Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems

Wu, Billy; Widanage, Widanalage Dhammika; Yang, Shichun; Liu, Xinhua

doi:10.1016/j.egyai.2020.100016

Cited by 188 publications

(57 citation statements)

References 64 publications

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“…The setting of a multi sensor integration platform that fuses the established models and state indicators algorithms developed via AI is essential for making the concept of battery digital twins a reality. Several authors have recently suggested the development of battery digital twins in both the manufacturing domain and at the BMS level [116,117] and below we discuss how such approaches can be fused with the models presented.…”

Section: Multisensory Functionalitiesmentioning

confidence: 99%

Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches

Vegge¹,

Tarascon

Edström

2021

Advanced Energy Materials

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With an exponentially growing demand for rechargeable batteries, the development of new ultra‐performant, fully scalable, and sustainable battery technologies and materials must be accelerated. Creating a holistic, closed‐loop infrastructure for materials discovery, manufacturing, and battery testing that utilizes a common data infrastructure and autonomous workflows to bridge big data from all domains of the battery value chain, can pave the way for a transformative reduction in the required time to discovery. By embedding multisensory and self‐healing capabilities in future battery technologies and integrating these with AI and physics‐aware machine learning models capable of predicting the spatio‐temporal evolution of battery materials and interfaces, it will, in time, be possible to identify, predict and prevent potential degradation and failure modes. This will facilitate enhanced battery quality, reliability, and life, for example, by preemptively changing the battery charging conditions or releasing self‐healing additives from the separator membrane, akin to preemptive medicine, and form the basis for inverse design of new battery materials, interfaces, and additives. The large‐scale and long‐term European research initiative BATTERY 2030+ seeks to make this longer‐than ten‐year vision a reality through the development of a versatile and chemistry neutral “Battery Interface Genome—Materials Acceleration Platform” infrastructure (BIG‐MAP).

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Section: Multisensory Functionalitiesmentioning

confidence: 99%

Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches

Vegge¹,

Tarascon

Edström

2021

Advanced Energy Materials

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show abstract

“…We et al [ 75 ] proposed the integration of battery modeling with AI‐based methods for developing the digital twin of the Li‐ion batteries. The accurate battery modeling and its monitoring needs the accurate knowledge of the electrochemistry associated with it and the deeper understanding of mechanical, electrical, thermal, and material aspects of the battery.…”

Section: Future Research Directionsmentioning

confidence: 99%

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

Panwar

Singh²,

Garg

et al. 2021

Energy Tech

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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.

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“…The future EVs can provide a cost and effort reduction in the system design, verification, testing and time-to-market thanks to the EV digital twin. Five technology trends are developing in a complementary way to enable digital twins, namely, the IoT, cloud computing, APIs and open standards, artificial intelligence (AI) and digital reality technologies [22,23]. Figure 1 depicts the digital twin concept containing the real space's physical device, which is a commercial EV drivetrain (e.g., battery, power electronics converters and motor) with sensors and control units.…”

Section: Digital Twin Development For Evs and Its Associated Benefitsmentioning

confidence: 99%

Beyond the State of the Art of Electric Vehicles: A Fact-Based Paper of the Current and Prospective Electric Vehicle Technologies

Mierlo

Berecibar

Baghdadi

et al. 2021

WEVJ

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Today, there are many recent developments that focus on improving the electric vehicles and their components, particularly regarding advances in batteries, energy management systems, autonomous features and charging infrastructure. This plays an important role in developing next electric vehicle generations, and encourages more efficient and sustainable eco-system. This paper not only provides insights in the latest knowledge and developments of electric vehicles (EVs), but also the new promising and novel EV technologies based on scientific facts and figures—which could be from a technological point of view feasible by 2030. In this paper, potential design and modelling tools, such as digital twin with connected Internet-of-Things (IoT), are addressed. Furthermore, the potential technological challenges and research gaps in all EV aspects from hard-core battery material sciences, power electronics and powertrain engineering up to environmental assessments and market considerations are addressed. The paper is based on the knowledge of the 140+ FTE counting multidisciplinary research centre MOBI-VUB, that has a 40-year track record in the field of electric vehicles and e-mobility.

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Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems

Cited by 188 publications

References 64 publications

Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches

Toward Better and Smarter Batteries by Combining AI with Multisensory and Self‐Healing Approaches

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

Beyond the State of the Art of Electric Vehicles: A Fact-Based Paper of the Current and Prospective Electric Vehicle Technologies

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