Characterization of Li-ion battery and state of charge Estimation methods for diverse battery chemistries: a review

Exact evaluations of Li-ion battery conditions are necessary for efficient battery management. Various chemicals, capacity testing, hybrid pulse power characterization testing analysis, open-circuit voltage analysis, state of charge calculation techniques, direct shaping, adaptive including techniques, and nonlinear control techniques are all part of the research on Li-ion battery technology. Direct measurements require battery-charge designation once, while adaptive methods change estimates based on changing conditions. Nonlinear observer techniques use complex models and expect SoC to be appropriate. Learning algorithms with neural networks of deep learning for reality-based solutions for estimating specific SoCs. Hybrid methods combine particular techniques, such as adaptive neuro-fuzzy interference and artificial neural network systems, to improve the accuracy of SoC estimation. These various methods address complex conditions caused by Li-ion battery chemistry and environmental factors, and ensure reliable SoC estimation for optimal battery performance. This article describes various battery chemistries, unique quality tests, and commonly used methods for calculating energy levels, along with their advantages and disadvantages. Accurate State of Charge (SOC) estimation is vital for efficient Li-ion battery management. This study explores various battery chemistries, quality tests, and SOC estimation methods, including direct measurements, adaptive techniques, and nonlinear control methods. Advanced approaches like neural networks and hybrid techniques enhance SOC accuracy under complex conditions. The findings ensure reliable SOC estimation for optimal battery performance and longevity.