Estimation of battery SOC using a combined approach of temporal convolutional networks and Unscented Kalman Filter
Huipin Lin, Xiaoying Chen, Lei Zhang, Zhengyi Bao, Sai Tang · 2026
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Description
Huipin Lin and co-authors improved a key estimate for electric car batteries in 2026: how much charge is left. Drivers and safety systems depend on that number, but it cannot be measured directly and shifts with use.
Their method pairs a neural network built for time series with a filtering method that smooths noisy output. Tested on three standard driving cycles from a university data set, it beat common rival networks on accuracy and stayed steady as conditions changed. Errors sat near one percent. Better charge estimates mean more honest range predictions and batteries managed with less guesswork.
Catalog record
| CREATOR | Huipin Lin, Xiaoying Chen, Lei Zhang, Zhengyi Bao, Sai Tang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44369 |
| LICENSE | CC_BY |
| RIGHTS | CC BY |
| SOURCE | PLOS |