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State of charge estimation using different machine learning techniques
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AI
DOI：10.1080/02522667.2022.2042091.png)
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Abstract
En 中文
The advent of larger adoption of electric vehicles (F.Vs) and hybrid electric vehicles (HEVs) has resulted in the amelioration of battery technology. However, the accurate State-of-Charge (SoC) estimation remains to have scope of improvement. SoC is the ratio of available capacity and maximum possible charge that can be stored in a battery. SoC estimation is of prime importance with relation to battery safety and maintenance. This paper shows SoC estimation by three different techniques - linear regression, random forest regression and multilayer perceptron. Linear and random forest regression are techniques based on statistical premises while multilayer perceptron makes use of deep learning. An accurate SoC estimation can result in better battery performance.
Keywords:
State of Charge
Battery capacity
Machine learning
Deep learning
Journal
IF:
0.7
Papers:
14
Citations:
758
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