Predictive equivalent consumption minimization strategy for power-split hybrid electric vehicles using Monte Carlo algorithm

Purpose: The underlying research goal of this article is to put forward a reliable fuel saving performance based on the forecasted velocities of drive cycles for a power-split hybrid electric vehicle. Theory and Methods: The power distribution between energy sources is devised by utilizing the P-ECMS for the power-split hybrid electric vehicle using the uncertain drive cycle velocity estimation based on MC algorithm. Results: The effectiveness and accuracy of the method are evaluated under seven drive cycles. The MC provides good prediction results of the velocities. On the basis of it, the P-ECMS method decreases fuel consumption up to 6.01% under NEDC, up to 9.09% under WLTP, up to 6.33% under UDDS, up to 5.14% under HWFET, up to 1.96% under NYCC, up to 11.47% under LA-92, and up to 7.92% under ALL-CYC compared to a standard ECMS method. Conclusion: It is seen from the analysis results that battery SOC decreases slightly using the P-ECMS since the electric motor is actively used to meet power demand instead of the engine over the predicted speed profiles. In the end, the MC algorithm-based P-ECMS strategy can verify the optimal power distribution based on fuel-saving potentials as compared to the baseline ECMS strategy while keeping the battery SOC at a reasonable interval.