Batteries State of Health Estimation via Efficient Neural Networks With Multiple Channel Charging Profiles

Abstract

The prognostics and health management (PHM) plays the main role to handle the risk of failure before its occurrence. Next, it has a broad spectrum of applications including utility networks, energy storage systems (ESS), etc. However, an accurate capacity estimation of batteries in ESS is mandatory for their safe operations and decision making policy. ESS comprises of different storage mechanisms such as batteries, capacitors, etc. Consequently, the measurement of different charging profiles (CPs) has a strong relation to battery capacity. These profiles include temperature (T), voltage (V), and current (I) where the CPs patterns vary as the battery ages with cycles. Consequently, estimating a battery capacity, the conventional methods practice single channel charging profile (SCCP) and hop multiple channel CPs (MCCPs) that cause incorrect battery health estimation. To tackle these issues, this article proposes MCCPs based battery management system (BMS) to estimate batteries health/capacity through the deep learning (DL) concept where the patterns in these CPs are changed as the battery ages with time and cycles. Thus, we deeply investigate both machine learning (ML) and DL based methods to provide a concrete comparative analysis of our method. The adaptive boosting (AB) and support vector regression (SVR) are widely compared with long short-term memory (LSTM), multi-layer perceptron (MLP), bi-directional LSTM (BiLSTM), and convolutional neural network (CNN) to attain the appropriate approach for battery capacity and state of health (SOH) estimation. These approaches have a high learning capability of inter-relation between the battery capacity and variation in CPs patterns. To validate and verify the proposed technique, we use NASA battery dataset and experimentally prove that BiLSTM outperforms all the approaches and obtains the smallest error values for MAE, MSE, RMSE, and MAPE using MCCPs compared to SCCP.

A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods

“…With the arrival of big data and 5G cloud computing, the shortcoming of algorithm complexity can be made up for and alleviated by more powerful computing power. More complex deep learning algorithms can be applied in SOH estimation and life prediction of lithium batteries [104].…”

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos

“…In the current era, deep feature-based models have achieved great success in numerous domains of nonlinear high dimensional data, such as activity recognition [31] and video summarization [32], among many others [33,34]. Most of the previous literature is based on semi-supervised anomaly detection techniques in which the model is trained on normal data.…”

Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors

“…where [50]. Two layers of the network are concurrently processing the input data, with each one operating a particular function.…”

“…More precisely, another two layers also operate on sequence data but in a different direction, and in the last step, the final outcomes of both layers are combined with the appropriate method [51]. In this study, a hybrid model is proposed by integrating ConvLSTM [43] with BiLSTM [50] for energy data forecasting after extensive experiments and ablations study of various sequence learning models.…”