Heat exchange control method and device of motor, vehicle and medium

Abstract

The application discloses a heat exchange control method and device of a motor, a vehicle and a medium, and relates to the technical field of vehicles. The method comprises the following steps: predicting a future working condition load by fusing vehicle states, a map, navigation and historical data; then, quantitatively calculating an energy consumption benefit caused by the change of the electric drive efficiency and an energy consumption increase value caused by the change of the waste heat utilization state, and dynamically optimizing and determining an electric drive target heat exchange temperature according to the difference between the two values; and finally, stably following the target value by closed-loop control to make the actual temperature of the motor stable. In this way, the contradiction between the motor temperature rising demand and the waste heat recovery demand under low temperature working conditions is effectively solved, and the vehicle energy consumption is significantly reduced and the thermal management control precision is improved within the whole working condition range.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a heat exchange control method for an electric motor, a heat exchange control device for an electric motor, a computer-readable storage medium, and a vehicle. Background Technology

[0002] In the field of new energy vehicles, the thermal management of the electric drive system has a significant impact on the overall vehicle performance and energy consumption. In related technologies, electric drive thermal management systems typically employ a fixed heat exchange temperature control strategy, making it difficult to dynamically adjust based on actual driving conditions and the operating status of the electric drive system. On the one hand, if heat exchange is initiated too early when the motor temperature is low, the motor will not be able to fully utilize the efficiency gains brought about by the temperature rise, resulting in energy waste. On the other hand, under low-temperature conditions, if the electric drive system is not properly heat-exchanged, the thermal management system cannot effectively recover the waste heat from the motor, thus requiring additional electricity to maintain the temperature of the battery pack and other systems, leading to increased overall vehicle energy consumption. Summary of the Invention

[0003] This application aims to at least partially address the problem in related technologies where fixed heat exchange temperature control strategies cannot simultaneously improve electric drive efficiency and recover waste heat, resulting in high overall vehicle energy consumption. To this end, the first objective of this application is to propose a heat exchange control method for a motor. This method involves acquiring the vehicle's current state information, the motor's current heat exchange temperature, map information, navigation information, and historical driving data. Based on these information, the method determines the vehicle's predicted load information. It then determines the vehicle's predicted energy consumption, the motor's predicted input power information, and the motor's efficiency information based on the predicted load information. Based on the predicted load information, it determines the vehicle's predicted energy consumption, the motor's predicted input power information, and the motor's efficiency information. Based on the motor's predicted input power information and motor efficiency information, it determines the motor's energy consumption benefit value and, based on the predicted input power information, predicted energy consumption, and motor efficiency information, it determines the motor's energy consumption increase value. Based on a first difference between the motor's energy consumption benefit value and the motor's energy consumption increase value, it adjusts the motor's required heat exchange temperature to obtain the motor's target heat exchange temperature. Finally, based on the motor's current heat exchange temperature and the motor's target heat exchange temperature, it controls the heat exchange components to heat the motor.

[0004] This application predicts future operating load by integrating vehicle status, map, navigation, and historical data. It then quantifies the energy gain due to changes in electric drive efficiency and the energy increase due to changes in waste heat utilization, dynamically optimizing the difference between these two values ​​to determine the target heat exchange temperature for the electric drive. Finally, closed-loop control ensures the motor's actual temperature stably follows this target value. This effectively resolves the conflict between the motor's heating requirements and waste heat recovery needs under low-temperature conditions, achieving a significant reduction in overall vehicle energy consumption and improved thermal management control accuracy across all operating conditions.

[0005] The second objective of this application is to provide a heat exchange control device for an electric motor.

[0006] The third objective of this application is to provide a computer-readable storage medium.

[0007] The fourth objective of this application is to propose a vehicle.

[0008] To achieve the above objectives, the first aspect of this application proposes a heat exchange control method for an electric motor. This method involves acquiring current vehicle status information, current motor heat exchange temperature, map information, navigation information, and historical driving data; determining the vehicle's predicted load information based on the current status information, map information, navigation information, and historical driving data; determining the vehicle's predicted energy consumption, predicted motor input power information, and motor efficiency information based on the predicted load information; determining the motor's energy consumption benefit value based on the predicted motor input power information and motor efficiency information; determining the motor's energy consumption increase value based on the predicted motor input power information, predicted energy consumption, and motor efficiency information; adjusting the motor's required heat exchange temperature based on a first difference between the motor's energy consumption benefit value and the motor's energy consumption increase value to obtain the motor's target heat exchange temperature; and controlling the heat exchange components based on the motor's current heat exchange temperature and the motor's target heat exchange temperature to provide heat exchange for the motor.

[0009] According to one embodiment of this application, the motor's required heat exchange temperature is adjusted based on a first difference between the motor's energy consumption gain value and the motor's energy consumption increase value to obtain a target heat exchange temperature. This includes: in response to the first difference being greater than a preset threshold, determining a heat exchange temperature adjustment amount based on a first preset adjustment strategy, the first difference, and a preset temperature adjustment gradient; and adjusting the motor's required heat exchange temperature based on the heat exchange temperature adjustment amount to obtain the target heat exchange temperature; and in response to the difference being less than or equal to the preset threshold, determining the current heat exchange temperature of the motor as the target heat exchange temperature.

[0010] According to one embodiment of this application, controlling a heat exchange component to exchange heat for the motor based on the current heat exchange temperature and the target heat exchange temperature of the motor includes: obtaining a second difference between the current heat exchange temperature and the target heat exchange temperature of the motor; determining a control amount of the heat exchange component based on a second preset adjustment strategy and the second difference; and controlling the heat exchange component based on the control amount of the heat exchange component to maintain the actual temperature of the motor at the target heat exchange temperature of the motor.

[0011] According to one embodiment of this application, the predicted load information includes the predicted load corresponding to each sampling point in the current prediction period, the predicted motor input power information includes the predicted motor input power corresponding to each sampling point in the current prediction period, and the motor efficiency information includes the first motor efficiency and the second motor efficiency corresponding to each sampling point in the current prediction period. Determining the predicted energy consumption required by the vehicle, the predicted motor input power information, and the motor efficiency information based on the predicted load information includes: calculating the predicted motor input power information corresponding to each sampling point based on the predicted load and vehicle dynamics formula; calculating the predicted energy consumption required based on the predicted motor input power and a preset sampling interval; determining the first motor efficiency corresponding to each sampling point by searching a preset motor efficiency spectrum based on the predicted load and the current heat exchange temperature of the motor; and determining the second motor efficiency corresponding to each sampling point by searching a preset motor efficiency spectrum based on the predicted load and the required heat exchange temperature of the motor.

[0012] According to one embodiment of this application, the motor energy consumption benefit value is determined based on the motor predicted input power information and the motor efficiency information, including: calculating the efficiency difference corresponding to each sampling point based on the second motor efficiency and the first motor efficiency corresponding to each sampling point; calculating the instantaneous energy consumption benefit corresponding to each sampling point based on the efficiency difference corresponding to each sampling point, the motor predicted input power and the preset sampling interval; and summing the instantaneous energy consumption benefits corresponding to each sampling point to obtain the motor energy consumption benefit value.

[0013] According to one embodiment of this application, determining the increase in motor energy consumption based on predicted input power information, predicted required energy consumption, and motor efficiency information includes: calculating the predicted heat generation of the motor based on the predicted input power of the motor corresponding to each sampling point, the second motor efficiency, and a preset sampling interval; determining the available residual heat based on the minimum value between the predicted required energy consumption and the predicted heat generation of the motor; calculating the predicted additional heat based on the difference between the predicted required energy consumption and the available residual heat; calculating the first predicted energy consumption based on the predicted additional heat and the efficiency of the heat exchange component, wherein the first predicted energy consumption is used to characterize the predicted energy consumption of the heat exchange component when utilizing the residual heat of the motor for heat exchange; calculating the second predicted energy consumption based on the predicted required energy consumption and the efficiency of the heat exchange component, wherein the second predicted energy consumption is used to characterize the predicted energy consumption of the heat exchange component when not utilizing the residual heat of the motor for heat exchange; and calculating the increase in motor energy consumption based on the second predicted energy consumption and the first predicted energy consumption.

