fig1

Advancements in the estimation of the state of charge of lithium-ion battery: a comprehensive review of traditional and deep learning approaches

Figure 1. Flowchart depicting traditional ML methods for estimating SOC. Input variables (voltage, current, and temperature) undergo data cleaning and feature transformation before being processed by machine learning models: KNN, Decision Trees, SVM, ELM and GPR. The final step involves results processing, accuracy evaluation, and error analysis. SOC: State of charge; KNN: k-nearest neighbor; SVM: support vector machine; ELM: extreme learning machine; GPR: gaussian process regression.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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