Electric vehicle thermal management method and system
By collecting battery current and cooling system parameters to calculate heat transfer residuals and dynamically adjusting detection sensitivity, the problem of temperature signal misleading during loop switching in electric vehicle thermal management detection is solved, enabling accurate monitoring of battery thermal status and timely risk identification.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- MICROSPECTRUM AUTO RESEARCH (GUANGZHOU) TESTING TECHNOLOGY SERVICE CO LTD
- Filing Date
- 2025-12-09
- Publication Date
- 2026-07-14
AI Technical Summary
Existing electric vehicle thermal management detection methods suffer from low detection accuracy and inability to identify potential thermal risks in a timely manner under complex operating conditions such as loop switching, due to temperature signals being misled by pseudo-steady-state conditions.
By collecting battery current, battery temperature measured by the cooling system, coolant flow rate, and circuit status signals, the estimated heat generation power of the battery and the heat exchange power of the cooling system are calculated, the heat exchange residual is determined, and the detection sensitivity is dynamically adjusted to identify unexpected thermal behavior and potential risk intensity.
During circuit switching, the internal thermal state of the battery is accurately identified, avoiding false alarms and improving the accuracy and timeliness of thermal management detection, adapting to detection strategies with different risk levels.
Smart Images
Figure CN121469387B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of industrial testing technology, and in particular to a thermal management method and system for electric vehicles. Background Technology
[0002] Electric vehicle battery thermal management systems typically employ liquid cooling, using cooling loops to remove heat generated during battery operation and maintain the battery within a suitable temperature range. To adapt to different operating conditions, modern thermal management systems are often designed with a multi-loop structure, such as two heat exchange loops: battery-engine and vehicle cooling system-battery-engine. Switching between these different cooling loops is achieved through valves.
[0003] During the operation of a thermal management system, the thermal state of the battery needs to be monitored in real time to prevent safety issues such as overheating or thermal runaway. Existing detection methods mainly rely on temperature sensors to collect the inlet and outlet temperatures of the battery pack coolant and the surface temperature of individual battery cells, and compare these temperature signals with preset temperature thresholds. When the monitored temperature exceeds the set threshold, the system triggers an alarm or takes cooling measures, such as increasing the coolant flow rate, activating the heat pump system, or limiting the battery output power.
[0004] However, existing technologies still have some problems, which means that the accuracy of thermal management detection for electric vehicles needs to be improved. Summary of the Invention
[0005] To solve the above-mentioned technical problems, or at least partially solve them, this application provides a thermal management method and system for electric vehicles, which can improve the accuracy of thermal management detection for electric vehicles.
[0006] In a first aspect, this application provides a method for testing the thermal management of an electric vehicle, the method comprising the following steps:
[0007] Collect battery current, battery temperature measured through the cooling system, coolant flow rate, coolant temperature, and circuit status signals;
[0008] The estimated heat output of the battery is calculated based on the battery current.
[0009] Calculate the heat exchange power of the cooling system based on the coolant flow rate and coolant temperature;
[0010] Based on the estimated heat generation power of the battery, the heat exchange power of the cooling system, the battery temperature measured by the cooling system, and the circuit status signal, determine whether there is any unexpected thermal behavior;
[0011] When unexpected thermal behavior occurs, adjust the method for detecting the battery's thermal state.
[0012] Optionally, determining whether unexpected thermal behavior exists based on the estimated heat generation power of the battery, the heat exchange power of the cooling system, the battery temperature measured by the cooling system, and the circuit status signal includes:
[0013] Determine whether the change in battery temperature measured by the cooling system is inconsistent with the estimated heat generation power of the battery;
[0014] When the judgment result is yes, it is determined that there is unexpected thermal behavior.
[0015] Optionally, determining whether the change in battery temperature measured by the cooling system is inconsistent with the estimated heat generation power of the battery includes:
[0016] The heat transfer residual is calculated based on the estimated heat generation power of the battery and the heat transfer power of the cooling system. The heat transfer residual is the difference between the estimated heat generation power of the battery and the heat transfer power of the cooling system.
