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Solving Limited Data Challenges in Battery Parameter Estimators by Using Generative Adversarial Networks
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DOI：10.1109/POWERAFRICA52236.2021.9543237.png)
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There has been an increased demand for Energy Storage Systems (ESS) as they power most of today's remote devices and are needed to sustain the ongoing transportation electrification revolution to curb pollution levels. State-of-Charge (SOC) and State-of-Health (SOH) assessment of batteries is vital to prevent unexpected shutdown of battery powered consumer electronics and to determine the expected range from an Electric Vehicle. SOC & SOH estimators demand high-quality curated battery parameter data such as current, temperature and voltage over several charge/discharge cycles. Experiments needed to collect such data are expensive and tedious. Lack of diversity in open access battery datasets have hindered research in this area. This paper is focused on synthetic generation of battery parameters from sparse datasets. The goal is to produce high-fidelity heterogeneous time-series datasets which help to overcome the problem of lack of data in the ESS domain.
Keyword:
Generative Adversarial Network
Battery
Energy Storage
Synthetic Data
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