In the context of global efforts toward carbon neutrality and the rapid evolution of new energy vehicle (NEV) technologies, NEVs have emerged as a pivotal direction in the automotive industry due to their superior fuel economy and comprehensive performance. Among the various subsystems, the air conditioning (AC) system stands out as a major consumer of electrical energy, significantly impacting vehicle range and energy调度. Concurrently, the battery management system (BMS), particularly its thermal management component, is crucial for maintaining the thermal stability and operational efficiency of the battery, motor, and engine. The interplay between the AC system and the battery thermal management system involves both thermal energy exchange and competition for cooling resources, making their coordinated control strategies and energy path configuration decisive for overall vehicle thermal efficiency. As vehicle functional integration deepens, the coupling between these systems intensifies, elevating the coordinated调度 of multi-system thermal energy to a critical research frontier for enhancing NEV thermal efficiency and performance.
From a first-person research perspective, this study addresses the synergistic optimization of the AC system and the battery thermal management system in NEVs. We aim to develop integrated strategies that improve energy utilization, response adaptability, and system stability. The battery management system (BMS) plays a central role in this endeavor, as it monitors and regulates battery temperature, directly influencing both safety and efficiency. Through this work, we seek to contribute methodologies that bridge gaps in current thermal management practices, leveraging advanced control frameworks and heat pump technologies to achieve holistic optimization.
The协同operation原理 between the AC system and the battery thermal management system in NEVs is rooted in their inherent energy flow and control logic couplings. The AC system primarily relies on an electric compressor for cooling, whose operational demands directly increase the electrical load on the battery, thereby exacerbating battery temperature rise. On the other hand, the battery thermal management system, often integrated within the broader BMS, is responsible for temperature control across multiple loops encompassing the engine, motor, and battery pack. This system typically调节 coolant temperatures within a range of 60–90°C, with thermal loads exhibiting significant dynamic variations. In practice, the electric compressor’s operation adds to the battery’s thermal burden, necessitating prompt responses from the power system cooling loop to adjust heat dissipation. Conversely, waste heat generated by the engine during operation can be redirected to the AC system’s heating环节, reducing reliance on electrical heating elements like PTC (Positive Temperature Coefficient) heaters. This dynamic thermal balance enables整车energy optimization, but it requires precise coordination to avoid conflicts and inefficiencies.
Despite the potential benefits, several persistent issues hinder the协同operation of these systems. First, low thermal energy utilization efficiency is a major concern. In many NEVs, the AC system’s heating function relies heavily on PTC heating, which consumes substantial electrical power. When the engine is running, its coolant contains abundant waste heat, but the lack of an effective thermal energy共享path between the battery thermal management system and the AC system prevents real-time transfer of this heat to the cabin for供暖. This results in significant wastage of thermal resources. Moreover, heat generated during the cooling process of the battery thermal management system is not utilized to compensate for AC loads, indicating a lack of耦合mechanisms in energy flow that limits overall thermal efficiency.
Second, there is a lack of coordination in control systems. The AC system and the battery thermal management system often operate under independent controllers, with no unification in information acquisition, decision logic, or execution strategies. For instance, when the electric compressor is activated for cooling and battery temperature rises simultaneously, both systems may concurrently request cooling resources, leading to conflicts in coolant flow and散热capacity调度. Without clear control priorities, such situations cause response delays or control mismatches, impairing the real-time performance and stability of整车thermal management and reducing system coordination levels.
Third, poor adaptability to varying operating conditions is evident. In real-world driving, NEVs frequently encounter non-steady-state conditions, such as low-temperature cold starts, high-temperature traffic congestion, or rapid acceleration. Existing systems lack unified planning in response mechanisms to load changes, making it difficult to conduct joint judgments and predictions of thermal demands. Consequently, thermal control strategies often respond滞后ly and struggle to maintain thermal balance, compromising both comfort and efficiency.
To overcome these challenges, we propose a series of协同optimization strategies centered on enhancing the integration between the AC system and the battery thermal management system. These strategies focus on thermal energy sharing, integrated control调度, and adaptive multi-condition operation, all while emphasizing the critical role of the battery management system (BMS) in monitoring and regulation.
