Real-time energy management for commute HEVs using modified A-ECMS with traffic information recognition

To further improve fuel consumption performance of hybrid electric vehicles (HEVs) running on commute route in the face of time-varying traffic information, this paper investigates a real-time energy management strategy based on the adaptive equivalent consumption minimization strategy (A-ECMS) framework with traffic information recognition. The proposed management strategy integrates the global near optimization and the real-time performance. The simple traffic recognition is constructed by utilising k-means clustering algorithm to deal with the historical traffic data to form four clusters. The adaptive equivalence factor of the A-ECMS is designed as a three-dimensional mapping on each cluster and the system states by employing stochastic dynamic programming (SDP) policy iteration to solve offline the stochastic optimal control problem formulated by each cluster statistical characteristic. In real-time energy management controller online, the instantaneous power split is performed by the ECMS with a proper equivalent factor, which is obtained from mappings according to the cluster recognised by the current traffic situation and the state-of-charge (SOC). The effectiveness of the designed control strategy is verified by the simulation test conducted on GT-suite HEV simulator over real driving cycles.