As advancements in new energy vehicle technology and the implementation of environmental policies drive the adoption of pure electric vehicles, these vehicles have become an increasingly popular choice among consumers. However, during the operation of pure electric vehicles, core components such as the battery and motor generate substantial heat, particularly under high-load or extreme environmental conditions. Accumulated heat can adversely affect vehicle performance, safety, and range. Therefore, efficient thermal management and system stability in pure electric vehicles have emerged as critical research topics. In this paper, we delve into the control strategies for the thermal management system of pure electric vehicles, addressing the complexity of thermal demands under various operating conditions. We model the thermal management system, including sub-models for the battery, drive motor, and air conditioning system, and propose strategies with different control objectives to enhance system efficiency and stability, thereby significantly improving overall vehicle performance.
The thermal management system of a pure electric vehicle is highly integrated and complex, requiring real-time balance adjustment among multiple heat sources and thermal loads. We begin by constructing a comprehensive model based on thermal balance principles, focusing on the thermal loads of the battery, motor, and air conditioning system to ensure that subsystem temperatures fluctuate within reasonable ranges. The overall system model is derived from the heat balance equation:
$$Q_{total} = C \times \frac{dT}{dt} + h \times A \times (T_{env} – T)$$
Here, \(Q_{total}\) represents the total heat within the system, \(C\) is the equivalent thermal capacity, \(T\) is the current temperature, \(t\) is time, \(\frac{dT}{dt}\) is the rate of temperature change over time, indicating how quickly the system temperature changes, \(T_{env}\) is the environmental temperature, \(h\) is the heat transfer coefficient, and \(A\) is the heat dissipation surface area. This equation describes the processes of heat generation, storage, and dissipation. The thermal management system comprises multiple subsystems, including the battery thermal management subsystem, drive motor cooling subsystem, air conditioning system, coolant circulation system, and heat pump system. Each subsystem has specific functions, and their coordinated operation is essential to optimize the overall thermal management efficiency of the vehicle.
In building the subsystem models, we focus on the battery, drive motor, and air conditioning system. The battery generates significant heat during charging and discharging, especially under high-load states. The Joule heat from the battery, \(Q_{battery}\), can be expressed as:
$$Q_{battery} = I^2 \times R_{internal}$$
where \(I\) is the battery operating current and \(R_{internal}\) is the internal resistance. To maintain the battery temperature within the optimal operating range, the battery cooling system must adjust cooling intensity based on current and internal resistance. This forms the theoretical basis for designing the battery cooling system, which is often integrated with the battery management system (BMS) for precise control. The BMS monitors parameters like temperature and current, enabling dynamic adjustments to cooling strategies.
For the drive motor system, heat generated from Joule heating in windings and rotational friction causes a notable temperature rise. The thermal management model for the motor focuses on heat dissipation from the core winding parts:
$$Q_{motor} = h_{motor} \times A_{motor} \times (T_{motor} – T_{coolant})$$
where \(T_{motor}\) and \(T_{coolant}\) are the temperatures of the motor winding and coolant, respectively, \(h_{motor}\) is the heat transfer coefficient between the motor and coolant, and \(A_{motor}\) is the cooling surface area. By controlling coolant flow rate and temperature, motor stability under high loads can be ensured.
The air conditioning system model primarily regulates cabin temperature. Its cooling or heating capacity is based on refrigerant cycles, calculated as:
$$Q_{AC} = m_{refrigerant} \times c_{refrigerant} \times (T_{evaporator} – T_{condenser})$$
Here, \(m_{refrigerant}\) is the refrigerant flow rate, \(c_{refrigerant}\) is the specific heat capacity, and \(T_{evaporator}\) and \(T_{condenser}\) are the evaporator and condenser temperatures, respectively. This guides adjustments in system power and cycle rates to meet cabin comfort needs.
The establishment of these models allows for effective prediction and evaluation of the thermal management system’s performance under various conditions, providing a solid theoretical foundation for designing and implementing control strategies.
However, several challenges persist in the thermal management of pure electric vehicles. For the battery, thermal management issues arise due to significant heat generation under different loads and environmental temperatures. Test data indicate that battery temperature changes markedly in both low and high temperature environments, limiting charging and discharging efficiency. For instance, in temperatures ranging from -10°C to 0°C, battery discharge capacity can decrease by over 20%, while at temperatures above 50°C, performance declines significantly, with risks of thermal runaway due to overheating. The core of battery thermal management lies in stabilizing temperature within the optimal range of 20°C to 30°C to ensure efficiency and lifespan. Current load also greatly impacts battery temperature; at a 2C discharge rate, temperature rises rapidly, exceeding 50°C and surpassing the safe range, necessitating quick cooling by the thermal management system. This highlights the need for real-time adjustments in temperature control devices and cooling systems based on current load and environmental temperature to effectively dissipate heat and avoid performance degradation and safety hazards. In low-temperature conditions, battery heating is required to maintain discharge performance, underscoring the importance of an adaptive battery management system (BMS).
