High energy density lithium-ion battery state of charge prognosis

The state of charge (SOC) of lithium batteries is one of the most important parameters, and the accuracy of its estimation has a great impact on the management of lithium batteries. To improve the accuracy of SOC estimation of lithium-ion batteries, this chapter analyzes several factors affecting the accuracy of battery SOC estimation, including unavoidable aging effects, strong nonlinear operating characteristics, and complex noise information. The Bayesian filtering algorithm, Kalman filtering algorithm, and its improvement algorithm as well as the correction estimation method under the influence of multiple factors are used to estimate the SOC, respectively. The results are verified and analyzed through a variety of typical working condition experiments.