Utilizing Situational Awareness for Efficient Control of Powertrain in Parallel Hybrid Electric Vehicles

An optimal power management strategy is the key to benefit from hybridization of a vehicle powertrain. Designing such a strategy requires knowledge of the vehicle energy requirements during its drive cycle. Therefore, information available through Intelligent Transportation Systems (ITS) can play a critical role in designing such an optimal powertrain management. To avoid the implementation and practical issues of global optimal solutions, sub-optimal methods such as Equivalent Consumption Minimization Strategy (ECMS) have been introduced for the power distribution in Hybrid Electric Vehicles (HEV). However, the dependency of ECMS on prior knowledge about the driving cycle is a deterring effect for real-time implementation. Accordingly, on-line decision making about the equivalent factor value used in ECMS, which translates the electrical energy into the equivalent fuel energy, is the challenge captivating researches´´ attention. In this paper, an adaptive method for enhancement of the ECMS based on the prediction of driving conditions is proposed. Using the approximated future energy requirement of the vehicle over the prediction time horizon, the sub-optimal value of equivalent factor is updated. Simulation results validate the effectiveness of the proposed method to decrease fuel consumption while charge sustainability is satisfied.