A predictive thermal management method for an electric drive system of an urban logistics electric light truck

Abstract

The application discloses a kind of electric drive system predictive thermal management methods for urban logistics electric light truck, it is related to new energy vehicle technical field, including the following steps: obtaining motor current, power, vehicle speed, acceleration, IGBT temperature, motor winding temperature and reducer gear oil temperature, and synchronous acquisition driving road condition information, form the real-time data set for representing the urban logistics electric light truck operating condition and thermal state of real-time data set.Forming real-time data set for representing the urban logistics electric light truck operating condition and thermal state.The present application realizes the advance regulation of electric drive system temperature through predictive active thermal management, effectively reduces temperature peak, improves the reliability and service life of motor and reducer;By gently adjusting cooling power, reduce the energy consumption of cooling system, improve the vehicle endurance;In high intensity condition, guarantee continuous power output, avoid overheating limit power;At the same time, through the collaborative control of multiple heat sources, optimize the cooling resource allocation, improve the overall energy efficiency, and have good adaptability and practical value.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle technology, and specifically to a predictive thermal management method for an electric drive system of an electric light truck used in urban logistics. Background Technology

[0002] As a core carrier for urban freight transportation, electric light trucks for urban logistics operate under significantly complex and unique conditions. They typically require frequent starts and stops, rapid acceleration and deceleration in urban road environments, while also facing typical challenges such as traffic congestion and short-distance, high-frequency deliveries. Under these conditions, key components of the electric drive system, such as the motor and reducer, experience drastically fluctuating thermal loads, with winding temperatures and gear oil temperatures exhibiting a rapid, sawtooth-like rise and fall. The temperature level of the electric drive system directly affects the vehicle's operational reliability, lifespan, and continuous output capacity. Excessive temperatures not only accelerate motor insulation aging and damage IGBT devices but also reduce reducer lubrication performance, potentially leading to system failures. Furthermore, when the temperature exceeds a safe threshold, the system triggers a thermal protection mechanism, limiting output power and impacting vehicle transportation efficiency. Therefore, efficient and precise thermal management of the electric drive system has become one of the key technologies for ensuring the stable operation of electric light trucks for urban logistics.

[0003] Currently, electric drive systems for urban logistics electric light trucks generally adopt a passive reactive thermal management scheme based on temperature threshold triggering. This involves real-time monitoring of the motor and reducer temperatures using temperature sensors, activating the cooling system when the temperature reaches a preset threshold, and stopping cooling once the temperature drops to a set range. However, this approach has significant drawbacks in practical applications: First, because cooling control relies on temperature triggering, there is a significant lag. During rapid temperature increases in the electric drive system, the cooling system cannot intervene in time, easily leading to excessively high temperature peaks, thus affecting component reliability and lifespan. Second, the cooling system needs to operate at high power and frequently start and stop after reaching high temperatures, resulting in increased energy consumption and reduced vehicle range. Third, under high-intensity operating conditions such as continuous uphill climbing or prolonged traffic jams, insufficient cooling response may trigger system thermal protection, limiting power output and affecting the vehicle's continuous operating performance. Furthermore, existing solutions typically control a single heat source independently, lacking a coordinated thermal management mechanism between the motor and reducer, failing to achieve reasonable allocation of cooling resources, easily causing localized overheating or energy waste, and failing to meet the demand for efficient thermal management under complex urban logistics conditions.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a predictive thermal management method for an electric drive system of an electric light truck for urban logistics, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a predictive thermal management method for an electric drive system of an electric light truck for urban logistics, comprising the following steps: The system acquires motor current, power, vehicle speed, acceleration, IGBT temperature, motor winding temperature, and reducer gear oil temperature, and simultaneously acquires road condition information to form a real-time data set that characterizes the operating conditions and thermal state of electric light trucks for urban logistics. Based on real-time data sets, and using a pre-built thermal model of the electric drive system, the temperature rise trend of the motor and reducer in the next 30-60 seconds is calculated, and the corresponding heat load change trend and operating condition prediction results are obtained. Based on the trend of heat load change and the prediction results of operating conditions, the cooling actuator is activated in advance and the cooling power is gradually adjusted when the high heat generation condition is predicted. When the low heat generation or high heat dissipation condition is predicted, the operating power of the cooling actuator is reduced in advance, thereby realizing predictive active cooling control of the motor and reducer. Based on the trends of motor winding temperature, reducer gear oil temperature, and thermal load changes, the motor cooling circuit and reducer cooling circuit are adjusted differently. When the motor thermal load is dominant, the motor cooling power is increased and the reducer cooling power is reduced. When both are under high thermal load, the corresponding cooling power is increased simultaneously to achieve multi-heat source coordinated thermal management of the electric drive system for urban logistics electric light trucks.

[0007] Preferably, the operating condition characteristics of the electric drive system are processed by fusing multi-source operating information to form a unified data input structure, and the steps are as follows: The system collects motor current and power signals, and simultaneously acquires vehicle speed and acceleration change data. It also collects IGBT temperature, motor winding temperature and reducer gear oil temperature, and performs initial filtering on various signals to remove abnormal fluctuations. It receives driving route information output by the navigation module, marks the slope changes, road segment types and driving directions in the route, and performs segmentation processing on the route data; Data from different sources are aligned according to a unified time base, and interpolation or resampling operations are performed to form a continuous and consistent data sequence from multiple sources. The data sequences are classified and reorganized to construct a comprehensive dataset containing operating status parameters, thermal status parameters, and path characteristic parameters.

