Collaborative thermal management of power battery and passenger cabin for energy efficiency optimization

Collaborative thermal management is a promising approach for improving the energy efficiency of electric vehicles by optimizing both the battery and passenger cabin temperatures. This study proposes a novel collaborative thermal management system that addresses key performance aspects such as battery safety, cabin comfort, and system efficiency. The system utilizes a vehicle speed prediction-nonlinear model predictive control strategy (SP-NMPC) based on genetic algorithm. Experimental testing validates the effectiveness and accuracy of the proposed method. Temperature variations in the battery and passenger cabin are compared at ambient temperatures of 30℃ and 40℃ using the SP-NMPC and prototype original control strategy. The Mean Square Error (MSE) values are calculated and compared. Results show that the original control strategy yielded MSE values of 0.0722 and 0.3334 for the passenger cabin temperature at 30℃ and 40℃, respectively. In contrast, employing SP-NMPC under similar conditions achieved significantly lower MSE values of 0.0513 and 0.0521. This highlights the superior accuracy and resilience of SP-NMPC in maintaining passenger cabin temperature, reducing fluctuations, and enhancing passenger comfort. Moreover, the system achieves energy savings, reducing thermal management energy consumption by 5.59% and 5.20% at ambient temperatures of 30 °C and 40 °C, respectively, thereby extending the vehicle's range.