Progress in the prognosis of battery degradation and estimation of battery states

Yuan, Jun; Qin, Zhili; Huang, Haikun; Gan, Xingdong; Wang, Ziwei; Yang, Yichen; Liu, Shujiang; Wen, An; Bi, Chuang; Li, Baihai; Sun, Chenghua

doi:10.1007/s40843-023-2665-8

Cited by 4 publications

(1 citation statement)

References 171 publications

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“…Yuan et al [15] reviewed the latest developments in predicting the state of charge (SOC), state of health (SOH), and remaining useful life (RUL) of lithium-ion batteries (LIBs), particularly via machine learning techniques, delving into the degradation mechanisms of LIBs and their underlying theories, and providing an in-depth analysis of the strengths and limitations of various machine learning techniques used to predict SOC, SOH and RUL.…”

mentioning

confidence: 99%

Editorial: special topic on computation-assisted materials screening and design

Wang,

Sun,

Dong

2024

Sci. China Mater.

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mentioning

confidence: 99%

Editorial: special topic on computation-assisted materials screening and design

Wang,

Sun,

Dong

2024

Sci. China Mater.

Self Cite

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A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges

Xie,

Wang,

Zhang

et al. 2024

Journal of Energy Chemistry

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Evaluation of Fast Charging Efficiency under Extreme Environmental Conditions in Brazil

Pires,

Pontes,

Souza

et al. 2024

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">Fast chargers are necessary for the success of vehicle electrification. These devices can achieve a battery charge rate greater than 4C, significantly increasing the amount of heat generated by the battery. Additionally, the operating temperature of the storage device directly influences the device’s efficiency and lifespan. Given the importance of operation temperature, the Battery Management System (BMS) plays a key role in mitigating heat generation and degradation effects. Despite BMS optimizing battery operation under all possible conditions, the use of fast chargers in extremely hot and cold environments still lowers overall efficiency. In these two worst-case scenarios, the thermal system must manage the ideal charging temperature by consuming part of the energy supplied by the charger. The present work aims to evaluate the charging energy efficiency and time with fast charger utilization, considering the Brazil’s minimum and maximum temperatures registered in 2020. In order to establish the same comparison basis, a vehicle with battery capacity of 42 kWh is modeled and a simplified BMS charging strategy is defined. Experimental tests are done for an electric vehicle submitted to fast charging to validate the model. The results point out that charging under the extreme hot temperature is more efficient than the extreme cold temperature. In comparison to charging at 30°C, charging under the extreme hot temperature decreases the overall efficiency by 1.32% and raises the total charge time by 97 seconds. For the colder temperature case, the efficiency reduction obtained is 4.36% and the time increase by 10 minutes.</div></div>

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Progress in the prognosis of battery degradation and estimation of battery states

Cited by 4 publications

References 171 publications

Editorial: special topic on computation-assisted materials screening and design

Editorial: special topic on computation-assisted materials screening and design

A review of data-driven whole-life state of health prediction for lithium-ion batteries: Data preprocessing, aging characteristics, algorithms, and future challenges

Evaluation of Fast Charging Efficiency under Extreme Environmental Conditions in Brazil

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