[0008] Preferably, the process of constructing a temperature rise trend analysis by introducing parameters related to heat generation and heat dissipation includes the following steps: The motor current and power information are converted into heat intensity parameters, and segmented calibration is performed for different load ranges. The vehicle speed data is divided into intervals and mapped to corresponding heat dissipation capacity parameters, while also considering the impact of vehicle speed change trends on heat dissipation conditions. The motor winding temperature and the gear oil temperature of the reducer are used as state variables to smooth temperature changes and eliminate the impact of short-term fluctuations. The calculation process inputs heating parameters, heat dissipation parameters, and temperature state variables to continuously predict temperature changes over the next 30-60 seconds and outputs temperature rise trend data.

[0009] Preferably, a high-heat identification and response process is established based on the characteristics of changes in operating conditions, with the following steps: The driving path data is analyzed to extract continuous uphill sections and gradient change intervals, and the corresponding locations are marked. By jointly analyzing acceleration and vehicle speed signals, frequent start-stop, rapid acceleration, and load fluctuation states can be identified. The path characteristics and operational characteristics are combined to form a high-heat status indicator, and the duration of the indicator is recorded. The operating parameters of the cooling actuators are preset according to the high heat status indicator, and the fan speed and water pump flow are gradually adjusted according to the time sequence.

[0010] Preferably, a low heat load identification process is constructed based on the characteristics of changes in vehicle operating status, and the steps are as follows: The driving path is analyzed to identify downhill sections and areas where the gradient decreases, and the duration of these sections is recorded. Detect changes in vehicle speed and identify stable driving ranges and speed fluctuation ranges; The motor's operating status is analyzed to identify regenerative braking and low-load operating states, and corresponding feature parameters are extracted. By comprehensively processing path characteristics, vehicle speed characteristics, and operating status, a low heat load status indicator is generated, and the operating parameters of the cooling actuators are adjusted.

[0011] Preferably, a refined cooling regulation control process is formed based on the low heat load state identifier. The operating parameters of the cooling actuator are matched and allocated according to the slope change trend, vehicle speed change range and load characteristics corresponding to the motor operating state, and are continuously adjusted according to the operating state change process to maintain the consistency of the thermal state changes of the motor and reducer.

[0012] Preferably, a continuous adjustment process is formed by combining the operating characteristics of the actuators, and the steps are as follows: Receive cooling control commands and parse fan speed control parameters, and perform range verification and limit processing on the parameters; Receive cooling control commands and parse the electronic water pump flow control parameters, and process the flow parameters in segments; Establish a corresponding relationship curve based on the matching relationship between fan speed and water pump flow rate, and perform interpolation calculations; The fan speed and water pump flow rate are continuously adjusted according to changes in control parameters, so that both change synchronously with changes in input parameters.

[0013] Preferably, a differentiated regulation process is constructed based on the load distribution relationship between different heat sources, and the steps are as follows: Acquire motor winding temperature and reducer gear oil temperature data and update them in real time; By comparing and analyzing the two types of temperature data, the temperature difference and rate of change are calculated to form parameters for heat load difference. The heat load difference parameters are converted into cooling distribution coefficients, and the distribution coefficients are normalized. Adjust the operating parameters of the motor cooling circuit and the reducer cooling circuit according to the allocation coefficient to distribute the cooling capacity proportionally.

[0014] Preferably, a correction and adjustment process is constructed based on the difference between temperature feedback and predicted information, with the following steps: Collect data on the temperature changes of the motor and reducer after cooling is performed, and record the time series of these changes. The temperature change data is compared with the temperature rise trend prediction data, and the deviation change curve is calculated. The deviation curve is analyzed and processed to extract the deviation amount and its trend, and correction parameters are generated. The correction parameters are input into the cooling control process to continuously adjust the operating parameters of the cooling actuators.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention demonstrates significant advantages in reducing peak temperatures in electric drive systems through a predictive active thermal management scheme. By anticipating high-heat operating conditions, cooling regulation is initiated before the temperature reaches the traditional control threshold, resulting in smoother temperature changes in the motor and reducer, thus suppressing rapid temperature rises at the source. Compared to passive control methods, this approach effectively reduces peak temperature levels, minimizes the impact of thermal shock on critical components, and reduces the risks of motor insulation aging, IGBT device damage, and reducer gear oil performance degradation. This overall improves the reliability and stability of the electric drive system, extends the service life of core components, and further reduces the probability of failure and maintenance costs during vehicle operation.

[0016] This invention effectively reduces cooling energy consumption by optimizing the cooling system's operating mode. By predicting heat load changes, the cooling actuators adopt a smooth adjustment method during operation, avoiding energy waste caused by frequent start-stop cycles and instantaneous high-power operation in traditional control. Cooling capacity is gradually increased during high-load phases and cooling power is promptly reduced during low-load or high-heat-dissipation phases, ensuring the cooling system always operates in a state that matches heat demand. This control method reduces ineffective energy consumption, lowers the overall vehicle power consumption level, and thus improves the range of urban logistics electric light trucks, providing a more stable energy guarantee for long-term operation.

[0017] This invention also offers significant advantages in ensuring the continuous performance of the electric drive system. Under high-intensity operating conditions such as continuous uphill climbing, frequent start-stop cycles, or prolonged traffic congestion, by initiating cooling in advance and dynamically adjusting cooling capacity, the system enters an effective heat dissipation state before the heat load increases, thereby preventing the temperature from exceeding the safe range. This control method effectively prevents power limiting caused by overheating, ensuring that the electric drive system maintains stable power output under complex operating conditions, improving the vehicle's continuous driving performance and transportation efficiency, and meeting the high-intensity operation requirements of urban logistics.

