Thermal management control method and device, electronic equipment and vehicle

Abstract

The invention relates to a thermal management control method and device, electronic equipment and a vehicle, relates to the technical field of vehicle control, and at least solves the technical problem that the accuracy of thermal management of a power battery of a vehicle is poor in the related art. The long-distance journey is a journey with a journey distance greater than a preset distance; under the condition that the current journey is a long journey, based on historical operation data of the vehicle, heat management parameters of the current journey are predicted, and the heat management parameters are used for controlling operation temperature parameters of a power battery of the vehicle; based on the real-time operation data of the vehicle, the thermal management parameters are adjusted; and performing thermal management control on the vehicle based on the thermal management parameters after parameter adjustment. The thermal management method and device are used for improving the thermal management accuracy of the power battery of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, specifically to a thermal management control method, device, electronic equipment, and vehicle. Background Technology

[0002] With the rapid development of new energy vehicle technology, battery thermal management technology has become a core technology for ensuring the safety of power batteries. Thermal management of power batteries is essential to ensure efficient vehicle operation and prevent overheating that could affect battery life and safety.

[0003] In related technologies, an energy management strategy is determined based on external environmental information (such as road conditions and traffic information) and internal vehicle status information. This strategy is then used to adjust vehicle operating parameters (such as motor drive strategy and kinetic energy recovery strategy) to optimize energy efficiency under the current driving conditions. However, during vehicle operation, many parameters affect the thermal management of the power battery, and relying solely on the parameters discussed in these technologies results in poor accuracy in thermal management.

[0004] Another related technology determines whether navigation is activated based on vehicle driving information, and then uses a temperature prediction model to predict the battery temperature based on this information, thereby performing thermal management of the battery. However, this model-based method of predicting battery temperature involves a large computational load and has poor real-time performance, making it difficult to determine timely thermal management methods. Furthermore, this technology involves fewer parameters when predicting battery temperature, failing to provide comprehensive thermal management based on a complete set of relevant parameters. Therefore, current methods for thermal management of vehicle power batteries have relatively poor accuracy. Summary of the Invention

[0005] This application provides a thermal management control method, device, electronic device, and vehicle. The purpose of this application is to at least solve the technical problem of poor accuracy in thermal management of vehicle power batteries in related technologies.

[0006] To achieve the above objectives, the technical solution adopted in this application is as follows:

[0007] According to a first aspect provided in this application, a thermal management control method is provided, comprising: identifying whether the current trip of a vehicle is a long-distance trip, wherein a long-distance trip is a trip with a distance greater than a preset distance; if the current trip is a long-distance trip, predicting thermal management parameters for the current trip based on the vehicle's historical operating data, wherein the thermal management parameters are parameters used to control the operating temperature of the vehicle's power battery; adjusting the thermal management parameters based on the vehicle's real-time operating data; and performing thermal management control on the vehicle based on the adjusted thermal management parameters.

[0008] Based on the aforementioned technical means, when this application identifies that the vehicle's current journey is a long-distance trip, it can first predict the thermal management parameters for the current journey based on the vehicle's historical operating data. Thus, the predicted thermal management parameters can be used to perform thermal management on the vehicle's power battery during the current journey. Furthermore, during vehicle operation, the thermal management parameters are adjusted based on the vehicle's real-time operating data, and thermal management control of the vehicle's power battery is then performed based on these adjusted parameters. In this way, by combining predicted thermal management parameters with real-time operating data, the thermal management parameters can be adjusted in real time during vehicle operation, thereby improving the accuracy of thermal management of the vehicle's power battery.

[0009] In one possible implementation, identifying whether the vehicle's current trip is a long-distance trip includes: if navigation information for the current trip is obtained, identifying whether the current trip is a long-distance trip based on the distance between the vehicle's current location and the destination in the navigation information; or, if navigation information is not obtained, identifying whether the current trip is a long-distance trip based on the vehicle's historical trip information and historical parking location information.

[0010] Based on the aforementioned technical means, this application can directly determine whether the current trip is a long-distance trip based on the navigation information obtained. If navigation information is not obtained, it can further predict whether the current trip is a long-distance trip based on the vehicle's historical trip information and historical parking location information. Thus, by accurately determining whether the current trip is a long-distance trip, it is possible to accurately determine whether thermal management of the vehicle's power battery is required during the current trip.

[0011] In one possible implementation, the historical trip information includes: the start time and end time of the historical trip; the above-mentioned identification of whether the current trip is a long-distance trip based on the vehicle's historical trip information and historical parking location information includes: determining the vehicle's travel time period based on the historical trip start time and historical trip end time; determining the vehicle's travel area based on the historical parking location information; and identifying whether the current trip is a long-distance trip based on the travel time period, travel area, and the vehicle's current location.

[0012] Based on the aforementioned technical means, this application can determine the daily travel time of a vehicle by using the historical trip start and end times. Furthermore, it can determine the daily travel area of ​​the vehicle based on historical parking location information. Thus, based on the vehicle's daily travel time and area, the destination of the vehicle's current trip can be predicted, and by combining this with the vehicle's current location, it can be accurately determined whether the current trip is a long-distance trip.

[0013] In one possible implementation, the above-mentioned determination of the vehicle's travel area based on historical parking location information includes: dividing multiple historical parking locations in the historical parking location information into multiple clusters based on a preset aggregation radius; and determining the vehicle's travel area based on the location information of the multiple clusters.

[0014] Based on the aforementioned technical means, this application can aggregate multiple historical parking locations from historical parking location information based on a preset aggregation radius to obtain multiple clusters. Thus, by using the location information of multiple clusters, the location distribution of frequently visited destinations of vehicles can be determined. Then, by analyzing the location information of multiple clusters, the travel areas of vehicles can be accurately predicted.

[0015] In one possible implementation, determining the vehicle's travel area based on the location information of multiple clusters includes: identifying at least one cluster from the multiple clusters whose aggregation density is greater than or equal to a preset density; and determining the area corresponding to the location information of the at least one cluster as the vehicle's travel area.

[0016] Based on the aforementioned technical means, this application determines at least one cluster with an aggregation density greater than or equal to a preset density from multiple clusters. Then, based on the aggregation density of historical parking locations included in each of the multiple clusters, the area where the vehicle's most frequently traveled destination is located can be determined from the multiple clusters. Thus, based on the area corresponding to the location information of the selected at least one cluster, the vehicle's travel area can be accurately determined.

[0017] In one possible implementation, the above-mentioned identification of whether the current trip is a long-distance trip based on the travel time period, travel area, vehicle's current location, and current time includes: predicting the destination of the current trip based on the travel time period, travel area, current location, and current time; and identifying whether the current trip is a long-distance trip based on the travel distance between the vehicle's current location and the predicted destination of the current trip.

[0018] Based on the aforementioned technical means, this application analyzes the travel time period, travel area, current location, and current time to determine the destination matching the current location and current time, which is then used as the predicted destination for the current trip. Furthermore, by determining the distance between the vehicle's current location and the predicted destination, it is possible to accurately identify whether the current trip is a long-distance trip.

