A Benchmark Study for Real-Time Optimal Control of Connected HEVs: Design, Integration, and Evaluation

Developing advanced control for connected hybrid electric vehicles (HEVs) has become anticipated to enhance fuel economy and mobility, while there exists a gap between the abundance of controller developments and the scarcity of benchmarking baselines. To fill this gap, this article presents a benchmark study for real-time optimal control of connected HEVs, which was recognized with the Gold Award in a benchmark challenge competition. The developed benchmark controller formulates a two-layer hierarchical predictive control framework, integrating eco-velocity planning in the upper layer and a power management strategy (PMS) in the lower layer, to minimize fuel consumption while enforcing constraints and satisfying multiple traffic rules. Furthermore, the performance of the proposed controller is comprehensively evaluated with a high-fidelity, industrial-level, simulator of a Toyota HEV. In particular, the benefit of integrating different layer controllers represents a significant reduction in fuel consumption compared against the conventional controller, and the sensitivity analysis on traffic scenarios guides the direction of parameter tuning. In addition, the real-time feasibility is assessed experimentally via a vehicle control unit (VCU) embedded system. This study helps for making reference of future efforts and opportunities for connected vehicle efficiency improvement.