Baseline Strategy for Remaining Range Estimation of Electric Motorcycle Applications

Accurate predicting the remaining range of electric motorcycles (EMs) is important to help optimizing the energy consumption and improving the utilization of remaining energy in the batteries and therefore extending their life. In this paper, a range estimation strategy is developed to estimate the elapsed travel distance of the motorbike application and hence, the remaining range can be predicted. Then, daily riding cycles of the EM are identified and classified through machine learning technique based on the training and testing dataset of various standard ride cycles, which are combined with the proposed range estimation strategy to estimate the remaining travel distance of the motorcycle as the baseline to underpin and support the energy management system of the electric vehicle applications. The developed complete model is finally evaluated on a mixed daily riding cycles showing the effectiveness of the approach.