A method and device for thermal management of power batteries

Abstract

This application provides a power battery thermal management method and device, relating to the field of vehicle battery technology. The method includes, if a vehicle is detected entering a first preset road segment, acquiring the first battery state, first vehicle speed, and first estimated duration of the first preset road segment, and using a power battery thermal prediction model to predict the first temperature prediction curve of the power battery within the first estimated duration. Based on the first temperature prediction curve, it predicts whether the target predicted temperature of the power battery after the vehicle leaves the first preset road segment reaches a preset safe temperature threshold. If the target predicted temperature does not reach the preset safe temperature threshold, a first control command is sent to the cooling system to shut down the compressor and / or reduce the speed of the cooling water pump. By generating a temperature prediction curve through the thermal prediction model and assessing safety risks, a control command is finally sent to the cooling system to reduce its energy consumption.

Description

Technical Field

[0001] This invention relates to the field of vehicle battery technology, and more specifically, to a method and device for thermal management of power batteries. Background Technology

[0002] With the large-scale application of electric vehicles and large-scale energy storage systems, the safety and lifespan of lithium-ion batteries have become core bottlenecks restricting the high-quality development of the industry. Battery operating temperature is a key factor affecting its performance output, cycle life, and safety stability. Practice shows that the ideal battery operating temperature range needs to be controlled between 20℃ and 40℃, and the temperature difference between individual cells within the battery pack should be strictly controlled within 5℃. This requirement places extremely high demands on the precise control capabilities of the thermal management system. Therefore, developing efficient and reliable battery thermal management control strategies has become an important issue for ensuring the overall performance of battery systems and promoting the continuous progress of related industries.

[0003] Currently, the most widely used thermal management system for electric vehicle batteries is the proportional-integral-differential (PID) control strategy based on current temperature feedback. Its core logic is to monitor the battery temperature in real time using a temperature sensor, activate the cooling system (such as a compressor or cooling water pump) when the temperature exceeds a preset threshold, and stop cooling when the temperature drops below the threshold, thus achieving initial temperature regulation. However, this reactive control mode has significant drawbacks: severe energy waste, with the cooling system frequently starting and stopping near the temperature critical point, generating a large amount of unnecessary energy consumption; control lag, due to battery thermal inertia, when cooling is activated after the temperature exceeds the standard, the actual temperature has already exceeded the ideal range, while after the temperature drops to the threshold, the battery's residual heat can still maintain a safe state, leading to inappropriate cooling timing; and an overly conservative control strategy, setting a large temperature safety margin to avoid safety risks, resulting in excessively long cooling system operation time, further exacerbating energy consumption problems. Furthermore, some existing technologies attempt to introduce predictive control schemes, but these are mostly based on simple linear models or empirical formulas, resulting in insufficient prediction accuracy and difficulty in achieving effective energy-saving control while ensuring battery safety. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of the prior art by providing a power battery thermal management method and device. This method and device can accurately obtain key parameters such as battery status, vehicle speed, and road segment duration by combining navigation information to identify preset road segments, generate temperature curves based on thermal prediction models, assess safety risks, and finally send targeted control commands to the cooling system, thus realizing a shift in control logic from passive response to proactive prediction.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, embodiments of this application provide a power battery thermal management method, the method comprising:

[0007] If a vehicle is detected to have entered a first preset road segment, the first battery state, first vehicle speed, and first estimated duration of the first preset road segment are obtained.

[0008] Based on the first battery state, the first preset road section, the first vehicle speed, and the first estimated duration, the thermal prediction model of the power battery is used to predict the first temperature prediction curve of the power battery within the first estimated duration.

[0009] Based on the first temperature prediction curve, predict whether the target predicted temperature of the power battery after the vehicle leaves the first preset road section reaches the preset safe temperature threshold.

[0010] If the target predicted temperature does not reach the preset safe temperature threshold, a first control command is sent to the refrigeration system to cause the refrigeration system to shut down the compressor and / or reduce the speed of the cooling water pump.

[0011] In an optional implementation, the step of using the thermal prediction model of the power battery to predict the first temperature prediction curve of the power battery within the first estimated time period based on the first battery state, the first preset road segment, the first vehicle speed, and the first estimated duration includes:

[0012] Based on the first battery state, the first preset road section, the first vehicle speed, and the first estimated duration, the thermal prediction model is used to predict the temperature prediction curves of multiple battery modules in the power battery within the first estimated duration.

[0013] The temperature prediction curve of the power battery within the first expected time period is obtained based on the temperature prediction curves of the multiple battery modules within the first expected time period.

[0014] In an optional implementation, the first battery state includes: the first temperature of multiple locations in each battery module and the current state of charge (SOC) parameter of the power battery;

[0015] The step of using the thermal prediction model to predict the temperature of multiple battery modules in the power battery within the first estimated time period based on the first battery state, the first preset road segment, the first vehicle speed, and the first estimated duration includes:

[0016] Obtain the simulation models of the multiple battery modules pre-created in the preset simulation environment. The simulation model of each battery module includes simulation modules of multiple parts in each battery module.

[0017] Based on the first temperature of the multiple parts, the current SOC parameter, the first vehicle speed, the first estimated duration, and the first preset road segment, the simulation module of the multiple parts is used to make predictions to obtain the temperature prediction sequence of the multiple parts in each battery module within the first estimated duration.

[0018] Based on the temperature prediction sequence of the multiple parts in each battery module within the first estimated time, a temperature prediction curve for each battery module within the first estimated time is determined.

[0019] In an optional embodiment, the first temperature of the plurality of parts includes: the first temperature of the plurality of cell parts in each battery module, the first temperature of the water-cooled plate in contact with the last cell part, the first temperature of the coolant in the water-cooled plate, and the inlet temperature of the coolant; the simulation module of the plurality of parts includes: the simulation module of the plurality of cell parts, the simulation module of the water-cooled plate, and the simulation module of the coolant.

[0020] The step involves using simulation modules for each of the multiple locations to predict the temperature of the multiple locations within the first estimated time, based on the first temperature of the multiple locations, the current SOC parameter, the first vehicle speed, the first estimated duration, and the first preset road segment. This results in a temperature prediction sequence for each of the multiple locations in each battery module within the first estimated duration, including:

[0021] In the preset simulation environment, the temperature prediction sequence of the multiple battery cell parts within the first estimated time is calculated based on the first temperature of the multiple battery cell parts, the current SOC parameters, the preset specific heat capacity of the multiple battery cell parts, the first estimated time, and the first preset road segment.

[0022] In the preset simulation environment, the temperature prediction sequence of the water-cooled plate within the first estimated time period is calculated based on the first temperature of the water-cooled plate, the first vehicle speed, the first temperature of the coolant, and the first estimated time period.

[0023] In the preset simulation environment, the temperature prediction sequence of the coolant within the first estimated time period is calculated based on the first temperature of the coolant, the inlet temperature of the coolant, and the first estimated time period.

[0024] In an optional implementation, the step of calculating the temperature prediction sequence of the plurality of battery cell parts within the first estimated time period based on the first temperature of the plurality of battery cell parts, the current SOC parameter, the preset specific heat capacity of the plurality of battery cell parts, the first estimated time period, and the first preset road segment in the preset simulation environment includes:

[0025] The reference heat of the multiple battery cell parts is determined based on the first temperature of the multiple battery cell parts, the current SOC parameter, and the current parameter corresponding to the first preset circuit segment.