[0019] In one possible implementation, the historical operating data includes: historical driving data, historical environmental data, and historical cell temperature data of the battery pack; the above-mentioned prediction of thermal management parameters for the current trip based on the vehicle's historical operating data includes: determining the historical temperature distribution characteristics of the battery pack based on the historical cell temperature data; determining the historical driving habit characteristics of the vehicle based on the historical driving data; fusing the historical temperature distribution characteristics, historical driving habit characteristics, and historical environmental data to obtain historical fused data; and predicting the thermal management parameters for the current trip based on the historical fused data.

[0020] Based on the aforementioned technical means, this application can determine the historical temperature distribution characteristics of the battery pack by analyzing historical cell temperature data. Furthermore, it can determine the historical driving habit characteristics of the vehicle by analyzing historical driving data. By fusing historical temperature distribution characteristics, historical driving habit characteristics, and historical environmental data, multi-dimensional historical data can be integrated to obtain fused data. Based on this fused data, the thermal management parameters for the current journey can be accurately predicted, building upon the multi-dimensional historical data.

[0021] In one possible implementation, the method further includes: before predicting the thermal management parameters of the current trip based on the vehicle's historical operating data, aligning the historical cell temperature data, various data from historical driving data, and various data from historical environmental data by interpolation; wherein the historical driving data includes at least one of the following: driving speed, vehicle power, battery pack state of charge, and motor torque; and the historical environmental data includes at least one of the following: ambient temperature, ambient humidity, and altitude.

[0022] Based on the aforementioned technical means, this application can pre-align historical cell temperature data, historical driving data, and historical environmental data through interpolation. This allows for more accurate and efficient data processing based on the aligned data during subsequent data processing, thereby improving the accuracy and efficiency of predicting thermal management parameters for the current journey.

[0023] In one possible implementation, the real-time operating data includes at least one of the following: real-time driving data, real-time environmental data, and real-time cell temperature data of the battery pack; the above-mentioned adjustment of thermal management parameters based on the vehicle's real-time operating data includes: determining the real-time thermal management parameters for the current trip based on the real-time driving data, real-time environmental data, and real-time cell temperature data; and adjusting the thermal management parameters to the real-time thermal management parameters.

[0024] Based on the aforementioned technical means, during the operation of the vehicle based on the predicted thermal management parameters, more accurate real-time thermal management parameters can be determined based on the real-time driving data, real-time environmental data, and real-time cell temperature data generated during vehicle operation. This allows for more accurate control of vehicle operation based on the more accurate real-time thermal management parameters, enabling more precise thermal management of the power battery and further improving the accuracy of thermal management.

[0025] According to a second aspect of this application, a thermal management control device is provided, comprising: an identification module, a prediction module, a processing module, and a control module; the identification module is used to identify whether the current trip of the vehicle is a long-distance trip, wherein a long-distance trip is a trip with a distance greater than a preset distance; the prediction module is used to predict the thermal management parameters of the current trip based on the historical operating data of the vehicle, wherein the thermal management parameters are parameters used to control the operating temperature of the vehicle's power battery; the processing module is used to adjust the thermal management parameters based on the real-time operating data of the vehicle; and the control module is used to perform thermal management control on the vehicle based on the adjusted thermal management parameters.

[0026] In one possible implementation, the identification module is specifically used to identify whether the current trip is a long-distance trip based on the distance between the vehicle's current location and the destination in the navigation information when the navigation information of the current trip is obtained; or, the identification module is specifically used to identify whether the current trip is a long-distance trip based on the vehicle's historical trip information and historical parking location information when the navigation information is not obtained.

[0027] In one possible implementation, the historical trip information includes: the start time and end time of the historical trip; the processing module is further configured to determine the travel time period of the vehicle based on the start time and end time of the historical trip; the processing module is further configured to determine the travel area of ​​the vehicle based on historical parking location information; and the identification module is specifically configured to identify whether the current trip is a long-distance trip based on the travel time period, travel area, current location of the vehicle, and current time.

[0028] In one possible implementation, the processing module is specifically used to divide multiple historical parking locations in the historical parking location information into multiple clusters based on a preset aggregation radius; the processing module is specifically used to determine the travel area of ​​the vehicle based on the location information of the multiple clusters.

[0029] In one possible implementation, the processing module is specifically configured to determine at least one cluster from multiple clusters whose aggregation density is greater than or equal to a preset density; the processing module is specifically configured to determine the area corresponding to the location information of the at least one cluster as the travel area of ​​the vehicle.

[0030] In one possible implementation, the prediction module is further configured to predict the destination of the current trip based on the travel time period, travel area, current location, and current time; the identification module is specifically configured to identify whether the current trip is a long-distance trip based on the travel distance between the vehicle's current location and the predicted destination of the current trip.

[0031] In one possible implementation, the historical operating data includes: historical driving data, historical environmental data, and historical cell temperature data of the battery pack; the processing module is further configured to determine historical temperature distribution characteristic data of the battery pack based on the historical cell temperature data; the processing module is further configured to determine historical driving habit characteristic data of the vehicle based on the historical driving data; the processing module is further configured to fuse the historical temperature distribution characteristic data, historical driving habit characteristic data, and historical environmental data to obtain historical fused data; and the prediction module is specifically configured to predict the thermal management parameters of the current trip based on the historical fused data.

[0032] In one possible implementation, the processing module is further configured to, before predicting the thermal management parameters of the current trip based on the vehicle's historical operating data, align the historical cell temperature data, various data from the historical driving data, and various data from the historical environmental data by interpolation; wherein, the historical driving data includes at least one of the following: driving speed, vehicle power, battery pack state of charge, and motor torque; and the historical environmental data includes at least one of the following: ambient temperature, ambient humidity, and altitude.

[0033] In one possible implementation, the real-time operating data includes at least one of the following: real-time driving data, real-time environmental data, and real-time cell temperature data of the battery pack; the processing module is specifically used to determine the real-time thermal management parameters for the current trip based on the real-time driving data, real-time environmental data, and real-time cell temperature data; the processing module is specifically used to adjust the thermal management parameters to real-time thermal management parameters.

[0034] According to a third aspect provided in this application, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the first aspect described above and any possible implementation thereof.

[0035] According to a fourth aspect provided in this application, a computer-readable storage medium is provided that, when computer-executable instructions stored in the computer-readable storage medium are executed by a processor of an electronic device, causes the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0036] According to the fifth aspect provided in this application, a computer program product is provided, the computer program product including computer instructions, which, when executed on an electronic device, cause the electronic device to perform the method described in the first aspect and any possible implementation thereof.

[0037] According to a sixth aspect provided in this application, a vehicle is provided, the vehicle including a thermal management control device as described in the second aspect, the vehicle being used to implement the method described in the first aspect and any possible implementation thereof.

