Remaining capacity estimation for lithium-ion batteries via co-operation of multi-machine learning algorithms

• SVM is proposed to construct the relationship between voltage and capacity. • The peak value of incremental capacity curve is extracted as the health feature. • The preliminary capacity values are achieved based on the SVM, LSTM and GPR. • The random forest algorithm is employed to fuse the preliminary capacity values. Accurate capacity estimation of lithium-ion batteries is of great significance to guarantee their reliability and safety operation. In this paper, a fused capacity estimation method is devised via the co-operation of multi-machine learning algorithms. First, the peak value of incremental capacity curve is extracted as the health feature, and the support vector machine is engaged in data processing and mitigation of the noise-induced unfavorable interference. Then, the preliminary remaining capacity values are estimated based on the incorporation of support vector machine, long short-term memory network and Gaussian process regression with the support of the abstracted health feature. Finally, the random forest algorithm is employed to supply more accurate capacity estimation to fuse the preliminary remaining capacity values. The experimental validations showcase that the advanced algorithm enables to fuse the advantages of individual learners and improve the estimation accuracy. The results indicate that the proposed method can estimate the remaining capacity with the root mean square error of less than 2.4%. In addition, the robustness to noise corruption and the generality to different battery cells are also verified.