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Electric city bus gains popularity in recent years for its low greenhouse gasemission, low noise level, etc. Different from a passenger car, the weight of acity bus varies significantly with different amounts of onboard passengers.After analyzing the importance of battery aging and passenger load effects onan optimal energy management strategy, this study introduces the passenger loadprediction into the hybrid-electric city buses energy management problem, whichis not well studied in the existing literature. The average model, DecisionTree, Gradient Boost Decision Tree, and Neural Networks models are compared inthe passenger load prediction. The Gradient Boost Decision Tree model isselected due to its best accuracy and high stability. Given the predictedpassenger load, a dynamic programming algorithm determines the optimal powerdemand for supercapacitor and battery by optimizing the battery aging andenergy usage leveraging cloud techniques. Then, rule extraction is conducted ondynamic programming results, and the rule is real-time loaded to the vehicleonboard controller to handle prediction errors and uncertainties. The proposedcloud-based Dynamic Programming and rule extraction framework with thepassenger load prediction show 4% and 11% lower bus operating costs in off-peakand peak hours, respectively. The operating cost by the proposed framework isless than 1% of the dynamic programming with the true passenger loadinformation.

A Cloud-Based Energy Management Strategy for Hybrid Electric City Bus Considering Real-Time Passenger Load Prediction