[0014] According to one embodiment of this application, determining the predicted load information of a vehicle based on current state information, map information, navigation information, and historical driving data includes: analyzing historical driving data based on a time series analysis algorithm to obtain a first load change curve of the vehicle; determining the acceleration change trend of the vehicle based on the current state information, and determining the motion state information of the vehicle based on the vehicle dynamics model and the current state information, and determining the motor load corresponding to each sampling point based on the motion state information and the acceleration change trend to generate a second load change curve of the vehicle; determining the load change of the vehicle's electric drive system based on map information, and determining the vehicle's start-stop frequency and electric drive operating state based on navigation information, and determining the motor load corresponding to each sampling point based on the electric drive system load change, vehicle start-stop frequency, and electric drive operating state to generate a third load change curve of the vehicle; and weightedly fusing the first load change curve, the second load change curve, and the third load change curve based on a preset fusion algorithm to obtain the predicted load information.

[0015] To achieve the above objectives, a second aspect of this application provides a heat exchange control device for an electric motor, comprising: an acquisition module for acquiring current vehicle status information, current motor heat exchange temperature, map information, navigation information, and historical driving data; a first determination module for determining predicted vehicle load information based on the current status information, map information, navigation information, and historical driving data; a second determination module for determining predicted energy consumption required by the vehicle, predicted motor input power information, and motor efficiency information based on the predicted load information; a third determination module for determining a motor energy consumption benefit value based on the predicted motor input power information and motor efficiency information, and determining a motor energy consumption increase value based on the predicted motor input power information, predicted required energy consumption, and motor efficiency information; an adjustment module for adjusting the motor required heat exchange temperature based on a first difference between the motor energy consumption benefit value and the motor energy consumption increase value to obtain a target motor heat exchange temperature; and a control module for controlling the heat exchange components based on the current motor heat exchange temperature and the target motor heat exchange temperature to heat the motor.

[0016] To achieve the above objectives, a third aspect of this application provides a computer-readable storage medium storing a heat exchange control program for a motor, which, when executed by a processor, implements the aforementioned heat exchange control method for the motor.

[0017] To achieve the above objectives, a fourth aspect of this application provides a vehicle including a memory, a processor, and a motor heat exchange control program stored in the memory and capable of running on the processor. When the processor executes the motor heat exchange control program, it implements the aforementioned motor heat exchange control method.

[0018] According to the embodiments of this application, the motor heat exchange control method, device, vehicle, and medium acquire the current state information of the vehicle, the current heat exchange temperature of the motor, map information, navigation information, and historical driving data, and determine the predicted load information of the vehicle based on the current state information, map information, navigation information, and historical driving data; determine the predicted energy consumption required by the vehicle, the predicted input power information of the motor, and the motor efficiency information based on the predicted load information; determine the motor energy consumption benefit value based on the predicted input power information and the motor efficiency information, and determine the motor energy consumption increase value based on the predicted input power information, the predicted energy consumption required, and the motor efficiency information; adjust the motor required heat exchange temperature based on a first difference between the motor energy consumption benefit value and the motor energy consumption increase value to obtain the motor target heat exchange temperature; and control the heat exchange components based on the current heat exchange temperature and the motor target heat exchange temperature to heat the motor. This application predicts future operating load by integrating vehicle status, map, navigation, and historical data. It then quantifies the energy gain due to changes in electric drive efficiency and the energy increase due to changes in waste heat utilization, dynamically optimizing the difference between these two values ​​to determine the target heat exchange temperature for the electric drive. Finally, closed-loop control ensures the motor's actual temperature stably follows this target value. This effectively resolves the conflict between the motor's heating requirements and waste heat recovery needs under low-temperature conditions, achieving a significant reduction in overall vehicle energy consumption and improved thermal management control accuracy across all operating conditions. Attached Figure Description

[0019] Figure 1 This is a flowchart of a heat exchange control method for an electric motor according to some embodiments of this application; Figure 2 This is a block diagram of a heat exchange control device for an electric motor according to some embodiments of this application; Figure 3 This is a block diagram of a vehicle according to some embodiments of this application. Detailed Implementation

[0020] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0021] The heat exchange control method, apparatus, vehicle, and medium of the motor according to embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0022] Figure 1 This is a flowchart of a heat exchange control method for a motor according to some embodiments of this application. (Refer to...) Figure 1 The heat exchange control method for the motor in this application embodiment may include the following steps: S110 acquires the vehicle's current status information, the motor's current heat exchange temperature, map information, navigation information, and historical driving data, and determines the vehicle's predicted load information based on the current status information, map information, navigation information, and historical driving data.

[0023] Specifically, the vehicle's current status information includes, but is not limited to, vehicle speed, acceleration, steering wheel angle, accelerator pedal position, and brake pedal position. Among these, vehicle speed can be acquired by a vehicle speed sensor, acceleration can be acquired by an acceleration sensor, steering wheel angle can be acquired by a steering angle sensor, and accelerator pedal position and brake pedal position can be acquired by an accelerator pedal position sensor and a brake pedal position sensor, respectively.

[0024] The current heat exchange temperature of the motor may include the inlet and outlet temperatures of the motor coolant, the stator winding temperature, and the rotor temperature. The current heat exchange temperature of the motor can be acquired by a PT100 platinum resistance temperature sensor or a thermocouple sensor.

[0025] Navigation information includes, but is not limited to, speed limits, traffic lights, and road congestion. Map information includes, but is not limited to, road gradients, curvature, locations of traffic signal facilities, road segment types, and the locations of intersections and ramps. Both navigation and map information can be acquired through the vehicle's wireless communication module.

[0026] Historical driving data includes vehicle speed sequences, acceleration sequences, motor speed sequences, and torque sequences, as well as road segment types, time characteristics, and ambient temperature characteristics corresponding to each driving period. Among them, vehicle speed sequences are used to mine trends and periodic features through time series analysis, while road segment types, time characteristics, and ambient temperature characteristics are used to match similar historical operating conditions.

[0027] After acquiring the vehicle's current status information, map information, navigation information, and historical driving data, the predicted load information of the vehicle is determined based on these data. For example, the current status information, map information, navigation information, and historical driving data can be input into the load information prediction model to output the vehicle's predicted load information.

[0028] To further illustrate the above embodiments, in this application embodiment, determining the predicted load information of a vehicle based on current state information, map information, navigation information, and historical driving data includes: analyzing historical driving data based on a time series analysis algorithm to obtain a first load change curve for the vehicle; determining the acceleration change trend of the vehicle based on the current state information, and determining the vehicle's motion state information based on the vehicle dynamics model and the current state information, and determining the motor load corresponding to each sampling point based on the motion state information and the acceleration change trend, to generate a second load change curve for the vehicle; predicting road congestion information and vehicle start-stop frequency based on navigation information, and calculating a third load change curve in conjunction with the vehicle dynamics model; determining the load change of the vehicle's electric drive system based on map information, and determining the vehicle's start-stop frequency and electric drive operating state based on navigation information, and determining the motor load corresponding to each sampling point based on the electric drive system load change, vehicle start-stop frequency, and electric drive operating state, to generate a third load change curve for the vehicle; and weightedly fusing the first load change curve, the second load change curve, and the third load change curve based on a preset fusion algorithm to obtain the predicted load information.

[0029] Specifically, to improve the accuracy and comprehensiveness of future operating condition predictions, multi-source predictions are conducted from different time dimensions and data sources. For example, time-series feature mining is performed on historical driving data to obtain a first load change curve reflecting long-term vehicle operating condition patterns and driver habits; driving intention recognition and motion state prediction are performed on current state information to obtain a second load change curve reflecting current real-time driving behavior and short-term dynamic response; and road feature extraction and traffic state analysis are performed on map and navigation information to obtain a third load change curve reflecting changes in the driving environment and external operating conditions ahead. By acquiring these three types of prediction sequences, operating condition information at three levels—historical experience patterns, real-time driving intentions, and future road environments—can be covered, compensating for the shortcomings of single data sources in prediction range and accuracy. Based on this, a preset fusion algorithm is used to perform weighted fusion processing on the first, second, and third load change curves. The fused motor load time-series data is used as predicted load information, thereby providing a more accurate and comprehensive decision-making basis for the dynamic control of electric drive heat exchange temperature, effectively improving the energy efficiency of the vehicle and the energy-saving effect of the thermal management system.

