A battery module dynamic thermal management method and system

Abstract

The present invention relates to the technical field of battery thermal management and provides a method and system for dynamic thermal management of a battery module, the method comprising the following steps: when a preset trigger condition is met, performing a local environment heat dissipation capacity assessment and generating an assessment result; based on the assessment result and the current thermal state of the local battery, generating heat dissipation adjustment parameters for the heat dissipation system; performing data monitoring and recording to generate reported data information; the reported data information includes data related to the local environment heat dissipation capacity assessment, the assessment result data, the heat dissipation adjustment parameter data for the heat dissipation system, and the battery thermal state change data after the heat dissipation system performs the adjustment; uploading the reported data information to a cloud platform so that the cloud platform performs continuous learning and optimization operations of the thermal management strategy based on the reported data information. The present invention has the advantages of improving the adaptability and security of thermal management and supporting the continuous optimization of cloud strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of battery thermal management, and in particular to a dynamic thermal management method and system for a battery module. Background Art

[0002] In modern automated warehousing and logistics systems, unmanned electric engineering vehicles undertake high-intensity operational tasks, and their power battery modules face continuous charge and discharge cycles and complex and changing load conditions. To ensure the overall operational efficiency of the fleet and battery safety, cloud platforms are generally responsible for globally optimizing the energy scheduling and thermal management strategies of the entire fleet. However, the operating environment of these vehicles is unique, such as operating in narrow aisles between multi-layer, high-density racks. The physical structure of such environments restricts the natural circulation of air. Combined with factors such as the equipment's own heat dissipation and the stacking structure of the goods, the local ambient temperature may be significantly higher than the average warehouse temperature, forming local hot spots. When a vehicle operates in a narrow aisle, the hot air discharged from its own thermal management system is difficult to dissipate quickly, which may cause the local ambient temperature to rise further, adversely affecting the actual heat dissipation conditions of subsequent or adjacent vehicles.

[0003] Therefore, when there is a significant discrepancy between the global optimization strategy issued by the cloud and the local actual operating conditions perceived in real time by the device, a mechanism is urgently needed to ensure that the device's autonomous adjustments are carried out within a carefully designed, clearly constrained framework. When deciding whether and how to make autonomous adjustments, the device controller must not only consider its own battery temperature status but also comprehensively consider auxiliary information that indirectly or directly reflects the current local environment's heat dissipation capacity. More importantly, the complete decision-making process for each autonomous adjustment on the device side and its resulting multi-dimensional effects need to be systematically and structuredly recorded and uploaded to the cloud platform. This high-quality "extreme operating condition experience" data is crucial for the cloud model's continuous learning, iterative optimization of the robustness of its global thermal management strategy, and improvement of its dynamic allocation and guidance mechanisms for autonomous adjustment permissions or parameter ranges on the device side.

[0004] In view of the above problems, the existing technology is in urgent need of improvement. Summary of the Invention

[0005] In order to address the shortcomings of the existing technology, the present application provides a dynamic thermal management method and system for a battery module, which has the advantage of being able to dynamically adjust the thermal management strategy according to the actual heat dissipation capacity of the local environment in which the vehicle is located, and feed back the adjustment process and results to the cloud, thereby improving the adaptability and safety of thermal management, and supporting the continuous optimization of cloud strategies.

[0006] This application provides a dynamic thermal management method for battery modules, the technical key points of which are:

[0007] The following steps are involved:

[0008] When the preset trigger conditions are met, the local environment heat dissipation capacity is evaluated and the evaluation results of the local environment heat dissipation capacity are generated. The preset trigger conditions include an abnormal increase in the vehicle battery temperature or a deviation between the cloud instruction from the cloud platform and the local perception exceeding a preset threshold. When the preset trigger conditions are met, the local environment heat dissipation capacity is evaluated and the evaluation results of the local environment heat dissipation capacity are generated, including:

[0009] Acquire prior environmental information representing the heat dissipation conditions of the local environment in which the vehicle is located and battery status information of the local battery;

[0010] Determine the disturbance amplitude and disturbance duration for evaluating the heat dissipation capacity of the local environment based on prior environmental information and battery status information;

[0011] Controlling a cooling system representing a vehicle and applying disturbances according to the disturbance amplitude and duration;

[0012] Monitor the battery thermal response caused by disturbances and generate an assessment of the local environment's heat dissipation capacity based on the battery thermal response;

[0013] Based on the evaluation results of the local environment's heat dissipation capacity and the current thermal state of the local battery, heat dissipation adjustment parameters of the heat dissipation system are generated; Based on the evaluation results of the local environment's heat dissipation capacity and the current thermal state of the local battery, heat dissipation adjustment parameters of the heat dissipation system are generated, including:

[0014] Obtaining heat dissipation adjustment information of adjacent vehicles, the heat dissipation adjustment information including the position and heat dissipation adjustment parameters of the adjacent vehicles;

[0015] Based on the vehicle's position, the positions of neighboring vehicles, and heat dissipation adjustment parameters, the thermal impact of neighboring vehicles on the vehicle's local environment is evaluated to generate a thermal impact assessment value.

[0016] Generate heat dissipation adjustment parameters for the heat dissipation system based on the evaluation results of the local environment's heat dissipation capacity, the current thermal state of the local battery, and the thermal impact evaluation value;

[0017] During the battery thermal management process, data is monitored and recorded to generate and report data information; the reported data information includes data related to the local environment heat dissipation capacity assessment operation, the local environment heat dissipation capacity assessment result data, the heat dissipation adjustment parameter data of the heat dissipation system, and the battery thermal state change data after the heat dissipation system is adjusted;

[0018] The reported data information is uploaded to the cloud platform so that the cloud platform can perform continuous learning and optimization operations of the thermal management strategy based on the reported data information.

[0019] Through the above solution, the thermal management strategy can be dynamically adjusted according to the actual heat dissipation capacity of the local environment in which the vehicle is located, and the adjustment process and results can be fed back to the cloud, thereby improving the adaptability and safety of thermal management and supporting the continuous optimization of cloud-based strategies; the thermal impact of neighboring vehicles is taken into account, making the generation of heat dissipation adjustment parameters more accurate and avoiding adverse effects on surrounding vehicles.

[0020] To improve the solution, this application also proposes generating heat dissipation adjustment parameters for the heat dissipation system based on the evaluation results of the local environment's heat dissipation capacity, the current thermal state of the local battery, and the thermal impact evaluation value, including:

[0021] Obtain environmental structural information that characterizes the physical structure or cargo stacking form within the vehicle operation area;

[0022] The heat dissipation adjustment parameters of the heat dissipation system are generated based on the evaluation results of the local environment heat dissipation capacity, the current thermal state of the local battery, the thermal impact evaluation value, and the environmental structure information.

[0023] Through the above scheme, the environmental structure information in the operating area is further considered, making the generation of heat dissipation adjustment parameters more refined and adaptable to complex physical environments.

[0024] To improve the solution, this application also proposes monitoring the battery thermal characteristic response caused by disturbances and generating an assessment result of the local environment's heat dissipation capacity based on the battery thermal characteristic response, including:

[0025] In the process of monitoring the battery thermal characteristic response, obtaining interference indication information corresponding to the operating state of the non-heat dissipation system of the vehicle or the ambient thermal disturbance state of the environment in which the vehicle is located;

[0026] adjusting the battery thermal characteristic response according to the interference indication information and the battery thermal characteristic response caused by the disturbance, removing or suppressing the influence of the interference source indicated by the interference indication information from the battery thermal characteristic response before the adjustment, and obtaining an adjusted battery thermal characteristic response;

[0027] Based on the adjusted battery thermal characteristic response, an assessment result of the local environment heat dissipation capacity is generated.

[0028] Through the above scheme, the interference caused by non-heat dissipation system operation or environmental thermal disturbances is further taken into account. The accuracy of battery thermal characteristic response monitoring is improved through adjustment processing, thereby improving the reliability of the heat dissipation capacity evaluation results.

[0029] To further solve the problem, the present application also proposes that, after generating the heat dissipation adjustment parameters of the heat dissipation system based on the evaluation results of the local environment heat dissipation capacity, the current thermal state of the local battery, and the thermal impact evaluation value, the following also be included:

[0030] Determining initial heat dissipation adjustment parameters based on the heat dissipation adjustment parameters and the current thermal state of the battery of the vehicle;

[0031] Obtain operating status information of neighboring vehicles;

[0032] Based on the initial heat dissipation adjustment parameters and the operation status information, the heat impact value on the adjacent vehicles when the initial heat dissipation adjustment parameters are used for heat dissipation is predicted;

[0033] If the thermal impact value is greater than the preset impact threshold, the final heat dissipation adjustment parameters are adjusted based on the initial heat dissipation adjustment parameters while maintaining the vehicle battery temperature within a safe operating range.