[0038] It should be noted that the technical effects of any of the implementation methods in aspects two through six can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here.

[0039] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0041] Figure 1 This is a schematic diagram of the structure of a thermal management control system according to an exemplary embodiment;

[0042] Figure 2 This is a flowchart illustrating a thermal management control method according to an exemplary embodiment;

[0043] Figure 3 This is a flowchart illustrating yet another thermal management control method according to an exemplary embodiment;

[0044] Figure 4 This is a flowchart illustrating yet another thermal management control method according to an exemplary embodiment;

[0045] Figure 5 This is a flowchart illustrating yet another thermal management control method according to an exemplary embodiment;

[0046] Figure 6 This is a flowchart illustrating yet another thermal management control method according to an exemplary embodiment;

[0047] Figure 7 This is a flowchart illustrating yet another thermal management control method according to an exemplary embodiment;

[0048] Figure 8 This is a flowchart illustrating yet another thermal management control method according to an exemplary embodiment;

[0049] Figure 9 This is a block diagram illustrating a thermal management control device according to an exemplary embodiment;

[0050] Figure 10 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0051] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0052] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0053] Currently, vehicle thermal management can be achieved by setting temperature thresholds based on experience, activating fans or liquid cooling systems to lower the battery temperature when it exceeds the threshold. Alternatively, mathematical models can calculate optimal energy consumption and pre-plan cooling / heating strategies for battery thermal management. Artificial intelligence can also be used to learn from historical vehicle data, predicting temperature changes in real time and adjusting thermal management control strategies accordingly. However, these approaches have several drawbacks: experience-based strategies tend to become rigid, and extreme situations (such as sudden fast charging of the battery) can cause a rapid rise in battery temperature, leading to thermal management failure; while mathematical models provide accurate results, they are computationally intensive and lack real-time performance, making them unsuitable for handling unexpected situations; and artificial intelligence methods rely heavily on training data and may overlook the long-term effects of battery aging and environmental changes, causing the strategies to gradually deviate from actual needs.

[0054] The thermal management control method provided in this application embodiment can be applied to thermal management control systems. For example... Figure 1 As shown, the thermal management control system includes: controller 11, power battery 12 and database 13.

[0055] Among them, the controller 11 can be a vehicle controller or a battery controller, and the database 13 is used to store the vehicle's historical operating data and real-time operating data.

[0056] Specifically, the controller 11 is used to identify whether the vehicle's current trip is a long-distance trip, which is a trip with a distance greater than a preset distance.

[0057] The controller 11 is also used to predict the thermal management parameters of the current trip based on the vehicle's historical operating data when the current trip is a long trip. The thermal management parameters are parameters used to control the operating temperature of the vehicle's power battery 12.

[0058] The controller 11 is also used to adjust the thermal management parameters based on the vehicle's real-time operating data; and to perform thermal management control on the vehicle based on the adjusted thermal management parameters.

[0059] For ease of understanding, the thermal management control method provided in this application will be described in detail below with reference to the accompanying drawings. Figure 2 As shown, the thermal management control methods include S201-S204:

[0060] S201. Identify whether the vehicle's current trip is a long-distance trip.

[0061] Long-distance trips are those with a distance greater than the preset distance.

[0062] In this embodiment of the application, when a user travels using a vehicle, thermal management of the power battery is required during vehicle operation to regulate the operating temperature of the power battery, so that the power battery operates within the optimal temperature range and avoids the power battery's performance from being too high or too low.

[0063] It should be noted that the temperature of the power battery gradually increases with the distance (or duration) the vehicle travels. Therefore, when the vehicle travels a short distance, the power battery temperature will not be too high, and thermal management of the power battery is not required for short trips. However, thermal management of the power battery is required for long trips. Thus, it is necessary to first determine whether the vehicle's current trip is a long trip in order to determine whether thermal management of the power battery is required.

[0064] In one possible implementation, the trip can be classified as long-distance or short-distance based on the relationship between the travel distance and a preset distance. A trip greater than the preset distance is considered a long-distance trip, while a trip less than or equal to the preset distance is considered a short-distance trip. The preset distance can be determined based on the relationship between the vehicle's travel distance and the battery temperature. Specifically, when the vehicle has traveled the preset distance, the battery temperature can rise to a temperature value requiring thermal management (i.e., when the vehicle has traveled the preset distance, the battery temperature reaches the upper limit of the optimal temperature range, at which point the battery temperature needs to be reduced).

[0065] S202. When the current trip is a long-distance trip, predict the thermal management parameters of the current trip based on the vehicle's historical operating data.

[0066] Among them, thermal management parameters are parameters used to control the operating temperature of the vehicle's power battery.

[0067] It should be noted that during the current journey, when the vehicle has just started running, real-time operating data is not yet available, making it impossible to accurately determine the thermal management parameters for the power battery. Therefore, the thermal management parameters for the current journey can be predicted using the vehicle's historical operating data, and the power battery can be thermally managed based on these predicted parameters when the vehicle begins operation.

[0068] In some embodiments, historical operating data includes: historical driving data, historical environmental data, and historical cell temperature data of the battery pack.

[0069] In one possible implementation, when the current trip is a long-distance trip, historical operating data corresponding to the vehicle's operation during a historical time period can be obtained. Since the historical operating data obtained at this time is based on basic data acquired by sensors, the basic data needs to be preprocessed (e.g., data alignment, data missing data handling) before historical driving data, historical environmental data, and historical cell temperature data of the battery pack can be obtained.

[0070] In one possible implementation, because different sensors collect data at different frequencies, the acquired historical driving data, historical environmental data, and historical cell temperature data of the battery pack have different data frequencies. In this case, it is necessary to perform data alignment processing on the historical driving data, historical environmental data, and historical cell temperature data of the battery pack to make their frequencies the same.

[0071] In some embodiments, before predicting the thermal management parameters for the current trip based on the vehicle's historical operating data, the historical cell temperature data, various data from historical driving data, and various data from historical environmental data are aligned by interpolation.

[0072] Historical driving data includes at least one of the following: driving speed, vehicle power, battery pack state of charge, and motor torque; historical environmental data includes at least one of the following: ambient temperature, ambient humidity, and altitude.

[0073] In one possible implementation, dynamic time warping and sliding window compensation can be used to align historical driving data, historical environmental data, and historical cell temperature data of the battery pack to obtain aligned spatiotemporal data, which is then stored on a cloud server.

[0074] In one possible implementation, since the historical driving data, historical environmental data, and historical cell temperature data of the battery pack have different data frequencies, when aligning the historical driving data, historical environmental data, and historical cell temperature data of the battery pack, the cumulative distance matrix of the high-frequency sampling sequence and the low-frequency sampling sequence can be calculated by Dynamic Time Warping (DTW), the minimum cost path can be traced back, the low-frequency data can be interpolated and resampled, and the time axis can be forcibly aligned.

