By Sen Yan, PhD, DCU

With increasing concerns about traffic congestion and environmental degradation, the shift towards E-Mobility solutions in urban areas is becoming increasingly crucial. E-mobility devices, including E-bikes and E-scooters, may transform our travel patterns by offering more sustainable commuting options. Understanding the energy consumption patterns of these devices is crucial, as it directly affects their efficiency and the confidence and experience of urban commuters.

To address these issues, our team of researchers from Dublin City University established a detailed open dataset, collected from volunteers in Dublin, Ireland, for enhancing energy modelling for E-mobilities. The dataset fills a major gap in existing research by offering strong support for model validation and improvement. Specifically, it not only covers typical metrics like travel speeds and distances but also includes important factors such as rider weight, terrain changes, and weather conditions, all of which play significant roles in energy consumption prediction.

Building upon this dataset, our research team used various advanced AI-powered methods, presenting a remarkable improvement in predicting energy consumption based on machine learning models compared to traditional mathematical models. The results were impressive: AI-powered methods achieved up to 83.83% greater accuracy for e-bikes and 82.16% for e-scooters.

The corresponding paper has been accepted by a conference named ITEC 2024

You can read our paper here. More broadly, our research contributes to policymaking, society, and the environment. This research provides urban planners and policymakers with solid data to develop more efficient and reliable e-mobility options. Users are expected to benefit from more accurate estimations of travel distances and energy demand, minimising battery concerns and improving their overall experience of using e-mobility.

The widespread of e-mobilities lowers carbon emissions and reduces reliance on fossil fuels, leading urban transport toward a more sustainable future.