Multi-information integrated trip specific optimal power management for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEV) are widely received as a promising means of green mobility by utilizing more battery power. Recently, we have proposed a scheme of two-scale spatial-domain dynamic programming (DP) as a nearly global optimization approach to trip based optimal power management for PHEV through the combination with traffic data and trip modeling. Previously, the segment-wise power demand and SOC change was calculated through numerical integration based on the average speed and acceleration of the segment, and lookup tables were obtained. When more parameters are involved into power management, such as road grade and load change, such process becomes very tedious. In this paper, the spatial-domain DP is improved by calculating the power demand and SOC change in an analytical manner. The power demand is first calculated based on length, initial speed, acceleration, road grade, payload and wind of a road segment. The SOC change is then calculated for different PSR. An adjustable segment scheme used of analytical function is developed in order to improve the computation efficiency of the optimal power management without losing much of fuel economy. Simulation study shows that incorporating additional trip information such as road grade and predictable payload change into the optimization can significantly improve the fuel economy. The computational efficiency is also evaluated. The proposed method can greatly facilitate the development of optimal power management strategy for PHEV with multiple information inputs.