| dc.description.abstract | The drastic growth in the conventional transportation system raises serious air pollution concerns. Eco-friendly vehicles, in contrast, have been introduced as an alternative
to alleviate such environmental issues. To support the Canadian government’s goal of
achieving 100% sales of zero-emission vehicles by 2035, there is an increasing need for
advancements in charging infrastructure and the performance of Electric Vehicles (EVs).
These improvements aim to address range anxiety which is the primary concern of EV
consumers who fear running out of electricity during a journey and being unable to find
a charging point. However, so far, the main investment focus has been on the installation
of Fixed Charging Stations (FCSs) which requires significant budget contributions and
proper charging station placements. Therefore, to achieve higher EV popularity, this work
aims to elevate user satisfaction and alleviate Range Anxiety by developing an intelligent
system to manage EV charging demands, accurately estimating State of Charge (SoC) levels, and offering user-centric suitable service recommendations. Nevertheless, the scarcity
of EVs historical data for Artificial Intelligence (AI)-based predictions poses a significant
difficulty. To mitigate the aforementioned concern, we present a model based on Deep
Transfer Learning (DTL) between domain-variant data sets, to reduce the need for the
existence of a vast amount of EV data, including driving characteristics and patterns. [...] | en_US |