For the drive motor, thermal management problems occur under high-load or prolonged operation, with heat concentrated in windings, stators, and rotors. Rapid temperature increases can reduce motor efficiency, cause aging of winding insulation materials, demagnetize magnetic materials, and trigger overheating protection leading to power reduction. When motor operating temperature is below 85°C, efficiency remains high and stable; as temperature approaches or exceeds 120°C, efficiency drops significantly, and further increases accelerate insulation aging, shortening motor lifespan. The Curie temperature of magnetic materials directly affects output power; beyond a critical point, magnetic properties weaken, reducing torque output and causing power drops. Specific test data are summarized in Table 1.
| Motor Temperature / °C | Motor Efficiency / % | Winding Insulation Aging Rate (Relative Value) | Magnetic Material Remanence (Relative Value) |
|---|---|---|---|
| 85 | 97 | 1 | 100 |
| 100 | 93 | 1.5 | 98 |
| 110 | 90 | 2 | 96 |
| 120 | 85 | 3 | 92 |
| 130 | 80 | 5 | 85 |
Data in Table 1 show that above 100°C, motor efficiency declines noticeably, and winding insulation aging accelerates. Aging affects long-term performance and lifespan, with rates increasing rapidly above 120°C, potentially causing insulation failure and short circuits. Magnetic material remanence decreases with rising temperature, leading to unstable torque output under high loads. This cumulative loss can impair overall motor performance over time. In the thermal management system, temperature sensors play a key role in real-time monitoring, providing dynamic feedback for rapid adjustment of cooling parameters in response to load and environmental changes. Flow regulators and radiators, through intelligent control algorithms, automatically adjust flow and coolant temperature to dissipate heat promptly during increased thermal loads and avoid overcooling under low loads, enhancing system energy efficiency.
For the air conditioning system and cabin temperature control, issues relate to both occupant comfort and vehicle energy consumption. In extreme temperatures, air conditioning load rises significantly, impacting the range of pure electric vehicles. For example, when external temperature exceeds 35°C, the air conditioning system may consume over 30% of battery energy to maintain a comfortable cabin temperature of 22°C to 26°C. In low-temperature environments below -10°C, heating demand increases, leading to higher energy consumption. This affects overall energy efficiency and causes additional battery drain. Table 2 summarizes cabin temperature and air conditioning energy consumption ratios under different environmental temperatures and modes.
| Environmental Temperature / °C | Air Conditioning Mode | Cabin Temperature / °C | Air Conditioning Energy Consumption Ratio / % |
|---|---|---|---|
| -10 | Heating | 22 | 25 |
| 0 | Heating | 23 | 20 |
| 25 | Off | 26 | 0 |
| 35 | Cooling | 24 | 30 |
| 40 | Cooling | 26 | 35 |
Thus, air conditioning control must balance comfort and energy efficiency, especially given the energy constraints of pure electric vehicles.
To address these challenges, we propose specific control strategies for the thermal management system. For battery temperature control, the strategy aims to maintain the optimal range of 20°C to 30°C by rapidly dissipating heat in high temperatures and providing moderate heating in low temperatures. Liquid cooling is commonly used, with control over coolant flow rate and temperature to manage heat dissipation. The cooling system’s core function is to maintain dynamic thermal balance, with heat dissipation controlled by:
$$Q_{cooling} = m_{coolant} \times c_{coolant} \times (T_{coolantin} – T_{coolantout})$$
where \(m_{coolant}\) is the coolant flow rate, \(c_{coolant}\) is the specific heat capacity of the coolant, and \(T_{coolantin}\) and \(T_{coolantout}\) are the inlet and outlet temperatures of the coolant, respectively. When battery temperature rises, the system increases coolant flow or lowers coolant temperature to dissipate heat; when temperature is too low, flow decreases and temperature rises slightly to avoid excessive cooling. An intelligent temperature control system, often integrated with the battery management system (BMS), uses sensor feedback for precise adjustments. The BMS continuously monitors battery temperature and adjusts cooling parameters in real-time, ensuring safety and efficiency. For instance, the BMS can predict thermal behavior based on current and voltage data, enabling proactive cooling or heating. This enhances the overall reliability of the battery management system, making it a cornerstone of vehicle thermal management.