[0034] If the thermal impact value is less than or equal to the preset impact threshold, the initial heat dissipation adjustment parameter is determined as the final heat dissipation adjustment parameter.

[0035] Through the above scheme, after generating the heat dissipation adjustment parameters, the prediction and adjustment steps of the thermal impact of adjacent vehicles are added to ensure that the heat dissipation adjustment of the vehicle will not cause excessive thermal disturbances to adjacent vehicles, thus achieving thermal coordination among the fleet.

[0036] To improve the solution, this application also proposes to predict the thermal impact value on adjacent vehicles when using the initial heat dissipation adjustment parameters for heat dissipation based on the initial heat dissipation adjustment parameters and operating status information, including:

[0037] Obtain environmental structural information that characterizes the physical structure or cargo stacking form within the vehicle operation area;

[0038] The heat impact value on the adjacent vehicle is determined based on the initial heat dissipation adjustment parameters, the operating status information of the adjacent vehicle, the relative position of the host vehicle and the adjacent vehicle, and the environmental structure information.

[0039] Through the above scheme, the environmental structure information is further considered, making the prediction of the thermal impact of neighboring vehicles more accurate.

[0040] To further solve the problem, the present application also proposes that the final heat dissipation adjustment parameters are obtained by adjusting the initial heat dissipation adjustment parameters, including:

[0041] Generate a set of candidate final heat dissipation adjustment parameters based on the initial heat dissipation adjustment parameters, the operating status information of the neighboring vehicles, the current thermal state of the battery of the own vehicle, and the environmental structure information;

[0042] Predict the impact of a set of candidate final cooling adjustment parameters on the thermal impact of neighboring vehicles, the control effect on the battery temperature of the vehicle itself, the energy consumption level, and the life of the cooling system;

[0043] On the premise that the battery temperature of the vehicle is within a safe operating range, the candidate final heat dissipation adjustment parameter with the best prediction result is selected as the final heat dissipation adjustment parameter.

[0044] Through the above scheme, a method is provided to optimize and select the final heat dissipation adjustment parameters based on multiple objectives (the influence of neighboring vehicles, vehicle temperature control, energy consumption, and system life), thereby improving the overall performance of the thermal management strategy.

[0045] To improve the solution, this application also proposes that, under the premise that the battery temperature of the vehicle is within a safe operating range, the candidate final heat dissipation adjustment parameter with the best prediction result is selected as the final heat dissipation adjustment parameter, including:

[0046] Filtering a set of candidate final heat dissipation adjustment parameters from the prediction results that satisfy the constraint that the vehicle's battery temperature is within a safe operating range;

[0047] If the set of candidate final heat dissipation adjustment parameters includes a candidate final heat dissipation adjustment parameter, determining the candidate final heat dissipation adjustment parameter as the final heat dissipation adjustment parameter;

[0048] If the set of candidate final heat dissipation adjustment parameters includes at least two candidate final heat dissipation adjustment parameters, obtaining dynamic operating state information representing the vehicle;

[0049] evaluating each candidate final cooling adjustment parameter in the set of candidate final cooling adjustment parameters based on the dynamic operating state information and prediction results of the set of candidate final cooling adjustment parameters in terms of reducing thermal impact on neighboring vehicles, minimizing energy consumption increase, and ensuring the service life of the cooling system;

[0050] According to the evaluation result, a final heat dissipation adjustment parameter is determined from a set of candidate final heat dissipation adjustment parameters.

[0051] Through the above scheme, the selection process of the final heat dissipation adjustment parameters is further refined, especially taking into account the dynamic operation status information, so that the selection result is more adapted to the actual working conditions.

[0052] To further solve the problem, this application also proposes a battery module dynamic thermal management system for dynamic thermal management of vehicle battery modules, including:

[0053] The heat dissipation capacity assessment module is used to assess the heat dissipation capacity of the local environment and generate an assessment result of the heat dissipation capacity of the local environment when a preset trigger condition is met; when a preset trigger condition is met, the heat dissipation capacity assessment module is used to assess the heat dissipation capacity of the local environment and generate an assessment result of the heat dissipation capacity of the local environment, including:

[0054] Acquire prior environmental information representing the heat dissipation conditions of the local environment in which the vehicle is located and battery status information of the local battery;

[0055] Determine the disturbance amplitude and disturbance duration for evaluating the heat dissipation capacity of the local environment based on prior environmental information and battery status information;

[0056] Controlling a cooling system representing a vehicle and applying disturbances according to the disturbance amplitude and duration;

[0057] Monitor the battery thermal response caused by disturbances and generate an assessment of the local environment's heat dissipation capacity based on the battery thermal response;

[0058] An adjustment parameter generation module is configured to generate heat dissipation adjustment parameters for the heat dissipation system based on the evaluation results of the local environment heat dissipation capacity and the current thermal state of the local battery. The heat dissipation adjustment parameters for the heat dissipation system are generated based on the evaluation results of the local environment heat dissipation capacity and the current thermal state of the local battery, including:

[0059] Obtaining heat dissipation adjustment information of adjacent vehicles, the heat dissipation adjustment information including the position and heat dissipation adjustment parameters of the adjacent vehicles;

[0060] Based on the vehicle's position, the positions of neighboring vehicles, and heat dissipation adjustment parameters, the thermal impact of neighboring vehicles on the vehicle's local environment is evaluated to generate a thermal impact assessment value.

[0061] Generate heat dissipation adjustment parameters for the heat dissipation system based on the evaluation results of the local environment's heat dissipation capacity, the current thermal state of the local battery, and the thermal impact evaluation value;

[0062] A reporting data generation module is used to monitor and record data during the battery thermal management process and generate reporting data information; the reporting data information includes data related to the local environment heat dissipation capacity assessment operation, the local environment heat dissipation capacity assessment result data, the heat dissipation adjustment parameter data of the heat dissipation system, and the battery thermal state change data after the heat dissipation system performs the adjustment;

[0063] The data information reporting module is used to upload the reported data information to the cloud platform so that the cloud platform can perform continuous learning and optimization operations of the thermal management strategy based on the reported data information.

[0064] Through the above solution, a system for implementing the above dynamic thermal management method is provided, which provides a hardware or software carrier for the implementation of the method.

[0065] In summary, the present application provides a dynamic thermal management method and system for a battery module, which improves the adaptability and safety of thermal management by dynamically adjusting the thermal management strategy according to the actual heat dissipation capacity of the local environment in which the vehicle is located, and feeds back the adjustment process and results to the cloud, thereby improving the adaptability and safety of thermal management, and supporting the continuous optimization of cloud strategies. It has the advantages of being able to dynamically adjust the thermal management strategy according to the actual heat dissipation capacity of the local environment in which the vehicle is located, and feed back the adjustment process and results to the cloud, thereby improving the adaptability and safety of thermal management, and supporting the continuous optimization of cloud strategies.

[0066] To further understand the features and technical contents of the present invention, please refer to the following detailed description and drawings of the present invention. However, the drawings provided are only for reference and illustration and are not intended to limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 The figure is a flow chart of a method for dynamic thermal management of a battery module according to one embodiment of the present invention.

[0068] Figure 2 This is one of the method flow diagrams of a dynamic thermal management method for a battery module in another embodiment of the present invention.

[0069] Figure 3 This is a second flow chart of a method for dynamic thermal management of a battery module in another embodiment of the present invention.

[0070] Figure 4 This is a third flow chart of a method for dynamic thermal management of a battery module in another embodiment of the present invention.

[0071] Figure 5 This is a fourth flow chart of a method for dynamic thermal management of a battery module in another embodiment of the present invention.

[0072] Figure 6 This is the fifth method flow diagram of a dynamic thermal management method for a battery module in another embodiment of the present invention.

[0073] Figure 7 This is the sixth method flow diagram of a dynamic thermal management method for a battery module in another embodiment of the present invention.

[0074] Figure 8 This is the seventh method flow chart of a dynamic thermal management method for a battery module in another embodiment of the present invention.

[0075] Figure 9 This is the eighth method flow chart of a dynamic thermal management method for a battery module in another embodiment of the present invention.

[0076] Figure 10This is a system block diagram of a battery module dynamic thermal management system in one embodiment of the present invention. DETAILED DESCRIPTION

[0077] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0078] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0079] Traditional existing thermal management methods for unmanned electric engineering vehicle swarms face the problem of significant deviations between the global thermal management strategy issued by the cloud and the extreme or unexpected heat dissipation demands perceived locally, when the vehicles are operating in narrow aisles with multi-layered, high-density shelves with restricted air circulation, facing local hot spots and the superposition of thermal effects caused by intensive operation of multiple vehicles. This makes it difficult for the vehicle to autonomously execute an adaptive thermal management adjustment that can ensure that its own battery module temperature does not exceed the safety threshold while avoiding significant negative impacts on its own energy consumption and neighboring vehicles due to excessive heat dissipation. The decision-making basis, adjustment process and multi-dimensional effects can be structured and reported to support continuous optimization of the cloud model.