[0026] The heat flow rate of the multiple battery cell parts is determined based on the reference heat of the multiple battery cell parts and the corresponding calibration parameters of the multiple battery cell parts;

[0027] The heat flow between the multiple battery cell parts is determined based on the first temperature of the multiple battery cell parts and the contact area between the multiple battery cell parts;

[0028] Based on the heat flow of the multiple battery cell parts, the heat flow between the multiple battery cell parts, the preset specific heat capacity of the multiple battery cell parts, and the first estimated duration, a temperature prediction sequence for the multiple battery cell parts within the first estimated duration is determined.

[0029] In an optional implementation, calculating the temperature prediction sequence of the water-cooled plate within the first estimated time period based on the first temperature of the water-cooled plate, the first vehicle speed, the first temperature of the coolant, and the first estimated time period in the preset simulation environment includes:

[0030] The heat flow between the water-cooled plate and the environment is determined based on the first temperature of the water-cooled plate, the current ambient temperature, the first vehicle speed, and the contact area between the water-cooled plate and the environment.

[0031] The heat flow between the water-cooled plate and the coolant is determined based on the first temperature of the water-cooled plate, the first temperature of the coolant, and the contact area between the water-cooled plate and the coolant.

[0032] Based on the heat flow between the water-cooled plate and the environment, the heat flow between the water-cooled plate and the coolant, and the first estimated duration, a temperature prediction sequence for the water-cooled plate within the first estimated duration is determined.

[0033] In an optional implementation, calculating the temperature prediction sequence of the coolant within the first estimated time period based on the first temperature of the coolant, the inlet temperature of the coolant, and the first estimated time period in the preset simulation environment includes:

[0034] The heat flow rate of the coolant entering the water-cooled plate is determined based on the inlet temperature of the coolant.

[0035] The heat flow rate of the coolant exiting the water-cooled plate is determined based on the first temperature of the coolant.

[0036] Based on the heat flow rate of the coolant entering the water-cooled plate, the heat flow rate of the coolant exiting the water-cooled plate, and the first estimated duration, a temperature prediction sequence of the coolant within the first estimated duration is determined.

[0037] In an optional implementation, predicting whether the target predicted temperature of the power battery after the vehicle leaves the first preset road segment reaches a preset safe temperature threshold, based on the temperature prediction curve, includes:

[0038] Based on the temperature prediction curve, the predicted temperature characteristics of the power battery within the predicted duration are obtained; wherein, the predicted temperature characteristics include at least one of the following: predicted peak temperature, the arrival time of the predicted peak temperature, and the predicted temperature change rate;

[0039] Based on the predicted temperature characteristics, predict whether the target predicted temperature reaches the preset safe temperature threshold.

[0040] In an optional implementation, the method further includes:

[0041] If it is detected that the vehicle enters the second preset road segment from the first preset road segment after a preset future time, and the second estimated duration of the second preset road segment is obtained, the second battery state and the second vehicle speed of the power battery are obtained;

[0042] Based on the second battery state, the second preset road section, the second vehicle speed, and the second estimated duration, the thermal prediction model of the power battery is used to predict the second temperature prediction curve of the power battery within the second estimated duration.

[0043] Based on the first temperature prediction curve, the second battery state of the power battery, and the second estimated duration, calculate the first predicted temperature when the vehicle begins to enter the second preset road segment;

[0044] Based on the second temperature prediction curve, predict the time it takes for the power battery to rise from the first predicted temperature to the preset safe temperature threshold.

[0045] If the temperature rise time is less than or equal to the second predicted time, then the predicted lead time before the preset future time is calculated based on the second temperature prediction curve, the first predicted temperature, the preset response time of the refrigeration system, and the preset battery temperature safety margin.

[0046] Based on the expected lead time, a second control command is sent to the refrigeration system to cause the refrigeration system to start the compressor and / or increase the speed of the cooling water pump.

[0047] Secondly, embodiments of this application also provide a power battery thermal management device, the device comprising:

[0048] The acquisition module is used to acquire the first battery state, the first vehicle speed, and the first estimated duration of the first preset road segment if the vehicle is detected to have entered the first preset road segment.

[0049] The prediction module is used to predict the first temperature prediction curve of the power battery within the first estimated time period by using the thermal prediction model of the power battery based on the first battery state, the first preset road section, the first vehicle speed and the first estimated time period.

[0050] The prediction module is also used to predict, based on the first temperature prediction curve, whether the target predicted temperature of the power battery after the vehicle leaves the first preset road section reaches a preset safe temperature threshold.

[0051] The sending module is used to send a first control command to the refrigeration system if the target predicted temperature does not reach the preset safe temperature threshold, so that the refrigeration system shuts down the compressor and / or reduces the speed of the cooling water pump.

[0052] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores program instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to perform the steps of the power battery thermal management method as described in any of the first aspects.

[0053] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the power battery thermal management method as described in any of the first aspects.

[0054] The beneficial effects of this application are:

[0055] This application provides a power battery thermal management method and device, including: if a vehicle is detected entering a first preset road segment, acquiring the first battery state, first vehicle speed, and first estimated duration of the first preset road segment; using a power battery thermal prediction model to predict the first temperature prediction curve of the power battery within the first estimated duration based on the first battery state, the first preset road segment, the first vehicle speed, and the first estimated duration; predicting whether the target predicted temperature of the power battery after the vehicle leaves the first preset road segment reaches a preset safe temperature threshold based on the first temperature prediction curve; if the target predicted temperature does not reach the preset safe temperature threshold, sending a first control command to the cooling system to shut down the compressor and / or reduce the speed of the cooling water pump. This method, by combining navigation information to identify preset road segments, accurately acquiring key parameters such as battery state, vehicle speed, and road segment duration, generating temperature curves based on a thermal prediction model and assessing safety risks, and finally sending targeted control commands to the cooling system, achieves a shift in control logic from passive response to proactive prediction. This process can prevent the cooling system from operating ineffectively under low load conditions while ensuring that the battery temperature is always below the safety threshold. It significantly reduces the frequency of compressor start-stop and the duration of high-speed operation of water pump, thereby greatly reducing the energy consumption of the cooling system. At the same time, it avoids the temperature overshoot problem caused by thermal inertia in traditional PID control by using accurate temperature prediction, thus balancing battery safety and vehicle energy efficiency. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is one of the flowcharts illustrating a power battery thermal management method provided in an embodiment of this application;

[0058] Figure 2 A second schematic flowchart illustrating a power battery thermal management method provided in this application embodiment;

[0059] Figure 3 A third schematic flowchart illustrating a power battery thermal management method provided in this application embodiment;

[0060] Figure 4 A schematic diagram of a battery module provided in an embodiment of this application;

[0061] Figure 5 A fourth schematic flowchart illustrating a power battery thermal management method provided in this application embodiment;

[0062] Figure 6 Fifth of a flowchart illustrating a power battery thermal management method provided in this application embodiment;

[0063] Figure 7 A schematic flowchart of a power battery thermal management method provided in this application embodiment is shown in Figure 6.

[0064] Figure 8 The seventh schematic flowchart of a power battery thermal management method provided in this application embodiment;

[0065] Figure 9 This is the eighth flowchart illustrating a power battery thermal management method provided in this application embodiment;

[0066] Figure 10 A flowchart illustrating a power battery thermal management method provided in this application is shown in Figure 9.

[0067] Figure 11 This is a schematic diagram of the functional modules of a power battery thermal management device provided in an embodiment of this application;

[0068] Figure 12 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0070] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0071] In the description of this application, it should be noted that if the terms "upper", "lower", etc. appear to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship that the product of this application is usually placed in, it is only for the convenience of describing this application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0072] Furthermore, the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Additionally, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0073] It should be noted that, where there is no conflict, the features in the embodiments of this application can be combined with each other.

