Thermal management method and device of electric vehicle and non-volatile storage medium

Abstract

The invention discloses a thermal management method and device of an electric vehicle and a nonvolatile storage medium. The method comprises the following steps: acquiring temperature data and vehicle operation data in a target vehicle; based on the vehicle operation data and the temperature data, the heat production amount of a power system in the target vehicle is calculated; based on the heat production amount and the temperature data, predicting the temperature of the power system to obtain a temperature prediction value; and when the temperature prediction value is between a first preset threshold value and a second preset threshold value, adjusting the temperature of the power system based on a multi-objective optimization algorithm, a plurality of optimization targets corresponding to the multi-target optimization algorithm comprise at least two of the following targets: taking the minimum power consumption of the target vehicle as a target, taking the minimum temperature of a controller in the target vehicle as a target, and taking the minimum temperature difference corresponding to a battery in the target vehicle as a target. The technical problem that the energy utilization rate is low due to the fact that global heat management is difficult to perform due to independent operation of a plurality of systems in an existing electric vehicle is solved.

Description

Technical Field

[0001] The present invention relates to the field of new energy technologies, and in particular to a thermal management method and device for an electric vehicle and a non-volatile storage medium. Background Art

[0002] In current thermal management systems, the vehicle control unit (VCU), battery management system (BMS), motor controller (MCU), and thermal management system (TMS) typically operate independently, lacking coordinated optimization. This results in low energy efficiency and suboptimal thermal management. Furthermore, the complex information exchange between these different systems increases the difficulty and cost of system design.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] Embodiments of the present invention provide a thermal management method, device, and non-volatile storage medium for electric vehicles to at least solve the technical problem that multiple systems in electric vehicles currently operate independently and are difficult to perform global thermal management, resulting in low energy utilization.

[0005] According to one aspect of an embodiment of the present invention, a thermal management method for an electric vehicle is provided, comprising: obtaining temperature data and vehicle operation data collected by multiple temperature sensors in a target vehicle; calculating the heat generation of a power system in the target vehicle based on the vehicle operation data and the temperature data; predicting the temperature of the power system in the target vehicle based on the heat generation and the temperature data of the target vehicle to obtain a temperature prediction value; and adjusting the temperature of the power system in the target vehicle based on a multi-objective optimization algorithm when the temperature prediction value is between a first preset threshold and a second preset threshold, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of a domain controller in the target vehicle, and minimizing the temperature difference corresponding to a battery in the target vehicle.

[0006] Optionally, the heat generation of the power system in the target vehicle is calculated based on the vehicle operation data and temperature data, including: calculating the heat generation of the battery in the target vehicle based on the vehicle operation data and temperature data; establishing a motor loss model and a domain controller loss model; determining the heat generation of the motor in the target vehicle based on the vehicle operation data and the motor loss model; determining the heat generation of the domain controller in the target vehicle based on the vehicle operation data and the domain controller loss model; determining the heat generation of the power system in the target vehicle based on the heat generation of the battery, the heat generation of the motor and the heat generation of the domain controller.

[0007] Optionally, based on a multi-objective optimization algorithm, the temperature of the power system in the target vehicle is adjusted, including: randomly generating initial decision variables, wherein the decision variables include the coolant distribution ratio and the working status of the heat pump in the target vehicle; based on the initial decision variables, simulating the operation of the target vehicle under the temperature prediction value, and determining the target values ​​corresponding to multiple optimization objectives; based on the target values ​​corresponding to the multiple optimization objectives, adjusting the initial decision variables to obtain adjusted decision variables; repeating the steps of simulating the operation of the target vehicle under the temperature prediction value based on the adjusted decision variables, determining the target values ​​corresponding to the multiple optimization objectives and adjusting the decision variables according to the target values ​​corresponding to the multiple optimization objectives, until the target number of repetitions is reached to obtain the target decision variables; based on the target decision variables, adjusting the temperature of the power system in the target vehicle.

[0008] Optionally, when multiple temperature sensors malfunction, the current of the motor and the state of charge of the battery in the target vehicle are obtained; based on the current of the motor and the state of charge of the battery, the heat generation of the battery is calculated; based on the heat generation of the battery and the vehicle operation data, the heat generation of the target vehicle is determined.

[0009] Optionally, when the temperature prediction value is lower than a first preset threshold, the coolant flow rate in the target vehicle is controlled to be lower than a target flow rate threshold.

[0010] Optionally, when the temperature prediction value exceeds a second preset threshold, the driving power of the target vehicle is controlled to decrease and the coolant flow rate in the target vehicle is increased.

[0011] According to another aspect of an embodiment of the present invention, a thermal management system for an electric vehicle is provided, comprising: a temperature sensor, a water pump, and a domain controller, wherein the temperature sensor is connected to the domain controller and is used to upload temperature data of a target vehicle to the domain controller; the water pump is connected to the domain controller and is used to adjust the coolant flow in the target vehicle; and the domain controller is used to execute any one of the above-mentioned thermal management methods for an electric vehicle.

[0012] Optionally, the domain controller includes a vehicle controller, a battery management system, and a motor controller.

[0013] According to another aspect of an embodiment of the present invention, a thermal management device for an electric vehicle is also provided, including: an acquisition module for acquiring temperature data and vehicle operation data collected by multiple temperature sensors in a target vehicle; a calculation module for calculating the heat generation of a power system in the target vehicle based on the vehicle operation data and temperature data; a prediction module for predicting the temperature of the power system in the target vehicle based on the heat generation and temperature data of the target vehicle to obtain a temperature prediction value; and an adjustment module for adjusting the temperature of the power system in the target vehicle based on a multi-objective optimization algorithm when the temperature prediction value is between a first preset threshold and a second preset threshold, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of the domain controller in the target vehicle, and minimizing the temperature difference corresponding to the battery in the target vehicle.

