Predictive real-time energy management strategy for PHEV using lookup-table-based Dynamic Programming

This paper proposes a predictive real time energy management strategy for plug-in-hybrid electric vehicles (PHEV) based on an adaptation of Dynamic Programming (DP). The computational load of predictive real time strategies increases with the trip length. Therefore, for online computation by the onboard computer, they strongly depend on an efficient implementation. To reduce computation cost, current approaches for predictive strategies rely on strongly simplified intern vehicle models. The here proposed energy management strategy (EMS) uses a different approach, which is based on the use of precalculated lookup tables for the different operating points of the powertrain. This precalculation make the use of more exact vehicle models possible by using more detailed loss models of the powertrain components. The proposed EMS separates the optimization process, i.e. the calculation of the power distribution to engine and electric motor and gear in two calculation steps. The first step, which is computationally more intensive, has only to be executed once for a certain vehicle configuration. The obtained results are saved in lookup tables to avoid a later recomputation. In the second step, which is done online in the vehicle, a shortest path search algorithm is employed which is based on the predicted vehicle speed and rode slope of the trip. Techniques are integrated which decrease the rounding error caused by the use of lookup tables. The resulting difference of the consumed fuel mass between the lookup table based DP and standard DP is smaller than 0.03% by an approximately 50 times faster calculation. Using the proposed algorithm, even complex intern vehicle models do not affect the online computation cost and can be implemented by real time strategies.