[0030] Specifically, historical sequence data of vehicle speed changes over time can be extracted from historical driving data. Time series analysis algorithms are used to decompose the historical sequence data to extract trend and periodic feature components. At the same time, road segment type, time characteristics, and ambient temperature characteristics corresponding to each driving period are extracted from historical driving data, and similarity matching is performed with the current road segment type, time characteristics, and ambient temperature characteristics to filter out historical operating condition segments similar to the current operating condition. Based on the motor load data corresponding to the filtered historical operating condition segments, the first load change curve is generated by arranging them in chronological order.

[0031] Specifically, the system acquires the accelerator pedal opening signal and brake pedal opening signal from the current state information. Based on the rate of change and amplitude of the accelerator pedal opening signal, and the presence or absence of the brake pedal opening signal, it determines the driver's acceleration or deceleration intention. Then, based on a preset mapping relationship between driving intention and acceleration, it determines the acceleration change trend within a preset time period. Next, it acquires the current vehicle speed and steering wheel angle from the current state information and inputs them into the vehicle dynamics model. Combining this with the acceleration change trend, the model calculates the estimated vehicle speed and estimated wheel torque at each sampling point in the future. Then, based on the estimated vehicle speed and estimated wheel torque at each sampling point, and considering the transmission system speed ratio and motor efficiency characteristics, it back-calculates the required output torque and power values ​​of the motor at each sampling point, which serve as the motor load at each sampling point. Finally, it arranges the motor loads at each sampling point in chronological order to generate a second load change curve.

[0032] Specifically, the system can receive real-time traffic flow data from a cloud server via an onboard wireless communication module. Based on this data, it can extract the congestion level and estimated speed of each road segment along the navigation path. Then, based on changes in congestion levels of adjacent road segments and the location of traffic signal facilities, it can predict the vehicle's start-stop status and parking duration at each sampling point, thus obtaining the vehicle's start-stop frequency. Next, based on the real-time location coordinates obtained by the onboard positioning module, it can extract the slope and curvature sequences of the road ahead from the vehicle-mounted map database. The slope sequence is used to calculate the change in climbing resistance caused by the road slope at each sampling point, and the curvature sequence is used to estimate the vehicle's deceleration requirements when cornering, thus comprehensively obtaining the load change of the electric drive system. Then, the resistance requirements at each sampling point in the electric drive system load change, the start-stop status at each sampling point in the vehicle start-stop frequency, and the continuous operating state of the electric drive corresponding to the estimated speed are superimposed. Combined with the vehicle dynamics model, the required torque and power output values ​​of the motor at each sampling point are calculated as the motor load at each sampling point. Finally, the motor loads at each sampling point are arranged in chronological order to generate a third load change curve.

[0033] Finally, the motor load values ​​corresponding to each sampling point of the first load change curve, the second load change curve, and the third load change curve on the same time axis are obtained. Based on the current driving scenario and data confidence, the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient corresponding to the first load change curve, the second weighting coefficient, and the third weighting coefficient are determined. For each sampling point, the load value of the first load change curve is multiplied by the first weighting coefficient, the load value of the second load change curve is multiplied by the second weighting coefficient, and the load value of the third load change curve is multiplied by the third weighting coefficient, and then summed to obtain the fused load demand value of that sampling point. The fused load demand values ​​of each sampling point are arranged in chronological order to generate predicted load information.

[0034] The weighting coefficients are determined as follows: when the vehicle is on a historically high-frequency driving section, the value of the first weighting coefficient is increased; when the vehicle is in a condition where real-time driving intentions change frequently, the value of the second weighting coefficient is increased; when the vehicle is driving according to the navigation path and the road features ahead change significantly, the value of the third weighting coefficient is increased. The preset fusion algorithm includes either the Kalman filter algorithm or the Bayesian network algorithm.

[0035] S120 determines the vehicle's predicted energy consumption, motor predicted input power, and motor efficiency based on the predicted load information.

[0036] Specifically, the motor load values ​​corresponding to each sampling point in the future are extracted from the predicted load information. The predicted energy consumption of the vehicle can be determined by looking up a first preset relationship mapping table between the motor load value and the predicted energy consumption required by the vehicle; the predicted input power information of the vehicle's motor can be determined by looking up a second preset relationship mapping table between the motor load value and the predicted input power information of the vehicle's motor; and the motor efficiency information of the vehicle can be determined by looking up a third preset relationship mapping table between the motor load value and the vehicle's motor efficiency information.

[0037] The first preset relationship mapping table includes the motor load value corresponding to multiple sampling points, and the predicted energy consumption corresponding to each sampling point; the second preset relationship mapping table includes the motor load value corresponding to multiple sampling points, and the predicted input power information of the motor corresponding to each sampling point; the third preset relationship mapping table includes the motor load value corresponding to multiple sampling points, and the motor efficiency information corresponding to each sampling point.

[0038] To further illustrate the above embodiments, in this application embodiment, the predicted load information includes the predicted load corresponding to each sampling point in the current prediction period, the predicted motor input power information includes the predicted motor input power corresponding to each sampling point in the current prediction period, and the motor efficiency information includes the first motor efficiency and the second motor efficiency corresponding to each sampling point in the current prediction period. Determining the vehicle's predicted energy consumption, predicted motor input power information, and motor efficiency information based on the predicted load information includes: calculating the predicted motor input power information corresponding to each sampling point based on the predicted load and vehicle dynamics formula; calculating the predicted energy consumption based on the predicted motor input power and a preset sampling interval; determining the first motor efficiency corresponding to each sampling point by searching a preset motor efficiency spectrum based on the predicted load and the current heat exchange temperature of the motor; and determining the second motor efficiency corresponding to each sampling point by searching a preset motor efficiency spectrum based on the predicted load and the required heat exchange temperature of the motor.

[0039] Specifically, for each sampling point in the current prediction period, the predicted load value corresponding to that sampling point is obtained. The predicted load value includes the torque value required by the motor at that sampling point. Based on the current vehicle speed and transmission ratio, the torque value in the predicted load value is converted into the required motor output speed using vehicle dynamics formulas. The conversion process includes: dividing the current vehicle speed by the wheel rolling radius to obtain the wheel speed, and then multiplying the wheel speed by the transmission ratio to obtain the motor output speed. Based on the motor output speed and the torque value in the predicted load value, the motor output mechanical power corresponding to that sampling point is calculated using the formula: Motor output mechanical power = Motor output speed × Torque / C, where C represents a constant. The calculated motor output mechanical power is used as the predicted motor input power corresponding to that sampling point, or the motor output mechanical power is divided by a preset conversion efficiency coefficient to obtain the predicted motor input power corresponding to that sampling point. The above calculation process is performed sequentially for each sampling point in the current prediction period to obtain the predicted motor input power corresponding to each sampling point. The predicted motor input power of all sampling points together constitutes the motor predicted input power information.

[0040] For each sampling point within the current prediction period, obtain the predicted motor input power value corresponding to that sampling point, with the unit being kilowatts (kW). Determine the preset sampling interval between two adjacent sampling points, with the unit being seconds (s). For each sampling point, multiply the predicted motor input power value corresponding to that sampling point by the preset sampling interval to obtain the single-point energy consumption value for that sampling point. Sum the single-point energy consumption values ​​corresponding to all sampling points within the current prediction period to obtain the total energy consumption value, with the unit being kilojoules (kJ). Divide the total energy consumption value by the energy conversion constant 3600 to obtain the energy consumption required for prediction, expressed in kilowatt-hours (kWh).

