Abstract/Summary

The global rise of electrified transport is bringing significant attention to provision of charging infrastructure and subsequent increases in electricity demand. Whilst much research to date has concentrated on light vehicles these challenges are more extreme for Heavy Goods Electric Vehicles (HGEVs), with power demands exacerbated by larger batteries and the need for rapid turnaround when charging on-route. Colocation with Energy Storage Systems (ESS) could have potential to help, as could intelligent charge control. This paper presents a novel integrated elitist intelligent algorithm that can simultaneously optimise the multiple numerous technical and economic factors needed here, including long term, independent sizing of battery capacity and power-electronic rating, short term ESS management / charger dispatch, and consideration of dynamic electricity price variability. The work goes beyond previous studies by examining the particular challenges of heavy-duty vehicles, considering both charge management of individual vehicles and co-location of static battery storage, and also by contrasting plausible on route and depot-based charging cases. To support this, a method is developed to estimate patterns of HGV attendance at UK fuelling stations, applicable for other countries. Results highlight the economic challenge of on-route charging. Where fleet operation allows idle time at depots, smart control of vehicle charging can track the lowest price electricity time periods. Depot energy delivery cost was seen to reduce from 18.32 to 11.90 p/kWh comparing on-demand and managed charging (based on 2021, UK, half hourly wholesale electricity prices). On-route charging costs can be reduced by the co-location of static ESS but only to 15.74 p/kWh, without consideration of additional commercial costs. All day stations can deliver electricity at a lower average price than daytime only stations and can benefit from comparatively smaller ESS. Cost benefit analysis was applied for a range of assumptions, revealing insight into the non-linear relationship between battery capacity, charger rating, and subsequent energy delivery price.