2 research outputs found
Cloud-Based Dynamic Programming for an Electric City Bus Energy Management Considering Real-Time Passenger Load Prediction
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,
which is not well studied in existing literature. This study proposes a
passenger load prediction model using day-of-week, time-of-day, weather,
temperatures, wind levels, and holiday information as inputs. The average
model, Regression 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, dynamic programming algorithm determines
the optimal power demand for supercapacitor and battery by optimizing the
battery aging and energy usage in the cloud. Then rule extraction is conducted
on dynamic programming results, and the rule is real-time loaded to onboard
controllers of vehicles. The proposed cloud-based dynamic programming and rule
extraction framework with the passenger load prediction shows 4% and 11% fewer
bus operating costs in off-peak and peak hours, respectively. The operating
cost by the proposed framework is less than 1% shy of the dynamic programming
with the true passenger load information