[0074] The power battery thermal management method provided in this application will be explained in detail below with reference to the accompanying drawings and specific examples. Figure 1 This is one of the flowcharts illustrating a power battery thermal management method provided in an embodiment of this application; as shown below. Figure 1 As shown, the method includes:

[0075] S101. If the vehicle is detected to have entered the first preset road segment, the first battery state, the first vehicle speed, and the first estimated duration of the first preset road segment are obtained.

[0076] In this embodiment, the vehicle's navigation system detects the road conditions. If the navigation system detects that the vehicle is about to enter a first preset road segment, which is a congested road segment, then this road segment is a morning rush hour congested road segment, and the navigation displays the estimated travel time for the first preset road segment, i.e., the first estimated time, as 5 minutes (300 seconds). At this time, the first battery status and first vehicle speed of the power battery in the vehicle are obtained in real time through the vehicle's CAN bus. For example, the first battery status of the power battery includes the current temperature of the power battery being 35°C and the first vehicle speed being 40 km / h.

[0077] S102. Based on the first battery state, the first preset road section, the first vehicle speed, and the first estimated duration, a thermal prediction model for the power battery is used to predict the first temperature prediction curve of the power battery within the first estimated duration.

[0078] Specifically, the first battery state, the first preset road segment, the first vehicle speed, and the first estimated duration are input into the thermal prediction model of the power battery. The thermal prediction model predicts the temperature of the power battery within the first estimated duration and obtains the first temperature prediction curve of the power battery within the first estimated duration.

[0079] S103. Based on the first temperature prediction curve, predict whether the target predicted temperature of the power battery after the vehicle leaves the first preset road section reaches the preset safe temperature threshold.

[0080] S104. If the target predicted temperature does not reach the preset safe temperature threshold, a first control command is sent to the refrigeration system to make the refrigeration system shut down the compressor and / or reduce the speed of the cooling water pump.

[0081] Specifically, the preset safe temperature threshold is 45℃. If, according to the first temperature prediction curve, the target predicted temperature after the vehicle leaves the first preset road section is determined to be 35.5℃, then the target predicted temperature has not reached the preset safe temperature threshold.

[0082] At this time, a first control command is sent to the refrigeration system. The first control command is to immediately shut down the compressor and / or reduce the cooling water pump speed from the current 2000 rpm to the minimum speed of 500 rpm, maintaining only the basic circulation of coolant and entering the energy-saving mode.

[0083] In summary, this application provides a power battery thermal management method, comprising: if a vehicle is detected entering a first preset road segment, acquiring the first battery state, first vehicle speed, and first estimated duration of the first preset road segment; using a power battery thermal prediction model to predict the first temperature prediction curve of the power battery within the first estimated duration based on the first battery state, the first preset road segment, the first vehicle speed, and the first estimated duration; predicting whether the target predicted temperature of the power battery after the vehicle leaves the first preset road segment reaches a preset safe temperature threshold based on the first temperature prediction curve; and sending a first control command to the cooling system to shut down the compressor and / or reduce the speed of the cooling water pump if the target predicted temperature does not reach the preset safe temperature threshold. This method, by combining navigation information to identify preset road segments, accurately acquiring key parameters such as battery state, vehicle speed, and road segment duration, generating temperature curves based on a thermal prediction model and assessing safety risks, and finally sending targeted control commands to the cooling system, achieves a shift in control logic from passive response to proactive prediction. This process can prevent the cooling system from operating ineffectively under low load conditions while ensuring that the battery temperature is always below the safety threshold. It significantly reduces the frequency of compressor start-stop and the duration of high-speed operation of water pump, thereby greatly reducing the energy consumption of the cooling system. At the same time, it avoids the temperature overshoot problem caused by thermal inertia in traditional PID control by using accurate temperature prediction, thus balancing battery safety and vehicle energy efficiency.

[0084] This application also provides another possible implementation of the power battery thermal management method. Figure 2 This is a second schematic flowchart illustrating a power battery thermal management method provided in an embodiment of this application, as shown below. Figure 2As shown, based on the first battery state, the first preset road segment, the first vehicle speed, and the first estimated duration, a thermal prediction model for the power battery is used to predict the first temperature prediction curve of the power battery within the first estimated duration, including:

[0085] S201. Based on the first battery state, the first preset road section, the first vehicle speed, and the first estimated duration, a thermal prediction model is used to predict the temperature of multiple battery modules in the power battery within the first estimated duration.

[0086] S202. Based on the temperature prediction curves of multiple battery modules within the first estimated time period, obtain the temperature prediction curve of the power battery within the first estimated time period.

[0087] In this embodiment, the power battery is divided into multiple modules, which are three battery modules connected in sequence: front, middle and rear. A thermal prediction model is used to predict the temperature of each battery module within the first estimated time period based on the first battery state, the first preset road segment, the first vehicle speed and the first estimated time period.

[0088] Specifically, the temperature prediction curves of multiple battery modules within the first estimated time period are weighted and averaged to calculate the overall temperature prediction curve of the power battery within the first estimated time period.

[0089] The method provided in this application overcomes the limitations of traditional point-to-area temperature monitoring by generating temperature prediction curves for multiple battery modules and integrating them into an overall power battery curve. Since different modules within the battery pack may exhibit uneven temperatures due to variations in cell quantity and heat dissipation conditions, this step accurately captures the differences in temperature changes between modules, preventing safety hazards caused by the failure to identify abnormal temperatures in a single module. Simultaneously, the differentiated prediction and weighted integration based on module characteristics makes the overall temperature curve more closely match the actual thermal distribution of the battery pack, providing more accurate data support for subsequent energy-saving decisions and further improving the reliability and energy-saving effect of the control strategy.

[0090] This application embodiment also provides another possible implementation of the power battery thermal management method, wherein the first battery state includes: the first temperature of multiple parts in each battery module and the current state of charge (SOC) parameter of the power battery; Figure 3 This is the third flowchart illustrating a power battery thermal management method provided in this application. Figure 4 This is a schematic diagram of a battery module provided in an embodiment of this application. Figure 3 As shown, based on the first battery state, the first preset road segment, the first vehicle speed, and the first estimated duration, a thermal prediction model is used to predict the temperature of multiple battery modules in the power battery within the first estimated duration, including:

[0091] S301. Obtain simulation models of multiple battery modules pre-created in a preset simulation environment. The simulation model of each battery module includes simulation modules of multiple parts in each battery module.

[0092] In this embodiment, the preset simulation environment is the Simulink platform, and simulation models of multiple battery modules are pre-created. Each battery module's simulation model includes simulation modules for multiple parts within that module. For example... Figure 4 As shown, the battery module includes five parts: upper battery cell, middle battery cell, lower battery cell, water cooling plate, and coolant. The simulation modules for the upper battery cell, middle battery cell, lower battery cell, water cooling plate, and coolant are obtained respectively.

[0093] S302. Based on the first temperature of multiple parts, the current SOC parameter, the first vehicle speed, the first estimated duration, and the first preset road segment, the simulation modules of multiple parts are used to make predictions to obtain the temperature prediction sequence of multiple parts in each battery module within the first estimated duration.

[0094] Specifically, the first temperature of multiple parts, the current SOC parameter, the first vehicle speed, the first estimated duration, and the first preset road segment are input into the simulation modules of multiple parts respectively. The simulation modules of multiple parts predict the temperature of each part within the first estimated duration, thus obtaining the temperature prediction sequence of multiple parts in each battery module within the first estimated duration.

[0095] S303. Based on the temperature prediction sequence of multiple parts in each battery module within the first estimated time period, determine the temperature prediction curve of each battery module within the first estimated time period.

