DATA DRIVEN ENERGY ECONOMY PREDICTION FOR ELECTRIC CITY BUSES USING MACHINE LEARNING

Abstract

Electrification of transportation systems is increasing, in particular city buses raise enormous
potential. Deep understanding of real-world driving data is essential for vehicle design and fleet
operation. Various technological aspects must be considered to run alternative powertrains
efficiently. Uncertainty about energy demand results in conservative design which implies
inefficiency and high costs. Both, industry, and
academia miss analytical solutions to solve this problem due to complexity and interrelation of
parameters. Precise energy demand prediction enables significant cost reduction by optimized
operations. This paper aims at increased transparency of battery electric buses’ (BEB) energy
economy.We introduce novel sets of explanatory variables to characterize speed profiles, which we
utilize in powerful machine learning methods. We develop and comprehensively assess 5 different
algorithms regarding prediction accuracy, robustness, and overall applicability. Achieving a
prediction accuracy of more than 94%, our models performed excellent in combination with the
sophisticated selection of features. The presented methodology bears enormous potential for
manufacturers, fleet operators and communities to transform mobility and thus pave the way for
sustainable, public transportation.