Recent Advancements in Battery Management System for Li‐Ion Batteries of Electric Vehicles: Future Role of Digital Twin, Cyber‐Physical Systems, Battery Swapping Technology, and Nondestructive Testing

Abstract

The increasing popularity of the electric vehicles (EVs) is due to various environmental impacts of the gasoline‐/diesel‐based vehicles over the past few decades. EVs are commercialized in various parts of world but their full‐scale commercialization has not yet attained. Despite of many advantages, challenges associated with the use of EVs are their range anxiety, slow charging, and the performance/cost of battery. A thorough review from the year 2006 to 2020 is conducted in the field of battery management system (BMS). Herein, various functions, advantages, and disadvantages of methods used in BMS for cell balancing, thermal management, and protection of battery against over‐voltage and over current, estimation of state of health, and estimation of state of charge of battery are discussed. Additionally, critical gaps are identified and a framework for design of an efficient BMS is proposed. The deployment of advanced intelligent and smart technologies such as digital‐twin of battery pack, cyber‐physical systems, battery swapping technology, nondestructive testing, self‐reconfigurable batteries, and prudent recycling/reusability using automation are also discussed. In‐brief, critical gaps; advanced technologies and framework that researchers can use to develop comprehensive systems comprising advanced BMS; real‐time battery monitoring, and battery reusability and recycling; as a whole complete unit are provided.

Influence of the Intraday Electricity Market Structure on the Degradation of Li‐Ion Batteries Used to Firm Photovoltaic Production

“…[32] Diverse types of degradation models are available in the literature to predict the aging of the batteries. [33,34] These are usually classified into [35] : electrochemical models (detail and model the phenomena occurring into the battery), [36,37] equivalent circuit-based models (the battery is reduced as an equivalent circuit model), [38][39][40] analytical or semiempirical models with empirical fitting (estimation of aging parameters through measurements), [41][42][43] and statistical approaches or data-driven/ machine-learning-based models (mainly based on data, without any a priori knowledge). [44][45][46][47] However, we consider that among all of them, the semiempirical models are the best option for analyses such as those performed in this work in terms of complexity, computational burden, real-time response, and reliability trade-off.…”

CNC Machine Tool Fault Diagnosis Integrated Rescheduling Approach Supported by Digital Twin-Driven Interaction and Cooperation Framework

“…The feedback values are used to update the Q values in each behavior on the path during the backpropagation process. The feedback value R is calculated by using the unit index value v of the node in the termination state in this round, which can be calculated by (6). However, since the values of Cmax, SI, and F are taken to be as small as possible, the final feedback value obtained is the opposite of the unit index value, that is R=-v.…”

“…With the continuous development of new-generation information technologies, such as artificial intelligence, 5G, big data, and Internet of Things (IoT), the major multinational enterprises have combined their technological advantages to further develop DT, and new national advanced manufacturing strategies around the world have made DT a key research issue. DT has been wildly used in various industries, including manufacturing [5], automobile [6], healthcare [7], smart city [8], etc., effectively promoting their development process of digitalization, networking, and intellectualization.…”

Reconfigurable battery systems: Challenges and safety solutions using intelligent system framework based on digital twins

“…Recently, the notion of digital twin is receiving great attention as it can be used for real-time monitoring of complex multiphysics systems and is also responsible for bi-directional mapping of the actual physical system to its virtual system [6][7][8]. Digital twin is not one specific method but a comprehensive framework that is based on a combination of advanced technologies, such as artificial intelligence (AI) with clusters of machine learning (ML) algorithms, Internet of Things (IoT), blockchain, cloud storage and cloud computing, sensors, hardware etc., to achieve its primary goal of connecting virtual systems to real systems and predict and optimise its behaviour in real time [9,10].…”

“…To understand the state-of-the-art technologies and applicability of digital twin in the field of batteries, the authors referred to several review studies [6][7][8]. These surveys listed a qualitative framework for digital twin that can be applied to the batteries of fixed topology designs.…”