Performance Comparison of Rule-Based, ECMS, and DP Control Strategies for Mild Hybrid Electric Vehicles
Abstract
This study introduces and compares online rule-based and optimization-based energy management strategies for a mild hybrid electric vehicle, with their performance evaluated against an offline Dynamic Programming benchmark. A structured rule-based strategy is proposed to enforce engine operation along its optimal efficiency line, while the remaining power demand is balanced by the electric motor. To achieve charge-sustaining battery operation, a soft state of charge regulation mechanism is incorporated. An Equivalent Consumption Minimization Strategy (ECMS) is also developed using a precise formulation of battery equivalent fuel consumption computed from instantaneous engine and electric path efficiencies, instead of constant efficiencies used in the literature. DP, which provides a globally optimal solution over the entire driving cycle, is employed as a benchmark for assessing the rule-based and ECMS strategies. The control strategies are compared under charge-sustaining conditions, considering engine and motor operation characteristics, overall fuel consumption, and battery usage intensity. Furthermore, the influence of load shifting between the internal combustion engine and the electric motor on overall vehicle performance is analyzed. Fuel consumption decreases by 13.5% relative to the engine-only baseline with the proposed ECMS with precise equivalent fuel consumption, and DP yields an additional 1.6% benefit. Compared with the developed rule-based controller, ECMS nearly halves the battery usage intensity, and DP provides 3.1% further reduction relative to ECMS.