[0041] For each sampling point within the current prediction period, the predicted load value and the current heat exchange temperature of the motor corresponding to that sampling point are obtained. The predicted load value includes the torque and speed values ​​required by the motor at that sampling point, and the current heat exchange temperature is the measured temperature of the motor windings or coolant at the current moment. The torque, speed, and current heat exchange temperature values ​​from the predicted load value are used as inputs for a three-dimensional query. The corresponding efficiency value is directly located in a preset motor efficiency map, which is a three-dimensional data table calibrated through bench tests and reflects the efficiency distribution of the motor under different combinations of torque, speed, and temperature. The efficiency value obtained from the table lookup is taken as the first motor efficiency corresponding to that sampling point. The above table lookup process is performed sequentially for each sampling point within the current prediction period to obtain the first motor efficiency corresponding to each sampling point.

[0042] For each sampling point within the current prediction period, the predicted load value and the motor's required heat exchange temperature value are obtained. The predicted load value includes the torque and speed values ​​required by the motor at that sampling point, and the motor's required heat exchange temperature value is the target temperature value that the motor is expected to achieve at that sampling point, determined according to the thermal management control strategy. The torque, speed, and required heat exchange temperature values ​​from the predicted load value are used as three-dimensional query inputs to directly locate the corresponding efficiency value in a preset motor efficiency map. The preset motor efficiency map is a three-dimensional data table, calibrated beforehand through bench testing, reflecting the efficiency distribution of the motor under different combinations of torque, speed, and temperature. The efficiency value obtained from the table lookup is used as the second motor efficiency corresponding to that sampling point. The above table lookup process is performed sequentially for each sampling point within the current prediction period to obtain the second motor efficiency corresponding to each sampling point.

[0043] S130: Based on the predicted input power information and motor efficiency information, determine the motor energy consumption benefit value, and based on the predicted input power information, predicted required energy consumption and motor efficiency information, determine the motor energy consumption increase value.

[0044] Specifically, the motor energy consumption benefit value refers to the contribution of the increased electric drive efficiency due to the rise in motor temperature to the overall vehicle energy consumption. The internal resistance loss and electromagnetic conversion characteristics of a motor vary under different operating temperatures. A moderate increase in temperature can typically reduce winding copper losses and improve lubrication, thereby improving the overall operating efficiency of the electric drive system. If the motor is allowed to naturally heat up within a reasonable range without active heat exchange, some input energy may be saved due to increased efficiency. Conversely, if the heat exchange device is activated too early or the target heat exchange temperature is set too low, the efficiency gain from the natural temperature rise of the motor will be suppressed, causing the thermal management system to consume additional energy while actually increasing the net energy consumption of the vehicle. Therefore, it is necessary to quantitatively calculate the cumulative energy value of the power savings contributed by temperature changes within the prediction time range based on the predicted input power and motor efficiency information corresponding to each sampling point; this is the motor energy consumption benefit value. For example, the predicted input power information and motor efficiency information can be input into a preset motor energy consumption benefit value calculation formula to output the motor energy consumption benefit value.

[0045] To further illustrate the above embodiments, in this application embodiment, determining the motor energy consumption benefit value based on the motor predicted input power information and motor efficiency information includes: calculating the efficiency difference corresponding to each sampling point based on the second motor efficiency and the first motor efficiency corresponding to each sampling point; calculating the instantaneous energy consumption benefit corresponding to each sampling point based on the efficiency difference corresponding to each sampling point, the motor predicted input power, and the preset sampling interval; and summing the instantaneous energy consumption benefits corresponding to each sampling point to obtain the motor energy consumption benefit value.

[0046] Specifically, for each sampling point within the current prediction period, the difference between the efficiency of the second motor and the efficiency of the first motor is calculated. The purpose is to quantify the net contribution of temperature control to the efficiency of the electric drive system. Since both the first and second motor efficiencies are queried based on the same predicted load at the same sampling point (i.e., the torque and speed values ​​are identical), their only variable is the temperature input dimension. The first motor efficiency corresponds to the current actual heat exchange temperature, while the second motor efficiency corresponds to the target heat exchange temperature expected by the thermal management strategy. Therefore, the efficiency difference obtained by subtracting the first motor efficiency from the second motor efficiency purely reflects the efficiency change brought about by adjusting the motor temperature from the current state to the target state while keeping the load constant, thus eliminating the interference of load fluctuations on efficiency. When calculating the motor energy consumption benefit, this efficiency difference is multiplied by the predicted input power of the motor at the corresponding sampling point to obtain the input power saved at that sampling point due to active heat exchange temperature control. This cumulative effect yields the total energy savings contributed by temperature optimization throughout the entire prediction period, providing a direct quantitative basis for judging the economics of the heat exchange strategy.

[0047] For example, the predicted power information and motor efficiency information (first motor efficiency and second motor efficiency) can be input into the following formula to output the motor energy consumption benefit value:

[0048] in, This represents the predicted input power of the motor corresponding to the i-th sampling point; This represents the efficiency of the second motor corresponding to the i-th sampling point; This represents the efficiency of the first motor corresponding to the i-th sampling point; This represents the efficiency difference corresponding to the i-th sampling point; This represents the preset sampling interval corresponding to the i-th sampling point; n represents the number of sampling points in the current sampling period.

[0049] The increase in motor energy consumption primarily reflects the additional energy consumption incurred by the thermal management system due to the lack of residual heat from the electric drive when electric drive heat exchange is not performed under low-temperature conditions. In low-temperature environments, if the thermal management system fails to recover the residual heat generated by the motor in a timely manner, the motor surface temperature will be too low. This not only prevents the transfer of excess heat to the battery pack or passenger compartment to reduce the burden on other heat sources, but also causes the battery to consume more energy for self-heating due to increased internal resistance at low temperatures. Simultaneously, the passenger compartment's heating needs rely entirely on high-power PTC heaters or heat pump systems. This additional energy consumption forced upon the thermal management system due to the lack of utilization of residual heat from the electric drive constitutes the increase in energy consumption. By combining the predicted input power information and motor efficiency information, the actual heating power and usable residual heat of the motor under different heat exchange strategies can be estimated. Combined with the predicted required energy consumption, the additional energy consumption of the heat exchange components due to the failure to utilize the motor's residual heat can be further calculated, i.e., the increase in motor energy consumption. For example, the predicted input power information, predicted required energy consumption, and motor efficiency information can be input into the formula for calculating the increase in motor energy consumption to output the increase in motor energy consumption.

[0050] To further illustrate the above embodiments, in this application embodiment, determining the increase in motor energy consumption based on the predicted input power information of the motor, the predicted required energy consumption, and the motor efficiency information includes: calculating the predicted heat generation of the motor based on the predicted input power of the motor corresponding to each sampling point, the second motor efficiency, and the preset sampling interval; determining the available residual heat based on the minimum value between the predicted required energy consumption and the predicted heat generation of the motor; calculating the predicted additional heat based on the difference between the predicted required energy consumption and the available residual heat; calculating the first predicted energy consumption based on the predicted additional heat and the efficiency of the heat exchange component, wherein the first predicted energy consumption is used to characterize the predicted energy consumption of the heat exchange component when using the residual heat of the motor for heat exchange; calculating the second predicted energy consumption based on the predicted required energy consumption and the efficiency of the heat exchange component, wherein the second predicted energy consumption is used to characterize the predicted energy consumption of the heat exchange component when not using the residual heat of the motor for heat exchange; and calculating the increase in motor energy consumption based on the second predicted energy consumption and the first predicted energy consumption.

[0051] Specifically, if the waste heat generated during the operation of the electric drive system is not recovered and utilized, it must be independently supplied by high-power heating components to meet the heating requirements of the passenger compartment or battery pack, resulting in additional energy consumption. To quantify the impact of this additional loss on the overall vehicle energy consumption and to provide a comparable cost basis for thermal management strategies, firstly, for each sampling point, the predicted input power of the motor is multiplied by the efficiency of the second motor and the preset sampling interval, and then summed to calculate the predicted heat generation of the motor when operating at the current required heat exchange temperature. This heat generation represents the amount of heat that the electric drive system dissipates in the form of heat loss during energy conversion and has the potential for recovery.

