# Overcoming limited battery data challenges: A coupled neural network approach

arXiv: https://arxiv.org/abs/2111.15348 · HTML: https://mlsanity.com/paper/2111.15348/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 33 references that are in the map.
- First posted on arXiv 5 Oct 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Data Augmentation Using GANs](https://mlsanity.com/paper/1904.09135/) (2019)
- [Copula Flows for Synthetic Data Generation](https://mlsanity.com/paper/2101.00598/) (2021)
- [Conditional Infilling GANs for Data Augmentation in Mammogram Classification](https://mlsanity.com/paper/1807.08093/) (2018)

## Built on by (most cited first, 2 of 2)

- [Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on Behavior](https://mlsanity.com/paper/2406.06602/) (2024)
- [Zero-Shot Anomaly Detection in Battery Thermal Images Using Visual Question Answering with Prior Knowledge](https://mlsanity.com/paper/2505.16674/) (2025)

## Abstract (arXiv)

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.