[0080] For example, imagine multiple unmanned electric construction vehicles operating intensively in the narrow aisles of an automated warehouse. Air circulation in these aisles is restricted, making it difficult for hot air exhausted by the vehicles' cooling systems to dissipate quickly. A vehicle with a low battery health status receives a cooling instruction from the cloud based on its estimated health status and then enters an aisle where other vehicles are already operating at high loads. The actual ambient temperature in this aisle is higher than the average ambient temperature assumed when the cloud-based model strategy was generated, and the cooling systems of neighboring operating vehicles continue to release heat into the environment, further heating the local air. The actual temperature rise rate of the vehicle's battery exceeds the cloud-based strategy's expectations. If these issues are not addressed, strictly following cloud-based instructions on the vehicle side could cause the battery temperature to rapidly approach or exceed safety thresholds, posing a thermal safety risk.

[0081] In this regard, this application proposes a battery module dynamic thermal management method, combining Figure 1 As shown, the following steps are included:

[0082] S1, when a preset trigger condition is met, the local environment heat dissipation capacity is evaluated and an evaluation result of the local environment heat dissipation capacity is generated; the preset trigger condition includes an abnormal increase in vehicle battery temperature or a deviation between cloud instructions from the cloud platform and local perception exceeding a preset threshold;

[0083] S2, generating heat dissipation adjustment parameters of the heat dissipation system based on the evaluation results of the local environment heat dissipation capacity and the current thermal state of the local battery;

[0084] S3, during the battery thermal management process, monitoring and recording data to generate and report data information; the reported data information includes data related to the local environment heat dissipation capacity assessment operation, the local environment heat dissipation capacity assessment result data, the heat dissipation adjustment parameter data of the heat dissipation system, and the battery thermal state change data after the heat dissipation system performs the adjustment;

[0085] S4, uploading the reported data information to the cloud platform, so that the cloud platform can perform continuous learning and optimization operations of the thermal management strategy based on the reported data information.

[0086] Among them, the preset trigger conditions refer to the basis for starting the local environment heat dissipation capacity assessment, which may include an abnormal rise in the vehicle battery temperature, such as the battery temperature exceeds a certain safety threshold or the temperature rise rate exceeds a certain threshold, or the deviation between the cloud command from the cloud platform and the local perception exceeds the preset threshold, for example, the heat dissipation command issued by the cloud fails to effectively control the battery temperature, resulting in a large deviation between the local measured temperature and the target temperature.

[0087] Evaluating the local environment's heat dissipation capacity refers to obtaining information about the extent to which the vehicle's current microenvironment affects the battery's heat dissipation. This information can be obtained, for example, by analyzing data such as ambient temperature sensors, air inlet and outlet temperature difference sensors, and the battery temperature rise rate under the cooling system's current operating state, combined with historical data or a preset model for calculation.

[0088] The evaluation result of the local environment heat dissipation capacity refers to the quantitative information obtained after evaluating the local environment heat dissipation conditions, which can be a heat dissipation coefficient, a heat dissipation level, or an indicator reflecting the heat dissipation efficiency.

[0089] The current thermal state of the local battery refers to the current temperature, temperature rise rate, temperature distribution and other information of the vehicle's own battery module, which is obtained through the vehicle's onboard temperature sensors, battery management system, etc.

[0090] The heat dissipation adjustment parameters of the cooling system refer to the instructions or values ​​used to control the operating status of the vehicle battery cooling system, which may include the speed of the cooling fan, the flow rate of the coolant pump, the switching of cooling or heating mode, etc.

[0091] During the battery thermal management process, data monitoring and recording refers to the real-time collection and storage of battery thermal management-related data, which can be completed by the vehicle's battery management system or thermal management controller.

[0092] Reported data information refers to a data set formed by organizing and packaging the monitored and recorded data, which includes data related to the local environment heat dissipation capacity assessment operation, such as sensor data or test process information collected during the assessment process.

[0093] Uploading reported data to the cloud platform refers to sending the reported data to a remote cloud server via the vehicle's communication module, which can be achieved using wireless communication technology. Its purpose is to feed back actual operating data and experience from the end-side to the cloud, establishing a closed loop of end-cloud collaboration.

[0094] Based on the reported data, the cloud platform continuously learns and optimizes thermal management strategies using machine learning, data mining, and other technologies. This aims to leverage the client's experience in dealing with complex or abnormal operating conditions to enhance the robustness and accuracy of the cloud's global strategy and achieve continuous improvement in thermal management across the entire fleet.

[0095] In some preferred embodiments, when the vehicle battery temperature exceeds a preset temperature threshold, or the heat dissipation power instruction issued by the cloud fails to control the battery temperature rise rate within the target range, the local environment heat dissipation capacity evaluation is triggered. The evaluation process includes: the system temporarily increases the power of the heat dissipation system to a certain level, while monitoring the temperature rise rate of the battery temperature. By comparing the temperature rise rate with the temperature rise rate under the same heat dissipation power in a standard environment, a coefficient reflecting the current local environment heat dissipation efficiency is calculated as the evaluation result. Then, based on the heat dissipation efficiency coefficient, the current battery temperature and the battery health status information, a preset control algorithm or lookup table is used to calculate a new heat dissipation system control instruction, such as the precise speed of the fan or the flow rate of the coolant pump.

[0096] The heat dissipation adjustment parameters of the heat dissipation system generated based on the evaluation results of the local environment's heat dissipation capacity and the current thermal state of the local battery can be specifically as follows: when the vehicle is operating in a narrow alley, it can first impose a small thermal disturbance on the environment (such as briefly turning on the cooling fan) to monitor the response speed and amplitude of the battery temperature, thereby evaluating the current alley's air circulation and heat dissipation capacity. Then, combined with the current temperature, voltage and other conditions of the battery, it can calculate at what power the cooling fan should run, or at what flow rate the coolant circulation pump should work, to maintain the battery temperature within a safe range. In this way, the heat dissipation strategy can be dynamically adjusted according to the actual local environmental conditions of the vehicle and the battery's own state.

[0097] Optional, combined Figure 2 As shown, step S2: based on the evaluation result of the local environment heat dissipation capacity and the current thermal state of the local battery, generating heat dissipation adjustment parameters of the heat dissipation system, including:

[0098] S201, obtaining heat dissipation adjustment information of an adjacent vehicle, where the heat dissipation adjustment information includes a position of the adjacent vehicle and heat dissipation adjustment parameters;

[0099] S202, based on the position of the vehicle, the positions of the neighboring vehicles, and the heat dissipation adjustment parameters, evaluating the thermal impact of the neighboring vehicles on the local environment of the vehicle, and generating a thermal impact evaluation value;

[0100] S203 : Generate heat dissipation adjustment parameters of the heat dissipation system according to the evaluation result of the heat dissipation capacity of the local environment, the current thermal state of the local battery, and the heat impact evaluation value.

[0101] Among them, the heat dissipation adjustment information of neighboring vehicles refers to data related to the thermal management of other vehicles that are spatially close to the vehicle, including the position of the neighboring vehicles and the heat dissipation adjustment parameters; the position of the neighboring vehicle refers to the spatial coordinates of the neighboring vehicle in the operating area, which is represented by GPS coordinates, indoor positioning system coordinates or relative positions based on maps; the heat dissipation adjustment parameters refer to the parameters that control the working state of the vehicle's heat dissipation system, including fan speed, coolant flow or cooling power; thermal impact assessment refers to the process of quantifying the impact of the heat dissipation behavior of neighboring vehicles on the local ambient temperature of the vehicle, which is achieved by using a heat conduction model, an empirical formula or a machine learning model; the thermal impact assessment value refers to the output result of the thermal impact assessment process, which characterizes the degree of thermal impact of the neighboring vehicle on the local environment of the vehicle.

[0102] Optional, combined Figure 3 As shown, step S203: generating heat dissipation adjustment parameters of the heat dissipation system according to the evaluation result of the local environment heat dissipation capacity, the current thermal state of the local battery and the heat impact evaluation value, including:

[0103] S2031, obtaining environmental structure information representing the physical structure or cargo stacking form within the vehicle operation area;

[0104] S2032 : Generate heat dissipation adjustment parameters of the heat dissipation system according to the evaluation result of the heat dissipation capacity of the local environment, the current thermal state of the local battery, the heat impact evaluation value, and the environmental structure information.

[0105] Among them, environmental structure information refers to data that characterizes the physical structure or cargo stacking form within the vehicle's operating area. It can be collected through sensors or obtained through preset map data. Its purpose is to reflect the heat dissipation environment around the vehicle. For example, narrow alleys will restrict air circulation, and cargo stacking will block heat dissipation.