[0017] When the average value of the heat transfer residual over a preset time period is positive, it is determined that the change in battery temperature measured by the cooling system is inconsistent with the estimated heat generation power of the battery.
[0018] Optionally, determining whether the change in battery temperature measured by the cooling system is inconsistent with the estimated heat generation power of the battery further includes:
[0019] Determine if the following conditions are met simultaneously:
[0020] The battery current increase exceeds the first threshold;
[0021] The estimated increase in the battery's heat generation power exceeds the second threshold.
[0022] The change in battery temperature measured by the cooling system is below the third threshold.
[0023] The loop status signal indicates loop switching.
[0024] Optionally, the adjustment of the detection method for battery thermal state includes:
[0025] Calculate the potential risk intensity based on the estimated heat generation power of the battery and the heat exchange power of the cooling system;
[0026] The detection sensitivity is determined based on the potential risk intensity, and the battery thermal state is detected based on the detection sensitivity.
[0027] Optionally, determining the detection sensitivity based on the potential risk intensity includes:
[0028] A first risk threshold and a second risk threshold are preset, wherein the first risk threshold is a temperature alarm threshold, and the second risk threshold is less than the first risk threshold;
[0029] When the potential risk intensity is greater than the second risk threshold, the first risk threshold is reduced;
[0030] The battery temperature measured by the cooling system is compared with the adjusted first risk threshold. When the battery temperature measured by the cooling system exceeds the first risk threshold, a temperature alarm is triggered.
[0031] Optionally, the step of calculating the potential risk intensity based on the estimated heat generation power of the battery and the heat exchange power of the cooling system includes:
[0032] The first risk contribution value is determined based on the estimated heat generation power of the battery.
[0033] The second risk contribution value is determined based on the difference between the estimated heat generation power of the battery and the heat exchange power of the cooling system;
[0034] The potential risk intensity is calculated based on the first risk contribution value and the second risk contribution value.
[0035] Optionally, calculating the potential risk intensity based on the first risk contribution value and the second risk contribution value further includes:
[0036] The third risk contribution value is determined based on the multi-point temperature difference of the battery.
[0037] The potential risk intensity is calculated based on the first risk contribution value, the second risk contribution value, and the third risk contribution value.
[0038] Secondly, this application provides an electric vehicle thermal management detection system, the system including a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, the at least one instruction, the at least one program, the code set or instruction set being loaded and executed by the processor to implement the electric vehicle thermal management detection method as described in any of the first aspects.
[0039] The technical solution provided in this application has the following advantages compared with the prior art:
[0040] One of its beneficial effects and its working principle is as follows:
[0041] In electric vehicle thermal management systems employing multi-loop coupling structures, different loops possess differentiated heat exchange capabilities. Therefore, when responding to increased load or environmental changes, loop switching is necessary to match a more suitable thermal path. However, during loop switching, although both battery current and estimated heat generation power increase significantly, the change in battery temperature measured by the cooling system tends to be lower, exhibiting a flat state that does not match the increase in heat generation power.
[0042] This is because the circuit switching changes the heat flow topology. The heat exchange path, flow distribution, and temperature distribution of the heat exchanger in the new circuit need time to be re-established. During this period, the cooler coolant enters the battery circuit, and its enhanced heat absorption capacity temporarily suppresses the temperature rise.
[0043] Furthermore, if the controller increases the pump speed or starts the heat pump in advance at the same time, this control action will further enhance heat exchange, making the values measured by the temperature sensor more stable in a short period of time.
[0044] This transient phase in which the temperature signal is suppressed or distorted is called pseudo-steady state. During this phase, the values measured by the temperature sensor cannot accurately reflect the true thermal state inside the battery because there is a physical delay in the conduction of heat inside the battery, while the enhanced heat exchange of the external circuit rapidly affects the surface temperature. The asynchrony between the two may result in significant heat accumulation inside the battery, but the sensor still shows a stable reading.
