Online Monitoring of Lithium-Ion Battery Internal Temperature Using PWM Switching Oscillations

Xiang, Dan; Yang, Chen; Li, Hao; Zhou, Yiheng; Zhu, Sitian; Li, Yipeng

doi:10.1109/tpel.2022.3202939

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…v bat (t sw+ ) and i bat (t sw+ ) represent the voltage and current after t sw . Similarly, Xiang et al [244] demonstrated that a battery is equivalent to a series RL circuit at ultra-high frequencies (i.e., 10 kHz-1 MHz), as shown in the top of Fig. 26(e).…”

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confidence: 78%

“…As discussed above, PE-based monitoring schemes mainly refer to online impedance measurement. From complexity perspective, single-point impedance-based monitoring schemes [240], [244] are recommended in actual applications, such as EVs and ESPSs. However, the relationship between single-point impedance and the health status of LIBs should be further investigated.…”

Section: ) Suggested Feasible Solutionsmentioning

confidence: 99%

“…(d) Voltage and current waveforms of batteries in an SC system during a switching instant [240]. (e) TSEP characteristic of a battery at an ultra-high frequency [244]. (f) PWM switching oscillation in a battery system [244].…”

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confidence: 99%

“…(e) TSEP characteristic of a battery at an ultra-high frequency [244]. (f) PWM switching oscillation in a battery system [244]. R eq,in represent the equivalent reactance and resistance of the internal battery, X t and R t represent the equivalent reactance and resistance of external terminals.…”

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confidence: 99%

“…(e) TSEP characteristic of a battery at an ultra-high frequency[244]. (f) PWM switching oscillation in a battery system[244].…”

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confidence: 99%

See 4 more Smart Citations

Power Electronics-Based Safety Enhancement Technologies for Lithium-Ion Batteries: An Overview From Battery Management Perspective

Zhao

et al. 2023

IEEE Trans. Power Electron.

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Safety enhancement for lithium-ion batteries (LIBs) has received a lot of attention from academic and industrial fields. However, there is a lack of overview from the perspective of the application power electronics (PEs) in the systems. This article gives an overview of PE-based safety enhancement technologies for LIBs, mainly focusing on battery management. It introduces the latest advances in battery protection, balancing, monitoring, and lifetime improvement, all based on PE technologies. Detailed discussion and future research opportunities are given. This article aims to provide a reference for PE researchers who want to make some efforts in LIB safety.

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confidence: 78%

Section: ) Suggested Feasible Solutionsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…(e) TSEP characteristic of a battery at an ultra-high frequency[244]. (f) PWM switching oscillation in a battery system[244].…”

mentioning

confidence: 99%

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Power Electronics-Based Safety Enhancement Technologies for Lithium-Ion Batteries: An Overview From Battery Management Perspective

Zhao

et al. 2023

IEEE Trans. Power Electron.

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Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization

Zhao,

Chen,

Shu

et al. 2024

Reliability Engineering & System Safety

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Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

Omakor,

Miah,

Chaoui

2024

IEEE Access

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Lithium-ion (Li-ion) batteries are being used in electric vehicles to reduce the reliance on fossil fuels due to their high energy density, design flexibility, and efficiency compared to other battery technologies. However, they undergo complex nonlinear degradation and performance declines when abused, making their reliability crucial for effective electric vehicle performance. This survey paper presents a comprehensive review of state-of-the-art battery reliability assessments for electric vehicles. First, the operating principle of Li-ion batteries, their degradation patterns, and degradation models are briefly discussed. Afterwards, the reliability assessments of Li-ion batteries are detailed in qualitative and quantitative approaches. The qualitative approach encompasses failure modes mechanisms and effects analysis, X-ray computed tomography, and scanning electron microscopy. In contrast, quantitative approaches involve multiphysics modelling, electrochemical impedance spectroscopy, incremental capacity and differential voltage analysis, machine learning, and transfer learning. Each technique is examined in terms of its principles, advantages, limitations, and applicability in Li-ion batteries for electric vehicles. Comparative analysis reveals that qualitative methods are primarily used in early design stages to assess potential risks and in post-mortem battery analysis in the laboratory, while quantitative techniques such as machine learning and transfer learning offer real-time prognostic health management and anomaly prevention. Also, the quantitative techniques tend to be more cost-effective compared to their counterparts. The potential for consolidating reliability methods through standardization of testing protocols, real-world data integration, controller area network use, and policy regulation are highlighted to guide further research.INDEX TERMS Capacity fade, causes of failure, data-driven, degradation trajectory, electric vehicle, electrochemical impedance spectroscopy, failure modes mechanisms and effects analysis, incremental capacity and differential voltage analysis, Li-ion batteries, machine learning, model-based, power fade, qualitative analysis, quantitative analysis, remaining useful life, reliability, state of health, state of charge, transfer learning, X-ray computed tomography.

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Online Monitoring of Lithium-Ion Battery Internal Temperature Using PWM Switching Oscillations

Cited by 9 publications

References 32 publications

Power Electronics-Based Safety Enhancement Technologies for Lithium-Ion Batteries: An Overview From Battery Management Perspective

Power Electronics-Based Safety Enhancement Technologies for Lithium-Ion Batteries: An Overview From Battery Management Perspective

Online surface temperature prediction and abnormal diagnosis of lithium-ion batteries based on hybrid neural network and fault threshold optimization

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

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