A Two-Stage Real-Time Optimized EV Battery Cooling Control Based on Hierarchical and Iterative Dynamic Programming and MPC

The battery thermal management (BTM) system plays an increasingly important role today in the safety of electric vehicles (EVs) and hybrid electric vehicles (HEVs) as the battery capacity and power ratings keep growing. The BTM system is commonly coupled with the vehicle passenger cabin HVAC system. This integrated thermal system is a major onboard energy consumer, and its complexity brings challenges to its control. With the planned-ahead speed profile and the corresponding power trajectory obtained from the connected and automated vehicle (CAV) technology, predictive control makes a desirable option for the integrated system to maintain battery safety and passenger comfort while lowering energy consumption. However, in order to achieve both high accuracy and low cost in a wide control range, a very long prediction horizon and high sampling rate are both necessary. This will overwhelm the processing capacity of the onboard electronic control unit (ECU) if using conventional predictive control. In this paper, a two-stage predictive control strategy for the BTM and HVAC coupled system is proposed to solve this problem. In stage 1, based on the integrated cooling system efficiency features, a hierarchical and iterative dynamic programming (HIDP) scheme is designed to derive the optimal battery temperature trajectory to reach the set point at the end of the horizon with a modest computation burden. In stage 2, a control-oriented model is constructed for the cooling system and a model predictive controller (MPC) is accordingly built to track the trajectory from Stage 1 while enforcing the energy saving. A high set-point-tracking performance and as high as 10.61% energy saving for the cooling system in the UDDS cycle are verified by simulation results. The real-time implementation capability of the proposed strategy is demonstrated by the vehicle emulator experiments based on hardware in the loop (HIL) and a rapid control prototyping (RCP) platform.

Automated summary

The BTM system is crucial for the safety and comfort of electric vehicles, yet challenges arise in maintaining a balance between accuracy and cost efficiency. A two-stage predictive control strategy is proposed to optimize temperature trajectory and energy consumption effectively, demonstrating significant improvements in set-point tracking and energy saving.