The Relevance Vector Machine (RVM) which has uncertain expression and management capabilities becomes an effective method for estimating the state of charge (SOC) of Li-ion batteries. However, the algorithm has the problems of low multi-step prediction accuracy and poor online prediction adaptability, which limits its application for battery SOC estimation. So, an improved incremental RVM model is proposed to predict the SOC of Li-ion battery online. Through the ways of dynamic training model and on-line incremental learning to improve the prediction accuracy of the model, and the fast-sequence sparse Bayesian learning algorithm is selected for training to reduce the computational complexity and improve the computational efficiency of the algorithm. The study found that this model can guarantee higher prediction accuracy by adjusting the kernel width automatically. Experimental results show that this method has the characteristics of high prediction accuracy, fast calculation speed, and strong universality, it can provide a reference for Li-ion batteries SOC prediction and application.
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