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

• Published in: IEEE access 2021, Vol.9, p.7797-7813
• Main Authors: Khan, Noman, Ullah, Fath U Min, Afnan, Ullah, Amin, Lee, Mi Young, Baik, Sung Wook
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Batteries; Battery cycles; Battery management system; bi-directional LSTM; capacity; Charging; Decision making; deep learning; Energy storage; energy storage systems; ensemble learning; Estimation; Integrated circuit modeling; Lithium-ion batteries; lithium-ion battery; Machine learning; Multilayers; Neural networks; Prognostics and health management; Regression analysis; renewable energy sources; Root-mean-square errors; smart grid; state of health; Storage batteries; Storage systems; Support vector machines; Temperature measurement; transfer learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3047732

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.