[0052] Then, since the actual usable waste heat of the thermal management system is constrained by both the demand and supply sides, if the predicted heat generation of the motor is greater than the predicted energy consumption, it indicates that the waste heat generated by the electric drive system exceeds the current actual heat demand of the thermal management system. The excess cannot be effectively absorbed due to the lack of heat transfer temperature difference or heat storage capacity limitations, and can only be dissipated into the environment through the radiator. Therefore, the usable waste heat is only equal to the predicted energy consumption. Conversely, if the predicted heat generation of the motor is less than the predicted energy consumption, it indicates that the waste heat generated by the electric drive system is insufficient to fully cover the entire heat demand of the thermal management system. The waste heat can only undertake part of the heating task, and the remaining gap still needs to be supplemented by other heat sources. Therefore, the usable waste heat is only equal to the predicted heat generation of the motor. By comparing the predicted energy consumption and the predicted heat generation of the motor, the smaller value is taken as the usable waste heat. In this way, the true upper limit of waste heat recovery under the supply and demand matching constraint can be accurately reflected. To a certain extent, this avoids overestimating the contribution of waste heat or underestimating the burden of supplementary heat sources when calculating the first predicted energy consumption, thereby ensuring the rationality and accuracy of the energy consumption increase calculation results.

[0053] Available waste heat represents only the portion of the motor's waste heat that can actually be recovered by the thermal management system and used to meet heating demands, while the predicted energy consumption reflects the total heat supply required by the thermal management system to maintain the current required heat exchange temperature. When the available waste heat is less than the predicted energy consumption, it means that the motor's waste heat cannot fully cover all heat demands, and the remaining portion must be supplemented by active heating components such as PTC heaters or heat pumps. Therefore, by calculating the difference between the predicted energy consumption and the available waste heat, this heat gap that must rely on external energy input can be accurately extracted, i.e., the predicted heat to be supplemented. Dividing this predicted heat to be supplemented by the efficiency of the heat exchange components yields the energy consumed by the heat exchange components to fill this gap, i.e., the first predicted energy consumption. In the case where the motor's waste heat is not utilized at all, all heat demands of the thermal management system must be provided independently by the heat exchange components. Dividing the predicted energy consumption directly by the efficiency of the heat exchange components yields the energy consumed by the heat exchange components to meet all heat demands, i.e., the second predicted energy consumption.

[0054] Finally, subtracting the first predicted energy consumption from the second predicted energy consumption yields the additional electrical energy consumption of the heat exchange components due to the failure to utilize the motor's waste heat. In other words, when waste heat recovery is abandoned, the heat demand that could have been covered by the motor's waste heat is transferred to the heat exchange components, forcing them to consume additional electrical energy to compensate. This increase in energy consumption directly reflects the energy-saving benefits of the waste heat recovery strategy; a larger increase indicates a more significant contribution of waste heat recovery to reducing overall vehicle energy consumption; an increase approaching zero indicates limited energy-saving potential of waste heat recovery under current operating conditions.

[0055] S140, based on the first difference between the motor energy consumption benefit value and the motor energy consumption increase value, adjusts the motor demand heat exchange temperature to obtain the motor target heat exchange temperature.

[0056] Specifically, the motor energy consumption benefit value represents the energy saved due to the increased electric drive efficiency caused by the increase in motor temperature, which is a positive energy-saving contribution of the heat exchange strategy on the electric drive side. The motor energy consumption increase value represents the additional consumption incurred by the thermal management system due to the lack of residual heat from the electric drive when electric drive heat exchange is not performed under low-temperature conditions, which is a negative energy consumption cost of the heat exchange strategy on the thermal management side. The first difference obtained by subtracting the two is the net contribution value of the heat exchange strategy to the net energy consumption at the vehicle level.

[0057] After calculating the first difference, a closed-loop iterative optimization is performed based on this difference to find the target heat exchange temperature of the motor that minimizes the net energy consumption of the entire vehicle, rather than simply using a fixed empirical temperature value. Specifically, a positive first difference indicates that there is still room for adjustment towards a better temperature, while a negative or zero value indicates that the current temperature has already passed the optimal equilibrium point. Adjusting based on the sign and magnitude of the first difference is equivalent to using the net energy consumption of the entire vehicle as the optimization objective and the heat exchange temperature as the control variable, and gradually searching for the optimal solution along the direction of decreasing energy consumption.

[0058] For example, firstly, the first difference between the current motor's required heat exchange temperature and the target temperature is obtained. If the first difference is greater than zero, it indicates that continuing to adjust the temperature in the current direction based on the current temperature is expected to yield greater net energy savings. In this case, according to the preset temperature adjustment step size, a fixed increment is added or subtracted from the previous temperature value to obtain the corrected motor's required heat exchange temperature, and the first difference at the new temperature point is recalculated. If the first difference is less than or equal to zero, it indicates that the current temperature point has already achieved a local minimum in net energy consumption, or that further adjustment will lead to an increase in net energy consumption. In this case, the iterative adjustment is stopped, and the current motor's required heat exchange temperature is directly determined as the motor's target heat exchange temperature. Through the above iterative comparison and step correction process, the process eventually converges to the temperature point where the first difference approaches zero. This point is the motor's target heat exchange temperature that balances electric drive efficiency and waste heat utilization.

[0059] To further illustrate the above embodiments, in this application embodiment, the motor's required heat exchange temperature is adjusted based on a first difference between the motor's energy consumption gain value and the motor's energy consumption increase value to obtain the motor's target heat exchange temperature. This includes: responding to the first difference being greater than a preset threshold, determining a heat exchange temperature adjustment amount based on a first preset adjustment strategy, the first difference, and a preset temperature adjustment gradient, and adjusting the motor's required heat exchange temperature based on the heat exchange temperature adjustment amount to obtain the motor's target heat exchange temperature; responding to the difference being less than or equal to the preset threshold, determining the motor's current heat exchange temperature as the motor's target heat exchange temperature. The preset threshold can be determined according to actual conditions; for example, the preset threshold can be 0. The first preset adjustment strategy can be a PID (Proportional-Integral-Derivative) adjustment strategy, and no specific limitations are imposed here.

[0060] Specifically, within each control cycle, the first difference corresponding to the current motor's required heat exchange temperature is calculated in real time and compared with a preset threshold. When the first difference is greater than the preset threshold, it indicates that under the current temperature setting, the energy savings from improved efficiency on the electric drive side still outweigh the additional energy consumption from reduced waste heat on the thermal management side. The overall vehicle net energy consumption still has room for further reduction, and continuing to adjust the temperature in the current direction can still yield positive benefits. At this point, the first preset adjustment strategy uses the first difference as an error signal, combining its proportional, integral, and derivative adjustment characteristics to output a heat exchange temperature adjustment amount that matches the magnitude and trend of the error. This adjustment amount is then used to correct the motor's required heat exchange temperature, gradually moving the temperature towards a direction that brings the first difference closer to zero. As the temperature is iteratively adjusted, the first difference gradually decreases, and the adjustment step size also shrinks to avoid overshooting the optimal temperature point. When the first difference is less than or equal to the preset threshold, it indicates that the current temperature point has brought the benefits of electric drive to a state of balance with the costs of thermal management. If the adjustment continues, the first difference will turn from positive to negative, and the net energy consumption of the whole vehicle will increase instead of decrease. At this time, the system determines that the optimal energy-saving temperature range under the current operating conditions has been reached, and then stops the temperature adjustment action and determines the current effective heat exchange temperature of the motor as the target heat exchange temperature of the motor.

[0061] Thus, through the PID closed-loop control mechanism based on the first difference feedback, it can adaptively search for and maintain the electric drive heat exchange temperature that minimizes the energy consumption of the whole vehicle under complex and ever-changing driving conditions, thus taking into account both the energy-saving needs of electric drive efficiency optimization and waste heat recovery and utilization.

[0062] S150 controls the heat exchange components based on the current heat exchange temperature and the target heat exchange temperature of the motor to exchange heat for the motor.