[0018] This invention utilizes a multi-heat-source collaborative control mechanism to achieve a rational allocation of cooling resources between the motor and the reducer, thereby optimizing overall energy efficiency. Under different heat load conditions, the cooling circuit is differentiated and adjusted according to the actual temperature and thermal change trends of each component, avoiding overcooling or localized overheating problems caused by a single control strategy. This refined resource allocation improves the utilization efficiency of the cooling system, making thermal management more precise and efficient. Furthermore, the solution has a simple structure, requires no additional complex hardware, can be directly adapted to existing electric drive systems, and can be dynamically adjusted according to different urban road conditions, demonstrating good adaptability and promotional value. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 This is a schematic diagram of the predictive thermal management system structure of the electric drive system of the present invention.

[0021] Figure 2 This is a schematic diagram of the predictive thermal management method for the electric drive system of the present invention. Detailed Implementation

[0022] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0023] This invention provides, for example Figure 1 and Figure 2 The following are the specific steps of a predictive thermal management method for an electric drive system of an electric light truck used in urban logistics: The core of this invention is predictive active cooling, with the specific control logic executed in four steps, entirely led by the Vehicle Control Unit (VCU), achieving precise and efficient thermal management. At the beginning of the control process, the VCU comprehensively senses and collects real-time data on the vehicle's operating status and the thermal state of the electric drive system through various sensors. The VCU collects core data in real time through these sensors, including motor current, power (directly reflecting the motor's heating intensity, serving as a core heat source parameter), vehicle speed (reflecting the vehicle's driving state and indirectly determining heat dissipation conditions), IGBT temperature, motor winding temperature, and reducer gear oil temperature. Simultaneously, it obtains driving road condition information (such as whether there are continuous uphill sections) through the navigation module and determines the current driving conditions (such as rapid acceleration and stop-and-go traffic) through the acceleration sensor. All data is synchronized to the VCU in real time to ensure data timeliness. During the above data acquisition process, various signals are continuously updated at a high frequency, enabling the VCU to accurately grasp the transient change characteristics of the electric drive system under different operating conditions, thus providing a reliable data foundation for subsequent thermal trend analysis.

[0024] In practical implementation, motor current and power data directly reflect the motor's load level and heat intensity under current operating conditions, serving as key parameters for judging heat source change trends. Vehicle speed data not only reflects the vehicle's operating status but also directly affects external air cooling conditions and heat dissipation efficiency. Natural heat dissipation capacity is enhanced at high speeds, while it significantly decreases at low speeds or in congested conditions. Therefore, vehicle speed information has significant reference value in thermal management. IGBT temperature, motor winding temperature, and reducer gear oil temperature correspond to the real-time thermal state of different heat sources within the electric drive system. These temperature data can intuitively reflect the heat accumulation of each component, providing direct evidence for identifying potential overheating risks. Simultaneously, the navigation module provides driving condition information that can reflect road gradient and driving path characteristics in advance. For example, continuous uphill driving is often accompanied by higher loads and a sustained heat generation trend, while downhill or straight, unobstructed road sections correspond to lower heat generation levels. Accelerometers are used to identify the vehicle's current dynamic behavior characteristics, such as rapid acceleration and frequent starts and stops—typical urban logistics conditions. These behaviors cause rapid fluctuations in motor load, leading to rapid changes in thermal load.

[0025] Through the fusion and acquisition of multi-source information, the VCU can not only acquire the temperature status at a single moment, but also indirectly characterize the heat generation mechanism by combining current, power, vehicle speed, and operating condition information, thereby achieving a comprehensive perception of the thermal state. All acquired data is transmitted and synchronized to the VCU's internal processing unit in real time, providing fundamental support for subsequent thermal model calculations and predictive control while ensuring data integrity and timeliness. This process emphasizes data continuity and consistency, avoiding judgment biases caused by sampling delays or data gaps. It also lays the foundation for subsequent temperature rise trend prediction and dynamic adjustment of cooling strategies, thereby improving the overall thermal management response speed and control accuracy, enabling the electric drive system to maintain stable operation under complex and ever-changing urban logistics conditions.

[0026] Thermal Modeling and Temperature Rise Trend Prediction: The VCU incorporates a thermal model of the electric drive system. This model is built upon the heating characteristics of the motor and reducer (such as motor copper and iron losses, and reducer frictional heating) and heat dissipation characteristics (such as wind resistance heat dissipation corresponding to vehicle speed and cooling circuit heat dissipation). Based on real-time collected operating condition and temperature data, it can accurately predict the temperature rise trend of the motor and reducer within the next 30-60 seconds, clearly anticipating upcoming high-heat or low-heat operating conditions. In its implementation, the thermal model is based on the energy conversion process of the electric drive system, using the copper and iron losses generated during motor operation as the main heat source inputs. Simultaneously, it converts the frictional losses generated by the reducer during transmission into heat inputs. Through unified modeling of various heat sources, a computational framework reflecting the overall thermal behavior of the system is formed.

[0027] In the construction of the thermal model, not only are the inputs on the heat-generating side considered, but the influencing factors on the heat-dissipating side are also systematically described. Vehicle speed changes directly affect the external airflow, thus impacting wind resistance and heat dissipation. When the vehicle is at a higher speed, the external airflow is stronger, which facilitates heat dissipation; while at low speeds or in congested conditions, heat dissipation capacity decreases, and heat is more easily accumulated. Furthermore, the heat dissipation capacity of the cooling circuit is also incorporated into the model as an important factor. By describing the operating status of the cooling fan and electric water pump, the model can reflect the regulatory effect of active cooling on temperature changes. Through a comprehensive characterization of both heat generation and heat dissipation factors, the thermal model can accurately describe the thermal balance changes of the motor and reducer under different operating conditions.

