State of Power Capability Prediction of Lithium-Ion Battery From the Perspective of Electrochemical Mechanisms Considering Temperature Effect

Sun, Xiaodong; Xu, Naixi; Chen, Qi; Yang, Jufeng; Zhu, Jun; Jing, Xu; Zheng, Linfeng

doi:10.1109/tte.2022.3206452

Cited by 7 publications

(2 citation statements)

References 56 publications

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“…The highest rated LIBs can be remanufactured in automotive scenarios for OEMs or spare parts market [36]. The medium-score LIBs can be repurposed in less demanding scenarios such as grid-related ESS, electric delivery vehicles, and uninterruptible power system (UPS), depending on the evaluation score [37]. LIBs below the reuse standard will be recycled to obtain valuable materials.…”

Section: Circular Value Chain Of Retired Lithium-ion Batteriesmentioning

confidence: 99%

Sustainable value chain of retired lithium-ion batteries for electric vehicles

Hua

Zhou

Huang

et al. 2020

Journal of Power Sources

127

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Section: Circular Value Chain Of Retired Lithium-ion Batteriesmentioning

confidence: 99%

Sustainable value chain of retired lithium-ion batteries for electric vehicles

Hua

Zhou

Huang

et al. 2020

Journal of Power Sources

127

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“…Among the core techniques of BMS, battery capacity estimation plays an essential role in estimating battery state of health (SOH), remaining useful life, and energy capability, but it still poses significant challenges. This is because lithium-ion batteries always demonstrate time-variable and highly nonlinear dynamics, which are involved in complicated electrochemical mechanisms (Deng et al, 2022;Sun et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Robustness enhanced capacity estimation method for lithium-ion batteries based on multi-voltage-interval incremental capacity peaks

Guo

Zheng

et al. 2023

Front. Energy Res.

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Accurate battery capacity estimation can contribute to safe and reliable operations of lithium-ion battery systems. The incremental capacity (IC) based techniques provide promising estimates of battery capacity. However, curve smoothing algorithms are usually required in the IC-based methods, which introduce additional errors and are computationally burdensome. To address this issue, this work proposes a novel approach using multi-voltage-interval IC peaks combined with a back-propagation neural network (BPNN) for battery capacity estimation. Multiple voltage intervals covering relatively narrow and wide values are applied for computing IC curves to enhance the estimation robustness. In particular, there is no need to employ smoothing algorithms. A BPNN is then applied to approximate the correlation between multi-voltage-interval IC peak and capacity. Besides, a five-point moving window technique is proposed to capture multi-voltage-interval IC peaks online effectively. Experimental results show capacity estimates with the majority of relative errors of ±1% and the maximum error of 2%.

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