[0045] This application collects battery current, battery temperature measured by the cooling system, coolant flow rate, coolant temperature, and circuit status signals. It calculates the estimated heat generation power of the battery based on the battery current, and calculates the heat exchange power of the cooling system based on the coolant flow rate and coolant temperature. Then, it calculates the heat exchange residual based on the estimated heat generation power of the battery and the heat exchange power of the cooling system. The heat exchange residual is the difference between the estimated heat generation power of the battery and the heat exchange power of the cooling system. When the average value of this residual over a preset time period is positive, it indicates that the heat generated by the battery has not been sufficiently removed by the cooling system. In this case, it is determined that the change in battery temperature measured by the cooling system is inconsistent with the estimated heat generation power of the battery, i.e., unexpected thermal behavior exists.
[0046] Traditional methods rely solely on temperature signals for judgment, which can be misled by pseudo-steady states under complex operating conditions such as loop switching, leading to the mistaken assumption that the load is low or the system is stable, thus missing the optimal intervention window during the risk accumulation phase. In contrast, this application introduces the concept of heat transfer residual to directly assess the balance between battery heating and cooling system heat transfer, enabling the identification of potential thermal risks even during the heat transfer transient phase.
[0047] Its second beneficial effect and its working principle are as follows:
[0048] While different unexpected thermal behaviors can all lead to temperature signal distortion, the underlying potential risks vary. When the estimated heat generation power of the battery is low and the heat transfer residual is close to zero, even if there is a brief distortion in the temperature signal, the actual risk is relatively limited. However, when the estimated heat generation power of the battery is high and the heat transfer residual is consistently positive, it indicates that heat accumulation is occurring. If the detection sensitivity is not improved in time, the thermal risk may have developed to a more serious level before the temperature signal returns to normal. Using a fixed temperature alarm threshold and sampling frequency, this one-size-fits-all strategy may produce unnecessary false alarms in low-risk situations, while failing to provide sufficiently timely warnings in high-risk situations.
[0049] This application calculates the potential risk intensity by estimating the heat generation power of the battery and the heat exchange power of the cooling system, and then determines the detection sensitivity based on the potential risk intensity and detects the thermal state of the battery based on the detection sensitivity.
[0050] When determining the detection sensitivity, a first risk threshold and a second risk threshold are preset, where the first risk threshold is the temperature alarm threshold and the second risk threshold is less than the first risk threshold. When the potential risk intensity is greater than the second risk threshold, the first risk threshold is reduced, making it easier for the battery temperature measured by the cooling system to trigger the temperature alarm.
[0051] This technology, which dynamically adjusts detection sensitivity based on the intensity of potential risk, enables the detection process to adaptively adjust its response strategy according to the actual level of risk. In high-risk situations, lowering the temperature alarm threshold allows for earlier risk identification and intervention, preventing protective measures from being triggered only when the temperature rises rapidly after the pseudo-steady state ends. In low-risk situations, maintaining standard detection parameters reduces unnecessary false alarms and control interventions. Attached Figure Description
[0052] Figure 1 This is a schematic diagram illustrating an application scenario of the electric vehicle thermal management testing method provided in the embodiments of this application;
[0053] Figure 2 This is one of the flowcharts illustrating the electric vehicle thermal management testing method provided in this application embodiment;
[0054] Figure 3 A second schematic flowchart of the electric vehicle thermal management testing method provided in this application embodiment;
[0055] Figure 4 This is the third flowchart illustrating the electric vehicle thermal management testing method provided in this application embodiment. Detailed Implementation
[0056] The technical solutions in this application will now be described with reference to the accompanying drawings.
[0057] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the implementation methods of this application will be further described in detail below with reference to the accompanying drawings.
[0058] Before providing a detailed explanation of the embodiments of this application, let's first introduce the application scenarios involved in the embodiments of this application.
[0059] Figure 1 This is a schematic diagram illustrating an application scenario of the electric vehicle thermal management detection method provided in this application embodiment. For example... Figure 1 As shown, the electric vehicle thermal management system includes a battery pack, multiple cooling circuits, and multiple sensors. By collecting signals from each sensor and executing the detection method provided in this application, it can identify unexpected thermal behaviors and dynamically adjust the detection sensitivity under complex operating conditions such as circuit switching, thereby achieving accurate monitoring of the battery's thermal state.