Thermal Energy Sharing Path Construction Based on Heat Integration
The design of a thermal energy sharing path involves dynamic调度of heat flow between engine waste heat and the AC heating system. Using coolant as the heat conduction medium, we establish a heat exchange branch between the engine thermal management loop and the AC system. A three-way electro-controlled valve is implemented to regulate coolant direction, enabling分配and switching of waste heat among different demand modules. Structurally, the engine cooling loop is connected to a high-efficiency plate heat exchanger, with one side linked to the engine outlet hot coolant and the other to the AC heating circuit. In AC heating mode, the three-way valve opens the heat exchange path, transferring heat from the engine coolant to the AC循环water. In priority modes where battery system temperature control takes precedence, the valve closes the thermal branch, redirecting coolant primarily to the radiator or battery cooling unit.
The heat exchange power is calculated using the following formula:
$$ Q = k \times A \times \Delta T_m $$
where \( Q \) is the heat transfer rate, \( k \) is the heat transfer coefficient, \( A \) is the heat exchange area, and \( \Delta T_m \) is the logarithmic mean temperature difference between the exchange ends.
To ensure dynamic response capability in heat分配, the coolant pump speed is regulated by the control system based on feedback from temperature change rates. A proportional-integral (PI) control strategy is employed to maintain stable heat exchange efficiency and minimize temperature differential fluctuations during cold starts or high thermal loads. In urban driving cycles, the control system sets the engine coolant outlet temperature to remain at \( (85 \pm 2)^{\circ}C \), maintaining a temperature difference of 5–15°C with the AC return water inlet to guarantee heat exchange efficiency exceeding 70%.
Integrated Control Thermal Management调度System Design
The调度system design centers on an integrated control architecture to coordinate thermal control strategies between the AC system and the battery thermal management system. A thermal management domain controller is mounted under the整车controller, consolidating data exchange among the AC control unit, battery thermal management control unit (often part of the BMS), and engine control unit onto a thermal management CAN bus. This enables synchronous sharing of key parameters such as thermal demands, temperature states, and power requests.
Subsystem operating states are uniformly modeled using state machines. The AC system regulates variables like electric compressor frequency and condenser heat exchange rate, while the battery thermal management system adjusts variables such as battery coolant pump speed, engine thermal valve opening, and heat exchanger fan speed. The control model adopts a discrete time-domain finite-state control strategy, prioritizing actions based on thermal load change rates and allocating execution paths according to system weights. The control logic is formulated as:
$$ u(t) = \arg \min \left[ \alpha_1 \times J_{\text{thermal}}(u) + \alpha_2 \times J_{\text{power}}(u) \right] $$
where \( u(t) \) represents the control input or strategy at time \( t \) (e.g., coolant pump speed, fan speed, water valve opening), optimized within the feasible domain \( U \) that satisfies system constraints (e.g., maximum/minimum power, temperature limits). \( J_{\text{thermal}} \) is the temperature deviation cost function, \( J_{\text{power}} \) is the system energy consumption cost function, and \( \alpha_1 \), \( \alpha_2 \) are weighting factors used to dynamically adjust the调度priority between the AC and thermal management systems.
Coolant pumps, water valves, and fans operate under a centralized control speed modulation mode, driven by current thermal loads from the battery and compressor. The system periodically refreshes感知layer information and predicts thermal load trends for the next 30–60 seconds.感知parameters include compressor current, battery temperature gradient, and engine instantaneous load. These predictions are fed back to the整车controller, prompting actuators to enter预备states提前, thereby reducing adjustment delays.
In execution mechanisms, temperature requests from the AC system are first subjected to path judgment by the thermal management domain controller. If the system is under high thermal load, the调度system优先activates load peak-shaving mechanisms, such as reducing compressor operating frequency, increasing coolant pump rates, or switching the engine cooling branch to a bypass path to release thermal capacity redundancy.
Multi-Condition Adaptive Operation Strategy Based on Heat Pump Integration
The heat pump system serves as a bidirectional heating and cooling device, establishing a thermal energy bridge between the AC system and the battery thermal management system through refrigerant flow direction switching and heat exchange component layout. System configuration includes an electric compressor, a four-way reversing valve, evaporators, condensers, and electronic expansion valves, with heat exchange nodes installed in both the power battery cooling circuit and the cabin heating pipeline to support dynamic switching among cooling, heating, and hybrid modes.
The adaptive strategy employs a multi-parameter-driven logic controller as its core. Control inputs encompass external ambient temperature, vehicle speed, battery state of charge (SOC), and estimated thermal load, while outputs include key execution variables such as heat pump operating mode, compressor power, expansion valve opening, and fan speed. The control process first identifies the current condition category to determine the entry into cooling, heating, or hybrid mode, then allocates thermal energy paths and control resources based on specific thermal management needs.