[0096] Specifically, the maximum and minimum temperatures of the upper, middle, and lower battery cells, water-cooling plate, and coolant in each battery module are determined from the predicted temperatures at each moment, and averaged to determine the predicted temperature value of each battery module at each moment. This results in multiple predicted temperature values ​​for each battery module within the first estimated duration. Based on these multiple predicted temperature values, a temperature prediction curve for each battery module within the first estimated duration is determined.

[0097] The method provided in this application relies on a modular simulation model in a preset simulation environment to decompose each part of the power battery into independent yet interconnected simulation sub-modules, thereby achieving refined modeling and prediction of the battery thermal management system. This approach can quickly generate temperature prediction sequences for each part through preset physical property parameters and simulation calculations, significantly reducing development and testing costs. Simultaneously, the reusability and parameter adjustability of the simulation model allow it to adapt to different vehicle models and battery pack structures, improving the method's versatility.

[0098] This application embodiment also provides another possible implementation of the power battery thermal management method, wherein the first temperature of multiple parts includes: the first temperature of multiple cell parts in each battery module, the first temperature of the water-cooled plate in contact with the last cell part, the first temperature of the coolant in the water-cooled plate, and the inlet temperature of the coolant; the simulation module of multiple parts includes: the simulation module of multiple cell parts, the simulation module of the water-cooled plate, and the simulation module of the coolant.

[0099] Figure 5 This is the fourth flowchart illustrating a power battery thermal management method provided in this application embodiment, as shown below. Figure 5 As shown, based on the first temperature of multiple locations, the current SOC parameter, the first vehicle speed, the first estimated duration, and the first preset road segment, simulation modules for multiple locations are used to make predictions, resulting in temperature prediction sequences for multiple locations within each battery module within the first estimated duration, including:

[0100] S401. In a preset simulation environment, calculate the temperature prediction sequence of multiple battery cell parts within the first predicted time based on the first temperature of multiple battery cell parts, the current SOC parameters, the preset specific heat capacity of multiple battery cell parts, the first estimated time, and the first preset road segment.

[0101] In this embodiment, based on the first temperature of multiple cell parts, such as 34.2℃ for the upper cell, 34℃ for the middle cell, and 33.8℃ for the lower cell, the current SOC parameter of 60%, the preset specific heat capacity of multiple cell parts, the first estimated duration of 300 seconds, and the current parameters corresponding to the first preset segment, the simulation module of multiple cell parts is used to predict and calculate the temperature prediction sequence of each cell part within the first estimated duration.

[0102] S402. In a preset simulation environment, calculate the temperature prediction sequence of the water-cooled plate within the first estimated time based on the first temperature of the water-cooled plate, the first vehicle speed, the first temperature of the coolant, and the first estimated time.

[0103] For example, based on the first temperature of the water-cooled plate being 32℃, the first vehicle speed being 40km / h, the first temperature of the coolant being 31.5℃, and the first estimated duration being 300 seconds, the simulation module of the water-cooled plate is used to predict and calculate the temperature prediction sequence of the water-cooled plate within the first estimated duration.

[0104] S403. In the preset simulation environment, calculate the temperature prediction sequence of the coolant within the first estimated time based on the first temperature of the coolant, the inlet temperature of the coolant, and the first estimated time.

[0105] For example, based on the first temperature of the coolant being 31.5°C, the inlet temperature of the coolant being 31°C, and the first estimated duration being 300 seconds, the coolant simulation module is used to predict and calculate the predicted temperature sequence of the coolant within the first estimated duration.

[0106] The method provided in this application calculates temperature prediction sequences for multiple key components such as battery cells, water-cooled plates, and coolants, achieving full-link thermal state monitoring of the battery thermal management system. Accurate prediction of the battery cells directly reflects the temperature changes of the core heat-generating components, providing a crucial basis for safety assessment. Separate predictions of the water-cooled plates and coolants allow for real-time monitoring of the cooling system's efficiency, preventing cooling failures caused by abnormal conditions of cooling components, such as reduced heat exchange efficiency of the water-cooled plates. The complete temperature sequence formed by combining these three parameters clearly presents the temperature loss and variation patterns along the heat transfer path, providing data support for subsequent optimization of the heat dissipation structure and adjustment of cooling parameters. Simultaneously, it ensures the comprehensiveness and accuracy of temperature prediction, avoiding localized overheating or overcooling.

[0107] This application also provides another possible implementation of the power battery thermal management method. Figure 6 This is the fifth flowchart illustrating a power battery thermal management method provided in this application embodiment, as shown below. Figure 6 As shown, in a preset simulation environment, based on the first temperature of multiple battery cell components, the current SOC parameters, the preset specific heat capacity of multiple battery cell components, the first estimated duration, and the first preset road segment, the temperature prediction sequence of multiple battery cell components within the first estimated duration is calculated, including:

[0108] S501. Determine the reference heat of multiple battery cell parts based on the first temperature of multiple battery cell parts, the current SOC parameter, and the current parameter corresponding to the first preset circuit segment.

[0109] In this embodiment, based on the first temperatures of multiple cell components, such as the first temperature T1 of the upper cell being 34.2℃, the first temperature T2 of the middle cell being 34℃, and the first temperature T3 of the lower cell being 33.8℃, the first temperatures of the upper, middle, and lower cells are averaged to obtain the first average temperature Tcell. Then, the Simulink lookup table module is used to look up the internal resistance R of the power battery based on the first average temperature Tcell, the current SOC parameter, and the current parameter I corresponding to the first preset road segment to obtain the internal resistance R of the power battery. Finally, the reference heat Q of multiple cell components is calculated using the reference heat calculation formula: Q=I²×R(SOC,Tcell,I).

[0110] S502. Determine the heat flow rate of multiple battery cell parts based on the reference heat of multiple battery cell parts and the corresponding calibration parameters of multiple battery cell parts.

[0111] For example, based on the calibration parameter k1 corresponding to the upper layer cell, the heat flow rate Q1 of the upper layer cell is determined using the heat flow rate calculation formula: Q1=k1×Q; based on the calibration parameter k2 corresponding to the middle layer cell, the heat flow rate Q2 of the middle layer cell is determined using the heat flow rate calculation formula: Q2=k2×Q; similarly, based on the calibration parameter k3 corresponding to the lower layer cell, the heat flow rate Q3 of the lower layer cell is determined using the heat flow rate calculation formula: Q3=k3×Q.

[0112] S503. Determine the heat flow between multiple battery cell parts based on the first temperature of multiple battery cell parts and the contact area between multiple battery cell parts.

[0113] For example, based on the first temperature T1 of the upper battery cell and the first temperature T2 of the middle battery cell, and the contact area A1 between the upper and middle battery cells, the heat flow between the upper and middle battery cells is determined. The heat flow Q4 between the upper and middle battery cells is calculated using the formula: Q4 = λ1 × A1 × (T1 - T2) / d, where λ1 and d are the material properties.

[0114] Similarly, the heat flow Q5 between the middle layer cell and the lower layer cell is calculated as follows: Q5 = λ1 × A2 × (T2 - T3) / d, where A2 represents the contact area between the middle layer cell and the lower layer cell.

[0115] Since the lower battery cell is in contact with the water-cooled plate, the heat flow rate Q6 between the lower battery cell and the water-cooled plate is determined based on the first temperature T3 of the lower battery cell, the first temperature T4 of the water-cooled plate, the heat transfer coefficient h3 from the lower battery cell to the water-cooled plate, and the contact area A3 between the lower battery cell and the water-cooled plate. The calculation formula is expressed as: Q6=h3×A3×(T3-T4).

