Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network

Abstract

The battery system is one of the core technologies of the new energy electric vehicle, so the frequent occurrence of safety accidents seriously limits the large-scale promotion and application. An innovative extreme learning machine optimized by genetic algorithm (GA-ELM)-based method is proposed to estimate the current system status, which improves the accuracy and timeliness of fault identification. It is feasible in the application of electric vehicles. To ensure the effectiveness of the signal, the proposed method is adopted using the simple mean filter to clean the data with eliminate wrong points. After the variance analysis, covariance, a horizontal variance of the filtered data, a modified feature parameters matrix is presented. The dimension is reduced by principal component analysis to improve the engineering application ability. Furthermore, a comprehensive GA-ELM-based identification method is proposed to reduce the resulting identification error of extreme learning machines due to the initial value change. More importantly, the sensitivity and accuracy of different solutions are compared and verified, which shows the technique has great potential in battery fault diagnosis based on the voltage signal.

Lithium-Ion Battery State-of-Health Estimation Method Using Isobaric Energy Analysis and PSO-LSTM

“…Commonly employed data-driven methods include support vector machine (SVM) [14], extreme learning machine (ELM) [15], relevance vector regression (RVR) [16], and long short-term memory neural network (LSTM NN) [17]. Among them, the input weights of the ELM algorithm are random and cannot be fne-tuned for changes in data features, which is less controllable and the output results of the model are unstable [18]. Te SVM method is difcult to divide large-scale data samples, and the computational cost will be greatly increased, which requires the selection of regularization parameters, kernel function, and kernel function parameters [15].…”

State of Health Estimation Based on the Long Short-Term Memory Network Using Incremental Capacity and Transfer Learning

“…Compared with the model-based method, the accuracy relies on the accuracy of parameter identification and the complexity of the model, so it is not easy to achieve high reliability and accuracy. With the development of data mining and computing power, the data-driven method has become a hot discussion for more researchers [ 14 , 15 , 16 ]—examples include the support vector machine [ 17 ], the neural network [ 18 ], the extreme learning machine [ 19 ], random forest [ 20 ], and Gaussian filtering [ 21 ].…”