Comparative analysis of state of charge based adaptive supervisory control strategies of plug-in Hybrid Electric Vehicles

摘要

In this era of vehicle electrification from mild hybrids to fully electric cars, the importance of fuel economy improvements has led to technological advancements in energy management strategies. The control algorithm is pivotal to the increase in the energy efficiency of a plug-in hybrid system. The existing energy management strategies lack the adaptiveness and utilization of advancements of vehicle to-vehicle technology. This paper proposes a Cost Optimization for Finite Horizon strategy and also an adaptive version of Equivalent Consumption Minimization Strategy (A-ECMS). The Adaptive-ECMS adds a battery State of Charge (SOC) based reference to ensure the most efficient blended operation for a charge-discharge cycle. The Cost Optimization for Finite Horizon strategy utilizes future driving condition information from vehicle-to-vehicle technology to assist fuel consumption. A forward-looking vehicle propulsion system simulator is developed in Simulink (R). A battery model is developed with parameters from the Nickel Cobalt Aluminum chemistry cell. To understand the extent of improvement, the proposed strategies are then compared with the prevalent Finite State Machine strategy (FSM) in three representative driving cycles. The results show an average fuel economy improvement of 5% when compared to the baseline strategy. Among the three strategies, Cost Optimization for Finite Horizon strategy is best suitable for urban driving conditions and Adaptive-ECMS is best suitable for highway driving conditions. For a conventional series-hybrid vehicle, implementing the proposed energy management strategies can help save approximately 8.5 gallons of fuel per year.(c) 2021 Elsevier Ltd. All rights reserved.