[0014] According to another aspect of an embodiment of the present invention, a non-volatile storage medium is provided, which includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any one of the above-mentioned thermal management methods for electric vehicles.

[0015] According to another aspect of an embodiment of the present invention, a computer device is provided. The computer device includes a processor, and the processor is used to run a program. When the program is run, any one of the above-mentioned thermal management methods for electric vehicles is executed.

[0016] According to yet another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, any one of the above-mentioned thermal management methods for an electric vehicle is implemented.

[0017] In an embodiment of the present invention, a thermal management method for an electric vehicle is adopted, by obtaining temperature data and vehicle operation data collected by multiple temperature sensors in a target vehicle; calculating the heat generation of a power system in the target vehicle based on the vehicle operation data and temperature data; predicting the temperature of the power system in the target vehicle based on the heat generation and temperature data of the target vehicle to obtain a temperature prediction value; when the temperature prediction value is between a first preset threshold value and a second preset threshold value, adjusting the temperature of the power system in the target vehicle based on a multi-objective optimization algorithm, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of the domain controller in the target vehicle, and minimizing the temperature difference corresponding to the battery in the target vehicle, thereby achieving the purpose of performing thermal management by collecting temperatures at multiple locations through multiple temperature sensors, thereby achieving the technical effect of improving energy utilization and thermal management effects, and further solving the technical problem that multiple systems in electric vehicles currently operate independently and it is difficult to perform global thermal management, resulting in low energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0019] Figure 1 A hardware structure block diagram of a computer terminal for implementing a thermal management method for an electric vehicle is shown;

[0020] Figure 2 is a flow chart of a thermal management method for an electric vehicle according to an embodiment of the present invention;

[0021] Figure 3 is a system architecture diagram of a thermal management system for an electric vehicle provided according to an embodiment of the present invention;

[0022] Figure 4 is a system architecture diagram of a thermal management system for an electric vehicle provided according to an optional embodiment of the present invention;

[0023] Figure 5 4 is a structural block diagram of a thermal management device for an electric vehicle provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] According to an embodiment of the present invention, a method embodiment of a thermal management method for an electric vehicle is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The figure shows a hardware structure block diagram of a computer terminal for implementing a thermal management method for electric vehicles. Figure 1 As shown, the computer terminal 10 may include one or more (illustrated as 102a, 102b, ..., 102n in the figure) processors (the processor may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices), a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0028] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0029] The memory 104 can be used to store software programs and modules for application software, such as the program instructions / data storage device corresponding to the thermal management method for electric vehicles in the embodiments of the present invention. The processor executes the software programs and modules stored in the memory 104 to execute various functional applications and data processing, thereby implementing the thermal management method for electric vehicles for the aforementioned application programs. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0030] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 .

[0031] Currently, the vehicle control unit (VCU), battery management system (BMS), and thermal management system (MCU) in electric vehicles operate independently and cannot share data. This leads to fragmented thermal management strategies, a lack of global optimization, and certain risks. Furthermore, traditional thermal management control strategies consume high energy consumption under extreme operating conditions, resulting in low energy utilization. To address these technical issues, the present invention proposes a thermal management method for electric vehicles. Figure 2 FIG. 1 is a flow chart of a thermal management method for an electric vehicle according to an embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0032] Step S202: Acquire temperature data and vehicle operation data collected by multiple temperature sensors in the target vehicle.

[0033] In this step, the target vehicle can be an electric vehicle. Multiple temperature sensors can be installed in an electric vehicle. For example, the battery pack internal temperature sensor can be used to monitor the temperature of battery cells or battery modules. Motor temperature sensors, including motor winding temperature sensors and motor housing temperature sensors, monitor the motor's temperature status. The domain controller's own temperature sensor monitors the controller's internal temperature to ensure it operates within a safe range. An ambient temperature sensor is used to obtain temperature information from the vehicle's external environment to adjust thermal management strategies.

[0034] Sensors can continuously collect temperature data and transmit it to the domain controller via the vehicle's bus system (such as the CAN bus). Simultaneously, vehicle operating data, such as speed, acceleration, SOC (state of charge), and torque, are also transmitted to the domain controller via the same method. Data collection should be performed continuously during vehicle operation to ensure real-time information.

[0035] Step S204 : Calculating the heat generation of the power system in the target vehicle based on the vehicle operation data and temperature data.

[0036] In this step, the power system in the target vehicle may include an electric motor, a power battery pack, a battery management system, a motor controller (MCU), a cooling system, etc. Therefore, to calculate the heat generation of the power system, the heat generation of different modules can be calculated separately and then the total can be calculated. The heat generation mechanism of the battery during the charging and discharging process can be identified, including ohmic heat (resistance heating) and chemical heat (heat generated by chemical reactions). Then, the sources of loss of the motor during operation are analyzed, mainly considering copper loss (heat generated by the resistance of the current passing through the winding) and iron loss (heat generated by eddy currents and hysteresis effects caused by magnetic field changes). The controller will also generate heat when operating under high load, and the conversion of power consumption of its internal components into heat energy can also be considered. For each heat source, its heat generation is calculated separately based on vehicle operation data and physical laws.

[0037] Through such steps, the domain controller can calculate the heat generation in real time based on vehicle operation data and temperature data, effectively monitor and manage the vehicle's thermal status, and ensure that key components such as batteries, motors and electronic controllers can be maintained within the optimal temperature range under various driving conditions, thereby improving the vehicle's performance, safety and energy efficiency.

[0038] Step S206 , based on the heat generation and temperature data of the target vehicle, predict the temperature of the power system in the target vehicle to obtain a temperature prediction value.