[0075] For example, a high-frequency sequence X = {x1, x2, ..., x...} m (For example, it could be current data with a frequency of 10Hz), the low-frequency sequence Y = {y1, y2, ..., y...} n} (For example, temperature data with a frequency of 1Hz), where m is greater than n. Then, construct the cumulative distance matrix and calculate the Euclidean distance matrix D and the cumulative distance matrix C between each point in the high-frequency and low-frequency sequences. The Euclidean distance matrix D is: D(i,j)=(x i -y j ) 2 The cumulative distance matrix C is: C(i,j)=D(i,j)+min(C(i-1,j),C(i,j-1),C(i-1,j-1)). C(i,j) represents the minimum cumulative cost from the starting point to position (i,j), D(i,j) represents the cost of position (i,j) itself (such as weights in the grid, pixel differences, etc.), and min(C(i-1,j),C(i,j-1),C(i-1,j-1)) represents the minimum cost path from the three possible predecessor positions to (i,j).

[0076] Furthermore, resampling and alignment are performed by interpolating the low-frequency sequence Y according to path P to generate an aligned low-frequency sequence Y. ， ={y j1 ,y j2 ,...,y jn Finally, the high-frequency sequence X and the aligned low-frequency sequence Y... ， They have the same timestamp.

[0077] In one possible implementation, a sliding window compensation method can be used to fill in the delayed data by forward interpolation within the sliding window for data timestamp deviations caused by network latency, and the historical window can be reversed and smoothed to dynamically adjust the window size to adapt to latency fluctuations.

[0078] Specifically, when processing delayed data using forward interpolation, if the data d t-Δ If the arrival time is delayed (Δ > 0), then the difference data is determined. With Insert at the corresponding position in the buffer.

[0079] In one possible implementation, historical errors can also be corrected through reverse correction, affecting the data {d} within the window. t-Δ+1 ,...,d t Perform linear smoothing to obtain the processed result. Furthermore, the adjustment range is limited to within the sensor's accuracy range. Therefore, during dynamic window adjustments, the window size is adaptively adjusted based on network latency statistics.

[0080] In one possible implementation, thermal management parameters may include: heating power of positive temperature coefficient (PTC) materials, heat pump mode power, refrigerant flow rate, coolant inlet temperature, etc.

[0081] In this application embodiment, the application can pre-process the historical cell temperature data, historical driving data and historical environmental data by interpolation, so that when processing these data in the future, more accurate and efficient data processing can be performed based on the aligned data, thereby improving the accuracy and efficiency of predicting the thermal management parameters of the current trip.

[0082] S203. Adjust thermal management parameters based on real-time vehicle operating data.

[0083] S204. Based on the adjusted thermal management parameters, perform thermal management control on the vehicle.

[0084] It should be noted that after thermal management of the power battery is performed based on the predicted thermal management parameters when the vehicle starts running, real-time operating data of the vehicle can be obtained in real time based on the vehicle's driving. This allows for the determination of more accurate thermal management parameters based on more accurate real-time operating data, enabling more accurate thermal management of the power battery during the subsequent journey of the vehicle.

[0085] In some embodiments, real-time operating data includes at least one of the following: real-time driving data, real-time environmental data, and real-time cell temperature data of the battery pack. Based on the vehicle's real-time operating data, adjusting thermal management parameters includes: determining real-time thermal management parameters for the current trip based on real-time driving data, real-time environmental data, and real-time cell temperature data; and adjusting the thermal management parameters to the real-time thermal management parameters.

[0086] In one possible implementation, real-time thermal management parameters can be determined based on real-time driving data, real-time environmental data, and real-time cell temperature data. These real-time thermal management parameters are then compared with predicted thermal management parameters to determine the error between them. The control parameters for thermal management are then dynamically adjusted using a gradient descent approach, enabling thermal management of the power battery during subsequent vehicle travel based on these real-time thermal management parameters.

[0087] In one possible implementation, the determined real-time thermal management parameters can be packaged into structured data, and then control commands containing the real-time thermal management parameters can be sent to the vehicle via the vehicle communication module to perform thermal management of the power battery during the subsequent journey of the vehicle.

[0088] In this way, by combining real-time operating data (i.e., real-time driving data, real-time environmental data, and real-time battery cell temperature data) with historical operating data (i.e., historical driving data, historical environmental data, and historical battery cell temperature data), the predicted thermal management parameters can be dynamically adjusted, which can improve the system's adaptability and robustness to complex operating conditions. Finally, the adjusted thermal management parameters are output to perform thermal management control on the vehicle.

[0089] In this embodiment of the application, during the operation of the vehicle based on the predicted thermal management parameters, more accurate real-time thermal management parameters can be determined based on the real-time driving data, real-time environmental data and real-time cell temperature data generated during vehicle operation. Thus, the vehicle operation can be controlled based on more accurate real-time thermal management parameters, and the power battery can be thermally managed more accurately, thereby further improving the accuracy of thermal management.

[0090] In this embodiment, when the vehicle's current journey is identified as a long-distance trip, the thermal management parameters for the current journey can be predicted based on the vehicle's historical operating data. Thus, the predicted thermal management parameters can be used to manage the vehicle's power battery during the current journey. Furthermore, during vehicle operation, the thermal management parameters are adjusted based on real-time operating data, and the adjusted parameters are used to control the thermal management of the vehicle's power battery. In this way, by combining predicted thermal management parameters with real-time operating data, the thermal management parameters can be adjusted in real time during vehicle operation, thereby improving the accuracy of thermal management of the vehicle's power battery.

[0091] In some embodiments, such as Figure 3 As shown, the above S201 may specifically include S301 or S302:

[0092] S301. When the navigation information of the current trip is obtained, the system identifies whether the current trip is a long-distance trip based on the distance between the vehicle's current location and the destination in the navigation information.

[0093] In one possible implementation, when a user has travel needs, they typically set navigation information on the vehicle's infotainment system, specifying the destination and route for the current trip. Therefore, when applying navigation information for the current trip, the system can directly determine whether the current trip is a long-distance trip based on the distance between the vehicle's current location and the destination in the navigation information.

[0094] If the user has not set navigation information on the vehicle's infotainment system, the destination and distance of the current trip cannot be directly determined, making it impossible to determine whether the current trip is a long-distance trip. In this case, the destination and distance of the current trip can be predicted based on the vehicle's historical trip information and historical parking location information, thereby predicting whether the current trip is a long-distance trip.

[0095] S302. In the absence of navigation information, identify whether the current trip is a long-distance trip based on the vehicle's historical trip information and historical parking location information.

[0096] The historical itinerary information includes: the start time of the historical itinerary and the end time of the historical itinerary.

[0097] In one possible implementation, when a user needs to travel by vehicle, the system can obtain the vehicle's historical trip information and historical parking location information within a historical time period, as well as the vehicle's current location and current time (i.e., the current point in time). The historical trip information can include the historical trip start time and historical trip end time for each trip within the historical time period, as well as the departure point, destination, route, vehicle start and stop times, vehicle location information when parked, and parking duration for each trip.

