Computationally efficient optimal power management for plug-in hybrid electric vehicles based on spatial-domain two-scale dynamic programming

The plug-in hybrid electric vehicles (PHEV), utilizing more battery power, has become a next-generation HEV with great promise of higher fuel economy. Global optimization charge-depletion power management would be desirable. This has so far been hampered due to the a priori nature of the trip information and the almost prohibitive computational cost of global optimization techniques such as dynamic programming (DP). We have recently developed a two-scale dynamic programming approach as a nearly globally optimized charge-depletion strategy for the PHEV power management, through the combination with the intelligent transportation systems (ITS). The trip models are obtained via GPS, GIS, real-time and historical traffic flow data and advanced traffic flow modeling. A computationally efficient algorithm is proposed in this paper to enhance the previously developed two-scale DP framework so that the computational efficiency meets the need of on-board implementation. The electric-vehicle mode is reinforced for predicted traffic stops. For the remainder of the trip, the route is divided into a number of segments of certain length. For each segment, the fuel consumption and SOC change are calculated in advance according to different level of power splitting ratio, speed and acceleration/deceleration. The optimization can then be solved in spatial domain, with much less dimension than that in time domain. Simulation study showed significant reduction of computational time with minor loss of fuel economy performance.