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.
Real-time energy management for commute HEVs using modified A-ECMS with traffic information recognition
IET Intelligent Transport Systems ; 13 , 4 ; 729-737
2019-01-17
9 pages
Article (Journal)
Electronic Resource
English
k-mean clustering algorithm , dynamic programming , energy management systems , optimal control , real-time performance , adaptive equivalent consumption minimization strategy , A-ECMS , proper equivalent factor , time-varying traffic information , current traffic situation , stochastic programming , hybrid electric vehicles , simple traffic recognition , state-of-charge , commute HEVs , real-time energy management strategy , fuel consumption performance , real-time energy management controller online , traffic information recognition , GT-suite HEV simulator , stochastic optimal control problem , cluster statistical characteristic , adaptive equivalence factor , stochastic dynamic programming policy iteration , historical traffic data
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