[0106] In some preferred embodiments, environmental structural information can be acquired using a vehicle-mounted perception system, for example, by scanning the vehicle's surroundings using a lidar (LiDAR) or camera, constructing a three-dimensional point cloud or image of the local environment. This information is then processed to identify physical structures such as walls, shelves, other vehicles, and stacked goods, as well as their relative positions and configurations, thereby generating environmental structural information. Alternatively, the vehicle can pre-load map data of the operating area, which contains information such as aisle widths, shelf layouts, and vent locations. The vehicle can then extract environmental structural information for the current area from this map data based on its own positioning information. When generating the cooling adjustment parameters for the cooling system, the evaluation results of the local environment's heat dissipation capacity, the current thermal state of the local battery, the thermal impact assessment value, and the acquired environmental structural information can be used as inputs to a pre-set control algorithm or machine learning model. This model is trained to comprehensively determine the optimal cooling strategy based on these inputs and output corresponding cooling adjustment parameters, such as the cooling fan speed, the cooling pump flow rate, and the selection of cooling or heating mode. For example, if the environmental structure information shows that the vehicle is deep in a narrow and poorly ventilated alley, even if the battery temperature has not yet reached a high threshold, the model may generate relatively aggressive heat dissipation parameters to cope with the potential rapid accumulation of heat; if the environmental structure information shows that there are heat-sensitive goods or equipment near the vehicle, the model may adjust the heat dissipation direction or reduce some heat dissipation power to reduce the thermal impact on the surrounding environment.

[0107] Optional, combined Figure 4 As shown, step S1 is further proposed: when a preset trigger condition is met, the local environment heat dissipation capacity is evaluated for the local environment, and an evaluation result of the local environment heat dissipation capacity is generated, including:

[0108] S101, obtaining a priori environmental information representing heat dissipation conditions of the local environment in which the vehicle is located and battery status information of the local battery;

[0109] S102, determining a disturbance amplitude and a disturbance duration for evaluating a local environment heat dissipation capability based on prior environmental information and battery status information;

[0110] S103, controlling a heat dissipation system representing the vehicle and applying a disturbance according to a disturbance amplitude and a disturbance duration;

[0111] S104 , monitoring the battery thermal characteristic response caused by the disturbance, and generating an evaluation result of the local environment heat dissipation capacity based on the battery thermal characteristic response.

[0112] Prior environmental information refers to static or slowly changing information known before the assessment that characterizes the local environmental cooling conditions. This information can be obtained using ambient temperature sensors, humidity sensors, wind speed sensors, or inferred based on vehicle location, warehouse map information, historical data, and so on. Battery status information refers to the real-time operating parameters of the local battery during the assessment. This information can be obtained using a battery management system (BMS), including battery temperature, voltage, current, state of charge (SOC), and state of health (SOH). Perturbation amplitude refers to the adjustment applied to the cooling system to assess cooling capacity. This information can be represented by an increment in cooling fan speed, coolant flow rate, or cooling power. Perturbation duration refers to the duration of the perturbation applied to the cooling system. This information can be determined by a preset duration or a dynamically calculated duration based on battery status information. The cooling system refers to the vehicle's device set used to control battery temperature. This system can employ air cooling, liquid cooling, or a combination of cooling systems. Battery thermal characteristic response refers to the temperature or heat changes exhibited by the battery after being disturbed by the heat dissipation system. It can be monitored by the temperature change rate of the battery cell, the average temperature change of the battery module, the change in the battery surface temperature distribution, or the change in the temperature difference between the coolant inlet and outlet.

[0113] In some preferred embodiments, the vehicle can first determine its current location using its built-in positioning module (e.g., GPS or an indoor positioning system) and, in conjunction with a pre-stored warehouse map database, obtain prior environmental information about the area, such as ventilation conditions and shelf layout density. Simultaneously, the vehicle's battery management system (BMS) can collect and provide real-time battery status information for the local battery, such as the battery pack's maximum, minimum, and average temperatures, current charge and discharge currents, state of charge (SOC), and state of health (SOH). Based on this prior environmental and battery status information, the control unit can calculate a relatively large perturbation amplitude (e.g., increasing the rated speed of the cooling fan by a certain percentage) and a relatively short perturbation duration (e.g., for tens of seconds) if, for example, it detects poor ventilation and high battery temperatures at the current location. The control unit can then send instructions to the cooling system's actuators (e.g., the cooling fan controller) to operate according to the calculated perturbation amplitude and duration. During the perturbation, the system can continuously monitor the average temperature change rate of the battery pack. For example, the battery temperatures before and after the perturbation are initiated can be recorded, and the temperature drop or increase per unit time can be calculated. Based on the monitored battery thermal characteristic response, such as the temperature drop rate, the system can generate an assessment result of the local environment's heat dissipation capacity, which can be a quantitative heat dissipation coefficient or a preset heat dissipation level.

[0114] Optional, combined Figure 5 As shown, step S104 is further proposed: monitoring the battery thermal characteristic response caused by the disturbance, and generating an evaluation result of the local environment heat dissipation capacity based on the battery thermal characteristic response, including:

[0115] S1041, in the process of monitoring the battery thermal characteristic response, obtaining interference indication information corresponding to an operating state of a non-heat dissipation system of the vehicle or an environmental thermal disturbance state of an environment in which the vehicle is located;

[0116] S1042, adjusting the battery thermal characteristic response based on the interference indication information and the battery thermal characteristic response caused by the disturbance, removing or suppressing the influence of the interference source indicated by the interference indication information from the battery thermal characteristic response before adjustment, and obtaining an adjusted battery thermal characteristic response;

[0117] S1043: Generate an evaluation result of the local environment heat dissipation capacity based on the adjusted battery thermal characteristic response.

[0118] Among them, interference indication information refers to data or signals used to quantify or describe the operating status of the vehicle's non-heat dissipation system or the environmental thermal disturbance status. It can be implemented using the output signals of various sensors (such as speed sensors, air conditioning status sensors, light sensors, wind speed sensors) or the status words of the vehicle's internal controller. Its purpose is to provide quantified interference information for subsequent adjustment processing; adjustment processing refers to the operation of correcting or filtering the original battery thermal characteristic response data, which can be implemented using mathematical modeling, signal filtering algorithms (such as Kalman filtering, least squares method) or machine learning-based correction models. Its purpose is to eliminate or weaken the superimposed influence of non-heat dissipation factors on the battery thermal characteristic response.

[0119] In some preferred embodiments, specifically, during the monitoring of the battery thermal response, the vehicle's speed signal can be obtained as interference information indicating the operating status of non-heat dissipation systems, while the ambient light intensity signal can be obtained as interference information indicating the state of ambient thermal disturbances. For example, when a vehicle is traveling at high speed, airflow promotes battery heat dissipation, but the vehicle's powertrain also generates heat. When exposed to direct sunlight, the battery absorbs additional heat. These factors can affect battery temperature fluctuations. After obtaining this information, a model can be established. The model inputs vehicle speed, light intensity, and the original battery temperature variation curve (i.e., the battery thermal response caused by the disturbance) and outputs a corrected battery temperature variation curve (i.e., the adjusted battery thermal response). This model can be a pre-trained regression model or a mathematical model based on physical heat transfer principles and incorporating empirical parameters. The model's purpose is to predict the additional impact of the current speed and light intensity on the battery temperature and subtract this impact from the original temperature variation. Finally, based on this interference-stripped or suppressed temperature variation curve, a more accurate local ambient heat dissipation coefficient is calculated to serve as an assessment of the local ambient heat dissipation capacity.

[0120] Optional, combined Figure 6 As shown, the present application further proposes that after step S203: generating the heat dissipation adjustment parameters of the heat dissipation system, the following steps are further included:

[0121] S204, determining initial heat dissipation adjustment parameters based on the heat dissipation adjustment parameters and the current thermal state of the battery of the vehicle;

[0122] S205, obtaining the operation status information of the adjacent vehicles;

[0123] S206, based on the initial heat dissipation adjustment parameters and the operation status information, predicting the heat impact value on adjacent vehicles when heat dissipation is performed using the initial heat dissipation adjustment parameters;

[0124] S207 , if the thermal impact value is greater than the preset impact threshold, adjusting the final heat dissipation adjustment parameter based on the initial heat dissipation adjustment parameter while maintaining the battery temperature of the vehicle within a safe operating range;

[0125] S208: If the thermal impact value is less than or equal to the preset impact threshold, the initial heat dissipation adjustment parameter is determined as the final heat dissipation adjustment parameter.