[0063] Specifically, the difference between the current heat exchange temperature and the target heat exchange temperature of the motor is calculated to obtain the current temperature deviation. This temperature deviation is used as a control input, and a preset control algorithm generates execution instructions for each heat exchange component. For example, when the current heat exchange temperature of the motor is lower than the target heat exchange temperature and the deviation is large, it indicates that the motor is in an overcooled state, and heat dissipation needs to be suppressed to promote temperature rise. At this time, the controller reduces the bypass ratio of coolant flowing through the radiator by adjusting the opening of the electronic thermostat, or reduces the water pump speed to reduce the coolant circulation flow, thereby reducing heat loss and allowing the motor temperature to gradually rise to the target heat exchange temperature. When the current heat exchange temperature of the motor is higher than the target heat exchange temperature, it indicates that the heat storage of the motor has exceeded the optimal range, and heat dissipation needs to be enhanced to suppress efficiency decay. At this time, the controller increases the opening of the thermostat to conduct the heat dissipation circuit, increases the water pump speed to accelerate coolant circulation, and starts the cooling fan for forced air cooling as needed, thereby removing excess heat from the electric drive system.

[0064] Throughout the control process, the controller continuously monitors the changing trend of temperature deviation and dynamically corrects the control duty cycle or speed command of the actuator until the current heat exchange temperature of the motor converges to near the target heat exchange temperature and remains stable, thereby ensuring that the electric drive system always operates within the expected temperature window that balances efficient output and waste heat utilization.

[0065] To further illustrate the above embodiments, in this application embodiment, the heat exchange component is controlled based on the current heat exchange temperature and the target heat exchange temperature of the motor to exchange heat for the motor. This includes: obtaining a second difference between the current heat exchange temperature and the target heat exchange temperature of the motor; determining a control quantity for the heat exchange component based on a second preset adjustment strategy and the second difference; and controlling the heat exchange component based on the control quantity to maintain the actual temperature of the motor at the target heat exchange temperature. The second preset adjustment strategy can be PID control, and no specific limitations are imposed here.

[0066] Specifically, the thermal management controller uses a temperature sensor to collect the current heat exchange temperature at the motor windings or coolant outlet in real time. It then calculates the difference between this temperature and the target heat exchange temperature of the motor to obtain a second difference value, representing the degree to which the actual temperature deviates from the target. This second difference value serves as the input variable for a second preset adjustment strategy, which can employ PID control or other closed-loop control algorithms.

[0067] Based on the magnitude and trend of the second difference, the controller dynamically calculates the control quantities of each heat exchange component and coordinates the actuators through PWM (Pulse-Width Modulation) signals or CAN (Controller Area Network) bus instructions. Specifically, when the second difference is positive and large, it indicates that the actual motor temperature is higher than the target temperature, requiring enhanced heat dissipation. In this case, the controller increases the opening of the electronic thermostat to increase the proportion of coolant flowing through the radiator, increases the water pump speed to accelerate the coolant circulation flow, and starts the radiator fan at high speed under high-temperature conditions to enhance forced heat exchange. When the second difference is negative, it indicates that the actual motor temperature is lower than the target temperature, requiring suppression of heat dissipation to promote temperature rise. In this case, the controller decreases the opening of the thermostat to bypass the heat dissipation circuit, reduces the water pump speed to reduce heat loss, and, as needed, starts the PTC (Positive Temperature Coefficient) heater or air conditioning system to compensate for heat in the passenger compartment or battery pack, thereby indirectly reducing the need to extract waste heat from the motor.

[0068] During the control process, the controller continuously monitors the change of the second difference and continuously adjusts the duty cycle or speed command of the actuator control signal through the closed-loop correction mechanism of the second preset adjustment strategy until the second difference converges to the allowable steady-state error range, so that the actual temperature of the motor stably follows the target temperature.

[0069] In addition, if the temperature sensor malfunctions and causes signal loss or exceeds the limit, the controller automatically switches to the preset default safe temperature as the alternative target value and sends a fault code to the instrument panel via the CAN bus to alert the driver, ensuring that the thermal management system can continue to operate in safe mode under abnormal conditions, thus avoiding performance degradation or hardware damage caused by overheating of the electric drive to a certain extent.

[0070] This application predicts future operating load by integrating vehicle status, map, navigation, and historical data. It then quantifies the energy gain due to changes in electric drive efficiency and the energy increase due to changes in waste heat utilization, dynamically optimizing the difference between these two values ​​to determine the target heat exchange temperature for the electric drive. Finally, closed-loop control ensures the motor's actual temperature stably follows this target value. This effectively resolves the conflict between the motor's heating requirements and waste heat recovery needs under low-temperature conditions, achieving a significant reduction in overall vehicle energy consumption and improved thermal management control accuracy across all operating conditions.

[0071] As a concrete example, taking the actual operation scenario of a pure electric vehicle in a low-temperature winter environment as an example, the implementation process of the electric drive heat exchange temperature dynamic control method and system of the present invention will be described in detail: 1. Data Acquisition Phase: After vehicle startup, the vehicle speed sensor collects vehicle speed signals at a sampling frequency of 100ms, while the acceleration sensor simultaneously collects acceleration data; the accelerator pedal position sensor monitors pedal position changes in real time; the PT100 platinum resistance temperature sensor (installed at the motor coolant inlet and outlet) collects coolant temperature every 50ms, and the thermocouple sensor (embedded in the stator winding) simultaneously monitors the winding temperature. These analog signals are filtered and amplified by the signal conditioning circuit, then converted into digital signals by the AD (Analog-to-Digital) converter and transmitted to the thermal management controller. Simultaneously, the ambient temperature sensor (installed in the front grille) collects an ambient temperature of -5℃, and the battery management system (BMS) transmits the battery pack temperature as 10℃ via the CAN bus.

[0072] 2. Future operating condition prediction stage: (1) Prediction based on historical data: The thermal management controller retrieves historical operating condition data of the same time period and similar driving area in the past week, and uses time series model analysis to predict that the vehicle speed will fluctuate between 15-35km / h in the next 10 minutes, and there will be multiple starts and stops; at the same time, it finds historical operating conditions with high similarity to the current ambient temperature, time information and road type, and the subsequent display shows that the vehicle will pass through multiple traffic light intersections and will have frequent acceleration and deceleration; (2) Prediction based on real-time vehicle status: The sensor collects that the driver frequently presses the accelerator pedal and the force changes greatly, and brakes many times. The machine learning algorithm judges it as aggressive driving. Style, predicting that the vehicle will have frequent acceleration and deceleration actions in the future; the vehicle dynamics model estimates that the vehicle needs to increase driving force and the electric drive system will generate more heat based on the current vehicle speed, acceleration and slight uphill information of the road ahead; (3) Prediction based on map and navigation information: the high-precision map shows that there are 4 consecutive traffic lights 800 meters ahead and the road has a 6% slope; the real-time navigation system indicates that the traffic flow of the road ahead is large and the expected speed is 10-20km / h, which is in a congested state; (4) Multi-source data fusion prediction: the Kalman filter algorithm is used to weight and fuse the above three prediction results to obtain the predicted load information of the vehicle in the next 10 minutes.

[0073] 3. Energy Consumption Calculation Stage: The thermal management controller retrieves the electric drive efficiency at two temperatures from the electric drive efficiency map stored in EEPROM (Electrically Erasable Programmable Read-Only Memory) based on the current heat exchange temperature of the motor (e.g., 20℃) and the required heat exchange temperature of the motor (e.g., 22℃) after correction based on the current heat exchange temperature. Combined with the predicted input power information of the vehicle motor determined based on the predicted load information, the controller calculates a motor energy consumption gain of 8Wh. Simultaneously, considering the low ambient temperature and the low battery pack temperature, the thermal management system keeps the PTC heater and compressor active to maintain the battery pack temperature. By using actuator status feedback to calculate the additional electrical energy consumption of the heat exchange components due to the unutilized motor waste heat under the same operating conditions, the controller calculates an additional motor energy consumption of 12Wh.

[0074] 4. Difference Calculation and Judgment Stage: The thermal management controller calculates the difference between the energy consumption gain and the energy consumption increase, ΔE = 8 - 12 = 4Wh. Since ΔE ≤ 0, the heat exchange temperature determination step is initiated.

[0075] 5. Heat exchange temperature determination stage: The current heat exchange temperature of the motor (20℃) is marked as the target heat exchange temperature of the motor. The thermal management controller stores this information and synchronizes it to the vehicle VCU (Vehicle Control Unit).

