Battery Energy Forecasting in Electric Vehicle Using Deep Residual Neural Network. (6th May 2022)

• Record Type: Journal Article
• Authors: Refaai, Mohamad Reda A.; Bharothu, Jyothilal Nayak; Kumar, T. V. V. Pavan; Srinivas, Chodagam; Sudhakar, M.; Bhowmick, Anirudh
• Other Names: Mohanavel V. Academic Editor.
• Is Part Of: International journal of photoenergy. Volume 2022(2022)
• Publication Date: 2022-05-06
• Subjects: Photochemistry -- Periodicals; Photobiology -- Periodicals; Chemistry, Physical and theoretical -- Periodicals; Photochimie; Photobiologie; Chimie physique et théorique; Chemistry, Physical and theoretical; Photobiology; Photochemistry; Electronic journals; Periodicals; 541.35
• DOI: 10.1155/2022/5959443
• Languages: English
• ISSNs: 1110-662X

Abstract

Abstract : In the recent decade, it is possible to use electric vehicles in a safe, cost-effective, and environmentally friendly manner, but only if accurate and trustworthy state parameter predictions are produced prior to their disposal. The state of health (SOH) of the lithium-ion batteries (LIBs) must be precisely forecasted in order to ensure that the LIB can operate safely. The inability of physical SOH estimators to cope with the dynamic character of SOH when operating in a highly nonlinear environment is a common limitation when operating in nonlinear environments. Traditional SOH estimation techniques have demonstrated that they have limits that can be overcome by data-driven methods. TCN, a new machine learning technique, combines the advantages of residual neural networks (ResNet) with the computing efficiency of neural networks to produce a technique that is both efficient and effective. The results of rgw simulation show that the proposed method has reduced placement cost, and also a TCN can accurately estimate the SOH of a LIB with an MSE error of less than 1% over the LIB lifetime. The performance of an electric car battery, which are numerous and diverse, can be anticipated more precisely using this approach.