[0039] In this step, the temperature of the target vehicle is predicted based on the heat production and temperature data of the target vehicle. A thermal state prediction model can be established, for example, a physics-based model (such as a coupled model of heat conduction, convection, and radiation), or a data-driven model (such as a machine learning model, a neural network model). The key parameters in the model, such as thermal conductivity, specific heat capacity, and environmental impact coefficient, can be determined based on historical data and experimental verification. The collected heat production and temperature data are used as input conditions for the model. Using the prediction model, the thermal state of the vehicle at the next moment (usually a few seconds to a few minutes later) is simulated, including the temperature change trend of each heat source. The prediction model can also include the impact of ambient temperature and wind speed on the heat dissipation effect of the vehicle to more accurately predict the temperature. Based on the simulation results, the temperature prediction values ​​of each key component (battery, motor, domain controller) of the target vehicle are calculated.

[0040] Through the above steps, based on the heat production and current temperature data of the target vehicle, future vehicle temperature changes can be predicted, so that measures can be taken in advance to adjust the temperature, ensuring that the vehicle can maintain a good thermal state under various conditions and improving overall performance and safety.

[0041] In certain embodiments, a hidden Markov model (HMM) can be used for temperature prediction. This model is often used for analyzing and predicting sequential data, particularly time series data. In the field of vehicle thermal management, HMMs can be used to predict the vehicle's driving pattern, load status, and other factors, thereby enabling proactive adjustments to the thermal management system and optimizing energy utilization and cooling / heating efficiency. Based on a series of current and past observations, HMMs can predict the vehicle's likely future operating conditions, providing predictive information to the thermal management system, enabling it to respond proactively and thus optimize the overall system's energy efficiency and thermal management performance.

[0042] Step S208: When the temperature prediction value is between the first preset threshold and the second preset threshold, the temperature of the power system in the target vehicle is adjusted based on a multi-objective optimization algorithm, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of the domain controller in the target vehicle, and minimizing the temperature difference corresponding to the battery in the target vehicle.

[0043] In this step, after obtaining the predicted temperature value, the corresponding strategy can be implemented based on the predicted temperature value. The range between the first and second preset thresholds can be understood as the safe temperature range. First, confirm whether the current predicted temperature value is between the first and second preset thresholds, that is, within the safe operating temperature range. Then, the objectives to be achieved by the multi-objective optimization algorithm can be determined. These typically include: minimizing power consumption, that is, reducing the vehicle's energy consumption for thermal management; minimizing domain controller temperature, that is, keeping the domain controller temperature within a safe range and as low as possible to extend its service life; and minimizing battery temperature difference, that is, ensuring that the temperature difference between each cell within the battery is kept to a minimum to avoid battery performance degradation caused by excessive temperature differences. Based on the current vehicle state, an initial set of control parameters can be generated, such as coolant flow rate and radiator fan speed. The multi-objective optimization algorithm (NSGA-III algorithm) can then be executed to adjust the temperature in the target vehicle. For example, the execution parameters of the thermal management system can be adjusted, such as adjusting the coolant distribution ratio, controlling the radiator fan speed, and optimizing the operating mode of the heating or cooling system.

[0044] After adjustments are made, the battery temperature, domain controller temperature, and vehicle power consumption are continuously monitored to ensure that thermal conditions are within the expected range and that the multi-objective optimization is effective. If the monitored data deviates significantly from the expected target or if operating conditions change, the domain controller should activate the feedback mechanism, recollect data, evaluate the effectiveness of the current solution, and restart the multi-objective optimization process if necessary.

[0045] Through the above steps, the thermal management system can be intelligently adjusted based on a multi-objective optimization algorithm, and the thermal management method can be optimized to ensure that the vehicle's operating efficiency, battery health, and domain controller performance are optimally balanced when the temperature forecast is within a safe range, thereby improving the overall vehicle performance and user experience, and achieving energy saving effects for the entire vehicle by regulating the temperature of the vehicle's power system.

[0046] Through the above steps, the purpose of collecting temperatures at multiple locations for thermal management through multiple temperature sensors can be achieved, thereby realizing the technical effect of improving energy utilization and thermal management effects, and further solving the technical problem that multiple systems in electric vehicles currently operate independently and are difficult to perform global thermal management, resulting in low energy utilization.

[0047] As an optional embodiment, the heat generation of the power system in the target vehicle is calculated based on the vehicle operation data and temperature data, including: calculating the heat generation of the battery in the target vehicle based on the vehicle operation data and temperature data; establishing a motor loss model and a domain controller loss model; determining the heat generation of the motor in the target vehicle based on the vehicle operation data and the motor loss model; determining the heat generation of the domain controller in the target vehicle based on the vehicle operation data and the domain controller loss model; determining the heat generation of the power system in the target vehicle based on the heat generation of the battery, the heat generation of the motor, and the heat generation of the domain controller.

[0048] Alternatively, the target vehicle's battery charge and discharge, as well as the voltage across the battery, can be determined based on vehicle operating data. The battery's heat generation rate can then be calculated using a formula. Battery heat generation typically includes two aspects: ohmic heat generation and chemical reaction heat generation rate. The ohmic heat generation rate is primarily related to the battery's internal resistance and current, while the chemical heat generation rate is related to the battery's chemical activity and charge and discharge rate. A motor loss model can then be established. Motor losses can include copper loss, iron loss, and mechanical loss. Iron loss primarily stems from hysteresis and eddy current loss. Based on these three types of losses, the motor's total heat generation rate can be derived. A domain controller loss model can then be established. The domain controller's power consumption is primarily determined by its power consumption characteristics and can be calculated based on the processor's operating state and power consumption characteristics. The heat generation rates of the battery, motor, and domain controller calculated above are summed to obtain the target vehicle's total heat generation rate.

