Hierarchical Model Predictive Control for Optimization of Vehicle Speed and Battery Thermal Using Vehicle Connectivity

Abstract

Extreme ambient temperatures cause electric vehicles’ batteries to deteriorate and have a major impact on driving range, which is a barrier for mass production of electric vehicles. Recently, much research on the optimized temperature of the battery and energy management of an electric vehicle has been conducted, but the vehicle-level optimization (i.e., vehicle speed and position optimizations) has not yet been considered together. This paper proposes a hierarchical model predictive control structure for vehicle-level and electric powertrain-level optimizations simultaneously. Specifically, using vehicle communication technologies to forecast future traffic, the required vehicle traction power coupled with battery dynamics can be predicted, and this predicted traction power is used when designing the thermal control of the battery. Furthermore, the computationally tractable control could be designed for real-time application through decoupling the vehicle and battery dynamics. The simulation results under highway and urban driving conditions show the efficacy of our approach by comparing the battery energy consumption with that of the baseline methodology, i.e., conventional control.

Keywords

• Battery electric vehicle
• battery thermal management
• connected and automated vehicle
• model predictive control
• energy-efficient driving