Regenerative braking-based hierarchical model predictive cabin thermal management for battery life extension of autonomous electric vehicles

Journal article, 2022

This paper conducts a regenerative braking-based cabin thermal management by developing a hierarchical model predictive control (MPC) strategy. At the higher layer, an MPC controller is developed for optimally planning the vehicle speed. The lower layer implements cabin thermal management based on the planned regenerative braking behavior from the upper layer, where the recuperative energy can be used to directly power the air conditioning (AC) system. The state-space equation of a re-constructed power flow of vehicle is constructed, and based on which, an optimization problem is formulated to minimize energy consumption for battery aging-conscious control. Experiments are conducted to verify the performance of the hierarchical MPC strategy. Simulation results show that the vehicle drives in adaptation to the road topographies, the traffic flow and traffic signals in an energy-efficient way. The shifts of AC power load and cabin thermal load are caused so that the recuperative energy is fully used and the battery charging/discharging (aging) is minimized. Compared to the benchmark method, the energy consumption is reduced by 3.38% and the battery cycling aging is reduced by up to 29.15%.