[0076] 6. Dynamic Control Phase: Based on the target heat exchange temperature of 20℃ for the motor, the thermal management controller uses a PWM signal to control the electronic thermostat to reduce the bypass opening, increasing the flow of coolant through the radiator. Simultaneously, it increases the water pump speed to accelerate coolant circulation and enhance heat dissipation. Additionally, it activates the PTC heater or air conditioning system to compensate for insufficient residual heat from the motor and maintain the battery pack temperature. During vehicle operation, the thermal management controller continuously collects the actual heat exchange temperature and compares it with the required temperature of 20℃. It then uses a PID algorithm to adjust the electronic thermostat opening, water pump speed, and PTC heater power in real time, forming a closed-loop control. If a temperature sensor malfunctions, the thermal management controller immediately switches to the default safe temperature of 70℃ and sends a fault code to the instrument panel via the CAN bus, prompting the driver to have it inspected.

[0077] In summary, this application dynamically adjusts the electric drive heat exchange temperature by combining future operating condition predictions. This not only fully utilizes the efficiency improvement brought about by moderate motor temperature rise to reduce energy consumption on the electric drive side, but also optimizes waste heat recovery and heat dissipation strategies in advance for complex driving scenarios such as congestion, hill climbing, and sharp turns. To a certain extent, it avoids energy waste caused by fixed temperature control. Compared with traditional control methods that rely solely on current operating conditions, the overall vehicle energy consumption is significantly reduced. At the same time, the multi-source prediction mechanism that integrates historical data, real-time status, and navigation information enables the thermal management system to respond in advance, effectively reducing temperature lag and fluctuations caused by sudden changes in operating conditions, and significantly enhancing the vehicle's adaptability and operational stability across the entire operating range. Furthermore, the combination of multi-source data fusion prediction and PID closed-loop control enables precise adjustment of the electric drive heat exchange temperature, allowing the actual motor temperature to accurately follow the optimal target temperature, reducing the impact of temperature fluctuations on electric drive performance and lifespan, and improving control accuracy and electric drive system reliability. Overall, the introduction of future operating condition prediction functionality endows the thermal management system with a certain degree of intelligent decision-making capability, promoting the development of new energy vehicle thermal management technology towards intelligence and refinement.

[0078] Corresponding to the above embodiments, this application also proposes a heat exchange control device for an electric motor.

[0079] Reference Figure 2 The heat exchange control device 200 for the motor includes: an acquisition module 210, a first determination module 220, a second determination module 230, a third determination module 240, an adjustment module 250, and a control module 260.

[0080] The system includes the following modules: Acquisition module 210 acquires the vehicle's current status information, the motor's current heat exchange temperature, map information, navigation information, and historical driving data. First determination module 220 determines the vehicle's predicted load information based on the current status information, map information, navigation information, and historical driving data. Second determination module 230 determines the vehicle's predicted energy consumption, the motor's predicted input power information, and the motor's efficiency information based on the predicted load information. Third determination module 240 determines the motor's energy consumption benefit value based on the motor's predicted input power information and motor efficiency information, and determines the motor's energy consumption increase value based on the motor's predicted input power information, predicted energy consumption, and motor efficiency information. Adjustment module 250 adjusts the motor's required heat exchange temperature based on a first difference between the motor's energy consumption benefit value and the motor's energy consumption increase value to obtain the motor's target heat exchange temperature. Control module 260 controls the heat exchange components based on the motor's current heat exchange temperature and the motor's target heat exchange temperature to provide heat exchange for the motor.

[0081] According to one embodiment of this application, the adjustment module 250 is specifically configured to: in response to a first difference being greater than a preset threshold, determine a heat exchange temperature adjustment amount based on a first preset adjustment strategy, the first difference, and a preset temperature adjustment gradient, and adjust the required heat exchange temperature of the motor based on the heat exchange temperature adjustment amount to obtain the target heat exchange temperature of the motor; in response to a difference being less than or equal to a preset threshold, determine the current heat exchange temperature of the motor as the target heat exchange temperature of the motor.

[0082] According to one embodiment of this application, the control module 260 is specifically configured to: obtain a second difference between the current heat exchange temperature of the motor and the target heat exchange temperature of the motor; determine the control quantity of the heat exchange component based on the second preset adjustment strategy and the second difference; and control the heat exchange component based on the control quantity of the heat exchange component so that the actual temperature of the motor is maintained at the target heat exchange temperature of the motor.

[0083] According to one embodiment of this application, the predicted load information includes the predicted load corresponding to each sampling point in the current prediction period, the predicted motor input power information includes the predicted motor input power corresponding to each sampling point in the current prediction period, and the motor efficiency information includes the first motor efficiency and the second motor efficiency corresponding to each sampling point in the current prediction period. The second determining module is specifically used to: calculate the predicted motor input power information corresponding to each sampling point based on the predicted load and vehicle dynamics formula; calculate the predicted energy consumption based on the predicted motor input power and a preset sampling interval; determine the first motor efficiency corresponding to each sampling point by searching a preset motor efficiency spectrum based on the predicted load and the current heat exchange temperature of the motor; and determine the second motor efficiency corresponding to each sampling point by searching a preset motor efficiency spectrum based on the predicted load and the required heat exchange temperature of the motor.

[0084] According to one embodiment of this application, the third determining module is specifically used to: calculate the efficiency difference corresponding to each sampling point based on the efficiency of the second motor and the efficiency of the first motor corresponding to each sampling point; calculate the instantaneous energy consumption benefit corresponding to each sampling point based on the efficiency difference corresponding to each sampling point, the predicted input power of the motor and the preset sampling interval; and sum the instantaneous energy consumption benefits corresponding to each sampling point to obtain the motor energy consumption benefit value.

[0085] According to one embodiment of this application, the first determining module is specifically used to: calculate the predicted heat generation of the motor based on the predicted input power of the motor corresponding to each sampling point, the second motor efficiency, and a preset sampling interval; determine the available residual heat based on the minimum value between the predicted required energy consumption and the predicted heat generation of the motor; calculate the predicted additional heat based on the difference between the predicted required energy consumption and the available residual heat; calculate the first predicted energy consumption based on the predicted additional heat and the efficiency of the heat exchange component, wherein the first predicted energy consumption is used to characterize the predicted energy consumption of the heat exchange component when using the residual heat of the motor for heat exchange; calculate the second predicted energy consumption based on the predicted required energy consumption and the efficiency of the heat exchange component, wherein the second predicted energy consumption is used to characterize the predicted energy consumption of the heat exchange component when not using the residual heat of the motor for heat exchange; and calculate the increase in motor energy consumption based on the second predicted energy consumption and the first predicted energy consumption.

[0086] According to one embodiment of this application, determining the predicted load information of a vehicle based on current state information, map information, navigation information, and historical driving data includes: analyzing historical driving data based on a time series analysis algorithm to obtain a first load change curve of the vehicle; determining the acceleration change trend of the vehicle based on the current state information, and determining the motion state information of the vehicle based on the vehicle dynamics model and the current state information, and determining the motor load corresponding to each sampling point based on the motion state information and the acceleration change trend to generate a second load change curve of the vehicle; determining the load change of the vehicle's electric drive system based on map information, and determining the vehicle's start-stop frequency and electric drive operating state based on navigation information, and determining the motor load corresponding to each sampling point based on the electric drive system load change, vehicle start-stop frequency, and electric drive operating state to generate a third load change curve of the vehicle; and weightedly fusing the first load change curve, the second load change curve, and the third load change curve based on a preset fusion algorithm to obtain the predicted load information.

[0087] It should be noted that the above explanation of the embodiments and beneficial effects of the heat exchange control method for motors also applies to the heat exchange control device for motors in the embodiments of this application. To avoid redundancy, it will not be elaborated in detail here.

[0088] Corresponding to the above embodiments, this application also proposes a computer-readable storage medium.

[0089] The computer-readable storage medium of this application stores a heat exchange control program for a motor, which, when executed by a processor, implements the aforementioned heat exchange control method for the motor.

[0090] It should be noted that the above explanation of the embodiments and beneficial effects of the heat exchange control method for motors also applies to the computer-readable storage medium of the embodiments of this application. To avoid redundancy, it will not be elaborated in detail here.

