Analysis of Optimal Shift Schedule Derivation for Electric City Buses Equipped with Automatic Manual Transmission

Mingjie Zhao, Jiang Yi, Cheng Lin


Since most of the electric city buses equipped with automated manual transmission (AMT) are currently implemented with a heuristic strategy, this paper proposes a systematic extraction method to optimize and accelerate the shift schedule design process. A dynamic model of the electric city bus with a four-speed AMT is established to study the energy management strategy. Dynamic programming (DP) algorithm is adopted to explore the offline global optimal control sequence considering the shift frequency. Then the feature of optimal operating points is analyzed and a time-saving derivation methodology is discussed to construct the gear shift schedule based on the clustering algorithm. The derived control strategy is proved to be efficient, flexible and online implementable. Simulation results show that the derived strategy can improve the energy economy by 4.45% compared with the preliminary empirical gear shift schedule, which is 57.8% better than back-propagation neural networks in a determined city route.


electric city bus, shift schedule derivation, dynamic programming (DP), neural networks (NN), clustering algorithm


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