A study of different machine learning algorithms for state of charge estimation in lithium-ion battery pack

Forecasting the state of charge (SOC) using battery control systems is laborious because of their longevity and reliability. Since battery degradation is typically nonlinear, predicting SOC estimation with significantly less degradation is laborious. So, the estimation of SOC is an increasingly major problem in ensuring the effectiveness and safety of the battery. To overcome these issues in SOC estimation, we found many methods in the scientific literature, with differing degrees of precision and intricacy. The SOC of lithium-ion batteries can now be precisely predicted using supervised learning approaches. Reliable assessment of the SOC of a battery ensures safe operation, extends battery lifespan, and optimizes system performance. This work compares and studies the performance, benefits, and drawbacks of five supervised learning techniques for SOC estimates. Different SOC estimate methods are discussed, including both conventional and contemporary methods. These consist of techniques using voltage and current measurements and more complex algorithms using electrochemical models, impedance spectroscopy, and machine learning methods, incorporating the use of artificial intelligence and machine learning for flexible SOC estimation. In the future, SOC estimates will be a crucial component of a larger ecosystem for energy management, allowing for the seamless integration of energy storage into smart grids and adopting more environmentally friendly energy habits. The five methods we compare are random forest RF, gradient boosting machines, extra tree regressor, XG Boost, and DT. In these five methods, we are going to investigate, review, and discuss the current algorithms and overcome them to select one of the most precise and accurate algorithms to predict the accurate estimation of lithium-ion battery SOC.