[0116] S504. Based on the heat flow of multiple battery cell parts, the heat flow between multiple battery cell parts, the preset specific heat capacity of multiple battery cell parts, and the first estimated duration, determine the temperature prediction sequence of multiple battery cell parts within the first estimated duration.

[0117] For example, based on the heat flux of the upper battery cell, the heat flux between the upper and middle battery cells, and the preset specific heat capacity Cp1 of the upper battery cell, the temperature change of the upper battery cell is determined. The calculation formula is expressed as: ΔT1=(Q1-Q4) / (Cp1×m1)×dt, where m1 is the mass of the upper battery cell. Using the integrator in Simulink: given the first temperature T1 of the upper battery cell and the temperature change ΔT1 of the upper battery cell as input, the output temperature prediction sequence of the upper battery cell within the first predicted time period is T1_out.

[0118] Similarly, based on the heat flux of the middle layer cell, the heat flux between the middle and lower layers, and the preset specific heat capacity of the middle layer cell, the temperature change of the middle layer cell is determined. Using an integrator in Simulink: given a first temperature of the middle layer cell and inputting the temperature change, the integrator outputs a predicted temperature sequence for the middle layer cell within a first estimated time period. Similarly, based on the heat flux of the lower layer cell, the heat flux between the lower layer cell and the water-cooling plate, and the preset specific heat capacity of the lower layer cell, the temperature change of the lower layer cell is determined. Using an integrator in Simulink: given a first temperature of the lower layer cell and inputting the temperature change, the integrator outputs a predicted temperature sequence for the lower layer cell within a first estimated time period.

[0119] The method provided in this application significantly improves the accuracy of cell temperature prediction by refining the calculation logic of the reference heat and heat flow of the cell and generating a temperature sequence by combining specific heat capacity and time parameters. This step considers the nonlinear effects of SOC, current, and temperature on the battery's internal resistance, and calculates the reference heat by combining table lookup and formulas, avoiding the heat generation deviation caused by traditional linear estimation. At the same time, it distinguishes between the cell's own heat flow and the heat flow between parts, which can accurately capture the internal heat conduction law of the cell and reflect the temperature differences of different cell parts (such as the temperature gradient between the upper and lower cells). The final generated temperature sequence is refined.

[0120] This application also provides another possible implementation of the power battery thermal management method. Figure 7 This is the sixth flowchart illustrating a power battery thermal management method provided in this application embodiment, as shown below. Figure 7 As shown, in a preset simulation environment, based on the first temperature of the water-cooled plate, the first vehicle speed, the first temperature of the coolant, and the first estimated duration, the predicted temperature sequence of the water-cooled plate within the first estimated duration is calculated, including:

[0121] S601. Determine the heat flow between the water-cooled plate and the environment based on the first temperature of the water-cooled plate, the current ambient temperature, the first vehicle speed, and the contact area between the water-cooled plate and the environment.

[0122] In this embodiment, the heat transfer coefficient h5 of the environment and the water-cooled plate is determined according to the first vehicle speed lookup table. Then, the heat flow Q7 between the water-cooled plate and the environment is determined according to the first temperature T4 of the water-cooled plate, the current ambient temperature Tenv, and the contact area A5 between the water-cooled plate and the environment. The formula for calculating the heat flow Q7 between the water-cooled plate and the environment is: Q7=h5(v)×A5×(Tenv-T4).

[0123] S602. Determine the heat flow between the water-cooled plate and the coolant based on the first temperature of the water-cooled plate, the first temperature of the coolant, and the contact area between the water-cooled plate and the coolant.

[0124] Based on the first temperature T4 of the water-cooled plate, the first temperature T5 of the coolant, and the contact area A4 between the water-cooled plate and the coolant, the heat flow rate Q8 between the water-cooled plate and the coolant is determined. The formula for calculating the heat flow rate Q8 between the water-cooled plate and the coolant is: Q8 = h4 × A4 × (T4 - T5), where h4 represents the heat transfer coefficient from the water-cooled plate to the coolant.

[0125] S603. Determine the temperature prediction sequence of the water-cooled plate within the first estimated time period based on the heat flow between the water-cooled plate and the environment, the heat flow between the water-cooled plate and the coolant, and the first estimated time period.

[0126] Based on the heat flow between the water-cooled plate and the environment, the heat flow between the water-cooled plate and the coolant, and the preset specific heat capacity of the water-cooled plate, the temperature change of the water-cooled plate is determined. Using the integrator in Simulink: given the first temperature of the water-cooled plate and the temperature change of the water-cooled plate, the integrator outputs the temperature prediction sequence of the water-cooled plate within the first predicted time.

[0127] The method provided in this application refines the calculation of heat flow to the water-cooled plate by combining vehicle speed, ambient temperature, and coolant temperature, effectively improving the accuracy of temperature prediction for key heat dissipation components. This step considers the dynamic impact of vehicle speed on the heat exchange efficiency between the water-cooled plate and the environment, such as low heat exchange efficiency at low speeds and high heat exchange efficiency at high speeds, avoiding prediction deviations caused by fixed heat transfer coefficients. Simultaneously, distinguishing the bidirectional heat flow between the water-cooled plate and the environment and coolant clearly reflects the bridging role of the water-cooled plate in the heat dissipation system, allowing real-time monitoring of its heat absorption and release efficiency. Based on the generated temperature sequence, it is possible to determine in advance whether the water-cooled plate has experienced heat exchange failure, ensuring stable operation of the heat dissipation system and preventing battery temperature runaway due to abnormal water-cooled plate conditions. It also provides a basis for optimizing the water-cooled plate structure and adjusting airflow adaptation strategies.

[0128] This application also provides another possible implementation of the power battery thermal management method. Figure 8 This is the seventh flowchart illustrating a power battery thermal management method provided in this application embodiment, as shown below. Figure 8 As shown, in a preset simulation environment, based on the first temperature of the coolant, the inlet temperature of the coolant, and the first estimated duration, the predicted temperature sequence of the coolant within the first estimated duration is calculated, including:

[0129] S701. Determine the heat flow rate of the coolant-side water-cooled plate based on the inlet temperature of the coolant.

[0130] In this embodiment, when determining the specific heat capacity Cp3(Tin) and density ρ3(Tin) of the coolant when using the coolant-side water-cooled plate, the inlet temperature Tin of the coolant is used. Then, the heat flow rate Q9 of the coolant-side water-cooled plate is determined based on the inlet temperature Tin, the specific heat capacity Cp3(Tin), and the density ρ3(Tin) of the coolant. The formula for calculating the heat flow rate Q9 of the coolant-side water-cooled plate is: Q9=Cp3(Tin)×ρ3(Tin)×q×Tin, where q represents the coolant flow rate.

[0131] S702. Determine the heat flow rate of the coolant outlet water-cooled plate based on the first temperature of the coolant.

[0132] Specifically, based on the first temperature T5 of the coolant, determine the specific heat capacity Cp3(T5) and density ρ3(T5) of the coolant when it exits the water-cooled plate on the coolant side. Then, based on the first temperature T5 of the coolant, the specific heat capacity Cp3(T5) and density ρ3(T5) of the coolant, determine the heat flow rate Q10 of the coolant exiting the water-cooled plate on the coolant side. The formula for calculating the heat flow rate Q10 of the coolant exiting the water-cooled plate on the coolant side is: Q10=Cp3(T5)×ρ3(T5)×q×T5, where q represents the coolant flow rate.

[0133] S703. Based on the heat flow rate of the coolant inlet plate, the heat flow rate of the coolant outlet plate, and the first estimated duration, determine the temperature prediction sequence of the coolant within the first estimated duration.

