A method for capacity prediction of lithium-ion batteries under small sample conditions

Abstract

Accurate life prediction of lithium-ion battery is very important for the safe operation of battery system. At present, the data-driven life prediction method is an effective method. However, it is difficult to obtain full life cycle data of long-life lithium batteries, which leads to low accuracy of prediction results. In addition, the degradation of lithium-ion batteries has different trends in different stages, the commonly used methods are insufficient to describe global time variables which make it difficult to adapt to changes in different stages of lithium-ion battery capacity degradation. To solve the above problems, the paper proposes a deep adaptive continuous time-varying cascade network based on extreme learning machines (CTC-ELM) under the condition of small samples. First, a virtual sample generation method based on multi-population differential evolution is proposed, which uses multi-distribution overall trend diffusion technology to adaptively determine the virtual sample range, and combines with the improved differential evolution algorithm to achieve small sample data amplification. Then, a new prediction network with CTC-ELM is constructed. Finally, it is verified on different data sets. Experiments show that the method proposed can effectively expand the sample set of lithium-ion batteries and achieve high accuracy in the estimation of lithium-ion battery capacity.