[0049] This process requires real-time updates to reflect the vehicle's changing thermal state under varying driving conditions, providing the thermal management system with precise heat generation information to optimize cooling or heating strategies and ensure that the temperatures of all vehicle components remain within safe and efficient ranges. Through meticulous data collection, model building, and calculations, the thermal management system can respond more intelligently to changes and achieve efficient management of the vehicle's thermal state.

[0050] In some embodiments, a three-dimensional dynamic coupling equation of a battery thermal-electric coupling model, a motor loss model, and a domain controller loss model can be established to determine the heat generation of the target vehicle and thus predict the temperature of the target vehicle. The specific formula is as follows:

[0051] The battery thermal-electric coupling model is used to calculate the heat generation of the battery:

[0052]

[0053] Where Q bat is the total heat generated by the battery, I is the battery current, R0 is the battery internal resistance, V p is the polarization voltage, R p is the polarization internal resistance.

[0054] Motor loss model:

[0055] Q motor =Q Cu +Q Fe +Q m

[0056] Where Q motor is the total loss of the motor, Q Cu is the motor copper loss, Q Fe is the motor iron loss, Q m is the mechanical loss of the motor.

[0057] Domain Controller Loss Model:

[0058] Q iPDCU =nU DS (T j )·I DS (T j )+nU Diode (T j )·I Diode (T j )

[0059] Where Q iPDCU is the total heat generated by the domain controller, U DS is the voltage across the SiC chip DS, I DS is the current across the SiC chip DS, U Diode is the SiC chip diode voltage, I Diode is the SiC chip diode current.

[0060] As an optional embodiment, based on a multi-objective optimization algorithm, the temperature of the power system in the target vehicle is adjusted, including: randomly generating initial decision variables, wherein the decision variables include the coolant distribution ratio and the working status of the heat pump in the target vehicle; based on the initial decision variables, simulating the operation of the target vehicle under the temperature prediction value, and determining the target values ​​corresponding to multiple optimization objectives; based on the target values ​​corresponding to the multiple optimization objectives, adjusting the initial decision variables to obtain adjusted decision variables; repeating the steps of simulating the operation of the target vehicle under the temperature prediction value based on the adjusted decision variables, determining the target values ​​corresponding to the multiple optimization objectives and adjusting the decision variables according to the target values ​​corresponding to the multiple optimization objectives, until the target number of repetitions is reached, and obtaining the target decision variables; based on the target decision variables, adjusting the temperature of the power system in the target vehicle.

[0061] Alternatively, the target vehicle's temperature can be adjusted based on a multi-objective optimization algorithm. The core goal is to find a set of decision variables that achieves an optimal balance in temperature management for the target vehicle while considering multiple optimization objectives (such as minimizing power consumption, minimizing domain controller temperature, and minimizing battery temperature difference). This can be solved using a modified NSGA-III algorithm. First, an initial set of solutions can be randomly generated within the feasible space of decision variables. Decision variables can include coolant distribution ratio, heat pump operating mode, cooling fan speed, and so on. A range can be set for each decision variable to ensure that each decision variable is within its physical and engineering constraints. For example, the coolant distribution ratio can be between 0% and 100%, and the heat pump operating mode can be selected between energy-saving and high-performance modes. Using the current initial decision variables, the target vehicle's operation is simulated under predicted temperature conditions. The simulation considers the vehicle's thermal model (including the thermal characteristics of the battery, motor, and domain controller), as well as the responses of the cooling and heating systems. Target values ​​are then determined within the simulation. For example, based on the simulation results, the vehicle's total power consumption is calculated for the current decision variables. The temperature states of the battery, motor, and domain controller are evaluated to ensure they are within a safe operating temperature range. Calculate the maximum temperature difference between each battery cell as an indicator for evaluating battery health. Use a multi-objective optimization algorithm, such as the improved NSGA-III algorithm, to evaluate the performance of the current set of decision variables based on the various objective values ​​obtained in the previous step. The algorithm generates a set of non-dominated solutions, that is, a set that performs best on a certain objective and is at least as good as other solutions on other objectives. Dynamically adjust the weight of each optimization objective based on the current vehicle operating conditions and thermal management strategy to ensure that the algorithm can preferentially optimize the most important objectives. Use the decision variables in the non-dominated solution as new input, simulate and evaluate the objective value again, and iterate the optimization until the preset number of iterations or convergence conditions are reached. Monitor the changes in the objective value during the optimization process to ensure that the algorithm can converge stably within a limited number of iterations. Select a solution from the non-dominated solution set in the last iteration. This solution is considered optimal in the sense of multi-objective optimization, that is, it reaches an acceptable balance point on all objectives.

[0062] The finalized decision variables (such as coolant distribution ratio and heat pump operating status) are applied to the thermal management system. By adjusting actuator parameters such as coolant flow rate, heat pump operation mode, and cooling fan speed, a thermal management strategy is implemented to achieve the desired temperature management effect.

[0063] Through the above steps, thermal management strategies can be dynamically adjusted to maintain the ideal thermal state for the target vehicle while taking into account multiple objectives such as power consumption, domain controller temperature, and battery temperature differentials, ultimately achieving improved vehicle efficiency and safety. This process requires the close integration of vehicle operating data and temperature information, as well as the real-time computing power and execution efficiency of intelligent algorithms.

[0064] As an optional embodiment, when multiple temperature sensors are abnormal, the current of the motor and the charge state of the battery in the target vehicle are obtained; based on the current of the motor and the charge state of the battery, the heat generation of the battery is calculated; based on the heat generation of the battery and the vehicle operation data, the heat generation of the power system in the target vehicle is determined.