[0091] Corresponding to the above embodiments, this application also proposes a vehicle.

[0092] See Figure 3 As shown, the vehicle 300 of this application includes a memory 310, a processor 320, and a motor heat exchange control program stored in the memory 310 and run on the processor 320. When the processor executes the motor heat exchange control program, it implements the aforementioned motor heat exchange control method.

[0093] It should be noted that the above-described embodiments and explanations of the beneficial effects of the heat exchange control method for the motor also apply to the vehicles in the embodiments of this application. To avoid redundancy, they will not be elaborated in detail here.

[0094] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0095] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0096] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0097] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0098] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0099] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A heat exchange control method for an electric motor, characterized in that, include: The vehicle's current status information, the motor's current heat exchange temperature, map information, navigation information, and historical driving data are acquired, and the vehicle's predicted load information is determined based on the current status information, the map information, the navigation information, and the historical driving data. Based on the predicted load information, the predicted energy consumption required by the vehicle, the predicted input power of the motor, and the motor efficiency information are determined. Based on the predicted input power information of the motor and the motor efficiency information, the motor energy consumption benefit value is determined, and based on the predicted input power information of the motor, the predicted required energy consumption and the motor efficiency information, the motor energy consumption increase value is determined. Based on the first difference between the motor energy consumption benefit value and the motor energy consumption increase value, the motor required heat exchange temperature is adjusted to obtain the motor target heat exchange temperature. Based on the current heat exchange temperature of the motor and the target heat exchange temperature of the motor, the heat exchange components are controlled to exchange heat for the motor.

2. The heat exchange control method for a motor according to claim 1, characterized in that, The step of adjusting the motor's required heat exchange temperature based on a first difference between the motor's energy consumption gain value and the motor's energy consumption increase value to obtain the motor's target heat exchange temperature includes: In response to the first difference being greater than a preset threshold, a heat exchange temperature adjustment amount is determined based on a first preset adjustment strategy, the first difference, and a preset temperature adjustment gradient, and the required heat exchange temperature of the motor is adjusted based on the heat exchange temperature adjustment amount to obtain the target heat exchange temperature of the motor. In response to the difference being less than or equal to the preset threshold, the current heat exchange temperature of the motor is determined as the target heat exchange temperature of the motor.

3. The heat exchange control method for a motor according to claim 1, characterized in that, The step of controlling the heat exchange components based on the current heat exchange temperature and the target heat exchange temperature of the motor to exchange heat for the motor includes: Obtain the second difference between the current heat exchange temperature of the motor and the target heat exchange temperature of the motor; The control quantity of the heat exchange component is determined based on the second preset adjustment strategy and the second difference, and the heat exchange component is controlled based on the control quantity of the heat exchange component so that the actual temperature of the motor is maintained at the target heat exchange temperature of the motor.

4. The heat exchange control method for a motor according to claim 1, characterized in that, The predicted load information includes the predicted load corresponding to each sampling point in the current prediction period; the predicted motor input power information includes the predicted motor input power corresponding to each sampling point in the current prediction period; and the motor efficiency information includes the first motor efficiency and the second motor efficiency corresponding to each sampling point in the current prediction period. The step of determining the predicted energy consumption required by the vehicle, the predicted motor input power information, and the motor efficiency information based on the predicted load information includes: Based on the predicted load and vehicle dynamics formula corresponding to each sampling point, the predicted input power information of the motor corresponding to each sampling point is calculated. Based on the predicted input power of the motor corresponding to each sampling point and the preset sampling interval, the energy consumption required for the prediction is calculated. Based on the predicted load corresponding to each sampling point and the current heat exchange temperature of the motor, the first motor efficiency corresponding to each sampling point is determined by looking up a preset motor efficiency spectrum. Based on the predicted load and the required heat exchange temperature of the motor corresponding to each sampling point, the second motor efficiency corresponding to each sampling point is determined by looking up the preset motor efficiency spectrum.

5. The heat exchange control method for a motor according to claim 4, characterized in that, The step of determining the motor energy consumption benefit value based on the predicted input power information of the motor and the motor efficiency information includes: Based on the efficiency of the second motor and the efficiency of the first motor corresponding to each sampling point, the efficiency difference corresponding to each sampling point is calculated. Based on the efficiency difference corresponding to each sampling point, the predicted input power of the motor, and the preset sampling interval, the instantaneous energy consumption benefit corresponding to each sampling point is calculated. The instantaneous energy consumption gains corresponding to each sampling point are summed to obtain the motor energy consumption gain value.

6. The heat exchange control method for an electric motor according to claim 4, characterized in that, The step of determining the increase in motor energy consumption based on the predicted input power information of the motor, the predicted energy consumption required, and the motor efficiency information includes: Based on the predicted input power of the motor corresponding to each sampling point, the efficiency of the second motor, and the preset sampling interval, the predicted heat generation of the motor is calculated. The available residual heat is determined based on the minimum value between the predicted energy consumption and the predicted heat generation of the motor. The predicted additional heat is calculated based on the difference between the predicted energy consumption and the available surplus heat. Based on the predicted heat to be replenished and the efficiency of the heat exchange components, the first predicted energy consumption is calculated, wherein the first predicted energy consumption is used to characterize the predicted energy consumption of the heat exchange components when using the waste heat of the motor for heat exchange. Based on the predicted energy consumption and the efficiency of the heat exchange component, a second predicted energy consumption is calculated, wherein the second predicted energy consumption is used to characterize the predicted energy consumption of the heat exchange component when the waste heat of the motor is not utilized for heat exchange. The increase in motor energy consumption is calculated based on the second predicted energy consumption and the first predicted energy consumption.

7. The heat exchange control method for a motor according to claim 1, characterized in that, Determining the predicted load information of the vehicle based on the current state information, the map information, the navigation information, and the historical driving data includes: The historical driving data is analyzed based on a time series analysis algorithm to obtain the first load change curve of the vehicle. Based on the current state information, the acceleration change trend of the vehicle is determined, and based on the vehicle dynamics model, the motion state information of the vehicle is determined according to the current state information. Based on the motion state information and the acceleration change trend, the motor load corresponding to each sampling point is determined to generate the second load change curve of the vehicle. Based on the map information, the load change of the vehicle's electric drive system is determined, and based on the navigation information, the vehicle's start-stop frequency and electric drive operating status are determined. The motor load corresponding to each sampling point is determined according to the load change of the electric drive system, the vehicle's start-stop frequency, and the electric drive operating status, so as to generate the third load change curve of the vehicle. The first load change curve, the second load change curve, and the third load change curve are weighted and fused based on a preset fusion algorithm to obtain the predicted load information.

8. A heat exchange control device for an electric motor, characterized in that, include: The acquisition module is used to acquire the vehicle's current status information, the motor's current heat exchange temperature, map information, navigation information, and historical driving data; The first determining module is used to determine the predicted load information of the vehicle based on the current status information, the map information, the navigation information, and the historical driving data; The second determining module is used to determine the predicted energy consumption required by the vehicle, the predicted input power information of the motor, and the motor efficiency information based on the predicted load information. The third determining module is used to determine the motor energy consumption benefit value based on the motor predicted input power information and the motor efficiency information, and to determine the motor energy consumption increase value based on the motor predicted input power information, the predicted required energy consumption and the motor efficiency information. The adjustment module is used to adjust the motor's required heat exchange temperature based on a first difference between the motor's energy consumption gain value and the motor's energy consumption increase value, so as to obtain the motor's target heat exchange temperature. The control module is used to control the heat exchange components based on the current heat exchange temperature and the target heat exchange temperature of the motor, so as to exchange heat for the motor.

9. A computer-readable storage medium, characterized in that, It stores a heat exchange control program for the motor, which, when executed by the processor, implements the heat exchange control method for the motor according to any one of claims 1-7.

10. A vehicle, characterized in that, The method includes a memory, a processor, and a heat exchange control program for a motor stored in the memory and capable of running on the processor. When the processor executes the heat exchange control program for the motor, it implements the heat exchange control method for the motor according to any one of claims 1-7.

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