[0060] The electric vehicle thermal management detection method provided in this application embodiment can be loaded and executed by an electric vehicle thermal management detection system. The system includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the electric vehicle thermal management detection method as described in the following embodiments.
[0061] Reference Figures 2-4 The electric vehicle thermal management testing method described in this application includes the following steps:
[0062] S201: Collects battery current, battery temperature measured by the cooling system, coolant flow rate, coolant temperature, and circuit status signals;
[0063] Specifically, in this embodiment of the application, the following signals are collected in real time:
[0064] Battery current. In this embodiment, the battery pack consists of multiple battery modules connected in series and parallel, and the total current is measured by a Hall current sensor.
[0065] The battery temperature is measured by the cooling system, including the battery pack inlet temperature, outlet temperature, and individual cell surface temperature, at a total of eight measurement points. These temperature sensors are all installed in the cooling system's piping or on the battery surface in contact with the cooling system, and the measured temperatures reflect the temperature state that the cooling system can observe.
[0066] Coolant flow rate is measured by a flow sensor.
[0067] Coolant temperature, including coolant inlet temperature and coolant outlet temperature.
[0068] The loop status signal, provided by the valve position sensor of the three-way valve, indicates whether the current system is in the first cooling loop or the second cooling loop.
[0069] S202: Calculate the estimated heat generation power of the battery based on the battery current;
[0070] Based on the collected battery current and internal resistance, the estimated heat output of the battery is calculated using the Joule heating formula. The battery internal resistance is obtained by looking up a table based on the current battery temperature and state of charge (SOC). This application pre-configures and stores a battery internal resistance characteristic table, which was obtained through experimental calibration.
[0071] S203: Calculate the heat exchange power of the cooling system based on the coolant flow rate and coolant temperature;
[0072] This application calculates the actual heat exchange power of the cooling system based on the coolant flow rate, coolant inlet temperature, coolant outlet temperature, and coolant specific heat capacity, according to the heat calculation formula.
[0073] S204: Based on the estimated heat generation power of the battery, the heat exchange power of the cooling system, the battery temperature measured by the cooling system, and the circuit status signal, determine whether there is any unexpected thermal behavior;
[0074] Specifically, this includes determining whether the change in battery temperature measured by the cooling system is inconsistent with the estimated heat generation power of the battery. The determination process includes the following steps:
[0075] Specifically, it includes the following steps:
[0076] S301: Calculate the heat transfer residual based on the estimated heat generation power of the battery and the heat transfer power of the cooling system, wherein the heat transfer residual is the difference between the estimated heat generation power of the battery and the heat transfer power of the cooling system;
[0077] The heat transfer residual is calculated, which represents the difference between the heat generated by the battery and the heat removed by the cooling system. In this embodiment, the average heat transfer residual over the most recent 30 seconds is calculated.
[0078] S302: When the average value of the heat transfer residual within a preset time period is positive, it is further determined whether the following conditions are met simultaneously:
[0079] When the average heat transfer residual is positive, further determine whether the following four conditions are met simultaneously:
[0080] First, calculate the increase in battery current within a preset time period and determine whether the increase exceeds a preset first threshold.
[0081] Second, calculate the increase in the estimated heat generation power of the battery within a preset time period, and determine whether the increase exceeds a preset second threshold.
[0082] Third, analyze the variation range of the battery pack's average temperature. The average temperature of the battery pack is calculated by averaging the temperatures at multiple measuring points, and it is determined whether the rate of temperature rise is lower than a preset third threshold.
[0083] Fourth, detect whether the loop status signal changes within a preset time period to confirm whether a loop switch has occurred.
[0084] S303: When the judgment result is yes, it is determined that there is unexpected thermal behavior.