[0028] At the data-driven level, real-time collected data on motor current, power, vehicle speed, temperature, and driving conditions are continuously input into the thermal model calculation unit within the VCU, serving as a crucial basis for dynamically updating model parameters. Motor current and power characterize the instantaneous load level and heat intensity trends, temperature data reflects the current thermal state and corrects model calculation results, while vehicle speed and operating condition information are used to adjust heat dissipation conditions and future operating trends. Through continuous data input and updates, the thermal model maintains a high-precision description of the current state while also possessing the ability to extrapolate and predict short-term future states.

[0029] In the process of predicting temperature rise trends, based on the current operating data and thermal state, and combined with the heat generation and dissipation relationship within the thermal model, the temperature changes within the next 30-60 seconds are extrapolated. This time window not only covers the typical operating cycle of urban logistics vehicles but also ensures high real-time performance and executability of the prediction results. The prediction results include not only the magnitude of temperature change but also the rate and trend of temperature change, thus enabling the identification of upcoming rapid temperature rise phases or phases where the temperature stabilizes or even decreases.

[0030] Through the aforementioned prediction process, the VCU can identify potential high-heat-generating conditions before the temperature reaches the traditional control threshold, such as continuous high-load conditions caused by continuous uphill driving or frequent acceleration and deceleration. It can also identify low-heat or high-heat-generating conditions, such as high-speed constant-speed driving or downhill driving. This prediction result provides crucial input for subsequent cooling control strategies, transforming thermal management from a passive response solely reliant on current temperature to an active adjustment based on future trends, thereby improving the foresight and accuracy of control.

[0031] The entire thermal modeling and temperature rise trend prediction process emphasizes computational efficiency and real-time performance. While ensuring prediction accuracy, it meets the computational resource requirements of the onboard control environment, enabling the method to be stably executed during actual vehicle operation. Through unified modeling of heat generation and heat dissipation characteristics and continuous updating of real-time data, this process provides core support for achieving accurate and efficient predictive thermal management.

[0032] Predictive Cooling Control Execution: High-Heat Condition Prediction and Control: When the VCU predicts high-heat conditions such as continuous uphill driving or congested road sections (frequent starts and stops, rapid acceleration) based on current acceleration and vehicle speed, the cooling system is activated in advance, even if the current temperature of the motor and reducer has not reached the cooling start threshold in existing technologies. The variable-speed cooling fan speed and electric water pump flow rate are smoothly adjusted to gradually increase cooling power and begin heat dissipation ahead of time, preventing a rapid temperature rise. Low-Heat / High-Heat Condition Prediction and Control: When the VCU predicts low-heat or high-heat conditions such as approaching a downhill section (motor in regenerative braking state, reduced heat generation) or a smooth road section (stable vehicle speed, good heat dissipation), the cooling fan speed and electric water pump flow rate are reduced in advance to decrease cooling power output and avoid energy waste caused by excessive cooling.

[0033] In its implementation, predictive cooling control uses thermal trend predictions as direct input. By anticipating changes in heat load over a future period, it proactively adjusts the cooling system's operating status. Compared to control methods that rely on temperature threshold triggers, this control logic does not wait for the temperature to reach a set upper limit before initiating cooling. Instead, it intervenes in advance when the heat load is about to increase, thus achieving a higher degree of temporal matching between the cooling and heating processes. Under high-heat conditions, navigation information reflects road gradient trends, and acceleration and vehicle speed changes indicate the vehicle's current dynamic load. When typical operating conditions such as continuous uphill driving or frequent start-stop cycles occur, the motor load remains at a high level, and the heat intensity increases significantly. In this case, the cooling system is activated in advance, gradually increasing the cooling fan speed and electric water pump flow rate to smoothly increase cooling capacity. This avoids the energy consumption shock caused by a sudden high-power start-up of the cooling system in traditional methods, and also prevents the temperature from rapidly climbing to its peak value in a short period.

[0034] During cooling power regulation, the principle of gradual change in control is emphasized. By continuously adjusting the operating parameters of the actuators, the cooling capacity gradually adapts to changes in heat load, thereby reducing system oscillations and improving control stability. This regulation method can release some heat before it accumulates in large quantities, keeping the motor winding temperature, IGBT temperature, and reducer oil temperature within a relatively stable range, reducing temperature fluctuations and improving the reliability and service life of key components.

[0035] Predictive control also plays a crucial role under low-heat or high-heat conditions. When a vehicle is about to enter a downhill section, the motor may be in regenerative braking mode, significantly reducing heat generation. On sections with higher speeds and smoother operation, improved external air cooling conditions enhance the system's heat dissipation capacity. In such situations, reducing the cooling fan speed and electric water pump flow rate in advance can prevent energy waste caused by excessive cooling capacity. Simultaneously, appropriately reducing cooling power can also reduce the operating load on actuators, lowering the overall system energy consumption and contributing to improved vehicle range.

[0036] By predicting and adjusting both high-heat and low-heat operating conditions, the cooling system's operating status is dynamically matched to actual heat demand, achieving on-demand cooling control. This control logic not only improves the response speed of thermal management but also significantly optimizes energy utilization efficiency. Throughout the execution process, the VCU continuously receives real-time temperature feedback and operating condition change information, dynamically correcting the cooling strategy to form a continuously adjusting closed loop in the control process. This ensures that the electric drive system can maintain operation within a reasonable temperature range in the complex and ever-changing urban logistics environment, balancing thermal safety and energy efficiency.

