Extended Horizon ECMS Control of PHEVs With 2D Electricity Price Adaptation Policy

• Published in: IEEE transactions on intelligent transportation systems 2022-04, Vol.23 (4), p.3531-3542
• Main Author: Bashash, Saeid
• Format: Article
• Language: English
• Subjects: Adaptive control; Batteries; Dynamic programming; electric vehicles; Electricity; Electricity pricing; Electronic countermeasures; Energy costs; Energy management; fuel economy; Hybrid electric vehicles; intelligent vehicles; Mechanical power transmission; optimal control; Optimization; Powertrain; Real-time systems; Robust control; Search algorithms; State of charge; Trajectory; Trajectory optimization
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2020.3037884

This article proposes new improvements to the ECMS-based control of plug-in hybrid electric vehicles (PHEVs) using macro-level trip forecast information. First, the dynamic programming (DP) optimization is applied to a power-split PHEV model to obtain the optimal powertrain trajectories for benchmarking the energy economy of the proposed controllers. An instantaneous energy cost minimization process is then formulated to compute the optimal mix of fuel and electricity, subject to the state and input constraints. To improve the speed and accuracy of the energy management system, the Nelder-Mead simplex search algorithm is used for the instantaneous optimizations. To account for the battery capacity limitation, an adaptive electricity pricing policy is introduced to control battery discharge rate based on the remaining trip distance, average speed, and elevation forecasts. The parameters of the proposed policy are optimized for a group of 5 urban and highway drive cycles to obtain a robust controller which can serve a wide spectrum of driving patterns. To further improve the energy economy, an extended-horizon optimization algorithm is proposed to account for control input transitions between consecutive time steps. Finally, a revised speed forecast formula is implemented during the low-SOC intervals to lift battery SOC ahead of high-demand periods. The combination of the proposed schemes enables reaching over 99% average optimality compared to a finely discretized DP optimization for the adopted drive cycles, and above 98% optimality for a test PHEV drive cycle.