[0126] The initial heat dissipation adjustment parameters refer to the expected operating parameters of the vehicle's heat dissipation system, determined based on the initially generated heat dissipation adjustment parameters and the current thermal state of the vehicle's battery, before considering the impact on neighboring vehicles. These parameters can be implemented using rule-based, model-based calculations, or table lookup methods. Their purpose is to provide a benchmark for subsequent evaluation of the thermal impact on neighboring vehicles.

[0127] Neighboring vehicle operating status information refers to information that characterizes the neighboring vehicle's current operating mode, load, battery temperature, cooling system operating status, and other information that can reflect its heat generation and heat dissipation capabilities, as well as its sensitivity to external thermal disturbances. This information can be obtained through vehicle-to-vehicle communication (V2V), a central dispatch system, or a cloud platform. Its purpose is to provide the necessary input for predicting the impact of the vehicle's cooling behavior on neighboring vehicles.

[0128] The thermal impact value (TIV) quantifies the degree of heat transfer or temperature rise to neighboring vehicles when the vehicle uses initial cooling adjustment parameters. This value can be calculated using thermodynamic models, simulated predictions, or learned from historical data. Its purpose is to assess the potential adverse effects of the vehicle's cooling behavior on the surrounding environment and neighboring vehicles.

[0129] The preset impact threshold is an upper limit set for the thermal impact value. When the predicted thermal impact value exceeds this threshold, it is considered that the heat dissipation behavior of the vehicle may have an unacceptable impact on neighboring vehicles. The preset impact threshold can be pre-set based on factors such as vehicle type, operating environment, and safety standards. Its purpose is to define the acceptable range of the impact of the vehicle's heat dissipation behavior on neighboring vehicles.

[0130] The safe operating range (SOR) refers to the temperature range that the vehicle's battery modules must maintain to ensure normal performance and avoid damage or thermal runaway. It is typically determined by the battery manufacturer or system integrator based on battery characteristics. Its purpose is to provide basic constraints that must be met for vehicle cooling adjustments.

[0131] The final heat dissipation adjustment parameters refer to the final operating parameters actually applied to the vehicle's cooling system, determined after comprehensive consideration of the vehicle's battery safety and the thermal impact on neighboring vehicles. They can be obtained by reducing or fine-tuning the initial heat dissipation adjustment parameters. Their purpose is to optimize the impact on neighboring vehicles while ensuring the vehicle's own safety.

[0132] In some preferred embodiments, after generating cooling adjustment parameters for the cooling system based on the local environment's cooling capacity assessment, the current thermal state of the local battery, and the thermal impact assessment, the system may first determine an initial cooling fan speed percentage based on this parameter and the current battery temperature of the host vehicle, using an internal model or a lookup table, as the initial cooling adjustment parameter. Subsequently, the system may receive broadcast information from neighboring vehicles via the inter-vehicle communication module. This information may include information about the neighboring vehicle's battery temperature, whether it is currently charging or discharging, and the current cooling fan speed, constituting the neighboring vehicle's operating status information. The system then utilizes a pre-defined heat transfer model that considers the relative distance and direction between the host vehicle and the neighboring vehicle, as well as the initial cooling fan speed, combined with the neighboring vehicle's operating status information, to predict the potential increase in battery temperature in the neighboring vehicle caused by the host vehicle's cooling fan operating at this speed, using this percentage as the thermal impact value. The system then compares this predicted thermal impact value with a pre-defined impact threshold (e.g., 1.5 degrees Celsius). If the predicted thermal impact value exceeds 1.5 degrees Celsius, the system initiates the adjustment process. During this process, the system attempts to reduce the cooling fan speed while ensuring the vehicle's battery temperature does not exceed its upper safety limit (e.g., 55 degrees Celsius). For each attempted speed, the system re-estimates the thermal impact on neighboring vehicles until it finds a speed that both maintains the vehicle's battery temperature and minimizes the thermal impact on neighboring vehicles. This speed is then determined as the final cooling adjustment parameter. If the predicted thermal impact value is 1.5 degrees Celsius or less, the initial cooling fan speed is deemed acceptable and is directly set as the final cooling adjustment parameter.

[0133] Optional, combined Figure 7 As shown, step S206: based on the initial heat dissipation adjustment parameters and the operation status information, predicting the heat impact value on the adjacent vehicles when the initial heat dissipation adjustment parameters are used for heat dissipation, including:

[0134] S2061, obtaining environmental structure information representing the physical structure or cargo stacking form within the vehicle operation area;

[0135] S2062 : Determine the heat impact value on the neighboring vehicle based on the initial heat dissipation adjustment parameter, the operating status information of the neighboring vehicle, the relative position of the host vehicle and the neighboring vehicle, and the environmental structure information.

[0136] Environmental structure information refers to the physical layout and cargo stacking within the vehicle's operating area. This information can be obtained using sensor data, pre-set map information, or manually input warehouse layout data. Its purpose is to quantify the impact of the environment on heat transfer. Neighboring vehicle operating status information refers to the neighboring vehicle's current workload, battery temperature, cooling system status, and other information. This information can be obtained through inter-vehicle communication or a cloud platform. Its purpose is to reflect the neighboring vehicle's own heat load and heat dissipation. The relative position of the host vehicle and neighboring vehicles refers to the spatial distance and orientation between the host vehicle and the neighboring vehicle. This information can be obtained using GPS, UWB positioning, or relative positioning based on vision or lidar. Its purpose is to determine the path and intensity attenuation of heat transfer. The thermal impact value (TIV) refers to the degree of change in the temperature or thermal environment of the neighboring vehicle caused by the host vehicle's heat dissipation behavior. This information can be determined using heat conduction models, CFD simulations, or machine learning models based on historical data. Its purpose is to quantify the potential adverse effects of the host vehicle's heat dissipation on neighboring vehicles.

[0137] In some preferred embodiments, vehicle A is operating in a narrow aisle of an automated warehouse with its cooling system activated. Vehicle B is operating in an adjacent aisle or in the same aisle ahead. Environmental structural information can be obtained using a pre-established three-dimensional warehouse map or by using vehicle A's onboard LiDAR to scan the current area and identify information such as shelves, walls, and the height and density of stacked goods. Operating status information of neighboring vehicle B can be obtained through inter-vehicle communication or the cloud, including vehicle B's current battery temperature, motor load, and cooling fan speed. The relative position of vehicle A and neighboring vehicle B can be determined by obtaining their real-time coordinates through their positioning systems and calculating their relative distance and orientation. To determine the thermal impact value, initial cooling adjustment parameters (vehicle A's cooling fan power), vehicle B's operating status information, the relative positions of vehicles A and B, and environmental structural information can be input into a pre-defined thermal impact prediction model. This model can be a simplified computational fluid dynamics (CFD) model or a neural network model trained on historical data. For example, the model considers the obstruction of air flow by racks, calculates the diffusion path and velocity of the hot air exhausted by vehicle A within the aisle, and the temperature rise when it reaches vehicle B, thereby determining the thermal impact value.

[0138] Optional, combined Figure 8 As shown, step S208 is further proposed: adjusting the initial heat dissipation adjustment parameters to obtain the final heat dissipation adjustment parameters, including:

[0139] S2081, generating a set of candidate final heat dissipation adjustment parameters based on the initial heat dissipation adjustment parameters, the operating status information of the neighboring vehicles, the current thermal state of the battery of the own vehicle, and the environmental structure information;

[0140] S2082, respectively predicting the impact of a set of candidate final heat dissipation adjustment parameters on the thermal impact of neighboring vehicles, the control effect on the battery temperature of the own vehicle, the energy consumption level, and the life of the heat dissipation system;

[0141] S2083: On the premise that the battery temperature of the vehicle is within a safe operating range, select the candidate final heat dissipation adjustment parameter with the best prediction performance as the final heat dissipation adjustment parameter.

[0142] The initial heat dissipation adjustment parameters refer to parameters used for preliminary heat dissipation adjustment attempts, determined based on the initially generated heat dissipation adjustment parameters and the real-time thermal state of the vehicle's battery. These parameters can be obtained using methods such as rule-based, lookup table, or model calculation. The operating status information of neighboring vehicles refers to information reflecting the current operating conditions of neighboring vehicles, such as whether they are performing high-power operations, whether their cooling systems are operating, and the intensity of these operations. This information can be obtained through vehicle-to-vehicle communication, sensor sensing, or acquisition from a cloud platform. The current thermal state of the vehicle's battery refers to information reflecting the vehicle's real-time battery temperature, temperature rise rate, and temperature distribution. This information can be obtained through direct measurement with a temperature sensor or estimation using a battery model. Environmental structure information refers to information characterizing the physical structure or cargo stacking configuration within the vehicle's operating area, such as aisle width and height, shelf layout, and cargo stacking density. This information can be obtained using methods such as preset map data, sensor scanning, or visual recognition. A set of candidate final heat dissipation adjustment parameters refers to a set of multiple selectable parameters generated based on various relevant information and used for final heat dissipation adjustment. This information can be generated using methods such as optimization algorithms, machine learning models, or a preset strategy library. Prediction refers to estimating possible future outcomes based on current information and models. This involves estimating the various impacts of adopting different candidate cooling adjustment parameters. This can be achieved using physical models, simulation models, or data-driven models. The best prediction result refers to the candidate parameter that performs best overall or achieves the preset optimization goal across multiple evaluation dimensions, including the thermal impact on neighboring vehicles, the effectiveness of controlling the vehicle's battery temperature, energy consumption levels, and the lifespan of the cooling system, while maintaining the basic constraint that the vehicle's battery temperature is within a safe operating range. This can be evaluated and selected using weighted summation, multi-objective optimization, or priority sorting.

