Control strategy analysis of multistage speed compressor for vehicle air conditioning based on particle swarm optimization

For electric vehicles (EVs), there is an urgent need to reduce the energy consumption of the air conditioning (AC) system while ensuring the cabin's comfort. This paper proposes a particle swarm optimization-based multistage compressor speed (PSO-MCS) control strategy to reduce compressor energy consumption. The multistage compressor speed (MCS) allows the compressor speed to be as near to the high-efficiency cooling zone as possible, outputting different speeds in different temperature zones to achieve temperature control. The particle swarm optimization (PSO) algorithm is used to reduce the compressor energy consumption by searching for the optimal compressor speed. The AC system mathematical model based on PSO-MCS is built and compared with the conventional proportional-integral-differential (PID) control. The results show that PSO-MCS control can improve the coefficient of performance (COP) by 2.41% and 2.71%, respectively, compared to PID control at 35 °C and 40 °C. And PSO-MCS control can reduce 4.07% and 3.89% energy consumption, respectively.