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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
The proposed method is tested against a set of Ni-Mh batteries and it is concluded that this method is feasible for practical applications
📌 3 Key Takeaways
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Objective & Hypothesis:This work presents a battery capacity estimation method based on inverse distance weighting multivariate interpolation.
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Key Findings & Data:The proposed method uses a parametric approach based on a generic rechargeable battery model.
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Conclusion & Impact:The proposed method is tested against a set of Ni-Mh batteries and it is concluded that this method is feasible for practical applications
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Rechargeable batteries · extended Kalman filtering · inverse distance weighting
Abstract
This work presents a battery capacity estimation method based on inverse distance weighting multivariate interpolation. The proposed method uses a parametric approach based on a generic rechargeable battery model. Battery model parameters are estimated with an Extended Kalman Filtering based algorithm. Battery capacity estimation is performed with an inverse distance weighting multivariate interpolation model based upon the estimated battery parameters. The proposed method is tested against a set of Ni-Mh batteries and it is concluded that this method is feasible for practical applications
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Cite This Article
Barlak, C., Özkazanç, Y. (2010). BATTERY CAPACITY ESTIMATION WITH INVERSE DISTANCE WEIGHTING. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Volume: 52 Issue: 1. https://ajindex.com/articles/1117336