Overcoming limited battery data challenges: A coupled neural network approach

TL;DR

This paper introduces a coupled neural network approach to generate synthetic battery data, addressing the scarcity of diverse datasets for better battery state estimation in electric vehicles.

Contribution

It presents a novel dual neural network method for augmenting time-series battery data, improving the training data availability for battery health monitoring models.

Findings

Effective augmentation of battery datasets demonstrated

Improved battery state estimation accuracy shown

Method works on EV drive cycle data

Abstract

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.