[0098] Therefore, based on the vehicle's historical trip information and historical parking location information, the destination of the current trip can be predicted, and the distance between the destination and the vehicle's current location can be determined, thus determining whether the current trip is a long-distance trip.

[0099] In this embodiment, if navigation information for the current trip is available, the application can directly determine whether the current trip is a long-distance trip based on the navigation information. If navigation information is not available, it can further predict whether the current trip is a long-distance trip based on the vehicle's historical trip information and historical parking location information. Thus, by accurately determining whether the current trip is a long-distance trip, it is possible to accurately determine whether thermal management of the vehicle's power battery is required during the current trip.

[0100] In some embodiments, such as Figure 4 As shown, S302 can specifically include S401-S403:

[0101] S401. In the absence of navigation information, determine the vehicle's travel time period based on the historical trip start time and historical trip end time.

[0102] In one possible implementation, the user's daily travel time (i.e., the vehicle's travel time period) can be determined by analyzing the historical start and end times of each trip taken by the vehicle within a historical time period.

[0103] Specifically, based on the start and end times of historical trips, smoothing functions such as Gaussian kernels can be used to calculate the probability density distribution of travel time points, thereby obtaining the user's daily travel time (i.e., travel time period) and storing it to the cloud server.

[0104] For example, as shown in Formula 1, the probability density distribution of travel time points can be determined by Formula 1, and the user's daily travel time can be determined based on the local maximum point of the probability density function.

[0105]

[0106] in, Let represent the density estimate at point t, where n represents the sample size (i.e., the total number of historical start and end times of the journey), and t i Let represent the i-th sample, h represent the bandwidth, which controls the smoothness of the estimate and is a key parameter for kernel density estimation, and K() represent the kernel function (e.g., Gaussian kernel function), which satisfies the conditions of non-negativity and integral of 1, and is used to weight the sample points.

[0107] S402. Determine the vehicle's travel area based on historical parking location information.

[0108] In one possible implementation, the vehicle's historical parking location information can be determined based on the departure and destination locations corresponding to each trip of the vehicle in a historical time period. By analyzing the vehicle's historical parking location information, the user's place of residence and daily travel destinations (i.e., the vehicle's travel area) can be determined.

[0109] Furthermore, by combining the user's daily travel time (i.e., the vehicle's travel time period) and daily travel destination (i.e., the vehicle's travel area), the user's daily travel habits can be determined, that is, the user's daily travel destination and travel distance (i.e., when and where the user goes each day).

[0110] In one possible implementation, a density-based clustering algorithm (DBSCAN) is established based on historical parking location information to obtain the user's daily travel area and store it on a cloud server.

[0111] In some embodiments, S402 may specifically include: dividing multiple historical parking locations in the historical parking location information into multiple clusters based on a preset aggregation radius; and determining the travel area of ​​the vehicle based on the location information of the multiple clusters.

[0112] In some embodiments, determining the travel area of ​​a vehicle based on the location information of multiple clusters includes: identifying at least one cluster from the multiple clusters whose aggregation density is greater than or equal to a preset density; and determining the area corresponding to the location information of the at least one cluster as the travel area of ​​the vehicle.

[0113] Specifically, by selecting a certain mileage range (i.e., a preset aggregation radius, such as 1 or 2) as the radius for density aggregation, the minimum number of data points (i.e., historical parking location information) around a given location is selected to determine the core point. Points whose distance to the core point is less than the preset aggregation radius are considered outliers. Further, a distance calculation metric is selected to determine the density threshold (i.e., the preset density). Then, using the density reachability characteristics of the DBSCAN algorithm and the vehicle's current location information, several daily travel areas are aggregated according to the activity radius.

[0114] In this embodiment, the application can aggregate multiple historical parking locations based on a preset aggregation radius to obtain multiple clusters. Thus, by using the location information of multiple clusters, the location distribution of frequently visited destinations of the vehicle can be determined. Then, by analyzing the location information of the multiple clusters, the vehicle's travel area can be accurately predicted.

[0115] In this embodiment of the application, by determining at least one cluster with an aggregation density greater than or equal to a preset density from multiple clusters, the application can determine the area where the vehicle's most frequently traveled destination is located from multiple clusters based on the aggregation density of historical parking locations included in each cluster. Thus, based on the area corresponding to the location information of the selected at least one cluster, the vehicle's travel area can be accurately determined.

[0116] S403. Based on the travel time period, travel area, vehicle's current location, and current time, identify whether the current trip is a long-distance trip.

[0117] In this embodiment, the vehicle's daily travel time can be determined based on the historical trip start and end times. Furthermore, the vehicle's daily travel area can be determined based on historical parking location information. Thus, based on the vehicle's daily travel time and area, the destination of the vehicle's current trip can be predicted, and by combining this with the vehicle's current location, it can be accurately determined whether the current trip is a long-distance trip.

[0118] In some embodiments, such as Figure 5 As shown, the above S403 may specifically include S501-S502:

[0119] S501. Based on the travel time period, travel area, current location, and current time, predict the destination of the current trip.

[0120] In one possible implementation, a daily travel preference model based on the travel time period and travel area is established using a distributed gradient boosting library (XGBoost) to predict the destination of the current trip and the corresponding travel distance, and then stored on a cloud server.

[0121] Specifically, features are first constructed, including temporal features (e.g., the time interval since the last trip), spatial features (e.g., the number of trips to a certain travel area within a certain time period), and time series features (e.g., the rate of change of destination / travel area direction for n trips). Then, a model is built based on these features to obtain the objective function (e.g., a loss function with regularization). For example, the objective function is shown in Equation 2.

[0122]

[0123] Where θ represents the model parameters (such as weights, biases, etc.), n represents the number of samples (i.e., travel time period, travel area), and y i This represents the true label of the i-th sample. This represents the predicted value of the i-th sample. γT represents the loss function (such as cross-entropy, mean squared error, etc.), which measures the difference between the predicted and actual values. γT represents the task-related regularization term, where T may represent model complexity, number of features, or other penalty terms, and γ is the corresponding weight coefficient. The term represents the L2 regularization term (also known as weight decay), ‖w‖ 2 Let λ represent the sum of squares of the weight vectors, and λ represent the regularization strength.

[0124] Furthermore, a design tree structure is constructed. The tree splitting process involves calculating the gain of each feature splitting node and deciding whether to retain the split. The goal of each tree is to maximize the split gain, i.e., minimize the loss of the objective function after the split. For example, the gain can be expressed by Equation 3.

[0125]

[0126] Where L and R represent the sample sets of the left and right subtrees after the split, gl and h l gr and h represent the first derivative (gradient) and second derivative (Hessian matrix value) of the samples in the left subtree. r denoted by , we have the first and second derivatives of the samples in the right subtree, λ represents the L2 regularization coefficient (controlling the smoothness of the leaf node weights), and γ represents the leaf node complexity penalty term (controlling the complexity of the tree and preventing overfitting).

