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

TL;DR

This paper proposes a cloud-based energy management strategy for hybrid electric city buses that incorporates real-time passenger load prediction to optimize energy use, reduce costs, and extend battery life.

Contribution

It introduces a novel framework combining passenger load prediction, dynamic programming, and rule extraction for improved energy management in electric buses.

Findings

Achieved 4% and 11% reduction in operating costs during off-peak and peak hours.

Gradient Boost Decision Tree provided the best accuracy for passenger load prediction.

The proposed method reduces operating costs by less than 1% compared to using true passenger load data.

Abstract

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.