Rapid Model-Free State of Health Estimation for End-Of-First-Life Electric Vehicle Batteries Using Impedance Spectroscopy
| Autor: | Jamie Hathaway, Alireza Rastegarpanah, Rustam Stolkin |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Battery (electricity)
Technology Control and Optimization business.product_category State of health Computer science 020209 energy lithium-ion batteries Bayesian probability Energy Engineering and Power Technology 02 engineering and technology Reuse Electric vehicle 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Engineering (miscellaneous) electric vehicles battery second use state of health Hyperparameter Artificial neural network Renewable Energy Sustainability and the Environment screening 021001 nanoscience & nanotechnology Reliability engineering machine learning Equivalent circuit 0210 nano-technology business Energy (miscellaneous) |
| Zdroj: | Energies Volume 14 Issue 9 Energies, Vol 14, Iss 2597, p 2597 (2021) |
| ISSN: | 1996-1073 |
| Popis: | The continually expanding number of electric vehicles in circulation presents challenges in terms of end-of-life disposal, driving interest in the reuse of batteries for second-life applications. A key aspect of battery reuse is the quantification of the relative battery condition or state of health (SoH), to inform the subsequent battery application and to match batteries of similar capacity. Impedance spectroscopy has demonstrated potential for estimation of state of health, however, there is difficulty in interpreting results to estimate state of health reliably. This study proposes a model-free, convolutional-neural-network-based estimation scheme for the state of health of high-power lithium-ion batteries based on a dataset of impedance spectroscopy measurements from 13 end-of-first-life Nissan Leaf 2011 battery modules. As a baseline, this is compared with our previous approach, where parameters from a Randles equivalent circuit model (ECM) with and without dataset-specific adaptations to the ECM were extracted from the dataset to train a deep neural network refined using Bayesian hyperparameter optimisation. It is demonstrated that for a small dataset of 128 samples, the proposed method achieves good discrimination of high and low state of health batteries and superior prediction accuracy to the model-based approach by RMS error (1.974 SoH%) and peak error (4.935 SoH%) metrics without dataset-specific model adaptations to improve fit quality. This is accomplished while maintaining the competitive performance of the previous model-based approach when compared with previously proposed SoH estimation schemes. |
| Databáze: | OpenAIRE |
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