Two-Layer Model Predictive Battery Thermal and Energy Management Optimization for Connected and Automated Electric Vehicles

TL;DR

This paper introduces a two-layer Model Predictive Control strategy for electric vehicle battery thermal and energy management, leveraging long-term traffic data and real-time optimization to enhance energy efficiency and safety.

Contribution

The paper presents a novel two-layer MPC approach that improves battery energy efficiency and thermal management in connected electric vehicles, with reduced computational effort.

Findings

Achieves 2.8-7.9% energy savings over rule-based controllers.

Reduces computational complexity compared to single-layer MPC.

Effectively manages battery temperature within desired range.

Abstract

Future vehicles are expected to be able to exploit increasingly the connected driving environment for efficient, comfortable, and safe driving. Given relatively slow dynamics associated with the state of charge and temperature response in electrified vehicles with large batteries, a long prediction/planning horizon is needed to achieve improved energy efficiency benefits. In this paper, we develop a two-layer Model Predictive Control (MPC) strategy for battery thermal and energy management of electric vehicle (EV), aiming at improving fuel economy through real-time prediction and optimization. In the first layer, the long-term traffic flow information and an approximate model reflective of the relatively slow battery temperature dynamics are leveraged to minimize energy consumption required for battery cooling while maintaining the battery temperature within the desired operating range. In the second layer, the scheduled battery thermal and state of charge (SOC) trajectories planned to achieve long-term battery energy-optimal thermal behavior are used as the reference over a short horizon to regulate the battery temperature. Additionally, an intelligent online constraint handling (IOCH) algorithm is developed to compensate for the mismatch between the actual and predicted driving conditions and reduce the chance for battery temperature constraint violation. The simulation results show that, depending on the driving cycle, the proposed two-layer MPC is able to save 2.8-7.9% of the battery energy compared to the traditional rule-based controller in connected and automated vehicle (CAV) operation scenario. Moreover, as compared to a single layer MPC with a long horizon, the two-layer structure of the proposed MPC solution reduces significantly the computing effort without compromising the performance.