Adaptive Real-Time Energy Management of a Multi-Mode Hybrid Electric Powertrain

Meticulous design of the energy management control algorithm is required to exploit all fuel-saving potentials of a hybrid electric vehicle. Equivalent consumption minimization strategy is a well-known representative of on-line strategies that can give near-optimal solutions without knowing the future driving tasks. In this context, this paper aims to propose an adaptive real-time equivalent consumption minimization strategy for a multi-mode hybrid electric powertrain. With the help of road recognition and vehicle speed prediction techniques, future driving conditions can be predicted over a certain horizon. Based on the predicted power demand, the optimal equivalence factor is calculated in advance by using bisection method and implemented for the upcoming driving period. In such a way, the equivalence factor is updated periodically to achieve charge sustaining operation and optimality. To verify the performance of the adaptive strategy, simulation has been conducted under city and highway driving cycles. Optimal solutions of the equivalence factor and the control outputs, i.e., engine speed and torque, are presented. Results show that the adaptive strategy can maintain battery charge sustaining operation, although there is a drawback that engine activation sometimes happens when vehicle is decelerating or braking. A comparative study is also conducted to verify the fuel economy of the proposed strategy. It is shown that with adaptive strategy, fuel consumption is increased by 9.737% in city driving and 2.409% in highway driving.