A Two-Layer Real-Time Optimization Control Strategy for Integrated Battery Thermal Management and HVAC System in Connected and Automated HEVs

Abstract

With the increase in the capacity and power rating of batteries in today's HEVs and EVs, the battery thermal management (BTM) system bears growing importance in vehicle safety and efficiency. A practical BTM system is commonly coupled with the passenger cabin heating-ventilation-air-conditioning (HVAC) system, which makes a major energy consumer and a challenging control object. Thanks to the connected and automated vehicle (CAV) technology, predictions of the vehicle speed profile and power trajectory can be obtained, providing the possibility for predictive control of the BTM and HVAC coupled system in order to maintain battery safety, passenger comfort, and to reduce energy consumption. However, the tradeoff among higher energy saving potential and wider control range from a long and sparse horizon, and higher accuracy from a short and dense horizon is inevitable in the conventional predictive control. In this paper, a two-layer predictive control strategy for warm/hot weather is proposed to address the aforementioned tradeoff. The upper layer controller firstly plans the optimized battery temperature trajectory according to the look-ahead speed preview and battery power profiles provided by the CAV network and the integrated BTM and HVAC system efficiency surface from off-line data. Then the lower layer model predictive controller tracks the planned trajectory and cabin temperature reference while enforcing the energy consumption optimization. Simulation results demonstrate that the proposed strategy exhibits accurate battery and passenger cabin temperature reference tracking while achieving up to 10.47% HVAC energy saving comparing to a baseline control strategy with UDDS profile and typical driving conditions. Implementation of the proposed strategy on a real-time vehicle emulator based on rapid control prototyping (RCP) and hardware-in-the-loop (HIL) platforms demonstrates the real-world implementation capability of the proposed two-layer framework.