[0127] Based on this, further training and optimization are performed to calculate the gradient of the sample features, thereby traversing the features to calculate each split point. After selecting the optimal split, subtrees are recursively generated to update the model's prediction values ​​and predict the destination of the current journey.

[0128] S502. Based on the vehicle's current location and the predicted distance between the current destination and the destination of the current trip, identify whether the current trip is a long-distance trip.

[0129] For example, such as Figure 6 As shown, the information acquisition module obtains vehicle information to determine whether navigation information is available for the current trip. If navigation information is available, the trip distance is determined based on this information. If no navigation information is available, the vehicle's historical trip information is processed using a density-based clustering algorithm in the trip prediction module to obtain the vehicle's travel area. Furthermore, the vehicle's historical parking location information is processed using a Gaussian kernel and other smoothing functions to obtain the vehicle's travel time period. Further, based on the vehicle's travel area and travel time period, the trip distance is predicted using a distributed gradient enhancement library. Then, it is determined whether the trip distance is greater than a preset distance. If the trip distance is greater than the preset distance, the current trip is determined to be a long-distance trip; if the trip distance is less than or equal to the preset distance, the current trip is determined to be a short-distance trip.

[0130] In this embodiment, by analyzing the travel time period, travel area, current location, and current time, this application can determine the destination matching the current location and current time based on the travel time period and travel area, and use this destination as the predicted destination for the current trip. Furthermore, by determining the distance between the vehicle's current location and the predicted destination, it can accurately identify whether the current trip is a long-distance trip.

[0131] In some embodiments, such as Figure 7 As shown, the above S202 may specifically include S701-S704:

[0132] S701. When the current journey is a long journey, determine the historical temperature distribution characteristics of the battery pack based on historical cell temperature data.

[0133] In one possible implementation, feature extraction can be performed on historical cell temperature data, historical driving data, and historical environmental data to obtain historical temperature distribution feature data (i.e., spatiotemporal heat distribution), historical driving habit feature data (i.e., driving behavior), and environmental condition-related features. Then, these multimodal features are jointly modeled offline to obtain an optimized model, thereby generating offline optimized thermal management parameters (i.e., predicted thermal management parameters for the current trip) and storing them on a cloud server.

[0134] Specifically, when determining the historical temperature distribution characteristics of the battery pack based on historical cell temperature data, a 3D Convolutional Neural Network (3D-CNN) architecture can be used to extract high-resolution thermal distribution features from the historical cell temperature data, suppress background noise, and enhance the ability to identify local overheating and poor heat dissipation. Then, the historical cell temperature data (i.e., battery pack temperature field data) is divided into N×M×L grid cells to construct a three-dimensional temperature matrix. This three-dimensional temperature matrix is ​​then input into the 3D convolutional neural network, initially using a 5-layer 3D convolutional structure, with each layer configured with Conv3D (kernel_size = 3×3×3, stride = 1, padding = 1) and the activation function being LeakyReLU. Subsequently, max pooling (MaxPool3D) is used to gradually reduce the spatial resolution, generating a thermal distribution map of the battery pack (i.e., historical temperature distribution characteristic data) to characterize the high semantic features of the battery pack's spatiotemporal thermal distribution.

[0135] S702. Based on historical driving data, determine the historical driving habit characteristics of the vehicle.

[0136] In one possible implementation, historical driving data (i.e., driving behavior over time) is dynamically modeled using a bidirectional Long Short-Term Memory (LSTM) network and Fourier transform. This allows for the synchronous modeling of temporal dynamic evolution and periodic behavioral patterns from the historical driving data, providing driving scenario context information for battery thermal management. Furthermore, the mean and variance of the data within each window are calculated according to a fixed window size (initially set to 10), and Z-score standardization is performed to obtain standardized data. This standardized data is then input into a bidirectional LSTM network, initially configured with four stacked bidirectional LSTM layers, each with 256 hidden units, for a total of 512 hidden units. The hidden state of the last layer is taken and compressed into a low-dimensional temporal feature vector through a fully connected layer, representing short-term abrupt changes (such as rapid acceleration) and long-term trends (such as the decay of state of charge) in driving behavior. The time-series features output by the LSTM are subjected to a Short-Time Fourier Transform (STFT) to preserve low-frequency energy and suppress high-frequency noise, generating a frequency domain feature vector that represents the user's periodic driving habits (such as the frequency of rapid acceleration and gear shifting) to obtain the vehicle's historical driving habit feature data.

[0137] S703. Historical temperature distribution characteristic data, historical driving habit characteristic data and historical environmental data are fused together to obtain historical fused data.

[0138] S704. Based on historical fusion data, predict the thermal management parameters for the current journey.

[0139] In one possible implementation, historical temperature distribution feature data, historical driving habit feature data, and historical environmental data are fused together. In the cross-modal fusion layer, robust alignment of heterogeneous modalities can be achieved through a Transformer encoder and dynamic weight allocation, thereby improving the reliability of thermal management decisions in complex scenarios (such as tunnels or GPS failures).

[0140] Specifically, historical environmental data, historical temperature distribution feature data, and historical driving habit feature data are merged. The merged data is then input into a Transformer encoder for alignment (the multi-head self-attention layer is initially set to 6 stacked layers, number of heads = 8, key dimension = 512, feedforward network dimension = 2048, and the position encoding uses a sine function to enhance sequence order perception). The aligned data is then dynamically weighted, and the modal weight α = Softmax(W*[E] is calculated using a learnable matrix. env ,E_b ehavior E spatial The contribution level is dynamically adjusted to obtain historical fusion data.

[0141] Furthermore, by generating offline optimized thermal management parameters (i.e. predicted thermal management parameters), and adjusting the predicted thermal management parameters based on the vehicle's real-time operating data, the generalization ability of the model under complex operating conditions can be improved.

[0142] Specifically, as shown in Formula 4, the Mean Squared Error (MSE) loss function is constructed.

[0143]

[0144] Among them, L mse This represents the mean squared error loss value, used to measure the average deviation between the predicted and actual values. N represents the sample size (e.g., historical fused data). This represents the predicted value of the i-th sample. This represents the true value of the i-th sample. This represents the square of the L2 norm, which is the sum of the squares of the vector elements.

[0145] As shown in Formula 5, the Dynamic Time Warping (DTW) loss function is constructed.

[0146] L DTW =DTW(π) pred ,π true Formula 5.

[0147] Among them, L DTW π represents the DTW loss value, which is a measure of similarity between two time series. pred Represents the predicted time series, π true Representing the true reference time series, DTW(π) pred ,π true ) represents the dynamic time warping algorithm, which calculates the optimal matching path and the corresponding cumulative distance between two sequences.

[0148] Furthermore, based on the mean squared error loss function shown in Formula 4 and the dynamic time warping loss function shown in Formula 5, the total loss function shown in Formula 6 can be obtained.

