Real-Time Optimal Energy Management for Hybrid and Plug-In Hybrid Electric Vehicles

Department of Mechanical Engineering and Institute for Integrated Energy Systems, University of Victoria, Canada

Abstract: In this work, a systematic approach for real-time optimal energy management of hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV) has been introduced and validated through two HEV/PHEV case studies. Firstly, a new analytical model of the optimal control problem for Toyota Prius HEV with both offline and real-time solutions was presented and validated through Hardware-in-Loop (HIL) real-time simulation. Secondly, the new online or real-time optimal control algorithm was extended to a multi-regime PHEV by modifying the optimal control objective function and introducing a real-time implementable control algorithm with an adaptive coefficient tuning strategy. A number of practical issues in vehicle control, including drivability, controller integration, etc. are also investigated. The newly proposed real-time optimal control algorithm identifies the optimal operational mode and the corresponding torque split among each components at each time step. The control objective was to minimize the well-to-wheel energy use (PEU and GHG), where both the fuel and electric energy consumption was taken into account. The optimal torque split was computed based on Pontryagin’s Minimum Principle. To reduce computational burden, the original 2 degree of freedom (DOF) powertrain control problem has been converted into a 1-DOF search algorithm in the optimization search. For practical implementation, an adaptive technique was utilized to update the equivalence factor based on battery SOC and current driving distance. The newly proposed fast PMP algorithm was investigated through Model-in-the-loop (MIL) simulation tests using the simplified vehicle model, showing improved PEU consumption by 3-5%, comparing to the baseline rule-based controller for which the battery SOC is just depleted at the end of the trip. The new algorithm was also validated on various driving cycles using both Model-in-Loop (MIL) and HIL environment. This research better utilizes the energy efficiency and emissions reduction potentials of hybrid electric powertrain systems, and forms the foundation for the developments of next generation HEVs and PHEVs.