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