  Electric Vehicle (EV) fleets have dramatically expanded over the past several
years. There has been significant increase in interest to electrify all modes
of transportation. EVs are primarily powered by Energy Storage Systems such as
Lithium-ion Battery packs. Total battery pack capacity translates to the
available range in an EV. State of Charge (SOC) is the ratio of available
battery capacity to total capacity and is expressed in percentages. It is
crucial to accurately estimate SOC to determine the available range in an EV
while it is in use. In this paper, a Temporal Convolution Network (TCN)
approach is taken to estimate SOC. This is the first implementation of TCNs for
the SOC estimation task. Estimation is carried out on various drive cycles such
as HWFET, LA92, UDDS and US06 drive cycles at 1 C and 25 {\deg}Celsius. It was
found that TCN architecture achieved an accuracy of 99.1%.
