Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends

By Haifeng Dai, Bo Jiang, Xiaosong Hu, Xianke Lin, Xuezhe Wei, Michael G. Pecht. 2020

342 citations recorded by the source corpus.

Abstract Lithium-ion batteries are promising energy storage devices for electric vehicles and renewable energy systems. However, due to complex electrochemical processes, potential safety issues, and inherent poor durability of lithium-ion batteries, it is essential to monitor and manage batteries safely and efficiently. This study reviews the development of battery management systems during the past periods and introduces a multilayer design architecture for advanced battery management, which consists of three progressive layers. The foundation layer focuses on the system physical basis and theoretical principle, the algorithm layer aims at providing a comprehensive understanding of battery, and the application layer ensures a safe and efficient battery system through sufficient management. A comprehensive overview of each layer is presented from both academic and engineering perspectives. Future trends in research and development of next-generation battery management are discussed. Based on data and intelligence, the next-generation battery management will achieve better safety, performance, and interconnectivity.

Subfields: Automotive Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4256ed2dac1ce85f916470a18191d85053114de3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

In Electrical and Electronic Engineering

Citations in the corpus

A review of the internal short circuit mechanism in lithium‐ion batteries: Inducement, detection and prevention (2021, methodology): “The ISC can be diagnosed by quantitative or qualitative analysis methods.(37) The quantitative analysis methods are further divided into analytical model methods and datadriven methods.”