In heating mode, the heat pump evaporator is positioned in the external environment to absorb heat, while the condenser is connected to the cabin heat exchanger to release heat. When the engine is operational and coolant temperature is within a stable range, the control system switches to hybrid mode, introducing engine waste heat into the heat pump circuit to achieve superimposed heating. In cooling mode, the heat pump evaporator and cabin heat exchanger operate协同ly, and the battery cooling channel can be并联connected to the condenser端, enabling dual-output of cooling energy.
The control strategy is executed based on state prediction logic. For example, before the vehicle enters a high-temperature area, the controller increases condenser fan speed to enhance heat exchange capacity and optimizes expansion valve opening to reduce system back pressure. Prior to entering urban congestion or low-speed states, the system lowers compressor frequency, entering intermittent operation mode to save electrical consumption. Target temperatures and power levels in each mode are dynamically adjusted by the整车controller according to battery thermal state and passenger comfort priorities. The control equation centers on temperature闭环regulation, with compressor frequency calculated as:
$$ f_{\text{comp}} = f_{\text{base}} + K_p (T_{\text{set}} – T_{\text{real}}) $$
where \( f_{\text{comp}} \) is the compressor frequency, \( f_{\text{base}} \) is the base operating frequency, \( T_{\text{set}} \) is the target cabin temperature, \( T_{\text{real}} \) is the actual measured temperature, and \( K_p \) is the proportional adjustment coefficient. When battery SOC is low, ambient temperatures are extreme, or system thermal load exceeds limits, the controller gradually reduces the compressor power proportion, activating PTC auxiliary heating or coolant pump bypass pathways to alleviate compressor burden.
Performance Validation of Cooperative Optimization
The validation of the协同optimization strategy combines joint simulation analysis with condition comparison tests. The simulation platform is built on AMESim to establish a multi-physics thermal-fluid-control model of the整车thermal management system, coupled with整车control logic developed in Simulink. The model encompasses the electric compressor, PTC heater, engine cooling loop, battery thermal management system, and heat pump circuit. The control strategy integrates thermal energy共享调度mechanisms, integrated thermal load controllers, and multi-condition prediction switching logic, with separate structures for “pre-optimization” and “post-optimization” thermal control strategies. The simulation cycle is set to 200 ms.
Test conditions are selected to simulate scenarios such as low-temperature cold start (-10°C), urban traffic congestion, high-speed operation, and dynamic switching, representing北方winter morning starts, suburban commuting cycles, and high-speed driving phases. During testing, metrics like time to achieve target cabin temperature, electric heater power, engine waste heat utilization rate, battery temperature control accuracy, and electric compressor power variation curves are recorded to assess system coordination and responsiveness under different strategies. Initial SOC is set at 70%, with initial coolant and battery temperatures aligned with ambient temperature to ensure data comparability and reproducibility.
The performance comparison analysis focuses on four dimensions: thermal energy utilization efficiency, system response speed, energy consumption performance, and协同control capability among multiple systems. Simulation results, summarized in Table 1, indicate that after implementing the协同optimization strategy, waste heat from the engine coolant is effectively directed to the AC heating module. The activation time of the electric heater is delayed by approximately 120 seconds, with peak power reduced by 32%. The time required for cabin temperature to rise to the target value of 22°C shortens from 11.5 minutes to 8.2 minutes, significantly improving passenger comfort. Compressor operating frequency fluctuations decrease, indicating more stable thermal load调度.
Control system response delays are markedly reduced. Under rapid condition switching, the system can predict cooling needs提前and execute pre-adjustment actions, effectively lowering the frequency of thermal mismatch occurrences. The heat pump operating mode automatically switches based on load predictions in high-speed and urban low-speed conditions, with battery temperature control偏差confined to ±2.5°C, meeting thermal safety requirements. Overall, the system’s electrical energy consumption per unit thermal output decreases by approximately 11%, demonstrating high levels of system integration and energy协同utilization. These data validate that the thermal control system based on协同optimization strategies can effectively enhance vehicle operating performance across multiple conditions, improving the adaptability and economy of整车thermal energy调度, and holds strong potential for engineering推广.