[0085] When the average heat transfer residual is positive and all four conditions mentioned above are met simultaneously, unexpected thermal behavior is identified, and an unexpected thermal behavior label is generated. This label indicates that the battery temperature currently measured by the cooling system cannot accurately reflect the true thermal state inside the battery, and the reliability of the temperature signal is reduced.
[0086] S205: When unexpected thermal behavior occurs, adjust the detection method for battery thermal state.
[0087] Specifically, the adjustment of the battery thermal state detection method includes:
[0088] S401: Calculate the potential risk intensity based on the estimated heat generation power of the battery, the heat exchange power of the cooling system, and the multi-point temperature difference of the battery.
[0089] The first risk contribution value is determined based on the estimated heat generation power of the battery.
[0090] The second risk contribution value is determined based on the difference between the estimated heat generation power of the battery and the heat exchange power of the cooling system;
[0091] The third risk contribution value is determined based on the multi-point temperature difference of the battery.
[0092] The potential risk intensity is calculated based on the first risk contribution value, the second risk contribution value, and the third risk contribution value.
[0093] Specifically, in the embodiments of this application:
[0094] The first risk contribution value is determined based on the estimated heat generation power of the battery.
[0095] A segmented mapping method is used to map the estimated heat generation power of the battery to the corresponding risk contribution value. The higher the heat generation power, the greater the first risk contribution value.
[0096] The second risk contribution value is determined based on the difference between the estimated heat generation power of the battery and the heat exchange power of the cooling system. This difference is the heat exchange residual calculated above. The magnitude of the heat exchange residual is mapped to the corresponding risk contribution value. The larger the heat exchange residual, the more significant the heat accumulation, and the larger the second risk contribution value.
[0097] The third risk contribution value is determined based on the multi-point temperature difference of the battery. The multi-point temperature difference of the battery is defined as the difference between the highest and lowest temperatures among multiple measuring points. This temperature difference is mapped to the corresponding risk contribution value. The larger the temperature difference, the more uneven the internal temperature distribution of the battery, and the larger the third risk contribution value.
[0098] The potential risk intensity is calculated by combining the first, second, and third risk contribution values through a weighted summation. The potential risk intensity ranges from 0 to 1, with a higher value indicating a higher potential risk in the current thermal state.
[0099] S402: Determine the detection sensitivity based on the potential risk intensity, and detect the battery thermal state based on the detection sensitivity, including:
[0100] A first risk threshold and a second risk threshold are preset, wherein the first risk threshold is a temperature alarm threshold, and the second risk threshold is less than the first risk threshold;
[0101] When the potential risk intensity is greater than the second risk threshold, the first risk threshold is reduced;
[0102] The battery temperature measured by the cooling system is compared with the adjusted first risk threshold. When the battery temperature measured by the cooling system exceeds the first risk threshold, a temperature alarm is triggered.
[0103] Specifically, in this embodiment, a first risk threshold and a second risk threshold are preset. The first risk threshold is a temperature alarm threshold, used to directly compare with the battery temperature measured by the cooling system to determine whether a temperature alarm is triggered. The second risk threshold is a threshold for judging the intensity of potential risk, used to determine whether to adjust the first risk threshold, and the second risk threshold is less than the first risk threshold.
[0104] Determine if the potential risk intensity is greater than the second risk threshold. If the potential risk intensity is greater than the second risk threshold, it indicates that there is a high potential thermal risk. Lower the first risk threshold to make the temperature alarm easier to trigger, thereby improving detection sensitivity.
[0105] The battery temperature measured by the cooling system is compared with an adjusted first risk threshold. When the battery temperature measured by the cooling system exceeds the first risk threshold, a temperature alarm is triggered, and a power limiting request can be sent to the vehicle or the cooling system operation can be strengthened.
[0106] By dynamically adjusting the detection sensitivity as described above, earlier warnings can be provided in high-risk situations, while maintaining regular detection parameters in low-risk situations, thus avoiding unnecessary false alarms.
[0107] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital versatile discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).