[0065] Alternatively, if a temperature sensor anomaly occurs, the target vehicle's temperature cannot be acquired. To ensure the proper operation of the thermal management system, a fault-tolerance mechanism can be implemented. In the event of a sensor anomaly, the instantaneous motor output current can be obtained by monitoring the feedback circuit of the motor controller (MCU) or by using the remaining functioning current sensors. The battery state of charge (SOC) can be estimated using algorithms within the battery management system (BMS) based on a comprehensive calculation of voltage, current, and temperature (even if some temperature sensors fail). To account for battery temperature sensor anomalies, a battery heat generation model based on current and SOC can be employed. This model is primarily based on the battery's equivalent circuit model and chemical reaction thermodynamics. The battery's ohmic heat loss is estimated using the motor current and the battery's internal resistance (which can be found in a SOC internal resistance table or calculated using a battery internal resistance model). The battery's chemical reaction heat loss can be estimated based on the battery type and electrochemical reaction thermal effect model at the SOC, typically incorporating the battery's charge / discharge rate and chemical activity factor. The total heat generation of the battery under the current operating conditions is calculated by summing these two heat losses.

[0066] In actual situations, in addition to sensor abnormalities, the domain controller may also overheat, resulting in poor thermal management. In this case, non-real-time tasks can be migrated to edge computing nodes to achieve the effect of cooling the domain controller.

[0067] Through the above steps, even in the absence of some sensor data, the system can still estimate battery heat generation and the vehicle's overall thermal state based on motor current and battery SOC, adjusting and optimizing the thermal management strategy to ensure vehicle safety and performance. This approach demonstrates the robustness and adaptability of the thermal management system under complex operating conditions.

[0068] As an optional embodiment, when the temperature prediction value is lower than a first preset threshold, the coolant flow rate in the target vehicle is controlled to be lower than a target flow rate threshold.

[0069] Optionally, a coolant flow reduction threshold can be pre-set. This threshold should be low enough to reduce the energy consumption of the cooling system, but not too low to be unable to respond quickly when the temperature suddenly rises. When information is received that the temperature is lower than the first preset threshold, the coolant flow reduction strategy can be activated. A new coolant pump control signal is generated to instruct the coolant pump to reduce the output flow to below the target flow threshold. After receiving the signal from the domain controller, the coolant pump is adjusted to a lower operating speed or a reduced working stroke to reduce the coolant flow. The adjustment of the coolant pump will be immediately reflected in the cooling system, and the coolant flow will be reduced accordingly. Continue to monitor the actual temperature of each component of the vehicle to ensure that the temperature remains within a safe range even when the coolant flow is reduced. Monitor the working status of the cooling system to ensure that there are no abnormalities due to the reduction in coolant flow, such as air blockage or local overheating.

[0070] Through these steps, the thermal management system can effectively conserve energy by reducing coolant flow when the predicted temperature falls below the safe operating temperature limit, while also ensuring that vehicle component temperatures remain within safe ranges. This achieves intelligent and energy-efficient thermal management. This strategy is suitable for low-temperature winter environments or other mild operating conditions, significantly improving the vehicle's energy efficiency.

[0071] As an optional embodiment, when the temperature prediction value exceeds a second preset threshold, the driving power of the target vehicle is controlled to decrease and the coolant flow rate in the target vehicle is increased.

[0072] Optionally, when the temperature prediction value exceeds a second preset threshold, it means that a key component of the vehicle (such as a battery, motor or domain controller) may be about to enter an overheating state, which will have a negative impact on the safety and performance of the vehicle. At this time, the thermal management system needs to take emergency measures to reduce the temperature. The domain controller can control the reduction of driving power. For example, the motor torque output can be adjusted to reduce the power output. If the vehicle is in an accelerating state, the vehicle can be instructed to enter a coasting or deceleration state. At the same time, the actuators of the cooling system, such as the coolant pump, can also be controlled. Increase the coolant flow by increasing the speed of the coolant pump or opening more coolant circulation paths. For example, if a three-way or four-way electronic water valve is used, the domain controller can adjust the water valve position so that more coolant can flow through the hot component. Adjust the coolant distribution ratio so that more coolant flows to the part of the system where the temperature prediction value is too high.

[0073] Through the above steps, the thermal management system can take timely action to reduce driving power and increase coolant flow when it predicts that the temperature may be too high, so as to protect key vehicle components from overheating damage while ensuring the safety and stability of vehicle operation.

[0074] In some embodiments, different response modes are implemented based on the predicted temperature value. For example, in emergency mode (temperature ≥ 90% of the safety threshold), the drive power can be forced to reduce by 20% and the maximum cooling flow can be activated; in optimization mode (temperature 50%-90% of the safety range), the NSGA-III algorithm can be used to solve the coolant distribution and heat pump operating point; and in energy-saving mode (temperature < 50% of the threshold), only the minimum cooling flow can be maintained, using the ambient temperature difference for passive heat dissipation.

[0075] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0076] Through the description of the above embodiments, those skilled in the art can clearly understand that the thermal management method for electric vehicles according to the above embodiments can be implemented by software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a more preferred embodiment. Based on this understanding, the technical solution of the present invention, or the part that contributes to the existing technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0077] According to an embodiment of the present invention, a thermal management system for an electric vehicle is also provided. Figure 3 is a system architecture diagram of a thermal management system for an electric vehicle according to an embodiment of the present invention. Figure 3 As shown, the system includes: a temperature sensor, a water pump and a domain controller, wherein the temperature sensor is connected to the domain controller and is used to upload the temperature data of the target vehicle to the controller; the water pump is connected to the domain controller and is used to adjust the coolant flow in the target vehicle; the domain controller is used to execute any one of the above-mentioned thermal management methods for electric vehicles.

