Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation

By Weihan Li, Monika Rentemeister, J. Badeda, Dominik Jöst, D. Schulte, Dirk Uwe Sauer. 2020

520 citations recorded by the source corpus.

Abstract Battery management is critical to enhancing the safety, reliability, and performance of the battery systems. This paper presents a cloud battery management system for battery systems to improve the computational power and data storage capability by cloud computing. With the Internet of Things, all battery relevant data are measured and transmitted to the cloud seamlessly, building up the digital twin for the battery system, where battery diagnostic algorithms evaluate the data and open the window into battery’s charge and aging level. The application of equivalent circuit models in the digital twin for battery systems is explored with the development of cloud-suited state-of-charge and state-of-health estimation approaches. The proposed state-of-charge estimation with an adaptive extended H-infinity filter is robust and accurate for both lithium-ion and lead-acid batteries, even with a significant initialization error. Furthermore, a state-of-health estimation algorithm with particle swarm optimization is innovatively exploited to monitor both capacity fade and power fade of the battery during aging. The functionalities and stability of both hardware and software of the cloud battery management system are validated with prototypes under field operation and experimental validation for both stationary and mobile applications.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a484bc8748291467fae3c773201b21e3305ed007

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Citations in the corpus

Online capacity estimation of lithium-ion batteries with deep long short-term memory networks (2021, background): “Models can be of several types, such as equivalent circuit models [3,10 – 16] or electrochemical models [17 – 24].”
The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities (2021, methodology): “tems [112], battery systems [113], plasma radiation detection in metal absorber–metal resistor bolometer [114], as discussed in Section VII-B.”
State-of-Health Estimation of Lithium-Ion Batteries by Fusing an Open Circuit Voltage Model and Incremental Capacity Analysis (2022, methodology): “However, this method is quite sensitive to parameter determination and model accuracy, which are difficult to guarantee under dynamic conditions over the lifetime of LIBs [26], [27].”