[0037] Multi-heat source coordinated control: The VCU uses real-time temperature data collected by the motor temperature sensor and the reducer oil temperature sensor, combined with the thermal model to predict the heat load of both, to independently or collaboratively control the motor cooling circuit and the reducer cooling circuit. When the motor heat load is high and the reducer heat load is low, the cooling power of the motor cooling circuit is increased, and the cooling power of the reducer is appropriately reduced. When both heat loads are high, the cooling power of both is increased collaboratively to achieve optimal allocation of cooling resources and ensure maximum overall thermal management efficiency.

[0038] In the specific control process, multi-heat source coordinated regulation is based on real-time temperature data and heat load prediction results. By identifying the differences in thermal state between different heat sources, dynamic allocation of cooling resources is achieved. As the main heat sources in the electric drive system, the motor and reducer have different heating mechanisms and thermal response characteristics. Motor heating is mainly affected by changes in current and load, exhibiting rapid changes, while reducer heating originates more from mechanical friction, with relatively lagging changes. Therefore, under different operating conditions, the heat loads of the two often exhibit inconsistent states. Using only a uniform cooling strategy can easily lead to insufficient local cooling or increased overall energy consumption.

[0039] By continuously collecting the temperatures of the motor windings and the gear oil in the reducer, and combining this with thermal model predictions of future heat load changes, the differences in temperature rise trends between the two heat sources can be identified in advance. When the motor's heat load is detected to be significantly higher than that of the reducer, cooling capacity is prioritized for the motor's cooling circuit to increase the corresponding cooling intensity, while the power output of the reducer's cooling circuit is appropriately reduced. This directs cooling resources towards the side with higher demand, effectively suppressing the rapid rise in motor temperature. During this process, the cooling fan speed and electric water pump flow rate are continuously adjusted to ensure a smoother heat distribution process and prevent system fluctuations caused by cooling switching.

[0040] When both the motor and the reducer are under high heat load, the cooling capacity of their respective cooling circuits is synchronously increased to match the overall heat dissipation capacity with the system's heat generation level. In this state, the cooling system enters its high-efficiency operating range. By coordinating and adjusting the various actuators, the temperatures of both the motor and the reducer are controlled within safe limits, preventing overheating of a single heat source from causing system performance degradation or protection-related limitations. This coordinated adjustment method can improve the overall utilization efficiency of the cooling system while ensuring thermal safety and reducing unnecessary energy waste.

[0041] Under certain operating conditions, the motor and reducer may exhibit asynchronous thermal change trends. For example, the motor load may decrease while the reducer remains at a high temperature. In such cases, by independently controlling each cooling circuit, cooling resources can be allocated more precisely, avoiding overcooling of components with low heat loads and thus further optimizing energy consumption. Through this differentiated adjustment method, the cooling system no longer operates as a single unit but is instead controlled specifically for different heat sources, improving the overall system response flexibility.

[0042] The entire multi-heat-source collaborative control process emphasizes real-time perception and dynamic adjustment of heat load distribution. Through the combination of thermal model prediction and real-time data feedback, the cooling strategy can be continuously optimized according to changes in operating conditions. In complex urban logistics conditions, such as frequent start-stop, short-distance transportation, and variable road conditions, this control method can effectively cope with rapid changes in heat load and achieve precise control of motor and reducer temperatures. Simultaneously, by rationally allocating cooling resources, the ineffective operating time of the cooling system is reduced, thereby improving the overall vehicle energy efficiency and enhancing the system's adaptability and reliability in practical applications while ensuring the stable operation of the electric drive system.

[0043] The specific implementation process of this invention revolves around predictive thermal management control logic, covering the complete operating cycle from system startup to system shutdown. Each link is interconnected and provides continuous feedback, forming a stable and reliable closed-loop regulation mechanism.

[0044] System Initialization: Upon vehicle startup, the thermal management system starts synchronously. All sensors, VCUs, and cooling actuators complete self-checks to ensure normal equipment operation. The thermal model loads initial parameters and enters standby mode. During initialization, the vehicle controller performs status checks on the motor temperature sensor, reducer oil temperature sensor, vehicle speed sensor, and acceleration sensor to confirm the signal acquisition link is normal. Simultaneously, it performs basic verification of the driving capabilities of the cooling fan and electric water pump to ensure the actuators can respond stably to control commands. At the same time, the thermal model loads preset parameters, including the basic thermal characteristic parameters of the motor and reducer, as well as the initial temperature state, enabling the system to have basic calculation capabilities during startup, providing support for subsequent real-time control. After completing the above preparations, the overall operating state enters standby mode, ready to respond to subsequent operational needs.

[0045] Real-time data acquisition: Various sensors continuously collect data such as motor current, power, IGBT temperature, vehicle speed, motor temperature, and reducer oil temperature, transmitting this data to the VCU in real time. During operation, various data are input to the control unit in continuous stream form. Motor current and power reflect load changes, IGBT temperature and winding temperature reflect the thermal state of the core electric drive components, reducer oil temperature reflects the degree of heat accumulation in the mechanical transmission parts, and vehicle speed information is used to assist in judging heat dissipation conditions. Data acquisition emphasizes high frequency and low latency characteristics to ensure that the collected information can reflect current operating condition changes in a timely manner. After entering the control unit, all types of data are processed and stored uniformly, providing a complete data foundation for subsequent operating condition judgment and thermal trend prediction.

