Battery Lifetime Prognostics
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TLDR
A timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches is presented, analyzed, and compared.About:
This article is published in Joule.The article was published on 2020-02-19 and is currently open access. It has received 471 citations till now. The article focuses on the topics: Battery (electricity) & Prognostics.read more
Citations
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A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems
TL;DR: In this article, a systematic review of the most commonly used battery modeling and state estimation approaches for BMSs is presented, including the physics-based electrochemical models, the integral and fractional order equivalent circuit models, and data-driven models.
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Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives
TL;DR: In this paper, the effects of different internal side reactions on lithium-ion battery degradation are discussed based on the anode, cathode, and other battery structures and the influence of different external factors on the aging mechanism is explained, in which temperature can exert the greatest impact compared to other external factors.
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Future smart battery and management: Advanced sensing from external to embedded multi-dimensional measurement
TL;DR: The transition from conventional LIB system towards higher smartness and the incurred advantages/challenges are overviewed, and the potential change of system-level smart battery integration is further discussed as an open outlook.
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Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems
TL;DR: The state-of-the-art in battery modelling, in-vehicle diagnostic tools, data driven modelling approaches, and how these elements can be combined in a framework for creating a battery digital twin are presented.
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Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends
TL;DR: A multilayer design architecture for advanced battery management, which consists of three progressive layers, which aims at providing a comprehensive understanding of battery, and the application layer ensures a safe and efficient battery system through sufficient management.
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