[0143] In some exemplary embodiments, the specific implementation is as follows. Assume that a vehicle is currently in a narrow lane, and a neighboring vehicle is performing a high-power discharge operation. The vehicle's battery temperature is approaching a safe upper limit. Based on the steps of the previous solution, an initial heat dissipation adjustment parameter has been determined, for example, the cooling fan is operating at 80% power. Prediction indicates that if heat dissipation continues at 80% power, the predicted thermal impact on the neighboring vehicle will exceed a preset threshold. In this case, the final heat dissipation adjustment parameter needs to be adjusted according to the method of the present application. First, based on the current initial parameter of 80% power, the high-power discharge state of the neighboring vehicle, the vehicle's battery temperature approaching the upper limit, and the environmental structure of the narrow lane, a set of candidate final heat dissipation adjustment parameters is generated. This set of candidate parameters may include reducing fan power to 70% or 60%, adopting an intermittent heat dissipation mode (for example, running at 80% power for 1 minute and then stopping for 30 seconds), or adjusting the heat dissipation outlet angle (if supported by the system). Prediction is then performed for each of these candidate parameters. For example, the prediction may reveal: the 70% power solution reduces the thermal impact on neighboring vehicles, keeping the battery temperature of the vehicle safe, reducing energy consumption, and minimizing the impact on vehicle lifespan; the 60% power solution further reduces the thermal impact on neighboring vehicles, but may exceed the battery temperature limit for the vehicle itself, resulting in lower energy consumption and a smaller impact on vehicle lifespan; the intermittent cooling solution reduces the thermal impact on neighboring vehicles, keeping the battery temperature safe for the vehicle itself, reducing energy consumption, and minimizing the impact on vehicle lifespan; and the air outlet angle adjustment solution reduces the thermal impact on neighboring vehicles, keeping the battery temperature safe for the vehicle itself, maintaining energy consumption, and minimizing the impact on vehicle lifespan. Finally, under the premise that the battery temperature of the vehicle itself is within the safe operating range, the candidate parameter with the best predicted performance is selected. In this example, the 60% power solution may be eliminated because it cannot ensure a safe battery temperature for the vehicle itself. Among the remaining solutions, selection can be made based on the pre-set optimization objective (e.g., prioritizing reducing the thermal impact on neighboring vehicles over reducing energy consumption). For example, if the intermittent heat dissipation solution performs better than the 70% power solution and the air outlet angle adjustment solution in reducing thermal impact and energy consumption, and can ensure the safety of the vehicle's battery temperature, it will be determined as the final heat dissipation adjustment parameter.

[0144] Optional, combined Figure 9 As shown, step S2083 is further proposed: on the premise that the battery temperature of the vehicle is within a safe operating range, selecting the candidate final heat dissipation adjustment parameter with the best prediction result as the final heat dissipation adjustment parameter, including:

[0145] S20831, selecting a set of candidate final heat dissipation adjustment parameters from the prediction results that satisfy the constraint that the vehicle battery temperature is within a safe operating range;

[0146] S20832: If the set of candidate final heat dissipation adjustment parameters includes a candidate final heat dissipation adjustment parameter, determine the candidate final heat dissipation adjustment parameter as the final heat dissipation adjustment parameter;

[0147] S20833: If the set of candidate final heat dissipation adjustment parameters includes at least two candidate final heat dissipation adjustment parameters, obtain dynamic operating state information representing the vehicle;

[0148] S20834: Evaluate each candidate final cooling adjustment parameter in the set of candidate final cooling adjustment parameters based on the dynamic operating state information and prediction results of the set of candidate final cooling adjustment parameters in terms of reducing thermal impact on neighboring vehicles, minimizing energy consumption increase, and ensuring the service life of the cooling system;

[0149] S20835 : Determine a final heat dissipation adjustment parameter from a set of candidate final heat dissipation adjustment parameters according to the evaluation result.

[0150] Prediction results refer to the expected performance data on multiple preset targets when using different candidate final cooling adjustment parameters. These predictions can be achieved through simulations based on physical models, data-driven models, or hybrid models. A set of candidate final cooling adjustment parameters refers to a set of selectable cooling adjustment parameters derived through a generation mechanism (e.g., based on initial parameters, neighboring vehicle status, the vehicle's battery status, and environmental structural information). These parameters may include one or more specific combinations of cooling power, fan speed, coolant flow, and other parameters. Maintaining the vehicle's battery temperature within the safe operating range (SOR) means maintaining the vehicle's battery module temperature within the manufacturer's specified temperature range to ensure proper battery function and lifespan. This can be achieved by setting upper and lower temperature limits. Dynamic operating status information refers to data reflecting the vehicle's real-time operating status, such as its current load, driving speed, acceleration and deceleration, and mission type. This information can be generated using data collected by internal vehicle sensors or information provided by the task scheduling system. Reducing the thermal impact on neighboring vehicles refers to adjusting the vehicle's cooling behavior to reduce the additional heat load or temperature rise imposed on surrounding vehicles. This can be achieved by predicting the diffusion of heat discharged by the vehicle's cooling system into the environment and its impact on the temperatures of neighboring vehicles. Minimizing energy consumption increases means minimizing the energy consumed by the operation of the cooling system while meeting the cooling requirements. This can be achieved by predicting the system power consumption under different cooling parameters. Ensuring the service life of the cooling system means optimizing the operating mode of the cooling system to reduce the wear or fatigue of system components (such as fans and water pumps) and extend their service life. This can be achieved by predicting the stress of components or the cumulative working time under different operating modes. The evaluation result refers to the quantitative or ranking result obtained after comprehensive consideration of the prediction results of each parameter in the candidate final cooling adjustment parameter set on multiple objectives. It can be achieved by using weighted scoring, multi-objective optimization algorithm or rule-based decision logic.

[0151] The solution of this application selects a set of candidate final cooling adjustment parameters from the prediction results that satisfy the vehicle's battery temperature within the safe operating range, ensuring that subsequent selection is based on meeting the most basic thermal safety requirements. If the set contains only one parameter, it is directly determined, simplifying the process. If the set contains multiple parameters, dynamic operating status information representing the vehicle is further obtained. This information reflects the vehicle's current operating environment and mission requirements and is an important basis for a refined evaluation. Next, each candidate parameter is evaluated based on the dynamic operating status information and the predicted results for multiple objectives, such as reducing the thermal impact on neighboring vehicles, minimizing energy consumption increases, and ensuring the operating life of the cooling system. This step comprehensively considers multiple factors, including the vehicle's own safety, impact on the surrounding environment, its own energy consumption, and system health, avoiding the negative externalities or internal losses that may result from focusing solely on its own temperature. By predicting and evaluating multiple objectives, the comprehensive performance of different candidate parameters can be quantified. Finally, based on the evaluation results, the final cooling adjustment parameters are determined from the candidate set. This evaluation and selection mechanism, based on multi-objective predictions and dynamic state information, selects the optimal overall parameters when multiple options satisfy safety constraints. This minimizes the impact on neighboring vehicles, reduces energy consumption, and extends system life, all while ensuring battery safety. Compared to solutions that simply generate a set of candidate parameters and predict their impact on limited objectives, such as the vehicle's own temperature control, this provides a more comprehensive and refined basis for parameter selection, enabling a better balance of multiple requirements. This comprehensive optimization and selection capability is crucial for improving the overall operational efficiency and stability of a fleet, especially in complex, multi-vehicle collaborative operating environments.