[0149] L total =λ1L mse +λ2L mse (λ1+λ2=1) Formula 6.

[0150] For example, such as Figure 8As shown, historical operating data (historical driving data, historical environmental data, and historical cell temperature data of the battery pack) is acquired, and data alignment is performed on these data using dynamic time warping and sliding window compensation. Then, data analysis is conducted based on the aligned historical driving data, historical environmental data, and historical cell temperature data of the battery pack to determine historical temperature distribution characteristics and historical driving habit characteristics. Subsequently, through cross-modal fusion, the historical temperature distribution characteristics, historical driving habit characteristics, and historical environmental data are fused to obtain historical fused data. Thermal management parameters for the current trip are then predicted based on this historical fused data. Further, based on the acquired real-time operating data (real-time driving data, real-time environmental data, and real-time cell temperature data), the predicted thermal management parameters are adjusted. By determining the real-time error and gradient descent, the adjusted thermal management parameters are obtained and sent to the vehicle's infotainment system. Thermal management control of the power battery is then performed based on these adjusted thermal management parameters.

[0151] In this embodiment, by analyzing historical cell temperature data, the historical temperature distribution characteristics of the battery pack can be determined. Furthermore, by analyzing historical driving data, historical driving habit characteristics of the vehicle can be determined. By fusing historical temperature distribution characteristics, historical driving habit characteristics, and historical environmental data, multi-dimensional historical data can be integrated to obtain fused data. Based on this fused data, the thermal management parameters for the current journey can be accurately predicted, building upon the multi-dimensional historical data.

[0152] The purpose of this application is to address the shortcomings of existing battery thermal management strategies. It aims to optimize the dynamic response and global energy consumption balance of the thermal management system for long-distance driving by integrating offline optimization and online learning mechanisms. This improves the real-time performance, energy efficiency ratio, and environmental adaptability of temperature control, thereby extending battery life, reducing energy consumption, and enhancing the range stability of new energy vehicles under complex operating conditions. Compared to the general thermal management strategy's indiscriminate temperature control across all scenarios, this application constructs a multi-dimensional trip prediction model by integrating user charging activity periods, high-frequency charging areas, and real-time navigation route information. This model accurately identifies driving scenarios for short and long distances. Since the energy-saving effect of thermal management optimization is not strongly perceived for short distances, the system defaults to disabling the high-precision thermal management optimization module, maintaining only basic temperature control functions. This avoids frequent multi-modal data fusion calculations on the cloud server, reducing platform resource consumption.

[0153] Furthermore, compared to the over-reliance on single parameters (such as current deviation percentage) in general data-driven solutions, this application integrates driving data (vehicle speed / power), environmental data (temperature and humidity / altitude), and user behavior data (charging preferences / trip patterns) to construct a multimodal spatiotemporal data-driven online optimization model. Through a cloud-based, real-time updated multimodal feature library, the system can dynamically capture the interaction between changes in battery internal resistance, refrigerant flow, and electrothermal coupling effects. Upon vehicle startup, it only needs to call pre-stored trip prediction results (short / long) to activate the high-precision thermal management module on demand, reducing real-time control latency from seconds to milliseconds. This is particularly suitable for rapid temperature control response in congested urban traffic conditions.

[0154] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the thermal management control device or electronic device includes corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0155] This application embodiment can, according to the above method, exemplarily divide a thermal management control device or electronic device into functional modules. For example, the thermal management control device or electronic device may include functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0156] Reference Figure 9 The thermal management control device 900 includes: an identification module 901, a prediction module 902, a processing module 903, and a control module 904; the identification module 901 is used to identify whether the current trip of the vehicle is a long-distance trip, which is a trip with a distance greater than a preset distance; the prediction module 902 is used to predict the thermal management parameters of the current trip based on the historical operating data of the vehicle when the current trip is a long-distance trip, and the thermal management parameters are used to control the operating temperature of the vehicle's power battery; the processing module 903 is used to adjust the thermal management parameters based on the real-time operating data of the vehicle; and the control module 904 is used to perform thermal management control of the vehicle based on the adjusted thermal management parameters.

[0157] In one possible implementation, the identification module 901 is specifically used to identify whether the current trip is a long-distance trip based on the distance between the vehicle's current location and the destination in the navigation information when the navigation information of the current trip is obtained; or, the identification module 901 is specifically used to identify whether the current trip is a long-distance trip based on the vehicle's historical trip information and historical parking location information when the navigation information is not obtained.

[0158] In one possible implementation, the historical trip information includes: the start time and end time of the historical trip; the processing module 903 is further configured to determine the travel time period of the vehicle based on the start time and end time of the historical trip; the processing module 903 is further configured to determine the travel area of ​​the vehicle based on the historical parking location information; the identification module 901 is specifically configured to identify whether the current trip is a long-distance trip based on the travel time period, travel area, current location of the vehicle and current time.

[0159] In one possible implementation, the processing module 903 is specifically used to divide multiple historical parking locations in the historical parking location information into multiple clusters based on a preset aggregation radius; the processing module 903 is specifically used to determine the travel area of ​​the vehicle based on the location information of the multiple clusters.

[0160] In one possible implementation, the processing module 903 is specifically used to determine at least one cluster from multiple clusters whose aggregation density is greater than or equal to a preset density; the processing module 903 is specifically used to determine the area corresponding to the location information of at least one cluster as the travel area of ​​the vehicle.

[0161] In one possible implementation, the prediction module 902 is further configured to predict the destination of the current trip based on the travel time period, travel area, current location, and current time; the identification module 901 is specifically configured to identify whether the current trip is a long-distance trip based on the travel distance between the vehicle's current location and the predicted destination of the current trip.

[0162] In one possible implementation, the historical operating data includes: historical driving data, historical environmental data, and historical cell temperature data of the battery pack; the processing module 903 is further configured to determine the historical temperature distribution characteristic data of the battery pack based on the historical cell temperature data; the processing module 903 is further configured to determine the historical driving habit characteristic data of the vehicle based on the historical driving data; the processing module 903 is further configured to fuse the historical temperature distribution characteristic data, the historical driving habit characteristic data, and the historical environmental data to obtain historical fused data; the prediction module 902 is specifically configured to predict the thermal management parameters of the current trip based on the historical fused data.

[0163] In one possible implementation, the processing module 903 is further configured to, before predicting the thermal management parameters of the current trip based on the vehicle's historical operating data, align the historical cell temperature data, various data from the historical driving data, and various data from the historical environmental data by interpolation; wherein, the historical driving data includes at least one of the following: driving speed, vehicle power, battery pack state of charge, and motor torque; and the historical environmental data includes at least one of the following: ambient temperature, ambient humidity, and altitude.

