An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space

By 2020, over 100 countries had expanded electric and plug-in hybrid vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner technologies due to the proven environmental health implications of internal combustion engine vehicles (ICEVs), as evidenced by recent COP26 meeting. This article proposes an agent-based model activity a tool for quantifying energy consumption simulating fleet EV/PHEVs within urban street network at various spatio-temporal resolutions. Driver behaviour plays significant role in consumption; thus, levels individual enhancing heterogeneity should provide more accurate results potential demand cities. The study found that (1) is lowest when speed limit adherence increases (low variance behaviour) highest acceleration/deceleration patterns vary (high behaviour); (2) travel shorter distances while abiding rules efficient compared those longer; (3) on average, tested vehicles, were £233.13 cheaper run than ICEVs across all experiment conditions. difference average fuel costs (electricity petrol) shrinks level driver less varied (more homogeneous). research allow policymakers quantify subsequent