[0152] In some preferred embodiments, when selecting a set of candidate final heat dissipation adjustment parameters from the prediction results that satisfy the constraint that the vehicle's battery temperature is within a safe operating range, an upper and lower battery temperature threshold can be set. Any candidate parameter predicted to cause the battery temperature to exceed this range will be eliminated. If only one candidate parameter remains after screening—for example, if the prediction results show that only parameter A can control the battery temperature within the safe range—parameter A is directly determined as the final heat dissipation adjustment parameter. If, after screening, multiple candidate parameters, such as parameters A, B, and C, all satisfy the battery temperature safety constraint, the system then obtains information about the vehicle's current dynamic operating state, such as when the vehicle is traveling at high speed and carrying a heavy object. Based on this dynamic state information and a pre-set model, the system predicts the thermal impact of parameters A, B, and C on neighboring vehicles (e.g., the magnitude of the temperature increase in neighboring vehicles), the system's own energy consumption (e.g., the power consumption of the cooling system), and the impact on the lifespan of key cooling system components (e.g., the cumulative operating time or speed of the fan) under the current state. For example, the prediction results may show that parameter A has the least impact on neighboring vehicles but slightly higher energy consumption, parameter B has the lowest energy consumption but a greater impact on neighboring vehicles, and parameter C is relatively balanced in all aspects. The system then evaluates parameters A, B, and C based on a pre-set evaluation strategy (for example, weighting neighboring impact, energy consumption, and lifespan to form a weighted sum), generating individual scores. Finally, based on the evaluation scores, the parameter with the highest score is selected as the final heat dissipation adjustment parameter, for example, parameter C with the highest overall score.

[0153] A battery module dynamic thermal management system is used to dynamically manage the thermal performance of vehicle battery modules. Figure 10 Shown, including:

[0154] The heat dissipation capacity assessment module is used to assess the heat dissipation capacity of the local environment and generate an assessment result of the heat dissipation capacity of the local environment when a preset trigger condition is met; when a preset trigger condition is met, the heat dissipation capacity assessment module is used to assess the heat dissipation capacity of the local environment and generate an assessment result of the heat dissipation capacity of the local environment, including:

[0155] Acquire prior environmental information representing the heat dissipation conditions of the local environment in which the vehicle is located and battery status information of the local battery;

[0156] Determine the disturbance amplitude and disturbance duration for evaluating the heat dissipation capacity of the local environment based on prior environmental information and battery status information;

[0157] Controlling a cooling system representing a vehicle and applying disturbances according to the disturbance amplitude and duration;

[0158] Monitor the battery thermal response caused by disturbances and generate an assessment of the local environment's heat dissipation capacity based on the battery thermal response;

[0159] An adjustment parameter generation module is configured to generate heat dissipation adjustment parameters for the heat dissipation system based on the evaluation results of the local environment heat dissipation capacity and the current thermal state of the local battery. The heat dissipation adjustment parameters for the heat dissipation system are generated based on the evaluation results of the local environment heat dissipation capacity and the current thermal state of the local battery, including:

[0160] Obtaining heat dissipation adjustment information of adjacent vehicles, the heat dissipation adjustment information including the position and heat dissipation adjustment parameters of the adjacent vehicles;

[0161] Based on the vehicle's position, the positions of neighboring vehicles, and heat dissipation adjustment parameters, the thermal impact of neighboring vehicles on the vehicle's local environment is evaluated to generate a thermal impact assessment value.

[0162] Generate heat dissipation adjustment parameters for the heat dissipation system based on the evaluation results of the local environment's heat dissipation capacity, the current thermal state of the local battery, and the thermal impact evaluation value;

[0163] A reporting data generation module is used to monitor and record data during the battery thermal management process and generate reporting data information; the reporting data information includes data related to the local environment heat dissipation capacity assessment operation, the local environment heat dissipation capacity assessment result data, the heat dissipation adjustment parameter data of the heat dissipation system, and the battery thermal state change data after the heat dissipation system performs the adjustment;

[0164] The data information reporting module is used to upload the reported data information to the cloud platform so that the cloud platform can perform continuous learning and optimization operations of the thermal management strategy based on the reported data information.

[0165] Among them, the heat dissipation capacity evaluation module refers to the hardware, software or a combination thereof used to perform the local environment heat dissipation capacity evaluation function, which can be implemented by one or more processors executing a preset algorithm program, with the purpose of obtaining the actual heat dissipation capacity information of the local environment in which the vehicle is located; the adjustment parameter generation module refers to a unit used to calculate the adjustment parameters of the heat dissipation system according to the evaluation results and the battery status, which can be implemented by one or more computing units executing a parameter generation model, with the purpose of determining a heat dissipation strategy that adapts to the current environment and battery status; the reporting data generation module refers to a unit used to collect, organize and format thermal management process data, which can be implemented by a data acquisition interface, a storage unit and data processing logic, with the purpose of providing the necessary data basis for the learning optimization of the cloud platform; the data information reporting module refers to a communication unit used to send the generated data information to the cloud platform, which can be implemented by a wireless communication module (such as a Wi-Fi module, a cellular communication module), with the purpose of realizing the transmission of terminal-side data to the cloud platform.

[0166] The solution of the present application is implemented by decomposing the functions of the dynamic thermal management method of the battery module into four cooperating modules: a heat dissipation capacity evaluation module, an adjustment parameter generation module, a reporting data generation module, and a data information reporting module. The heat dissipation capacity evaluation module is activated when specific conditions are met, detects and quantifies the heat dissipation conditions around the vehicle, and generates an evaluation result. This evaluation result is then passed to the adjustment parameter generation module, which combines the current temperature, health status and other information of the local battery to calculate the adjustments that need to be made to the heat dissipation system, such as parameters such as fan speed and coolant flow. Throughout the thermal management process, the reporting data generation module continuously monitors key data, including the details of the evaluation process, the evaluation results, the generated adjustment parameters, and the changes in the battery temperature after the heat dissipation system performs the adjustment, and integrates this data into structured reporting data information. Finally, the data information reporting module is responsible for reliably transmitting these reporting data information to the remote cloud platform. It is precisely because of this modular system structure that the various steps of the dynamic thermal management method described above can be specifically executed on the vehicle side. The heat dissipation capacity assessment module provides the foundation for environmental perception, the adjustment parameter generation module provides decision-making capabilities, and the reporting data generation module and data information reporting module establish a data feedback loop between the end and the cloud, supporting more macro-level and intelligent strategy learning and optimization in the cloud. This system-level design makes the original method process operational and responsive in real time, effectively adapting to complex local thermal environments and battery state changes, thus resolving the problem of lacking a specific system structure to implement thermal management methods.

[0167] In some preferred embodiments, the present application is implemented as follows: a heat dissipation capacity assessment module can be implemented by a dedicated processing unit in the vehicle's thermal management controller. This processing unit is connected to environmental sensing devices such as temperature sensors, humidity sensors, and wind speed sensors. Upon receiving a trigger signal from the vehicle's main control unit, it initiates a preset environmental assessment algorithm, for example, by analyzing sensor data or performing an active perturbation test to assess the heat dissipation coefficient of the local environment and stores the assessment results in local memory. An adjustment parameter generation module can be implemented by another processing unit in the thermal management controller. This processing unit reads the assessment results generated by the heat dissipation capacity assessment module and battery temperature, voltage, current, and health status data provided by the battery management system (BMS), executes a rule- or model-based parameter generation logic, and calculates control parameters for the cooling system, such as the PWM duty cycle of the cooling fan and the speed of the cooling pump. A reporting data generation module can be integrated into the data processing unit of the thermal management controller. This unit collects the assessment process data, assessment results, generated control parameters, and the battery temperature change curve over time after control execution in real time, and packages this data in a preset data format. The data information reporting module can be implemented by the vehicle's communication unit, for example, through the vehicle-mounted T-Box module, using the cellular network or the vehicle local area network to send the reported data information to the cloud server.

[0168] Through the above technical solution, a system capable of executing a dynamic thermal management method for battery modules is provided. Through a modular design, the system enables the evaluation of environmental heat dissipation capacity, dynamic adjustment of heat dissipation parameters, and reporting of key data to be implemented specifically on the vehicle side, solving the problem of lack of a specific system structure to support the implementation of thermal management methods, improving the real-time and adaptability of thermal management strategies, and providing data support for continuous learning and optimization of cloud platforms.