[0134] Based on the heat flow rate of the inlet and outlet water cooling plates on the coolant side, and the preset specific heat capacity of the coolant, the temperature change of the coolant is determined. Using an integrator in Simulink: given the first temperature of the coolant and the temperature change of the coolant, the output is the temperature prediction sequence of the coolant within the first estimated time period.

[0135] It should be noted that the output temperature of the coolant in the previous battery module is used as the input temperature of the coolant in the next battery module, thus obtaining the temperature prediction sequence of the coolant in each battery module within the first estimated time period.

[0136] The method provided in this application calculates the heat flow rates of the inlet and outlet water by combining the coolant inlet temperature and physical property parameters, thus achieving accurate prediction of temperature changes in the core medium of the heat dissipation system. This step considers the influence of coolant temperature on its physical property parameters, obtaining real-time specific heat capacity and density by looking up tables, avoiding calculation deviations in heat flow rates caused by fixed parameters. Simultaneously, the difference between the inlet and outlet water heat flow rates reflects the heat-carrying capacity of the coolant, directly determining the working efficiency of the coolant circulation system. The generated temperature sequence can identify coolant temperature anomalies in advance, ensuring that the heat dissipation medium is always in a highly efficient working state, avoiding energy waste or heat dissipation failure in the refrigeration system due to coolant problems, and providing data support for optimizing coolant formulations and adjusting circulation flow rates.

[0137] This application also provides another possible implementation of the power battery thermal management method. Figure 9 This is the eighth flowchart illustrating a power battery thermal management method provided in this application embodiment, as shown below. Figure 9 As shown, based on the temperature prediction curve, it is predicted whether the target predicted temperature of the power battery will reach the preset safe temperature threshold after the vehicle leaves the first preset road section, including:

[0138] S801. Based on the temperature prediction curve, obtain the predicted temperature characteristics of the power battery within the expected duration.

[0139] The predicted temperature features include at least one of the following: predicted peak temperature, predicted peak temperature arrival time, and predicted temperature change rate.

[0140] S802. Based on the predicted temperature characteristics, determine whether the predicted target temperature has reached the preset safe temperature threshold.

[0141] In this embodiment, based on the first temperature prediction curve, the predicted temperature features are extracted: predicted peak temperature, peak arrival time, and temperature change rate. For example, the current temperature of the power battery is 35°C, the predicted peak temperature is 35.5°C, the predicted peak temperature arrival time is 5 minutes, and the predicted temperature change rate is that the battery temperature of the power battery will rise slowly at a rate of approximately 0.1°C / minute.

[0142] If the preset safe temperature threshold is 45℃, then compared to the predicted peak temperature of 35.5℃, and with a gradual rate of temperature change and no signs of accelerated heating, it can be determined that even if the compressor is immediately shut off, the target predicted temperature of 35.5℃ after the vehicle leaves the first preset road section has not reached the preset safe temperature threshold.

[0143] The method provided in this application extracts predicted temperature features to determine whether the target temperature meets the standard, providing a more scientific and comprehensive basis for energy-saving decisions. This step overcomes the limitations of traditional methods, combining temperature change trends and peak characteristics to more accurately assess long-term temperature safety risks. Simultaneously, by comprehensively judging multiple features, it avoids misjudgments caused by instantaneous temperature fluctuations, improving the reliability of decisions. Furthermore, clear temperature features provide direction for subsequent adjustments to control strategies, ensuring maximum energy-saving effects under safe conditions and avoiding overly conservative or risky control behaviors.

[0144] This application also provides another possible implementation of the power battery thermal management method. Figure 10 This is the ninth flowchart illustrating a power battery thermal management method provided in this application embodiment, as shown below. Figure 10 As shown, the method also includes:

[0145] S901. If it is detected that a vehicle will enter a second preset road segment from a first preset road segment after a preset future time, and the second estimated duration of the second preset road segment is obtained, the second battery state of the power battery and the second vehicle speed are obtained.

[0146] In this embodiment, after the vehicle has traveled on the first preset road segment, the navigation system detects that it will exit the first preset road segment and enter the second preset road segment, such as a long uphill section, in 150 seconds at a preset future time. The second estimated duration of the second preset road segment is 100 seconds. Then, the second battery status is acquired in real time. For example, if external factors cause the current average battery temperature to be 37.5℃ and the second vehicle speed to be 40km / h, the system will acquire the second battery status.

[0147] S902. Based on the second battery state, the second preset road section, the second vehicle speed, and the second estimated duration, a thermal prediction model for the power battery is used to predict the second temperature prediction curve of the power battery within the second estimated duration.

[0148] Based on the second battery state, the current parameters corresponding to the second preset road segment, the second vehicle speed, and the second estimated duration, a thermal prediction model for the power battery is used to predict the second temperature prediction curve of the power battery within the second estimated duration. For example, the predicted temperature change rate in the second temperature prediction curve is that the temperature will rise rapidly at a rate of 0.1℃ / s. The current parameters corresponding to the second preset road segment are preset current parameters. For example, the battery parameters in congested road segments are smaller preset current parameters, while those in long uphill road segments, where there is a high current demand, are larger preset current parameters.

[0149] S903. Based on the first temperature prediction curve, the second battery state of the power battery, and the second estimated duration, calculate the first predicted temperature when the vehicle begins to enter the second preset road segment.

[0150] For example, the predicted temperature change rate of the first temperature prediction curve is that the temperature will rise slowly at a rate of 0.1℃ / minute. Based on the rate of 0.1℃ / minute, the current average battery temperature of the power battery is 37.5℃. Based on the second estimated duration of 150 seconds, the first predicted temperature when the vehicle begins to enter the second preset road segment is determined to be: 37.5 + 0.1 × 2.5 = 37.75℃. Therefore, the first predicted temperature when the vehicle begins to enter the second preset road segment is determined to be 37.75℃.

[0151] S904. Based on the second temperature prediction curve, predict the time it takes for the power battery to rise from the first predicted temperature to the preset safe temperature threshold.

[0152] For example, the predicted temperature change rate of the second temperature prediction curve is that the temperature will rise rapidly at a rate of 0.1℃ / s. Then the time for the power battery to rise from the first predicted temperature to the preset safe temperature threshold is (45-37.75) / 0.1=72.5s. Thus, it is predicted that the power battery will reach the preset safe temperature threshold in 72.5s from the first predicted temperature.

[0153] S905. If the temperature rise time is less than or equal to the second predicted time, then calculate the predicted advance time before the preset future time based on the second temperature prediction curve, the first predicted temperature, the preset response time of the cooling system, and the preset battery temperature safety margin.

[0154] S906. Based on the expected lead time, send a second control command to the refrigeration system to cause the refrigeration system to start the compressor and / or increase the speed of the cooling water pump.

[0155] The temperature rise time is 72.5 seconds, which is less than the second estimated time of 100 seconds. This means that the power battery temperature has reached the preset safe temperature threshold before the vehicle has even left the second preset road section. In order to keep the power battery within the ideal range during the vehicle's journey on the second preset road section, i.e., within the preset safe temperature threshold, it is necessary to calculate the estimated lead time before the preset future time based on the second temperature prediction curve, the first predicted temperature, the preset response time of the cooling system, and the preset battery temperature safety margin. This lead time is then used to send a second control command to the cooling system, so that the cooling system can start the compressor and / or increase the speed of the cooling water pump in advance, so that the battery temperature begins to drop when the power battery temperature reaches the preset safe temperature threshold.

