Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge

Abstract

Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasis on machine learning for SOH estimation. Furthermore, data preprocessing has received much attention because it is an important step in determining the efficiency of machine learning methods. In this paper, we propose a new preprocessing method for improving the efficiency of machine learning for SOH estimation. The proposed method consists of the relative state of charge (SOC) and data processing, which transforms time-domain data into SOC-domain data. According to the correlation analysis, SOC-domain data are more correlated with the usable capacity than time-domain data. Furthermore, we compare the estimation results of SOC-based data and time-based data in feedforward neural networks (FNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM). The results show that the SOC-based preprocessing outperforms conventional time-domain data-based techniques. Furthermore, the accuracy of the simplest FNN model with the proposed method is higher than that of the CNN model and the LSTM model with a conventional method when training data are small.

On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles

“…Examples of data-driven methods include a support vector machine (SVM) [25][26][27][28][29], random forest (RF) [30,31], artificial neural networks (ANNs) [32][33][34][35][36][37], recurrent-neural networks (RNNs) [38], and variants such as long short-term memory (LSTM) [39][40][41][42][43][44] and a nonlinear autoregressive network with exogenous inputs (NARX) [45][46][47]. ANNs learn from historical data to predict future behavior, i.e., the learning procedure exploits a dataset representative of the battery behaviour to make predictions.…”

A Convolutional Neural Network Approach for Estimation of Li-Ion Battery State of Health from Charge Profiles

“…A CNN is combined with a gated recurrent neural network in [12] and two CNNs are used with random forest in [13], with both combinations showing improved SOH estimation. Finally, a different method of sampling data, at fixed SOC steps rather than fixed time steps, was shown to be beneficial for battery SOH estimation with a CNN, FNN, or LSTM [14].…”

State-of-Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Deep Neural Network

“…The authors of [24] have trained a CNN model to predict battery capacity based on the observed impedance and SoC variables collected during the experiment. After all that, it has been demonstrated that using a different sampling strategy, such as defined SoC phases rather than defined time steps, is useful for battery SoH estimation when using FNN, CNN, or LSTM [25]. SoC estimate using a Nonlinear Autoregressive Exogenous neural network (NARX) is of specific interest to Zhang [26], who plans to contrast the anticipated outcome with that obtained using EKF.…”