Abstract
Prediction of the residual useful life of Lithium-ion batteries is one of the hotspots presently. In order to further obtain the residual useful life prediction of Li-ion battery, the degeneration data of it obtained from university of Maryland are analyzed. Discrete data point filtering is performed on the degraded data to simplify the processing. Due to the defects of slow learning speed and easy to fall into local minimum of the Back Proragation Neural Network (BPNN), the fast speed of Levemberg Marquardt (LM) algorithm and the globally search advantage of Genetic Algorithm (GA) are used to deal with. The construction of GA-LM-BPNN is three layers and is used to predict the residual capacity of Li-ion batteries.
Highlights
The prediction of Li-ion battery is the focus of the whole residual useful life (RUL) prediction [1]
In order to overcome the problems of high dependence model and poor adaptability of single prediction method, Back Proragation Neural Network (BPNN) was improved, and the fusion data-driven method was adopted to predict the RUL of Li-ion battery [3]
According to the steps of Genetic Algorithm (GA)-Levemberg Marquardt (LM)-BPNN, the curve of fitness function changes in the GA optimization process decreases with the increase of algebra
Summary
The prediction of Li-ion battery is the focus of the whole residual useful life (RUL) prediction [1]. Dong proposed a sub-optimal method for health diagnosis of satellite Li-ion batteries based on Auto Vector Regression and Particle Filter (AVR-PF) [2]. In order to overcome the problems of high dependence model and poor adaptability of single prediction method, Back Proragation Neural Network (BPNN) was improved, and the fusion data-driven method was adopted to predict the RUL of Li-ion battery [3]
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