For drive motor temperature control, the strategy focuses on keeping winding and magnetic material temperatures within reasonable limits to ensure efficient operation and longevity. Liquid cooling is again primary, with adjustments to coolant flow and temperature based on motor load. When load is high and temperature rises quickly, coolant flow increases and temperature decreases to dissipate heat rapidly. To address rapid temperature fluctuations, intelligent control algorithms predict motor power output, allowing preemptive adjustments to coolant parameters and avoiding thermal accumulation. This predictive approach improves cooling efficiency and reduces energy consumption. For example, by analyzing driving patterns and ambient conditions, the system can anticipate high-load scenarios and ramp up cooling beforehand. This not only stabilizes motor performance but also extends component life, contributing to overall vehicle durability.
For air conditioning system optimization, the strategy balances comfort and energy consumption, maintaining cabin temperature between 22°C and 26°C while minimizing battery drain. Through real-time monitoring of indoor-outdoor temperature differences, the system dynamically adjusts power and airflow. To further reduce energy use, zoned cooling and intelligent control schemes are introduced. In high temperatures, cooling prioritizes the driver’s area, with other zones adjusted based on occupant presence and sensor feedback, avoiding unnecessary energy consumption. In low temperatures, optimization integrates seat heating and air conditioning heating for zoned warmth, reducing overall power output. Additionally, humidity control is optimized by lowering evaporator load pressure and improving system efficiency, reducing dehumidification time and adjusting airflow. Intelligent algorithms allow the air conditioning system to adapt to temperature and humidity changes in real-time, predict external conditions, and pre-adjust parameters, ensuring comfort with low energy use. In extreme environments, the optimized system maintains a comfortable cabin while alleviating battery stress, playing a key role in overall energy savings.
We also emphasize the integration of these strategies with the vehicle’s broader thermal management framework. For instance, the battery management system (BMS) can communicate with the motor and air conditioning controllers to coordinate cooling resources. This holistic approach prevents conflicts, such as simultaneous heating and cooling demands, and optimizes energy distribution. Moreover, advanced BMS features, like state-of-health monitoring, can inform thermal strategies to preemptively address degradation issues. By leveraging data from multiple sensors, the BMS enhances predictive capabilities, making the thermal management system more responsive and efficient. This synergy between subsystems is crucial for achieving the desired performance and safety outcomes.
To illustrate the effectiveness of these strategies, we can consider additional data points and scenarios. For example, under urban driving conditions with frequent stops and starts, the battery and motor experience variable loads, requiring agile thermal responses. A well-tuned BMS can modulate cooling based on real-time current profiles, preventing temperature spikes. Similarly, in highway driving with sustained high speeds, motor cooling becomes critical, and predictive algorithms can maintain optimal temperatures without overconsuming energy. These examples underscore the importance of adaptive control in diverse operating environments.
Furthermore, we explore the role of simulation and modeling in refining these strategies. Using the derived equations, we can simulate thermal behavior under various conditions to optimize control parameters. For instance, by varying \(h\) and \(A\) in the overall heat balance equation, we can assess different cooling designs. Similarly, battery heat generation models help in sizing cooling components and setting BMS thresholds. These simulations reduce development time and cost, enabling more robust thermal management systems. We also recommend incorporating machine learning techniques into the BMS for continuous improvement, allowing the system to learn from historical data and adapt to new patterns.
In conclusion, by addressing the thermal management needs of batteries, drive motors, and air conditioning systems in pure electric vehicles, we have constructed system models, analyzed key problems, and proposed control strategies. These strategies effectively maintain battery and motor temperatures while optimizing air conditioning energy consumption, thereby enhancing overall vehicle efficiency and stability. The integration of a sophisticated battery management system (BMS) is pivotal in achieving these goals, as it enables precise thermal regulation and coordination across subsystems. As technology evolves, further innovations in materials, control algorithms, and system integration will continue to improve thermal management, supporting the widespread adoption of pure electric vehicles. We believe that ongoing research in this area will yield even more efficient and reliable solutions, contributing to a sustainable automotive future.
To summarize the key formulas and their applications, we present the following list for quick reference:
- Overall heat balance: $$Q_{total} = C \times \frac{dT}{dt} + h \times A \times (T_{env} – T)$$
- Battery heat generation: $$Q_{battery} = I^2 \times R_{internal}$$
- Motor heat dissipation: $$Q_{motor} = h_{motor} \times A_{motor} \times (T_{motor} – T_{coolant})$$
- Air conditioning capacity: $$Q_{AC} = m_{refrigerant} \times c_{refrigerant} \times (T_{evaporator} – T_{condenser})$$
- Cooling system heat dissipation: $$Q_{cooling} = m_{coolant} \times c_{coolant} \times (T_{coolantin} – T_{coolantout})$$
These equations form the backbone of our modeling and control approach, enabling detailed analysis and optimization. Additionally, the tables provided offer empirical insights into temperature-dependent behaviors, guiding strategy development. Through iterative refinement and real-world testing, we aim to advance thermal management systems, ensuring they meet the demands of modern electric mobility while leveraging the full potential of the battery management system (BMS) for enhanced performance and safety.