State of Charge Estimation of Battery Based on Neural Networks and Adaptive Strategies with Correntropy

Abstract

Nowadays, electric vehicles have gained great popularity due to their performance and efficiency. Investment in the development of this new technology is justified by increased consciousness of the environmental impacts caused by combustion vehicles such as greenhouse gas emissions, which have contributed to global warming as well as the depletion of non-oil renewable energy source. The lithium-ion battery is an appropriate choice for electric vehicles (EVs) due to its promising features of high voltage, high energy density, low self-discharge, and long life cycles. In this context, State of Charge (SoC) is one of the vital parameters of the battery management system (BMS). Nevertheless, because the discharge and charging of battery cells requires complicated chemical operations, it is therefore hard to determine the state of charge of the battery cell. This paper analyses the application of Artificial Neural Networks (ANNs) in the estimation of the SoC of lithium batteries using the NASA’s research center dataset. Normally, the learning of these networks is performed by some method based on a gradient, having the mean squared error as a cost function. This paper evaluates the substitution of this traditional function by a measure of similarity of the Information Theory, called the Maximum Correntropy Criterion (MCC). This measure of similarity allows statistical moments of a higher order to be considered during the training process. For this reason, it becomes more appropriate for non-Gaussian error distributions and makes training less sensitive to the presence of outliers. However, this can only be achieved by properly adjusting the width of the Gaussian kernel of the correntropy. The proper tuning of this parameter is done using adaptive strategies and genetic algorithms. The proposed identification model was developed using information for training and validation, using a dataset made available in a online repository maintained by NASA’s research center. The obtained results demonstrate that the use of correntropy, as a cost function in the error backpropagation algorithm, makes the identification procedure using ANN networks more robust when compared to the traditional Mean Squared Error.

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

“…On the other hand, Navega et al [37] introduced a dual neural network fusion model for SOC estimation, consisting of a linear neural network LIBs model and a backpropagation (BP) neural network. This model is trained using dynamic stress test (DST) data to establish the relationship between open circuit voltage (OCV) and SOC, enabling accurate SOC estimations under various operational conditions.…”

“…Overall, both methodologies contribute to the advancement of LIBs monitoring and management, with Sun et al [36] focusing on real-time thermal fault detection Navega et al [37] concentrating on accurate SOC estimation. Both approaches offer promising solutions to challenges faced in LIBs applications and hold potential for practical implementation in real-world scenarios.…”

“…The works by Sun et al [36] and Navega et al [37] both contribute innovative methodologies for enhancing the monitoring and management of LIBs, specifically focusing on thermal fault detection and SOC estimation, respectively.…”

State-of-Health Estimation and Anomaly Detection in Li-Ion Batteries Based on a Novel Architecture with Machine Learning

“…Navega et al [22] employed a nonlinear autoregressive model to estimate the SOC with the current, voltage, temperature, and previous SOC features. They used the maximum correntropy criterion as the cost function to train the model.…”

Development of a Simulator for Household Refrigerator Using Equation-Based Optimization Control with Bayesian Calibration

“…An optimal model is determined after conducting a comparison study. Based on input inputs, decision tree models predict the capacity of batteries by learning the decision rules for forecasting battery capacity [37]. This model was used to forecast the performance of a lithium-ion battery based on a multimode degradation analysis, creating several decision trees and aggregating the prediction results of each tree.…”