[0046] Operating Condition Prediction and Thermal Trend Forecasting: The VCU combines collected data and navigation information to predict operating conditions, calculating the temperature rise trend for the next 30-60 seconds using a thermal model. During this stage, a comprehensive analysis of vehicle speed changes, acceleration changes, and navigation path information identifies the upcoming operating state of the vehicle, such as continuous uphill driving, congested roads, or unobstructed roads. Simultaneously, real-time collected current, power, and temperature data are input into the thermal model to dynamically calculate the heating and cooling processes of the motor and reducer, thereby predicting the temperature change trend over a future period. This prediction process not only outputs the direction of temperature change but also reflects the rate of temperature rise and the potential peak range, providing a basis for subsequent control decisions and transforming thermal management control from a current-state-driven approach to a future-trend-oriented adjustment method.

[0047] Cooling Strategy Decision and Issuance: Based on the predicted results, the VCU determines the operating status of the cooling system (start / stop, power adjustment) and issues control commands to the variable-speed cooling fans and electric water pumps. During the control decision-making process, if the prediction results indicate a rapid increase in future temperature, the system enters cooling mode ahead of schedule, gradually increasing fan speed and water pump flow to match the cooling capacity with the heat generation trend. If the prediction results indicate a decrease in heat load or improved heat dissipation conditions, the cooling intensity is appropriately reduced to avoid unnecessary energy consumption. The control commands are output in a continuously adjusting manner, ensuring smooth changes in the operating status of the actuators, thereby improving control stability and reducing system fluctuations.

[0048] Cooling Execution and Feedback: The cooling execution components operate according to instructions, while temperature sensors continuously collect temperature data and feed it back to the VCU, forming a closed-loop control. During the execution phase, the cooling fan adjusts its speed to change the airflow, and the electric water pump adjusts its flow rate to change the heat exchange capacity of the cooling circuit. Together, they regulate the temperature of the motor and reducer. Simultaneously, the temperature sensor continuously feeds back real-time temperature data, enabling the control unit to monitor actual temperature changes and compare them with predicted results, thereby evaluating the control effect. Through this real-time feedback mechanism, the control system can promptly detect and correct deviations, ensuring the accuracy of the thermal management process.

[0049] Dynamic Adjustment: The VCU dynamically adjusts the cooling strategy based on real-time temperature data and changes in operating conditions to ensure the electric drive system remains within a reasonable temperature range. During operation, vehicle conditions may continuously change, such as transitioning from congested to open roads or from flat roads to inclines, resulting in changes in heat load. The control unit continuously updates input data and prediction results to correct the cooling strategy in real time, ensuring that cooling capacity always matches the heat load. In this process, the control logic emphasizes continuous adjustment and gradual change, avoiding increased energy consumption and system shocks caused by frequent start-stops or abrupt adjustments, thereby achieving stable and efficient thermal management.

[0050] System Shutdown: After the vehicle is turned off, the thermal management system determines whether to delay shutting down the cooling system based on the remaining temperature of the electric drive components (if the component temperature is still high, it will delay operation for a period of time before shutting down) to ensure that residual heat is fully dissipated, and then completes the system shutdown. During the shutdown phase, the system assesses the temperature status of the motor and reducer to determine whether to continue cooling operation to prevent heat from accumulating inside the system and causing localized overheating. During the delayed operation period, the cooling capacity gradually decreases until the temperature drops to a safe range and then operation stops. Finally, the system shutdown is completed, ensuring that the entire thermal management process ends smoothly while maintaining safety.

[0051] Through the operation of the entire process described above, each link is interconnected, forming a complete closed-loop system from data acquisition, trend prediction, control decision-making to execution feedback and dynamic adjustment. This ensures that the electric drive system maintains a reasonable temperature range in the complex and ever-changing urban logistics operation environment, while also taking into account energy consumption control and system reliability requirements.

[0052] The predictive thermal management system consists of the following core components, which work together to achieve operating condition monitoring, thermal trend prediction, and cooling control: Vehicle Control Unit (VCU): As the core control unit of the system, it is responsible for receiving signals from various sensors, performing operating condition prediction, thermal trend calculation, cooling strategy decision-making, and issuing control commands; it is the brain of the entire thermal management system. Motor Controller: Linked with the VCU, it provides real-time feedback on motor operating parameters (such as IGBT temperature, current, power, etc.), receives cooling control commands from the VCU, and coordinates the adjustment of motor operating status and cooling system operating mode. Temperature Sensors: Including motor temperature sensors and reducer oil temperature sensors, these sensors collect real-time data on motor winding temperature and reducer gear oil temperature, respectively, and transmit the temperature data to the VCU in real time as the basis for thermal model calculation and control decisions. Cooling Actuators: Employing variable-speed cooling fans and electronic water pumps, these devices can flexibly adjust speed (fan) and flow rate (water pump) according to commands issued by the VCU, achieving stepless adjustment of cooling power and avoiding energy waste caused by fixed-power operation. Auxiliary monitoring components include vehicle speed sensors, acceleration sensors, and navigation modules, which collect real-time operating condition data such as vehicle speed, acceleration, and driving route to support VCU operating condition prediction.

[0053] In the overall architecture, the core components are tightly coupled through signal interaction, constructing a comprehensive control system that integrates data acquisition, state perception, trend prediction, and control execution. The vehicle controller, located at the system's center, is responsible for information aggregation and decision output. Through unified processing of multi-source data, it achieves a comprehensive understanding of the electric drive system's operating status. During operation, the motor controller continuously outputs key parameters such as motor current, power, and IGBT temperature. These parameters not only reflect the current load level but also indirectly characterize changes in heat intensity, providing crucial information for thermal trend analysis. Simultaneously, the motor controller adjusts the motor's operating status according to control commands, ensuring consistency between thermal management control and power output.