[0164] In one possible implementation, the real-time operating data includes at least one of the following: real-time driving data, real-time environmental data, and real-time cell temperature data of the battery pack; the processing module 903 is specifically used to determine the real-time thermal management parameters for the current trip based on the real-time driving data, real-time environmental data, and real-time cell temperature data; the processing module 903 is specifically used to adjust the thermal management parameters to real-time thermal management parameters.

[0165] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0166] like Figure 10 As shown, the electronic device 1000 includes, but is not limited to, a processor 1001 and a memory 1002.

[0167] The memory 1002 described above is used to store the executable instructions of the processor 1001. It is understood that the processor 1001 is configured to execute instructions to implement the thermal management control method described in the above embodiments.

[0168] It should be noted that those skilled in the art will understand that Figure 10 The electronic device structure shown does not constitute a limitation on the electronic device; the electronic device may include, but is not limited to, other electronic devices. Figure 10 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0169] The processor 1001 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 1002, and by calling data stored in the memory 1002, it performs various functions and processes data, thereby providing overall monitoring of the electronic device. The processor 1001 may include one or more processing units. Optionally, the processor 1001 may integrate an application processor and a modem processor. The application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 1001.

[0170] The memory 1002 can be used to store software programs and various data. The memory 1002 may primarily include a program storage area and a data storage area, wherein the program storage area may store the operating system, application programs required by at least one functional module, etc. Furthermore, the memory 1002 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0171] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 1002 including instructions, which can be executed by a processor 1001 of an electronic device 1000 to implement the thermal management control method in the above embodiments.

[0172] In actual implementation, Figure 9 The functions of the identification module 901, prediction module 902, processing module 903, and control module 904 can be derived from... Figure 10 The processor 1001 calls the computer program stored in the memory 1002 to implement the process. The specific execution process can be found in the description of the thermal management control method section of the previous embodiment, and will not be repeated here.

[0173] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device.

[0174] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 1001 of the electronic device 1000 to complete the thermal management control method in the above embodiments.

[0175] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of the electronic device, they implement the various processes of the above-described thermal management control method embodiments and achieve the same technical effects as the above-described thermal management control method. To avoid repetition, they will not be described again here.

[0176] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0177] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0178] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the classified units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0179] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0180] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, essentially, or the part that contributes to the prior art, or a complete or partial classification of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0181] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A thermal management control method, characterized in that, The thermal management control method includes: Identify whether the vehicle's current journey is a long-distance journey, where a long-distance journey is defined as a journey with a distance greater than a preset distance; In the case that the current trip is a long trip, based on the vehicle's historical operating data, the thermal management parameters for the current trip are predicted. These thermal management parameters are used to control the operating temperature of the vehicle's power battery. Based on the real-time operating data of the vehicle, the thermal management parameters are adjusted. Thermal management control is performed on the vehicle based on the adjusted thermal management parameters.

2. The thermal management control method according to claim 1, characterized in that, The process of identifying whether the vehicle's current journey is a long-distance trip includes: If the navigation information of the current trip is obtained, the system identifies whether the current trip is a long-distance trip based on the distance between the vehicle's current location and the destination in the navigation information. Alternatively, if the navigation information is not obtained, the current trip can be identified as a long-distance trip based on the vehicle's historical trip information and historical parking location information.

3. The thermal management control method according to claim 2, characterized in that, The historical trip information includes: the start time of the historical trip and the end time of the historical trip; The step of identifying whether the current trip is a long-distance trip based on the vehicle's historical trip information and historical parking location information includes: Based on the start and end times of the historical trips, the travel time period of the vehicle is determined; The travel area of ​​the vehicle is determined based on the historical parking location information; Based on the travel time period, the travel area, the vehicle's current location, and the current time, determine whether the current trip is a long-distance trip.

4. The thermal management control method according to claim 3, characterized in that, Determining the vehicle's travel area based on the historical parking location information includes: Based on a preset aggregation radius, multiple historical parking locations in the historical parking location information are divided into multiple clusters; The travel area of ​​the vehicle is determined based on the location information of multiple clusters.

5. The thermal management control method according to claim 4, characterized in that, Determining the travel area of ​​the vehicle based on the location information of multiple clusters includes: From the plurality of said clusters, at least one of said clusters is determined to have an aggregation density greater than or equal to a preset density; The region corresponding to the location information of at least one of the aforementioned clusters is determined as the travel area of ​​the vehicle.

6. The thermal management control method according to claim 3, characterized in that, The step of identifying whether the current trip is a long-distance trip based on the travel time period, the travel area, the vehicle's current location, and the current time includes: Based on the travel time period, the travel area, the current location, and the current time, predict the destination of the current trip; Based on the vehicle's current location and the predicted distance between the current destination and the destination of the current trip, it is determined whether the current trip is a long-distance trip.

7. The thermal management control method according to claim 1, characterized in that, The historical operating data includes: historical driving data, historical environmental data, and historical cell temperature data of the battery pack. The prediction of thermal management parameters for the current journey based on the vehicle's historical operating data includes: Based on the historical cell temperature data, the historical temperature distribution characteristics of the battery pack are determined; Based on the historical driving data, the historical driving habit characteristic data of the vehicle are determined; The historical temperature distribution feature data, the historical driving habit feature data, and the historical environmental data are fused to obtain historical fused data; Based on the historical fusion data, predict the thermal management parameters for the current journey.

8. The thermal management control method according to claim 7, characterized in that, The method further includes: Before predicting the thermal management parameters for the current trip based on the vehicle's historical operating data, the historical cell temperature data, various data from the historical driving data, and various data from the historical environmental data are aligned using interpolation. The historical driving data includes at least one of the following: driving speed, vehicle power, state of charge of the battery pack, and motor torque; the historical environmental data includes at least one of the following: ambient temperature, ambient humidity, and altitude.

9. The thermal management control method according to claim 1, characterized in that, The real-time operating data includes at least one of the following: real-time driving data, real-time environmental data, and real-time cell temperature data of the battery pack; The adjustment of the thermal management parameters based on the vehicle's real-time operating data includes: Based on the real-time driving data, the real-time environmental data, and the real-time battery cell temperature data, the real-time thermal management parameters for the current journey are determined. Adjust the thermal management parameters to the real-time thermal management parameters.

10. A thermal management control device, characterized in that, The thermal management control device includes: an identification module, a prediction module, a processing module, and a control module; The identification module is used to identify whether the vehicle's current trip is a long-distance trip, where a long-distance trip is a trip with a distance greater than a preset distance; The prediction module is used to predict the thermal management parameters of the current trip based on the vehicle's historical operating data when the current trip is a long trip. The thermal management parameters are parameters used to control the operating temperature of the vehicle's power battery. The processing module is used to adjust the thermal management parameters based on the real-time operating data of the vehicle. The control module is used to perform thermal management control on the vehicle based on the adjusted thermal management parameters.

11. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the thermal management control method as described in any one of claims 1-9.

12. A vehicle, characterized in that, The vehicle includes the thermal management control device as described in claim 10, and the vehicle is used to implement the thermal management control method as described in any one of claims 1-9.

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