  The Electric Vehicle (EV) Industry has seen extraordinary growth in the last
few years. This is primarily due to an ever increasing awareness of the
detrimental environmental effects of fossil fuel powered vehicles and
availability of inexpensive Lithium-ion batteries (LIBs). In order to safely
deploy these LIBs in Electric Vehicles, certain battery states need to be
constantly monitored to ensure safe and healthy operation. The use of Machine
Learning to estimate battery states such as State-of-Charge and State-of-Health
have become an extremely active area of research. However, limited availability
of open-source diverse datasets has stifled the growth of this field, and is a
problem largely ignored in literature. In this work, we propose a novel method
of time-series battery data augmentation using deep neural networks. We
introduce and analyze the method of using two neural networks working together
to alternatively produce synthetic charging and discharging battery profiles.
One model produces battery charging profiles, and another produces battery
discharging profiles. The proposed approach is evaluated using few public
battery datasets to illustrate its effectiveness, and our results show the
efficacy of this approach to solve the challenges of limited battery data. We
also test this approach on dynamic Electric Vehicle drive cycles as well.
