Connected and automated hybrid electric vehicles (CAHEVs) are a potential solution to the future transportation due to their improved fuel economy, reduced emissions, and capability to mitigate congestion and improve safety. The battery thermal management (BTM) in CAHEVs is one of the crucial problems, because the lithium-ion batteries are highly temperature sensitive. Therefore, a practical and energy-efficient BTM strategy is required for both improving the operating temperature of batteries and saving energy. In this study, the dynamic programming (DP) is implemented for a BTM system in CAHEVs for achieving the optimal cooling/heating energy savings for batteries. To enhance the real-time capability, an iterative approach is proposed to approximate the optimum control strategy iteratively in a multidimensional search space. The proposed iterative DP strategy can improve the system performance and energy-efficiency by fully exploiting the future road information in CAHEVs combined with a model predictive control method. The hardware-in-the-loop validation of the proposed strategy is conducted on the UDDS and the WLTC drive cycles based on a Toyota Prius PHEV model. The results demonstrate the feasibility and effectiveness of the proposed BTM strategy that leads to a considerable BTM energy reduction.
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Aerospace Engineering
- Electrical and Electronic Engineering
- Applied Mathematics