PSA-Optimized Compressor Speed Control Strategy of Electric Vehicle Thermal Management Systems

The thermal management system (TMS) of electric vehicles (EVs) plays a pivotal role in vehicle performance, driving range, battery lifespan, and passenger comfort. Precise control of compressor speed, informed by real-time sensor data, is essential for improving TMS efficiency and extending EV range. This study proposes a control strategy based on the PID Search Algorithm (PSA), ensuring optimal thermal management for an integrated battery and cabin TMS. A co-simulation platform combining AMESim and Simulink is developed for validation, utilizing various sensors to monitor system performance. Simulations are conducted under target temperatures of 20 °C and 25 °C to replicate various operating conditions. The optimized strategy is compared with the most commonly used PID controllers, fuzzy controllers, and PID fuzzy control strategies. The results demonstrate that the PSA-Optimized control strategy significantly outperforms the other three strategies. For a target of 25 °C, the PSA-Optimized control strategy shows a minimal temperature overshoot of 0.012 °C, with COP improvements of 0.06, 0.04, and 0.03 compared to the other three control strategies, respectively. For a target of 20 °C, the overshoot is further reduced to 0.010 °C, while the coefficient of performance (COP) increases by 0.14, 0.01, and 0.07 relative to the same benchmarks. Overall, the results indicate that the PSA-Optimized control strategy effectively utilizes sensor data to reduce cabin temperature overshoot, stabilize compressor speed fluctuations, slow the decay of the battery’s state of charge (SOC), and enhance the system’s COP.