[0078] In a thermal management system, the integration and collaboration of temperature sensors, water pumps, and domain controllers are crucial for maintaining safe and efficient operating temperatures across all vehicle components. Temperature sensors monitor and collect temperature data from key components, such as the battery, motor, and coolant. These sensors are directly connected to the domain controller, uploading temperature data in real time. The water pump circulates the coolant through the system, adjusting the temperature of the battery, motor, or domain controller by increasing or decreasing the coolant flow rate. The domain controller is responsible for data processing, policy formulation, and system coordination. It receives data from the temperature sensors and, combined with vehicle operating conditions (such as motor output power and battery state of charge), analyzes the current thermal state and predicts future trends. Based on this analysis, it calculates appropriate coolant flow and drive power adjustments according to pre-set thermal management strategies. It can send control commands to the water pump to adjust the coolant flow rate and, if necessary, signals to the domain controller to coordinate drive power adjustments for effective overall temperature management. Continuously monitoring system status and adjusting strategies based on real-time data feedback creates a closed-loop control system that ensures precise execution of temperature management strategies.

[0079] Through the collaboration of temperature sensors, water pumps, and domain controllers, the thermal management system effectively monitors and regulates the target vehicle temperature, ensuring optimal vehicle operation under various operating conditions. This mechanism not only improves vehicle safety and reliability, but also enhances the thermal management system's responsiveness and energy efficiency.

[0080] Optionally, the domain controller can be a four-in-one domain controller, using a heterogeneous computing architecture and an integrated liquid cooling baseplate. The domain controller integrates modules such as the vehicle controller, battery management system, and motor controller to achieve coordinated control, avoiding the poor thermal management effects caused by multiple independent systems and lack of data sharing.

[0081] Figure 4 is a system architecture diagram of a thermal management system for an electric vehicle according to an optional embodiment of the present invention. Figure 4As shown, the domain controller is the center of the entire thermal management system, integrating the functions of the VCU (vehicle control unit), BMS (battery management system), MCU (motor control unit), and TMS (thermal management system). Temperature sensors can include a variety of types, such as battery pack and cooling circuit temperature sensors, which monitor the temperatures of various cells within the battery pack. These sensors typically use NTC (negative temperature coefficient) resistors. Motor body and cooling circuit temperature sensors monitor the temperatures of the motor windings and motor housing, with a high sampling rate to ensure real-time and accurate data. The domain controller body temperature sensor uses a PTC (positive temperature coefficient) resistor to monitor the internal temperature of the domain controller, ensuring it operates within a safe temperature range. Actuators primarily include cooling water pumps, three-way electronic water valves, four-way electronic water valves, PTCs, heat exchangers, and other actuators. The coolant pump, also known as the water pump, adjusts the coolant flow in the battery and motor cooling circuits to meet cooling requirements. The pump flow rate is infinitely adjustable from 0 to 15 L / min. Electronic water valves, including three-way and four-way valves, control the flow and distribution of coolant, ensuring that it is delivered to the components that need it most according to thermal management strategies. A cooling fan enhances the heat dissipation of the domain controller itself, and its speed can be dynamically adjusted from 0 to 12,000 rpm to accommodate varying thermal load requirements.

[0082] Specifically, the domain controller uses the CAN bus and direct sensor signals to synchronously collect data from the VCU (vehicle speed, acceleration), BMS (cell temperature, SOH), and MCU (winding temperature, torque), with a sampling period of ≤10ms. Based on real-time temperature and vehicle operating data, it calculates the optimal coolant flow distribution plan and cooling fan speed. Actuators (coolant pump, electronic water valve, and cooling fan) translate the thermal management strategy into action, achieving precise control of battery, motor, and domain controller temperatures.

[0083] This architectural design enables the domain controller to efficiently collect and process temperature data, coordinate cooling system operations, and achieve global optimization of powertrain thermal management. This not only improves system energy efficiency and battery life, but also enhances system robustness and reliability.

[0084] According to an embodiment of the present invention, a thermal management device for an electric vehicle is provided for implementing the thermal management method for an electric vehicle. Figure 5 This is a structural block diagram of a thermal management device for an electric vehicle provided according to an embodiment of the present invention. As shown in the figure, the thermal management device for an electric vehicle includes: an acquisition module 502, a calculation module 504, a prediction module 506 and an adjustment module 508. The thermal management device for an electric vehicle is described below.

[0085] The acquisition module 502 is used to acquire temperature data and vehicle operation data collected by multiple temperature sensors in the target vehicle.

[0086] The calculation module 504 is connected to the acquisition module 502 and is used to calculate the heat generation of the power system in the target vehicle based on the vehicle operation data and temperature data.

[0087] The prediction module 506 is connected to the calculation module 504 and is used to predict the temperature of the power system in the target vehicle based on the heat generation and temperature data of the target vehicle to obtain a temperature prediction value.

[0088] The adjustment module 508 is connected to the prediction module 506 and is used to adjust the temperature of the power system in the target vehicle based on a multi-objective optimization algorithm when the temperature prediction value is between the first preset threshold and the second preset threshold, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of the domain controller in the target vehicle, and minimizing the temperature difference corresponding to the battery in the target vehicle.

[0089] It should be noted that the acquisition module 502, calculation module 504, prediction module 506, and adjustment module 508 correspond to steps S202 to S208 in the embodiment. The examples and application scenarios implemented by these modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules, as part of the device, can be run in the computer terminal 10 provided in the embodiment.

[0090] An embodiment of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0091] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the thermal management method and device for electric vehicles in the embodiments of the present invention. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the thermal management method for electric vehicles described above. The memory may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0092] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain temperature data and vehicle operation data collected by multiple temperature sensors in the target vehicle; calculate the heat generation of the power system in the target vehicle based on the vehicle operation data and temperature data; predict the temperature of the power system in the target vehicle based on the heat generation and temperature data of the target vehicle to obtain a temperature prediction value; when the temperature prediction value is between a first preset threshold and a second preset threshold, adjust the temperature of the power system in the target vehicle based on a multi-objective optimization algorithm, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of the domain controller in the target vehicle, and minimizing the temperature difference corresponding to the battery in the target vehicle.

