Extreme Learning Machine Using Bat Optimization Algorithm for Estimating State of Health of Lithium-Ion Batteries

Abstract

An accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and reliable operation of electric vehicles. As a single hidden-layer feedforward neural network, extreme learning machine (ELM) has the advantages of a fast learning speed and good generalization performance. The bat algorithm (BA) is a swarm intelligence optimization algorithm based on bat echolocation for foraging. In this study, BA was creatively applied to improve the ELM neural network, forming a BA-ELM model, and it was applied to SOH estimation for the first time. First, through Pearson and Spearman correlation analysis, six variables were determined as the input variables of the model. The actual remaining capacity of the battery was determined as the output variable. Second, BA was used to optimize the connection weights and bias in ELM to construct the BA-ELM model. Third, the battery data set was trained and tested with BA-ELM, ELM, Elman, back propagation (BP), radial basis function (RBF), and general regression neural network (GRNN) models. Five statistical error indicators, and the radar chart, scatter plot, and violin diagram were used to compare the estimation effects. The results show that the evaluation function of BA-ELM can converge quickly and effectively optimize the network model of ELM. The RMSE of the BA-ELM model was 0.5354%, and the MAE was 0.4326%, which is the smallest among the 6 models. The RMSE values of the other model were 2.27%, 3.53%, 3.07%, 3.86%, 3.24%, respectively, indicating the BA-ELM has good potential for future applications.

Using Machine Learning to Optimize Resource Use in Batteries and Engines: A Review

“…The last classifier used in battery design is a genetic algorithm boosted artificial neural network (Fini et al 2023). In the battery efficiency group, there were three different versions of Extreme Learning Machine with different variations of genetic algorithm (Ge et al 2022) (Jia et al 2022) (Zhou et al 2023). This group was dominated by this classifier, with a single outlier in the form of a Gradient Boosted Tree ensemble classifier from Rehman et al (2020).…”

“…While these studies focused on using machine learning to rapidly iterate on designs, others focused on using the predictive qualities of the technology to predict resource usage. Ge et al(2022) used a genetic algorithm, specifically Bat Optimization, alongside an extreme learning machine classifier to predict the state of health of a lithium-ion battery. Something that Zhou et al (2023) and Jia et al(2022) did as well with different genetic algorithms, the grey wolf optimization and improved sparrow search algorithm respectively.…”

Critical Review on Improved Electrochemical Impedance Spectroscopy-Cuckoo Search-Elman Neural Network Modeling Methods for Whole-Life-Cycle Health State Estimation of Lithium-Ion Battery Energy Storage Systems

“…Given the novel methods proposed in some recent papers, the superiority of ENN is no longer obvious. In [142], a bat method-extreme learning machine (BA-ELM) model is established for estimation. Five error metrics and three kinds of diagrams are provided to evaluate the estimation results between the ENN and BA-ELM models.…”

Classification Model for Diabetes Mellitus Diagnosis based on K-Means Clustering Algorithm Optimized with Bat Algorithm

“…This enlargement is accompanied by a shift from exploration mode to local intensive exploitation. BA also has been used for many applications, for example travelling salesman problem [18,19], resource scheduling [20,21], customer churn [22,23], brain tumor recognition [24,25], estimating state of health of lithium-ion batteries [26], detection of myocardial infarction [27] and features selection [28,29]. www.ijacsa.thesai.org Based on the background described, this article proposes the development of a method for diagnosing diabetes mellitus based on the K-means clustering algorithm optimized by BA.…”