Overview of Battery Models for Sustainable Power and Transport Applications

Tomasov, Marian; Kajanová, Martina; Bracinik, Peter; Motyka, David

doi:10.1016/j.trpro.2019.07.079

Cited by 43 publications

(21 citation statements)

References 22 publications

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“…There are several types of battery models, including electrochemical, electrical, thermal, and combinations of interdisciplinary models (electro-thermal, etc.) [45]. Each model can be used to assist fault diagnosis, depending on the requirements of the Li-ion battery application.…”

Section: Model-based Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges

Fowler

2020

Algorithms

177

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The usage of Lithium-ion (Li-ion) batteries has increased significantly in recent years due to their long lifespan, high energy density, high power density, and environmental benefits. However, various internal and external faults can occur during the battery operation, leading to performance issues and potentially serious consequences, such as thermal runaway, fires, or explosion. Fault diagnosis, hence, is an important function in the battery management system (BMS) and is responsible for detecting faults early and providing control actions to minimize fault effects, to ensure the safe and reliable operation of the battery system. This paper provides a comprehensive review of various fault diagnostic algorithms, including model-based and non-model-based methods. The advantages and disadvantages of the reviewed algorithms, as well as some future challenges for Li-ion battery fault diagnosis, are also discussed in this paper.Algorithms 2020, 13, 62 2 of 18 combustion and explosion [7]. The consequences of Li-ion battery faults can be minimized or eliminated by the BMS, as it prevents the battery from functioning outside its safe operational range, and also detects faults using various fault diagnostic methods [8]. It is an indispensable component in the Li-ion battery system since it can handle failures safely by going into failure mode, providing a safer environment for the users of Li-ion battery applications.Since fault diagnosis is a crucial part of Li-ion battery advancements, various methods for fault diagnosis have been studied and developed. Many fault diagnostic algorithms have been proposed, which can be categorized into model-based and non-model-based methods. Model-based methods include parameter estimation, state estimation, parity space, and structural analysis. Non-model-based methods include signal processing and knowledge-based methods [1,9]. Fault diagnosis research in other fields has shown that the most effective approach is often a combination of more than one method [9].Lu et al.[8] briefly presented fault diagnosis as one of the key issues for Li-ion battery management in electric vehicles. In [10], a review of sensor fault diagnosis for Li-ion battery systems was provided, but other types of faults were not discussed. Wu et al.[1] conducted a review on fault mechanism and diagnosis for Li-ion battery, but there have been many new developments in the field since then. Researchers have made significant progress in understanding the mechanism and operation of Li-ion batteries, and with that, many innovations in battery fault diagnosis have emerged. Therefore, there is a need to identify the current progress and future direction of Li-ion battery fault diagnosis research. The contribution of this paper is the comprehensive review of recent developments in Li-ion battery fault diagnosis, as well as the discussion of some future challenges that need to be addressed.The rest of this paper is organized as follows. Section 2 introduces different types of faults in Li-ion battery, and their causes, ...

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Section: Model-based Methodsmentioning

confidence: 99%

A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges

Fowler

2020

Algorithms

177

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“…The terminal voltage falls sharply after the nominal zone which needs to be avoided. [17][18][19]. The values obtained for the considered A123 Li-iron-phosphate ANR26650M1 battery are K=0.02, A=0.03 V, =3.443 V, B=13.04 Ah -1 , Q=2.3 Ah, and R=0.01 Ω.…”

Section: Simulation Results and Discussionmentioning

confidence: 95%

The performance analysis of modified generic model for Lithium-ion batteries

K¹,

Vasudha²

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Battery technology has been widely used in various applications such as electric vehicles, smart grids, renewable energy systems, and sustainable power applications in recent years. The type, rating, and operating characteristics of batteries vary from one application to another. For example, Electric Vehicles (EVs) need lightweight batteries capable of producing fast energy and withstanding frequent charge/discharge cycles. Renewable energy applications require batteries that can store large energy in the time of excessive electric power generation and can substitute renewable energy sources to supply power in times of non-generation. As batteries are an electro-chemical phenomenon and are generally expensive, it is essential to establish an effective battery model to analyze its behavior under different operating conditions. In this regard, this paper presents a comprehensive study of various battery modeling approaches widely used for the characterization of batteries employed as energy sources for electric vehicles. A simple modified generic battery model is developed and simulated for the ANR26650M1 Li-ion battery in a MATLAB-Simulink environment. The modified generic model includes a polarization voltage term to represent the open-circuit voltage accurately and the polarization resistance term is slightly modified. The model parameters are deduced from the data sheet provided by the battery manufacturers. The discharge curve obtained through simulation is validated with the one given in the datasheet.

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“…According to [19,20], one can separate the most common types of LIB models available on the literature into some categories: statistical models, electrochemical models, electric equivalent circuit models, or a combination of these.…”

Section: Literature Reviewmentioning

confidence: 99%

Open data model parameterization of a second-life Li-ion battery

Seger

Coron²,

Thivel³

et al. 2022

Journal of Energy Storage

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With the fast expansion of Electric and Hybrid Vehicles, the demand of Li-ion batteries is increasing exponentially. This creates an opportunity for a second life of these cells once they are no longer suitable for their original application. An accurate modelling of the second life can be a powerful tool for these new applications. In this work, we propose a complete second life model parameterization for a Li-Ion cell, with its parameters based on experimental data of NMC/LMO cells cycled through their first and second lives. The resulting model parameters span a State of Health from 80 % to 50 % of the cell original capacity, a State of Charge from 0 to 100 %, two current values and two first life temperature points. The model parameters are presented in an open data fashion in the form of look-up tables, ready to be implemented in simulation softwares.

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Overview of Battery Models for Sustainable Power and Transport Applications

Cited by 43 publications

References 22 publications

A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges

A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges

The performance analysis of modified generic model for Lithium-ion batteries

Open data model parameterization of a second-life Li-ion battery

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