[0054] Temperature sensors, as the direct source of thermal state perception, play a fundamental supporting role in the system. By continuously collecting data on the temperature of the motor windings and the gear oil in the reducer, they can reflect the temperature changes of different heat sources in real time. This temperature data, after being transmitted to the vehicle controller, forms the input variables for thermal model calculations along with operating parameters such as current and power, thus improving the accuracy of thermal trend prediction. Simultaneously, temperature data is also used in the feedback control process; by comparing the actual temperature with the predicted results, dynamic evaluation of the control effect can be achieved.

[0055] The cooling actuator, acting as the control output actuator, plays a crucial regulatory role in the entire system. Through the coordinated operation of a variable-speed cooling fan and an electric water pump, the cooling capacity can be continuously varied according to control commands, allowing for more precise matching of heat load changes compared to traditional fixed-power methods. Fan speed adjustment primarily affects the air-side heat dissipation capacity, while water pump flow rate adjustment affects the heat exchange efficiency in the cooling circuit. Together, they effectively remove heat from the motor and reducer, thereby maintaining the system temperature within a reasonable range.

[0056] Auxiliary monitoring components provide crucial supplementary information for operating condition identification and trend prediction. Vehicle speed sensors reflect vehicle operating status and external heat dissipation conditions, while accelerometers identify dynamic vehicle behavior characteristics, such as rapid acceleration or frequent starts and stops typical of urban logistics conditions. The navigation module provides driving route and road condition information, such as the presence of continuous uphill or downhill sections. This information collectively forms the basis for judging future changes in operating conditions, enabling the vehicle controller not only to grasp the current state but also to predict upcoming operating environments.

[0057] Through the coordinated operation of the aforementioned components, the system forms a complete closed loop from data acquisition to control execution. While continuously receiving various input data, the vehicle controller dynamically calculates the thermal model and adjusts the cooling strategy based on the prediction results. The cooling execution components respond to commands, while temperature sensors continuously provide feedback, allowing the control process to be constantly corrected and optimized. This structure not only ensures the real-time performance and accuracy of the control process but also adapts to the complex and ever-changing operating conditions in urban logistics environments, achieving efficient and stable thermal management.

[0058] This invention demonstrates significant advantages in reducing peak temperatures in electric drive systems through a predictive active thermal management scheme. By anticipating high-heat operating conditions, cooling regulation is initiated before the temperature reaches the traditional control threshold, resulting in smoother temperature changes in the motor and reducer, thus suppressing rapid temperature rises at the source. Compared to passive control methods, this approach effectively reduces peak temperature levels, minimizes the impact of thermal shock on critical components, and reduces the risks of motor insulation aging, IGBT device damage, and reducer gear oil performance degradation. This overall improves the reliability and stability of the electric drive system, extends the service life of core components, and further reduces the probability of failure and maintenance costs during vehicle operation.

[0059] This invention effectively reduces cooling energy consumption by optimizing the cooling system's operating mode. By predicting heat load changes, the cooling actuators adopt a smooth adjustment method during operation, avoiding energy waste caused by frequent start-stop cycles and instantaneous high-power operation in traditional control. Cooling capacity is gradually increased during high-load phases and cooling power is promptly reduced during low-load or high-heat-dissipation phases, ensuring the cooling system always operates in a state that matches heat demand. This control method reduces ineffective energy consumption, lowers the overall vehicle power consumption level, and thus improves the range of urban logistics electric light trucks, providing a more stable energy guarantee for long-term operation.

[0060] This invention also offers significant advantages in ensuring the continuous performance of the electric drive system. Under high-intensity operating conditions such as continuous uphill climbing, frequent start-stop cycles, or prolonged traffic congestion, by initiating cooling in advance and dynamically adjusting cooling capacity, the system enters an effective heat dissipation state before the heat load increases, thereby preventing the temperature from exceeding the safe range. This control method effectively prevents power limiting caused by overheating, ensuring that the electric drive system maintains stable power output under complex operating conditions, improving the vehicle's continuous driving performance and transportation efficiency, and meeting the high-intensity operation requirements of urban logistics.

[0061] This invention utilizes a multi-heat-source collaborative control mechanism to achieve a rational allocation of cooling resources between the motor and the reducer, thereby optimizing overall energy efficiency. Under different heat load conditions, the cooling circuit is differentiated and adjusted according to the actual temperature and thermal change trends of each component, avoiding overcooling or localized overheating problems caused by a single control strategy. This refined resource allocation improves the utilization efficiency of the cooling system, making thermal management more precise and efficient. Furthermore, the solution has a simple structure, requires no additional complex hardware, can be directly adapted to existing electric drive systems, and can be dynamically adjusted according to different urban road conditions, demonstrating good adaptability and promotional value.

[0062] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A predictive thermal management method for an electric drive system of an electric light truck for urban logistics, characterized in that, Includes the following steps: The system acquires motor current, power, vehicle speed, acceleration, IGBT temperature, motor winding temperature, and reducer gear oil temperature, and simultaneously acquires road condition information to form a real-time data set that characterizes the operating conditions and thermal state of electric light trucks for urban logistics. Based on real-time data sets, and using a pre-built thermal model of the electric drive system, the temperature rise trend of the motor and reducer in the next 30-60 seconds is calculated, and the corresponding heat load change trend and operating condition prediction results are obtained. Based on the trend of heat load change and the prediction results of operating conditions, the cooling actuator is activated in advance and the cooling power is gradually adjusted when the high heat generation condition is predicted. When the low heat generation or high heat dissipation condition is predicted, the operating power of the cooling actuator is reduced in advance, thereby realizing predictive active cooling control of the motor and reducer. Based on the trends of motor winding temperature, reducer gear oil temperature, and thermal load changes, the motor cooling circuit and reducer cooling circuit are adjusted differently. When the motor thermal load is dominant, the motor cooling power is increased and the reducer cooling power is reduced. When both are under high thermal load, the corresponding cooling power is increased simultaneously to achieve multi-heat source coordinated thermal management of the electric drive system for urban logistics electric light trucks.

