Evaluation of Present Accelerated Temperature Testing and Modeling of Batteries

Diao, Weiping; Xing, Yinjiao; Saxena, Saurabh; Pecht, Michael

doi:10.3390/app8101786

Cited by 14 publications

(3 citation statements)

References 32 publications

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“…However, accelerated degradation is abnormal and can cause severe problems in Li-ion battery applications. The degradation process is accelerated during storage at elevated temperatures [27]. External degradation is also accelerated due to factors, such as impedance increase, higher frequency of cycle, change in SOC, and voltage rates [28,29].…”

Section: Accelerated Degradationmentioning

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: Accelerated Degradationmentioning

confidence: 99%

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

Fowler

2020

Algorithms

177

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“…Overheating is also caused by an external heat input or a defect in the cooling system. In general, increased temperature leads to accelerated degradation [28], which is characterized by excessive capacity fading and resistance increase. Any of the internal faults can result in thermal runaway which is also the most catastrophic fault and is associated with battery swelling, electrolyte leakage, smoke, fire, and explosion [25,26].…”

Section: Fault Diagnosismentioning

confidence: 99%

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

et al. 2021

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This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

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“…Lithium-ion batteries have been widely used in portable electronic devices, electric and aerospace vehicles, and energy storage systems because they offer high energy and power density, and long cycle life operation [1,2]. The performance of lithium-ion batteries can be evaluated by capacity, stored energy, and internal resistance.…”

Section: Introductionmentioning

confidence: 99%

Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells

et al. 2019

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Lithium-ion batteries typically exhibit a transition to a more rapid capacity fade trend when subjected to extended charge-discharge cycles and storage conditions. The identification of the knee point can be valuable to identify the more severe degradation trend, and to provide guidance when scheduling battery replacements and planning secondary uses of the battery. However, a concise and repeatable determination of a knee point has not been documented. This paper provides a definition of the knee point which can be used as a degradation metric, and develops an algorithm to identify it. The algorithm is implemented on various data cases, and the results indicate that the approach provides repeatable knee point identification.

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Evaluation of Present Accelerated Temperature Testing and Modeling of Batteries

Cited by 14 publications

References 32 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

Critical Review of Intelligent Battery Systems: Challenges, Implementation, and Potential for Electric Vehicles

Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells

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