| Performance Indicator | Before Cooperative Optimization | After Cooperative Optimization |
|---|---|---|
| Time for Cabin Temperature to Reach 22°C (minutes) | 11.5 | 8.2 |
| Maximum Electric Heater Power (kW) | 5.1 | 3.5 |
| Engine Waste Heat Utilization Rate (%) | 45 | 67 |
| Battery Temperature Control Deviation Range (°C) | ±4.3 | ±2.5 |
| Compressor Operating Frequency Fluctuation Range (Hz) | 25–60 | 35–50 |
| Total Electrical Energy Consumption (kWh/100 km) | 14.2 | 12.6 |
To further elucidate the role of the battery management system (BMS) in this context, we emphasize that the BMS is integral to monitoring battery thermal states and providing critical inputs for control decisions. In our optimized framework, the BMS continuously tracks parameters like temperature gradients and SOC, feeding them into the integrated调度system. This enables precise thermal regulation, preventing overheating or overcooling of the battery pack, which is essential for longevity and safety. The协同between the BMS and AC system ensures that thermal loads are balanced, reducing strain on the battery and enhancing overall efficiency. For instance, during high-demand scenarios, the BMS can signal the control system to prioritize battery cooling, temporarily adjusting AC operation to maintain optimal temperatures. This dynamic interplay highlights the importance of a robust BMS in achieving effective thermal management协同.
Moreover, the mathematical formulations underpinning our strategies reinforce the synergy. Consider the heat transfer dynamics in the shared path: the efficiency of waste heat recovery is governed by the heat exchanger performance, which we model using the logarithmic mean temperature difference method. Similarly, the control optimization in the integrated调度system minimizes a composite cost function that incorporates both thermal and energy penalties, as expressed in earlier equations. These formulations are solved in real-time by the domain controller, leveraging BMS data to adapt to changing conditions. For example, the weighting factors \( \alpha_1 \) and \( \alpha_2 \) in the control logic can be tuned based on BMS alerts regarding battery thermal stress, ensuring that the system responds appropriately to preserve battery health while maintaining cabin comfort.
In terms of practical implementation, the integration of a heat pump with the battery thermal management system offers substantial benefits. The heat pump’s ability to transfer heat bidirectionally allows it to serve both cabin conditioning and battery temperature control needs. When the battery requires cooling, the heat pump can operate in cooling mode, with the evaporator absorbing heat from the battery coolant loop and rejecting it to the environment. Conversely, in cold conditions, the heat pump can extract heat from the outside air or engine waste heat to warm both the cabin and the battery, reducing reliance on energy-intensive PTC heaters. This dual functionality is managed through the adaptive strategy described earlier, where the BMS provides inputs on battery temperature and SOC to determine the optimal operating mode. Such integration not only improves energy efficiency but also enhances the responsiveness of the thermal management system to transient conditions, a key advantage in real-world driving scenarios.
The validation results underscore the tangible improvements achieved through our协同optimization approach. Beyond the quantitative metrics in Table 1, we observed enhanced stability in battery temperature profiles during dynamic cycles. For instance, in urban congestion simulations, the optimized system maintained battery temperatures within a narrower band compared to the baseline, reducing thermal cycling stress on the battery cells. This is attributed to the predictive capabilities of the调度system, which uses BMS data to anticipate thermal loads and pre-adjust cooling or heating outputs. Additionally, the reduction in total energy consumption translates directly to extended driving range, a critical factor for NEV adoption. We estimate that the 11% decrease in unit energy consumption could lead to a proportionate increase in range under typical operating conditions, although actual gains may vary with driving patterns and environmental factors.
Looking ahead, the principles developed in this study can be extended to more complex thermal management architectures involving additional components like fuel cells or advanced energy storage systems. The core idea of integrating control across multiple thermal domains remains applicable, with the BMS continuing to play a pivotal role in coordinating thermal flows. Future work could explore machine learning algorithms to further refine the predictive models, using historical BMS data to improve accuracy in load forecasting. Moreover, as vehicle-to-grid (V2G) technologies evolve, the thermal management system could interact with external energy networks, optimizing not only onboard energy use but also grid integration potentials. In such scenarios, the BMS would need to manage thermal constraints while accommodating bidirectional energy flows, adding another layer of complexity to the协同optimization challenge.
In conclusion, this research addresses the协同optimization of the AC system and the battery thermal management system in NEVs through the construction of thermal energy sharing paths, design of integrated control调度systems, and development of multi-condition adaptive strategies based on heat pump integration. The battery management system (BMS) is central to this effort, providing essential data and control interfaces for seamless coordination. Simulation and experimental validations confirm that our approach significantly improves thermal energy utilization, reduces electrical consumption, accelerates cabin temperature response, and enhances battery thermal stability. These outcomes offer methodological support for the一体化design of complex thermal management systems and lay a foundation for advancing the energy efficiency and thermal robustness of new energy vehicles. As NEV technologies continue to progress, we believe that such协同optimization frameworks will become increasingly vital in achieving sustainable and high-performance automotive solutions.