[0156] For example, the preset response time of the refrigeration system includes: compressor response delay of 20-30 seconds, heat transfer delay of 20-30 seconds, and coolant circulation establishment time of 30-40 seconds. The total preset response time is set to 100 seconds. The preset battery temperature safety margin is set to 3℃. The expected advance time is: 100-(45-3-37.75) / 0.1=57.5s. The expected advance time is approximately 60s, which can be understood as sending a second control command to the refrigeration system 60s before the preset future time.

[0157] The method provided in this application pre-plans cooling strategies for different road segments, effectively solving the problem of temperature exceeding limits caused by the lag in traditional control. This step combines navigation information to obtain road segment characteristics and duration in advance; by calculating the initial temperature and temperature rise time when entering the second preset road segment, the thermal risk window is accurately grasped; at the same time, the response delay, heat transfer delay and safety margin of the cooling system are fully considered to calculate a reasonable advance start time, ensuring that the cooling system can enter a high-efficiency working state before the battery temperature exceeds the limit, avoiding temperature overshoot; finally, by starting or strengthening the cooling in advance, it can ensure that the battery temperature is stable within a safe range in the second preset road segment, and avoid the surge in energy consumption caused by temporary emergency cooling, achieving a balance between safety and energy efficiency, which is especially suitable for dynamic thermal management needs under complex road conditions.

[0158] The following will continue to explain the power battery thermal management device and electronic device provided in any of the above embodiments of this application. The specific implementation process and the resulting technical effects are the same as those in the corresponding method embodiments. For the sake of brevity, the parts not mentioned in this embodiment can be referred to the corresponding content in the method embodiment.

[0159] Figure 11 This is a functional module diagram of a power battery thermal management device provided in an embodiment of this application. Figure 11 As shown, the power battery thermal management device 100 includes:

[0160] The acquisition module 110 is used to acquire the first battery state, the first vehicle speed, and the first estimated duration of the first preset road segment if the vehicle is detected to have entered the first preset road segment.

[0161] The prediction module 120 is used to predict the first temperature prediction curve of the power battery within the first estimated time period by using a thermal prediction model of the power battery based on the first battery state, the first preset road section, the first vehicle speed and the first estimated time period.

[0162] The prediction module 120 is also used to predict, based on the first temperature prediction curve, whether the target predicted temperature of the power battery after the vehicle leaves the first preset road section reaches the preset safe temperature threshold.

[0163] The sending module 130 is used to send a first control command to the refrigeration system if the target predicted temperature does not reach the preset safe temperature threshold, so that the refrigeration system shuts down the compressor and / or reduces the speed of the cooling water pump.

[0164] Optionally, the prediction module 120 is further configured to use a thermal prediction model to predict the temperature of multiple battery modules in the power battery within the first estimated time period based on the first battery state, the first preset road segment, the first vehicle speed, and the first estimated time period; and to obtain the temperature prediction curve of the power battery within the first estimated time period based on the temperature prediction curves of the multiple battery modules within the first estimated time period.

[0165] Optionally, the first battery state includes: the first temperature of multiple parts in each battery module and the current state of charge (SOC) parameter of the power battery; the prediction module 120 is further used to acquire simulation models of multiple battery modules pre-created in a preset simulation environment, the simulation model of each battery module including simulation modules of multiple parts in each battery module; based on the first temperature of multiple parts, the current SOC parameter, the first vehicle speed, the first estimated duration, and the first preset road segment, the simulation modules of multiple parts are used to make predictions to obtain the temperature prediction sequence of multiple parts in each battery module within the first estimated duration; based on the temperature prediction sequence of multiple parts in each battery module within the first estimated duration, the temperature prediction curve of each battery module within the first estimated duration is determined.

[0166] Optionally, the first temperature of multiple parts includes: the first temperature of multiple cell parts in each battery module, the first temperature of the water-cooled plate in contact with the last cell part, the first temperature of the coolant in the water-cooled plate, and the inlet temperature of the coolant; the simulation module of multiple parts includes: the simulation module of multiple cell parts, the simulation module of the water-cooled plate, and the simulation module of the coolant.

[0167] The prediction module 120 is also used to calculate the temperature prediction sequence of multiple battery cell parts within the first estimated time in a preset simulation environment based on the first temperature of multiple battery cell parts, the current SOC parameters, the preset specific heat capacity of multiple battery cell parts, the first estimated time, and the first preset road segment; to calculate the temperature prediction sequence of the water-cooled plate within the first estimated time in a preset simulation environment based on the first temperature of the water-cooled plate, the first vehicle speed, the first temperature of the coolant, and the first estimated time; and to calculate the temperature prediction sequence of the coolant within the first estimated time in a preset simulation environment based on the first temperature of the coolant, the inlet temperature of the coolant, and the first estimated time.

[0168] Optionally, the prediction module 120 is further configured to: determine the reference heat of multiple battery cell parts based on the first temperature of multiple battery cell parts, the current SOC parameter, and the current parameter corresponding to the first preset segment; determine the heat flow of multiple battery cell parts based on the reference heat of multiple battery cell parts and the calibration parameters corresponding to multiple battery cell parts; determine the heat flow between multiple battery cell parts based on the first temperature of multiple battery cell parts and the contact area between multiple battery cell parts; and determine the temperature prediction sequence of multiple battery cell parts within the first predicted time based on the heat flow of multiple battery cell parts, the heat flow between multiple battery cell parts, the preset specific heat capacity of multiple battery cell parts, and the first predicted time.

[0169] Optionally, the prediction module 120 is further configured to determine the heat flow between the water-cooled plate and the environment based on the first temperature of the water-cooled plate, the current ambient temperature, the first vehicle speed, and the contact area between the water-cooled plate and the environment; determine the heat flow between the water-cooled plate and the coolant based on the first temperature of the water-cooled plate, the first temperature of the coolant, and the contact area between the water-cooled plate and the coolant; and determine the temperature prediction sequence of the water-cooled plate within the first estimated time period based on the heat flow between the water-cooled plate and the environment, the heat flow between the water-cooled plate and the coolant, and the first estimated time period.

[0170] Optionally, the prediction module 120 is further configured to determine the heat flow rate of the coolant-side inlet water-cooled plate based on the inlet temperature of the coolant; determine the heat flow rate of the coolant-side outlet water-cooled plate based on the first temperature of the coolant; and determine the temperature prediction sequence of the coolant within the first estimated time period based on the heat flow rate of the coolant-side inlet water-cooled plate, the heat flow rate of the coolant-side outlet water-cooled plate, and the first estimated time period.

[0171] Optionally, the prediction module 120 is further configured to obtain the predicted temperature characteristics of the power battery within the expected time period based on the temperature prediction curve; wherein the predicted temperature characteristics include at least one of the following: predicted peak temperature, the arrival time of the predicted peak temperature, and the predicted temperature change rate; and based on the predicted temperature characteristics, to predict whether the target predicted temperature reaches the preset safe temperature threshold.

[0172] Optionally, the device further includes:

[0173] The acquisition module 110 is also used to acquire the second battery status and the second vehicle speed of the power battery if it detects that the vehicle will enter the second preset road segment from the first preset road segment after a preset future time, and the second estimated duration of the second preset road segment.

[0174] The prediction module 120 is also used to make predictions based on the second battery state, the second preset road section, the second vehicle speed and the second estimated duration, using the thermal prediction model of the power battery to obtain the second temperature prediction curve of the power battery within the second estimated duration.

[0175] The calculation module is used to calculate the first predicted temperature when the vehicle begins to enter the second preset road segment based on the first temperature prediction curve, the second battery state of the power battery, and the second estimated duration.

[0176] The prediction module 120 is also used to predict the time it takes for the power battery to rise from the first predicted temperature to a preset safe temperature threshold based on the second temperature prediction curve.

