A Finite-Set Model-Based Predictive Battery Thermal Management in Connected and Automated Hybrid Electric Vehicles

The connected and automated hybrid electric vehicles (CAHEVs) have the potential to improve safety by mitigating traffic accidents. A crucial problem of the CAHEVs is that the Lithium-ion batteries are highly temperature-sensitive and may be premature aging at high working temperatures. Consequently, an effective and efficient battery thermal management (BTM) system is required with the minimum possible cooling energy consumption. To achieve the multiple objectives, a finite-set model-based (FSMB) predictive control strategy for the BTM in a CAHEV is presented, in which an improved cost function is proposed for better performances. Based on the predictive model of battery temperatures, the optimum cooling approach is determined with consideration of the future road information and battery charge/discharge power. The hardware-in-the-loop (HIL) test based on a Toyota Prius HEV model and the UDDS road cycle is conducted, and the results demonstrate the effectiveness of the proposed BTM strategy in both temperature control and energy saving.