[0093] Optionally, the processor may also execute program code for the following steps: calculating the heat generation of the power system in the target vehicle based on vehicle operation data and temperature data, including: calculating the heat generation of the battery in the target vehicle based on the vehicle operation data and temperature data; establishing a motor loss model and a domain controller loss model; determining the heat generation of the motor in the target vehicle based on the vehicle operation data and the motor loss model; determining the heat generation of the domain controller in the target vehicle based on the vehicle operation data and the domain controller loss model; determining the heat generation of the power system in the target vehicle based on the heat generation of the battery, the motor and the domain controller.

[0094] Optionally, the processor may also execute the program code for the following steps: adjusting the temperature of the power system in the target vehicle based on a multi-objective optimization algorithm, including: randomly generating initial decision variables, wherein the decision variables include the coolant distribution ratio and the working status of the heat pump in the target vehicle; simulating the operation of the target vehicle under the temperature prediction value based on the initial decision variables, and determining the target values ​​corresponding to each of the multiple optimization objectives; adjusting the initial decision variables based on the target values ​​corresponding to each of the multiple optimization objectives to obtain adjusted decision variables; repeating the steps of simulating the operation of the target vehicle under the temperature prediction value based on the adjusted decision variables, determining the target values ​​corresponding to each of the multiple optimization objectives and adjusting the decision variables according to the target values ​​corresponding to each of the multiple optimization objectives, until the target number of repetitions is reached to obtain the target decision variables; adjusting the temperature of the power system in the target vehicle based on the target decision variables.

[0095] Optionally, the processor may also execute the program code of the following steps: when multiple temperature sensors have abnormalities, obtain the current of the motor and the state of charge of the battery in the target vehicle; calculate the heat generation of the battery based on the current of the motor and the state of charge of the battery; and determine the heat generation of the power system in the target vehicle based on the heat generation of the battery and vehicle operation data.

[0096] Optionally, the processor may further execute program code of the following steps: when the temperature prediction value is lower than a first preset threshold, controlling the coolant flow in the target vehicle to be lower than a target flow threshold.

[0097] Optionally, the processor may further execute program code of the following steps: when the temperature prediction value exceeds a second preset threshold, controlling the driving power of the target vehicle to decrease and increasing the coolant flow rate in the target vehicle.

[0098] An embodiment of the present invention provides a method for thermal management of electric vehicles, which obtains temperature data and vehicle operation data collected by multiple temperature sensors in a target vehicle; calculates the heat generation of a power system in the target vehicle based on the vehicle operation data and temperature data; predicts the temperature of the target vehicle based on the heat generation and temperature data of the power system in the target vehicle to obtain a temperature prediction value; and when the temperature prediction value is between a first preset threshold and a second preset threshold, adjusts the temperature of the power system in the target vehicle based on a multi-objective optimization algorithm, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of a domain controller in the target vehicle, and minimizing the temperature difference corresponding to a battery in the target vehicle, thereby achieving the purpose of performing thermal management by collecting temperatures at multiple locations through multiple temperature sensors, thereby achieving the technical effect of improving energy utilization and thermal management effects, and further solving the technical problem that multiple systems in electric vehicles currently operate independently and it is difficult to perform global thermal management, resulting in low energy utilization.

[0099] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a non-volatile storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0100] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store program codes executed by the thermal management method for an electric vehicle provided in the embodiment.

[0101] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0102] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: obtaining temperature data and vehicle operation data collected by multiple temperature sensors in the target vehicle; calculating the heat generation of the power system in the target vehicle based on the vehicle operation data and temperature data; predicting the temperature of the power system in the target vehicle based on the heat generation and temperature data of the target vehicle to obtain a temperature prediction value; when the temperature prediction value is between a first preset threshold and a second preset threshold, adjusting the temperature of the power system in the target vehicle based on a multi-objective optimization algorithm, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of the domain controller in the target vehicle, and minimizing the temperature difference corresponding to the battery in the target vehicle.

[0103] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: calculating the heat generation of the power system in the target vehicle based on vehicle operation data and temperature data, including: calculating the heat generation of the battery in the target vehicle based on the vehicle operation data and temperature data; establishing a motor loss model and a domain controller loss model; determining the heat generation of the motor in the target vehicle based on the motor loss model based on the vehicle operation data; determining the heat generation of the domain controller in the target vehicle based on the domain controller loss model based on the vehicle operation data; determining the heat generation of the power system in the target vehicle based on the heat generation of the battery, the heat generation of the motor and the heat generation of the domain controller.

[0104] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: adjusting the temperature of the power system in the target vehicle based on a multi-objective optimization algorithm, including: randomly generating initial decision variables, wherein the decision variables include the coolant distribution ratio and the operating status of the heat pump in the target vehicle; simulating the operation of the target vehicle under the temperature prediction value based on the initial decision variables, and determining the target values ​​corresponding to each of the multiple optimization objectives; adjusting the initial decision variables based on the target values ​​corresponding to each of the multiple optimization objectives to obtain adjusted decision variables; repeating the steps of simulating the operation of the target vehicle under the temperature prediction value based on the adjusted decision variables, determining the target values ​​corresponding to each of the multiple optimization objectives and adjusting the decision variables according to the target values ​​corresponding to each of the multiple optimization objectives, until the target number of repetitions is reached to obtain the target decision variables; adjusting the temperature of the power system in the target vehicle based on the target decision variables.

[0105] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: in the event that an abnormality occurs in multiple temperature sensors, obtaining the current of the motor and the state of charge of the battery in the target vehicle; calculating the heat generation of the battery based on the current of the motor and the state of charge of the battery; and determining the heat generation of the power system in the target vehicle based on the heat generation of the battery and vehicle operation data.

