Multi-Layer NMPC for Battery Thermal Management Optimization Strategy of Connected Electric Vehicle Integrated With Waste Heat Recovery

The performance of the battery decreases dramatically in the low temperature, and the heating system of the battery in the cold environment consumes considerable energy, which leads to reduced range and increased safety hazards of electric vehicle. This article focuses on an optimal control strategy of battery thermal management system (BTMS) with waste heat recovery for connected electric vehicle (CEV), which improves heating efficiency and minimizes energy consumption. Firstly, to compensate the insufficient heating capacity of heat pump system in low temperature, the battery pack heating system integrated with heat pump air conditioning system (HPACS), waste heat recovery and electric heater is designed. Secondly, to address the problems of the battery slow thermodynamic response and the increased system complexity due to waste heat recovery, a multi-layer nonlinear model predictive control (ML-NMPC) strategy is developed, which utilizes intelligent transportation system (ITS) information to optimize the energy consumption of the integrated system. The upper layer and lower layer controllers coordinate with each other using the long and short speed prediction respectively to solve the problems of multi-layer control, fast implementation and reference trajectory tracking. The simulation results show that the waste heat recovery system can improve the performance of the HPACS, which increases the coefficient of performance (COP) from 1.79 to 2.54. Compared to centralized NMPC, ML-NMPC reduces energy consumption by 10.2% and 13.7% in the insulation stage under NEDC and real driving condition with higher state of health and less computation time, which demonstrates the superior thermal management capability.