preset value and the error between the minimum predicted battery temperature and the actual minimum temperature does not exceed a preset value, constructing a predicted temperature curve according to the temperature prediction model and performing thermal management control according to the predicted temperature curve; If the error between the maximum predicted battery temperature and the actual maximum temperature exceeds a preset value or the error between the minimum predicted battery temperature and the actual minimum temperature exceeds a preset value, the temperature prediction model parameters are compensated and updated to obtain an updated temperature prediction model; Re-predicting the temperature and constructing a predicted temperature curve based on the updated temperature prediction model; Thermal management control is performed according to the predicted temperature curve and the actual temperature.

6. A battery thermal management control device, characterized in that: The device comprises: An acquisition module is used to obtain vehicle driving information; A judgment module, configured to judge whether the vehicle is turned on for navigation according to the vehicle driving information, and obtain a judgment result; an acquisition module, configured to obtain a predicted battery temperature based on the judgment result and the vehicle driving information; The acquisition module is also used to evaluate the preset temperature prediction model and obtain the evaluation results; If the evaluation result does not meet the prediction requirements, retrain the preset temperature prediction model until the control requirements are met; If the evaluation result meets the prediction requirements, the preset temperature prediction model is used as the final temperature prediction model; Obtaining a predicted battery temperature based on the judgment result and the vehicle driving information, comprising: applying the vehicle driving information to the temperature prediction model based on the judgment result to obtain a predicted battery temperature; The acquisition module is further configured to, when a result of determining that navigation is not enabled on the vehicle, obtain a date category, a first duration, a second duration, and a third duration based on the vehicle driving information, wherein the date category includes weekdays and weekends, the first duration is a time during which a speed continuously exceeds a preset speed, the second duration is a time during which the vehicle is continuously driven on a highway, and the third duration is a time during which the vehicle is driven uphill; Performing classification statistics according to the date categories to obtain the frequencies and distribution patterns of the first duration, the second duration, and the third duration under different date categories; Constructing a second preset temperature prediction model according to the frequency and distribution law; Applying the vehicle driving information to a second preset temperature prediction model to obtain a predicted battery temperature; The control module is configured to execute a thermal management control strategy for processing when the predicted battery temperature meets a preset condition.

7. A battery thermal management control device, characterized in that: The device includes: a memory, a processor, and a battery thermal management control program stored in the memory and executable on the processor, wherein the battery thermal management control program is configured to implement the steps of the battery thermal management control method according to any one of claims 1 to 5.

8. A medium, characterized in that The medium stores a battery thermal management control program, which, when executed by a processor, implements the steps of the battery thermal management control method according to any one of claims 1 to 5.

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