Real-time energy management based on ECMS with stochastic optimized adaptive equivalence factor for HEVs

For both globally suboptimal solution and implementable strategy, a real-time energy management strategy, based on equivalent consumption minimization strategy (ECMS), is proposed for commuter hybrid electric vehicles (HEVs) running on fixed routes. The determination of the adaptive equivalence factor is a focus. By the statistical characteristics deriving from historical driving data, the infinite-horizon stochastic dynamic programming (SDP) optimization with a discount factor is first formulated for finding proper equivalence factor according to uncertain driving cycles on a fixed route. And then, a mapping of equivalent factor on the system state is established off-line by stochastic optimal solution deriving from SDP policy iteration algorithm. In the power splits online, the equivalence factor of the implemented adaptive ECMS is obtained from the mapping according to the real time driving condition to achieve the near global optimal control objective that fuel consumption is minimized and the battery state of charge (SOC) is maintained within the boundaries over the whole driving route. Based on the HEV test platform established by specialized GT-Suite, simulation results and comparisons in some real driving cycles are presented to verify the effectiveness of the proposed strategy and to evaluate the advantages over other strategies.