[0108] In the various embodiments of this application, unless otherwise specified or logically conflicting, the terminology and / or descriptions between different embodiments are consistent and can be referenced mutually. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships. In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone, where A and B can be singular or plural. In the textual description of the embodiments of this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. In this application, "first," "second," and various numerical designations are only for ease of description and are not used to limit the scope of the embodiments of this application. For example, they are used to distinguish different messages, rather than to describe a specific order or sequence.
[0109] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.
[0110] Finally, it should be noted that the above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A thermal management method for electric vehicles, characterized in that, Includes the following steps: Collect battery current, battery temperature measured through the cooling system, coolant flow rate, coolant temperature, and circuit status signals; The estimated heat output of the battery is calculated based on the battery current. Calculate the heat exchange power of the cooling system based on the coolant flow rate and coolant temperature; Based on the estimated heat generation power of the battery, the heat exchange power of the cooling system, the battery temperature measured by the cooling system, and whether a circuit switch has occurred by detecting changes in the circuit status signal, it is determined whether there is any unexpected thermal behavior. When unexpected thermal behavior occurs, adjust the method for detecting the battery's thermal state; The adjustment of the method for detecting the battery's thermal state includes: Calculate the potential risk intensity based on the estimated heat generation power of the battery, the heat exchange power of the cooling system, and the multi-point temperature difference of the battery. The detection sensitivity is determined based on the potential risk intensity, and the battery thermal state is detected based on the detection sensitivity. The step of determining the detection sensitivity based on the potential risk intensity and detecting the battery thermal state based on the detection sensitivity includes: A first risk threshold and a second risk threshold are preset, wherein the first risk threshold is a temperature alarm threshold, and the second risk threshold is less than the first risk threshold; When the potential risk intensity is greater than the second risk threshold, the first risk threshold is reduced; The battery temperature measured by the cooling system is compared with the adjusted first risk threshold. When the battery temperature measured by the cooling system exceeds the first risk threshold, a temperature alarm is triggered. The step of calculating the potential risk intensity based on the estimated heat generation power of the battery, the heat exchange power of the cooling system, and the multi-point temperature difference of the battery includes: The first risk contribution value is determined based on the estimated heat generation power of the battery. The second risk contribution value is determined based on the difference between the estimated heat generation power of the battery and the heat exchange power of the cooling system; The third risk contribution value is determined based on the multi-point temperature difference of the battery, where the multi-point temperature difference of the battery is the difference between the highest and lowest temperatures among multiple battery temperature measurement points. The potential risk intensity is calculated based on the first risk contribution value, the second risk contribution value, and the third risk contribution value.
2. The electric vehicle thermal management method according to claim 1, characterized in that, The step of determining whether there is unexpected thermal behavior based on the estimated heat generation power of the battery, the heat exchange power of the cooling system, the battery temperature measured by the cooling system, and the circuit status signal includes: Determine whether the change in battery temperature measured by the cooling system is inconsistent with the estimated heat generation power of the battery; When the judgment result is yes, it is determined that there is unexpected thermal behavior.
3. The electric vehicle thermal management method according to claim 2, characterized in that, The step of determining whether the change in battery temperature measured by the cooling system is inconsistent with the estimated heat generation power of the battery includes: The heat transfer residual is calculated based on the estimated heat generation power of the battery and the heat transfer power of the cooling system. The heat transfer residual is the difference between the estimated heat generation power of the battery and the heat transfer power of the cooling system. When the average value of the heat transfer residual over a preset time period is positive, it is determined that the change in battery temperature measured by the cooling system is inconsistent with the estimated heat generation power of the battery.
4. The electric vehicle thermal management method according to claim 3, characterized in that, The step of determining whether the change in battery temperature measured by the cooling system is inconsistent with the estimated heat generation power of the battery further includes: Determine whether the following conditions are met simultaneously: The battery current increase exceeds the first threshold; The estimated increase in the battery's heat generation power exceeds the second threshold. The change in battery temperature measured by the cooling system is below the third threshold. The loop status signal indicates loop switching.
5. An electric vehicle thermal management system, the system comprising a processor and a memory, the memory storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the electric vehicle thermal management method as described in any one of claims 1-4.