Adaptive real-time control strategy for extended-range electric vehicles considering battery temperature maintenance and cabin thermal comfort in low-temperature environments

To ensure the cabin thermal comfort and battery temperature maintenance capability of Extended-range electric vehicles (EREVs) in low-temperature environments, the vehicle thermal management system (VTMS) plays a crucial role in providing the necessary heat. However, the energy consumption associated with cabin and battery heating is substantial, impacting the overall energy usage of EREVs. In response to these challenges, we propose a collaborative adaptive equivalent fuel consumption minimization strategy (A-ECMS) for EREVs, incorporating specific requirements for cabin and battery heating. To transform EREVs' fuel economy problem into a problem solved by convex optimization, we employ polynomial fitting and variable transformation, altering the differential state equation. The solution to this problem is obtained using an analytical method based on quadratic programming, getting the double Lagrange multiplier for predicting. We subsequently present a collaborative AECMS that is designed to optimally real-time control EREVs in real-time. Our results indicate that the proposed strategy outperforms traditional A-ECMS in terms of fuel economy, with a maximum improvement of nearly 11%. Compared to A-ECMS solved using numerical methods, the average efficiency has increased by 40-50 times. Compared with traditional A-ECMS and A-ECMS using a numerical method, the proposed strategy has advantages in fuel efficiency and real-time performance.