Optimal Thermal Management, Charging, and Eco-driving of Battery Electric Vehicles

TL;DR

This paper presents an optimization framework for battery electric vehicles that balances thermal management, charging, and eco-driving to enhance energy efficiency and reduce trip time, using a hybrid dynamical system model.

Contribution

It introduces a novel hybrid dynamical system approach with spatial and temporal modeling for driving and charging dynamics, improving computational efficiency and trip time reduction.

Findings

Trip time reduced by 44% with the proposed method.

Hybrid dynamical system modeling improves computational efficiency.

Optimal charging and thermal management enhance energy efficiency.

Abstract

This paper addresses optimal battery thermal management (BTM), charging, and eco-driving of a battery electric vehicle (BEV) with the goal of improving its grid-to-meter energy efficiency. Thus, an optimisation problem is formulated, aiming at finding the optimal trade-off between trip time and charging cost. The formulated problem is then transformed into a hybrid dynamical system, where the dynamics in driving and charging modes are modeled with different functions and with different state and control vectors. Moreover, to improve computational efficiency, we propose modelling the driving dynamics in a spatial domain, where decisions are made along the traveled distance. Charging dynamics are modeled in a temporal domain, where decisions are made along a normalized charging time. The actual charging time is modeled as a scalar variable that is optimized simultaneously with the optimal state and control trajectories, for both charging and driving modes. The performance of the proposed algorithm is assessed over a road with a hilly terrain, where two charging possibilities are considered along the driving route. According to the results, trip time including driving and charging times, is reduced by 44 %, compared to a case without battery active heating/cooling.