[0169] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A dynamic thermal management method for a battery module, characterized in that: The following steps are involved: When a preset trigger condition is met, the local environment heat dissipation capacity is evaluated and an evaluation result of the local environment heat dissipation capacity is generated; the preset trigger condition includes an abnormal increase in vehicle battery temperature or a deviation between cloud instructions from the cloud platform and local perception exceeding a preset threshold; When a preset trigger condition is met, the local environment heat dissipation capacity is evaluated for the local environment, and an evaluation result of the local environment heat dissipation capacity is generated, including: Acquire prior environmental information representing the heat dissipation conditions of the local environment in which the vehicle is located and battery status information of the local battery; Determining, based on the prior environmental information and the battery status information, a disturbance amplitude and a disturbance duration for evaluating a heat dissipation capability of the local environment; controlling a cooling system representing the vehicle to apply a disturbance according to the disturbance amplitude and the disturbance duration; monitoring a battery thermal characteristic response caused by a disturbance, and generating an evaluation result of the heat dissipation capacity of the local environment based on the battery thermal characteristic response; Generating a heat dissipation adjustment parameter of a heat dissipation system based on the evaluation result of the heat dissipation capacity of the local environment and the current thermal state of the local battery; generating the heat dissipation adjustment parameter of the heat dissipation system based on the evaluation result of the heat dissipation capacity of the local environment and the current thermal state of the local battery includes: Acquiring heat dissipation adjustment information of an adjacent vehicle, the heat dissipation adjustment information including a position of the adjacent vehicle and a heat dissipation adjustment parameter; Based on the position of the vehicle, the position of the neighboring vehicles, and the heat dissipation adjustment parameter, a thermal impact assessment of the neighboring vehicles on the local environment of the vehicle is performed to generate a thermal impact assessment value; generating a heat dissipation adjustment parameter of a heat dissipation system according to the evaluation result of the heat dissipation capacity of the local environment, the current thermal state of the local battery, and the thermal impact evaluation value; During the battery thermal management process, data is monitored and recorded to generate reporting data information; the reporting data information includes data related to the local environment heat dissipation capacity assessment operation, the assessment result data of the local environment heat dissipation capacity, the heat dissipation adjustment parameter data of the heat dissipation system, and the battery thermal state change data after the heat dissipation system performs the adjustment; The reported data information is uploaded to the cloud platform, so that the cloud platform performs continuous learning and optimization operations of the thermal management strategy based on the reported data information.

2. A battery module dynamic thermal management method according to claim 1, characterized in that: The generating of the heat dissipation adjustment parameters of the heat dissipation system according to the evaluation result of the heat dissipation capacity of the local environment, the current thermal state of the local battery, and the thermal impact evaluation value includes: Obtain environmental structural information that characterizes the physical structure or cargo stacking form within the vehicle operation area; A heat dissipation adjustment parameter of a heat dissipation system is generated according to the evaluation result of the heat dissipation capacity of the local environment, the current thermal state of the local battery, the thermal impact evaluation value, and the environmental structure information.

3. A battery module dynamic thermal management method according to claim 1, characterized in that: The monitoring of the battery thermal characteristic response caused by the disturbance and generating an evaluation result of the local environment heat dissipation capacity according to the battery thermal characteristic response includes: In the process of monitoring the battery thermal characteristic response, obtaining interference indication information corresponding to the non-heat dissipation system operating state of the vehicle or the environmental thermal disturbance state of the vehicle environment; adjusting the battery thermal characteristic response according to the interference indication information and the battery thermal characteristic response caused by the disturbance, removing or suppressing the influence of the interference source indicated by the interference indication information from the battery thermal characteristic response before the adjustment, and obtaining an adjusted battery thermal characteristic response; An evaluation result of the local environment heat dissipation capacity is generated according to the adjusted battery thermal characteristic response.

4. A battery module dynamic thermal management method according to claim 1, characterized in that: After generating the heat dissipation adjustment parameters of the heat dissipation system according to the evaluation result of the local environment heat dissipation capacity, the current thermal state of the local battery, and the thermal impact evaluation value, the method further includes: determining an initial heat dissipation adjustment parameter based on the heat dissipation adjustment parameter and a current thermal state of the battery of the vehicle; Obtain operating status information of neighboring vehicles; Based on the initial heat dissipation adjustment parameter and the operation status information, predicting a heat impact value on the adjacent vehicle when heat dissipation is performed using the initial heat dissipation adjustment parameter; If the thermal impact value is greater than a preset impact threshold, the final heat dissipation adjustment parameter is adjusted according to the initial heat dissipation adjustment parameter while maintaining the battery temperature of the vehicle within a safe operating range; If the thermal impact value is less than or equal to a preset impact threshold, the initial heat dissipation adjustment parameter is determined as the final heat dissipation adjustment parameter.

5. A battery module dynamic thermal management method according to claim 4, characterized in that: The predicting, based on the initial heat dissipation adjustment parameter and the operation status information, a heat impact value on the adjacent vehicle when heat dissipation is performed using the initial heat dissipation adjustment parameter includes: Obtain environmental structural information that characterizes the physical structure or cargo stacking form within the vehicle operation area; A heat impact value on the neighboring vehicle is determined based on the initial heat dissipation adjustment parameter, the operating status information of the neighboring vehicle, the relative position of the host vehicle and the neighboring vehicle, and the environmental structure information.

6. A battery module dynamic thermal management method according to claim 5, characterized in that: The adjusting the final heat dissipation adjustment parameter according to the initial heat dissipation adjustment parameter includes: generating a set of candidate final heat dissipation adjustment parameters based on the initial heat dissipation adjustment parameters, the operating status information of the neighboring vehicle, the current thermal state of the battery of the own vehicle, and the environmental structure information; respectively predicting the effects of the set of candidate final heat dissipation adjustment parameters on the thermal impact of neighboring vehicles, the control effect on the battery temperature of the own vehicle, the energy consumption level, and the life of the heat dissipation system; On the premise that the battery temperature of the vehicle is within a safe operating range, the candidate final heat dissipation adjustment parameter with the best prediction result is selected as the final heat dissipation adjustment parameter.

7. A battery module dynamic thermal management method according to claim 6, characterized in that: The method of selecting the candidate final heat dissipation adjustment parameter with the best prediction result as the final heat dissipation adjustment parameter under the premise that the battery temperature of the vehicle is within a safe operating range includes: Filtering a set of candidate final heat dissipation adjustment parameters from the prediction results that satisfy the constraint that the vehicle's battery temperature is within a safe operating range; If the set of candidate final heat dissipation adjustment parameters includes one candidate final heat dissipation adjustment parameter, determining the one candidate final heat dissipation adjustment parameter as the final heat dissipation adjustment parameter; If the set of candidate final heat dissipation adjustment parameters includes at least two candidate final heat dissipation adjustment parameters, obtaining dynamic operating state information representing the vehicle; evaluating each candidate final heat dissipation adjustment parameter in the set of candidate final heat dissipation adjustment parameters based on the dynamic operating state information and prediction results of the set of candidate final heat dissipation adjustment parameters in terms of reducing thermal impact on neighboring vehicles, minimizing energy consumption increase, and ensuring the service life of the heat dissipation system; According to the evaluation result, the final heat dissipation adjustment parameter is determined from the set of candidate final heat dissipation adjustment parameters.

8. A battery module dynamic thermal management system for dynamic thermal management of vehicle battery modules, characterized in that: include: The heat dissipation capacity evaluation module is configured to evaluate the heat dissipation capacity of the local environment and generate an evaluation result of the heat dissipation capacity of the local environment when a preset trigger condition is met. The heat dissipation capacity evaluation module is configured to evaluate the heat dissipation capacity of the local environment and generate an evaluation result of the heat dissipation capacity of the local environment when a preset trigger condition is met, including: Acquire prior environmental information representing the heat dissipation conditions of the local environment in which the vehicle is located and battery status information of the local battery; Determining, based on the prior environmental information and the battery status information, a disturbance amplitude and a disturbance duration for evaluating a heat dissipation capability of the local environment; controlling a cooling system representing the vehicle to apply a disturbance according to the disturbance amplitude and the disturbance duration; monitoring a battery thermal characteristic response caused by a disturbance, and generating an evaluation result of the heat dissipation capacity of the local environment based on the battery thermal characteristic response; An adjustment parameter generation module is configured to generate heat dissipation adjustment parameters for a heat dissipation system based on the evaluation result of the heat dissipation capacity of the local environment and the current thermal state of the local battery. The generation of the heat dissipation adjustment parameters for the heat dissipation system based on the evaluation result of the heat dissipation capacity of the local environment and the current thermal state of the local battery includes: Acquiring heat dissipation adjustment information of an adjacent vehicle, the heat dissipation adjustment information including a position of the adjacent vehicle and a heat dissipation adjustment parameter; Based on the position of the vehicle, the position of the neighboring vehicles, and the heat dissipation adjustment parameter, a thermal impact assessment of the neighboring vehicles on the local environment of the vehicle is performed to generate a thermal impact assessment value; generating a heat dissipation adjustment parameter of a heat dissipation system according to the evaluation result of the heat dissipation capacity of the local environment, the current thermal state of the local battery, and the thermal impact evaluation value; a reporting data generation module, configured to monitor and record data during the battery thermal management process and generate reporting data information; the reporting data information includes data related to the local environment heat dissipation capacity assessment operation, the assessment result data of the local environment heat dissipation capacity, the heat dissipation adjustment parameter data of the heat dissipation system, and the battery thermal state change data after the heat dissipation system performs the adjustment; The data information reporting module is used to upload the reported data information to the cloud platform so that the cloud platform can perform continuous learning and optimization operations of the thermal management strategy based on the reported data information.

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