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 strate...
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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 stochasticdynamicprogramming (SDP) policyiteration 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.
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