Enhancing on-board energy management for next generation zero emission buses with Internet of Vehicles

Using multiple feature selection and predictive modelling methods, the analysis identifies useful variables from real operational bus data, augmented with online extractable data to emulate an IoV environment. The resulting summary table aims to reduce ambiguity and lower barriers to adopting IoV enabled energy management.<br><br>A specific V2I use case is then examined, demonstrating how bus priority at traffic lights in Belfast City Centre can further improve energy consumption.

A method is presented for assigning priority based on route characteristics that offer the greatest potential efficiency gains, derived from real recorded data. This provides a simple solution to the multi vehicle priority problem identified in existing literature. A complementary approach predicts the maximum achievable change in energy consumption using only route mapping data, allowing operators to estimate the impact of V2I implementation.

Results show that routes with high positive gradients benefit most from priority signalling, achieving up to an 8.28% improvement in energy consumption performance.<br><br>Overall, the research demonstrates that existing IoV resources can effectively enhance energy management both on board and through external control, providing practical methods for next generation zero emission bus fleets.<br><br><i>Thesis is embargoed until 31 July 2031.</i>