[0106] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: when the temperature prediction value is lower than a first preset threshold, controlling the coolant flow in the target vehicle to be lower than a target flow threshold.

[0107] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: when the temperature prediction value exceeds a second preset threshold, controlling the driving power of the target vehicle to decrease and increasing the coolant flow in the target vehicle.

[0108] An embodiment of the present invention also provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can achieve: obtaining temperature data and vehicle operation data collected by multiple temperature sensors in a target vehicle; calculating the heat generation of the target vehicle based on the vehicle operation data and temperature data; predicting the temperature of the target vehicle based on the heat generation and temperature data of the target vehicle to obtain a temperature prediction value; when the temperature prediction value is between a first preset threshold and a second preset threshold, adjusting the temperature in the target vehicle based on a multi-objective optimization algorithm, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of the domain controller in the target vehicle, and minimizing the temperature difference corresponding to the battery in the target vehicle.

[0109] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0110] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0111] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0112] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0113] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0114] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program code.

[0115] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A thermal management method for an electric vehicle, characterized in that: include: Acquire temperature data and vehicle operation data collected by multiple temperature sensors in the target vehicle; calculating a heat generation of a power system in the target vehicle based on the vehicle operation data and the temperature data; Predicting the temperature of the power system in the target vehicle based on the heat generation of the power system and the temperature data to obtain a temperature prediction value; When the temperature prediction value is between a first preset threshold and a second preset threshold, the temperature of the power system in the target vehicle is adjusted based on a multi-objective optimization algorithm, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of the domain controller in the target vehicle, and minimizing the temperature difference corresponding to the battery in the target vehicle.

2. The method according to claim 1, characterized in that The calculating the heat generation of the power system in the target vehicle based on the vehicle operation data and the temperature data includes: calculating a heat generation amount of a battery in the target vehicle based on the vehicle operation data and the temperature data; Establish motor loss model and domain controller loss model; Determining the heat generation of the motor in the target vehicle based on the vehicle operation data and the motor loss model; Determining, based on the vehicle operation data and the domain controller loss model, a heat generation amount of a domain controller in the target vehicle; The heat generation of a power system in the target vehicle is determined based on the heat generation of the battery, the heat generation of the motor, and the heat generation of the domain controller.

3. The method according to claim 1, characterized in that The adjusting the temperature of the power system in the target vehicle based on the multi-objective optimization algorithm includes: Randomly generating initial decision variables, wherein the decision variables include a coolant distribution ratio in the target vehicle and an operating state of a heat pump; Based on the initial decision variables, simulating the operation of the target vehicle under the temperature prediction value, and determining the target values ​​corresponding to the multiple optimization objectives; Adjusting the initial decision variables based on target values ​​corresponding to the multiple optimization objectives to obtain adjusted decision variables; Repeating the steps of simulating the operation of the target vehicle under the temperature prediction value based on the adjusted decision variables, determining target values ​​corresponding to each of the plurality of optimization objectives, and adjusting the decision variables according to the target values ​​corresponding to each of the plurality of optimization objectives, until a target number of repetitions is reached, thereby obtaining a target decision variable; Based on the target decision variable, a temperature of a power system in the target vehicle is adjusted.

4. The method according to claim 1, wherein Also includes: When the plurality of temperature sensors are abnormal, obtaining the current of the motor and the state of charge of the battery in the target vehicle; calculating a heat generation of the battery based on a current of the motor and a state of charge of the battery; The heat generation of a power system in the target vehicle is determined based on the heat generation of the battery and the vehicle operation data.

5. The method according to claim 1, wherein Also includes: When the temperature prediction value is lower than the first preset threshold, the coolant flow rate in the target vehicle is controlled to be lower than a target flow rate threshold.

6. The method according to any one of claims 1 to 5, characterized in that Also includes: When the temperature prediction value exceeds the second preset threshold, the driving power of the target vehicle is controlled to decrease and the coolant flow rate in the target vehicle is increased.

7. A thermal management system for an electric vehicle, characterized in that: It includes temperature sensor, water pump and domain controller, among which, The temperature sensor is connected to the domain controller and is used to upload the temperature data of the target vehicle to the domain controller; The water pump is connected to the domain controller, wherein the water pump is used to adjust the coolant flow in the target vehicle; The domain controller is used to execute the thermal management method for an electric vehicle as described in any one of claims 1 to 6.

8. The system according to claim 7, characterized in that The domain controller includes a vehicle controller, a battery management system and a motor controller.

9. A thermal management device for an electric vehicle, characterized in that: include: An acquisition module is used to acquire temperature data and vehicle operation data collected by multiple temperature sensors in the target vehicle; a calculation module, configured to calculate a heat generation amount of a power system in the target vehicle based on the vehicle operation data and the temperature data; a prediction module, configured to predict the temperature of the power system in the target vehicle based on the heat generation of the power system and the temperature data, and obtain a temperature prediction value; an adjustment module, configured to adjust the temperature in the target vehicle based on a multi-objective optimization algorithm when the temperature prediction value is between a first preset threshold value and a second preset threshold value, wherein the multiple optimization objectives corresponding to the multi-objective optimization algorithm include at least two of the following: minimizing the power consumption of the target vehicle, minimizing the temperature of the domain controller in the target vehicle, and minimizing the temperature difference corresponding to the battery in the target vehicle.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the thermal management method for an electric vehicle according to any one of claims 1 to 6.

11. A computer device, characterized in that: include: memory and processor, The memory stores a computer program; The processor is configured to execute a computer program stored in the memory, and when the computer program is run, the processor is enabled to execute the thermal management method for an electric vehicle according to any one of claims 1 to 6.

12. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the thermal management method for an electric vehicle according to any one of claims 1 to 6 is implemented.

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