[0177] The calculation module is used to calculate the expected lead time before a preset future time based on the second temperature prediction curve, the first predicted temperature, the preset response time of the cooling system, and the preset battery temperature safety margin if the temperature rise time is less than or equal to the second predicted time.

[0178] The sending module 130 is used to send a second control command to the refrigeration system according to the expected lead time, so that the refrigeration system can start the compressor and / or increase the speed of the cooling water pump.

[0179] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0180] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more microprocessors, or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SOC).

[0181] Figure 12 This is a schematic diagram of an electronic device provided in an embodiment of this application. This electronic device can be used for thermal management of a power battery. Figure 12 As shown, the electronic device includes: a processor 210, a storage medium 220, and a bus 230.

[0182] Storage medium 220 stores machine-readable instructions executable by processor 210. When the electronic device is running, processor 210 communicates with storage medium 220 via bus 230, and processor 210 executes the machine-readable instructions to perform the steps of the above method embodiment. The specific implementation and technical effects are similar and will not be described again here.

[0183] Optionally, this application also provides a storage medium 220, on which a computer program is stored. When the computer program is run by a processor, it executes the steps of the above-described method embodiments. The specific implementation and technical effects are similar, and will not be repeated here.

[0184] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0185] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0186] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0187] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0188] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of thermal management of a power battery, characterized in that, The method comprises: if it is detected that the vehicle enters a first preset road section, obtaining a first battery state of a power battery in the vehicle, a first vehicle speed, and a first expected duration of the first preset road section; according to the first battery state, the first preset road section, the first vehicle speed, and the first expected duration, using a thermal prediction model of the power battery to perform prediction, to obtain a first temperature prediction curve of the power battery within the first expected duration; according to the first temperature prediction curve, predicting whether a target prediction temperature of the power battery after the vehicle exits the first preset road section reaches a preset safety temperature threshold; if the target prediction temperature does not reach the preset safety temperature threshold, sending a first control instruction to a refrigeration system, so that the refrigeration system turns off a compressor and / or reduces a rotating speed of a cooling water pump; if it is detected that the vehicle enters a second preset road section after a preset future time, and a second expected duration of the second preset road section, obtaining a second battery state of the power battery, a second vehicle speed; according to the second battery state, the second preset road section, the second vehicle speed, and the second expected duration, using the thermal prediction model of the power battery to perform prediction, to obtain a second temperature prediction curve of the power battery within the second expected duration; according to the first temperature prediction curve, the second battery state of the power battery, and the second expected duration, calculating a first prediction temperature when the vehicle starts to enter the second preset road section; according to the second temperature prediction curve, predicting a temperature rising duration of the power battery from the first prediction temperature to the preset safety temperature threshold; if the temperature rising duration is less than or equal to the second expected duration, according to the second temperature prediction curve, the first prediction temperature, a preset response duration of the refrigeration system, and a preset battery temperature safety margin, calculating a predicted advance duration before the preset future time; according to the predicted advance duration, sending a second control instruction to the refrigeration system, so that the refrigeration system turns on the compressor and / or increases the rotating speed of the cooling water pump.

2. The method of claim 1, wherein, The method comprises: according to the first battery state, the first preset road section, the first vehicle speed, and the first expected duration, using the thermal prediction model to perform prediction, to obtain temperature prediction curves of a plurality of battery modules in the power battery within the first expected duration; according to the temperature prediction curves of the plurality of battery modules within the first expected duration, obtaining the temperature prediction curve of the power battery within the first expected duration.

3. The method of claim 2, wherein, The first battery state comprises: a first temperature of a plurality of parts in each battery module, a current state of charge (SOC) parameter of the power battery; The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The first temperature of the plurality of parts includes the first temperature of a plurality of cell parts, the first temperature of a water-cooled plate in contact with the last cell part, the first temperature of a cooling liquid in the water-cooled plate, and the inlet temperature of the cooling liquid; and the simulation module of the plurality of parts includes the simulation module of the plurality of cell parts, the simulation module of the water-cooled plate, and the simulation module of the cooling liquid. The temperature prediction sequence of the plurality of cell parts in the first predicted time length is calculated in the preset simulation environment according to the first temperature of the plurality of cell parts, the current SOC parameter, the preset specific heat capacity of the plurality of cell parts, the first predicted time length, and the first preset road section. The reference heat of the plurality of cell parts is determined according to the first temperature of the plurality of cell parts, the current SOC parameter, and the current of the first preset road section.

4. The method of claim 3, wherein, The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length.

5. The method of claim 4, wherein, The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. 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The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. 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The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. The temperature prediction curve of each battery module in the first predicted time length is determined according to the temperature prediction sequence of the plurality of parts in the first predicted time length. 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The temperature prediction curve of each battery module in the first determine heat flow of the plurality of battery cell parts according to the reference heat of the plurality of battery cell parts and the calibration parameters corresponding to the plurality of battery cell parts; determine heat flow between the plurality of battery cell parts according to the first temperature of the plurality of battery cell parts and contact area between the plurality of battery cell parts; determine temperature prediction sequence of the plurality of battery cell parts within the first predicted time length according to the heat flow of the plurality of battery cell parts, the heat flow between the plurality of battery cell parts, preset specific heat capacity of the plurality of battery cell parts and the first predicted time length.

6. The method of claim 4, wherein, The step of calculating the temperature prediction sequence of the water-cooled plate within the first predicted time length in the preset simulation environment according to the first temperature of the water-cooled plate, the first vehicle speed, the first temperature of the cooling liquid and the first predicted time length comprises: determine heat flow between the water-cooled plate and the environment according to the first temperature of the water-cooled plate, current environment temperature, the first vehicle speed and contact area between the water-cooled plate and the environment; determine heat flow between the water-cooled plate and the cooling liquid according to the first temperature of the water-cooled plate, the first temperature of the cooling liquid and contact area between the water-cooled plate and the cooling liquid; determine the temperature prediction sequence of the water-cooled plate within the first predicted time length according to the heat flow between the water-cooled plate and the environment, the heat flow between the water-cooled plate and the cooling liquid and the first predicted time length.

7. The method of claim 4, wherein, The step of calculating the temperature prediction sequence of the cooling liquid within the first predicted time length in the preset simulation environment according to the first temperature of the cooling liquid, the inlet temperature of the cooling liquid and the first predicted time length comprises: determine heat flow of the cooling liquid into the water-cooled plate according to the inlet temperature of the cooling liquid; determine heat flow of the cooling liquid out of the water-cooled plate according to the first temperature of the cooling liquid; determine the temperature prediction sequence of the cooling liquid within the first predicted time length according to the heat flow of the cooling liquid into the water-cooled plate, the heat flow of the cooling liquid out of the water-cooled plate and the first predicted time length.

8. The method of claim 1, wherein, The step of predicting whether the target prediction temperature of the power battery after the vehicle drives out of the first preset road section reaches a preset safety temperature threshold according to the temperature prediction curve comprises: obtain prediction temperature characteristics of the power battery within the predicted time length according to the temperature prediction curve; wherein the prediction temperature characteristics comprise at least one of a prediction peak temperature, an arrival time of the prediction peak temperature and a prediction temperature change rate; predict whether the target prediction temperature reaches the preset safety temperature threshold according to the prediction temperature characteristics.

9. An electronic device, comprising: The method comprises: a processor, a storage medium and a bus, the storage medium stores program instructions executable by the processor, when the electronic device is running, the processor and the storage medium communicate through the bus, the processor executes the program instructions to execute the steps of the power battery thermal management method in any one of claims 1 to 8.

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