2. The predictive thermal management method for an electric drive system of an electric light truck for urban logistics according to claim 1, characterized in that, The operating condition characteristics of the electric drive system are processed by fusing multi-source operational information to form a unified data input structure. The steps are as follows: The system collects motor current and power signals, and simultaneously acquires vehicle speed and acceleration change data. It also collects IGBT temperature, motor winding temperature and reducer gear oil temperature, and performs initial filtering on various signals to remove abnormal fluctuations. It receives driving route information output by the navigation module, marks the slope changes, road segment types and driving directions in the route, and performs segmentation processing on the route data; Data from different sources are aligned according to a unified time base, and interpolation or resampling operations are performed to form a continuous and consistent data sequence from multiple sources. The data sequences are classified and reorganized to construct a comprehensive dataset containing operating status parameters, thermal status parameters, and path characteristic parameters.

3. The predictive thermal management method for an electric drive system of an electric light truck for urban logistics according to claim 1, characterized in that, The process of constructing a temperature rise trend analysis by introducing parameters related to heat generation and heat dissipation is as follows: The motor current and power information are converted into heat intensity parameters, and segmented calibration is performed for different load ranges. The vehicle speed data is divided into intervals and mapped to corresponding heat dissipation capacity parameters, while also considering the impact of vehicle speed change trends on heat dissipation conditions. The motor winding temperature and the gear oil temperature of the reducer are used as state variables to smooth temperature changes and eliminate the impact of short-term fluctuations. The calculation process inputs heating parameters, heat dissipation parameters, and temperature state variables to continuously predict temperature changes over the next 30-60 seconds and outputs temperature rise trend data.

4. A predictive thermal management method for an electric drive system of an electric light truck for urban logistics according to claim 1, characterized in that, A high-heat identification and response process was established based on the characteristics of changes in operating conditions. The steps are as follows: The driving path data is analyzed to extract continuous uphill sections and gradient change intervals, and the corresponding locations are marked. By jointly analyzing acceleration and vehicle speed signals, frequent start-stop, rapid acceleration, and load fluctuation states can be identified. The path characteristics and operational characteristics are combined to form a high-heat status indicator, and the duration of the indicator is recorded. The operating parameters of the cooling actuators are preset according to the high heat status indicator, and the fan speed and water pump flow are gradually adjusted according to the time sequence.

5. A predictive thermal management method for an electric drive system of an electric light truck for urban logistics according to claim 1, characterized in that, The low heat load identification process is constructed based on the characteristics of vehicle operating status changes, and the steps are as follows: The driving path is analyzed to identify downhill sections and areas where the gradient decreases, and the duration of these sections is recorded. Detect changes in vehicle speed and identify stable driving ranges and speed fluctuation ranges; The motor's operating status is analyzed to identify regenerative braking and low-load operating states, and corresponding feature parameters are extracted. By comprehensively processing path characteristics, vehicle speed characteristics, and operating status, a low heat load status indicator is generated, and the operating parameters of the cooling actuators are adjusted.

6. A predictive thermal management method for an electric drive system of an electric light truck for urban logistics according to claim 5, characterized in that, Based on the low heat load status identification, a refined cooling regulation control process is formed. The operating parameters of the cooling actuator are matched and allocated according to the slope change trend, vehicle speed change range and load characteristics corresponding to the motor operating status, and are continuously adjusted according to the change process of operating status to maintain the consistency of the thermal state changes of the motor and reducer.

7. A predictive thermal management method for an electric drive system of an electric light truck for urban logistics according to claim 1, characterized in that, The continuous adjustment process is formed by combining the operating characteristics of the actuators, and the steps are as follows: Receive cooling control commands and parse fan speed control parameters, and perform range verification and limit processing on the parameters; Receive cooling control commands and parse the electronic water pump flow control parameters, and process the flow parameters in segments; Establish a corresponding relationship curve based on the matching relationship between fan speed and water pump flow rate, and perform interpolation calculations; The fan speed and water pump flow rate are continuously adjusted according to changes in control parameters, so that both change synchronously with changes in input parameters.

8. A predictive thermal management method for an electric drive system of an electric light truck for urban logistics according to claim 1, characterized in that, To construct a differentiated regulation process based on the load distribution relationship between different heat sources, the steps are as follows: Acquire motor winding temperature and reducer gear oil temperature data and update them in real time; By comparing and analyzing the two types of temperature data, the temperature difference and rate of change are calculated to form parameters for heat load difference. The heat load difference parameter is converted into a cooling distribution coefficient, and the distribution coefficient is normalized. Adjust the operating parameters of the motor cooling circuit and the reducer cooling circuit according to the allocation coefficient to distribute the cooling capacity proportionally.

9. A predictive thermal management method for an electric drive system of an electric light truck for urban logistics according to claim 1, characterized in that, A correction and adjustment process is constructed based on the difference between temperature feedback and forecast information, and the steps are as follows: Collect data on the temperature changes of the motor and reducer after cooling is performed, and record the time series of these changes. The temperature change data is compared with the temperature rise trend prediction data, and the deviation change curve is calculated. The deviation curve is analyzed and processed to extract the deviation amount and its trend, and correction parameters are generated. The correction parameters are input into the cooling control process to continuously adjust the operating parameters of the cooling actuators.

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