Improvement of PHEV Equivalent Fuel Economy and Battery Life by Applying Traffic-Based SOC Management

Improvement of plug-in hybrid electric vehicle (PHEV) equivalent fuel consumption (EFC) and battery life are significant issues in the design of PHEVs. This research aims to develop an adaptive traffic-based controller to improve fuel economy and battery life. The optimal state of charge (SOC) trajectories in real-world and standard cycles are extracted using the modified dynamic programming (MDP) approach. The optimal data is clustered based on the driving features, under the light of fuzzy clustering. Then, a control map is formed based on the clustered data to estimate SOC trajectories as a reference in an adaptive control strategy. A battery life model is employed to investigate the effect of the proposed method on battery aging. This study reveals that traffic-based SOC management simultaneously reduces the operating and maintenance costs since it improves the equivalent fuel economy and battery life in various driving cycles. Moreover, the suggested energy management strategy is implemented on an embedded system by applying the model-based design (MBD) method. The comparison of hardware-in-the-loop (HIL) and model-in-the-loop (MIL) simulation shows that the suggested strategy is implementable and effective for real applications.