Lithium-ion battery lifetime extension: A review of derating methods

Ruan, Haijun; Barreras, Jorge Varela; Engstrom, Timothy; Merla, Yu; Millar, Robert P.; Wu, Billy

doi:10.1016/j.jpowsour.2023.232805

Cited by 33 publications

(11 citation statements)

References 99 publications

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“…where the b value reflects the type of the electrochemical reactions during the Na + insertion/extraction process, and it is equal to the slope of the log(v) against log(i) plots according to eqn (1). When the calculated values of b are 0.5 and 1.0, the electrochemical reactions are mainly controlled by an ion-diffusion and a pseudocapacitive process, respectively.…”

Section: Crystengcomm Papermentioning

confidence: 99%

“…Currently, lithium ion batteries (LIBs) are dominant in the electric vehicles. 1,2 However, the high price and uneven distribution of lithium have compelled the development of novel secondary batteries to partially replace LIBs. Sodium has similar physicochemical properties with lithium, and its price is much lower than that of lithium due to its natural abundance.…”

Section: Introductionmentioning

confidence: 99%

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FeSe₂nanocrystalline aggregated microspheres with ultrahigh pseudocapacitive contribution for enhanced sodium-ion storage

Wang¹,

Hou²,

Deng³

et al. 2023

CrystEngComm

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Section: Crystengcomm Papermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FeSe₂nanocrystalline aggregated microspheres with ultrahigh pseudocapacitive contribution for enhanced sodium-ion storage

Wang¹,

Hou²,

Deng³

et al. 2023

CrystEngComm

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“…As a result, Li-ion batteries are widely used in a variety of applications, including transportation systems, electronics, and grid storage, among others [6]. Battery degradation is a complex phenomenon that depends on thermal, mechanical, and electrochemical processes, whose behaviors depend on battery chemistry, cell design, operating conditions, and use patterns [7,8]. As a result, extending the battery life is a complex and challenging research topic that has a significant potential impact on the reliability and performance of battery-powered systems.…”

Section: Introductionmentioning

confidence: 99%

“…To ensure their optimal use, extend their life, improve the maintenance schedules of the battery-powered equipment [12], and minimize the environmental impact associated with battery disposal, accurate state of health (SOH) estimation and prediction is critical. The SOH is a key indicator for awareness of the battery condition and battery derating control [8]. In [13], the SOH is defined as the Q max /Q rated ratio, where Q max is the maximum charge the battery can hold at the present time and Q rated is the nominal or rated charge.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods

de la Vega,

Riba,

Ortega-Redondo

2023

Batteries

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Lithium-ion batteries are key elements in the development of electrical energy storage solutions. However, due to cycling, environmental, and operating conditions, battery capacity tends to degrade over time. Capacity fade is a common indicator of battery state of health (SOH) because it is an indication of how the capacity has been degraded. However, battery capacity cannot be measured directly, and thus, there is an urgent need to develop methods for estimating battery capacity in real time. By analyzing the historical data of a battery in detail, it is possible to predict the future state of a battery and forecast its remaining useful life. This study developed a real-time, simple, and fast method to estimate the cycle capacity of a battery during the charge cycle using only data from a short period of each charge cycle. This proposal is attractive because it does not require data from the entire charge period since batteries are rarely charged from zero to full. The proposed method allows for simultaneous and accurate real-time prediction of the health and remaining useful life of the battery over its lifetime. The accuracy of the proposed method was tested using experimental data from several lithium-ion batteries with different cathode chemistries under various test conditions.

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Multiscale modeling for enhanced battery health analysis: Pathways to longevity

Yang,

Zhang,

Wang

et al. 2024

Carbon Neutralization

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The issues of health assessment and lifespan prediction have always been prominent challenges in the large‐scale application of lithium‐ion batteries (LIBs). This paper reviews the multiscale modeling techniques and their applications in battery health analysis, including atomic scale computational chemistry, particle scale reaction simulations, electrode scale structural models, macroscale electrochemical models, and data‐driven models at the system level. Multiscale modeling offers a profound insight into material behavior and the aging process of batteries, thereby providing a valuable reference for both estimation and management strategies of battery state of health. To extend the battery lifespan, the utilization of artificial intelligence for material discovery and manufacturing process optimization, the implementation of end‐cloud collaborative battery management systems, and the design of a multiscale simulation integration platform are considered. A management framework aimed at extending battery life is further proposed. This framework offers a promising roadmap for addressing health analysis challenges in LIBs, ultimately leading to more reliable, efficient, and durable solutions for next‐generation batteries.

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Lithium-ion battery lifetime extension: A review of derating methods

Cited by 33 publications

References 99 publications

FeSe₂nanocrystalline aggregated microspheres with ultrahigh pseudocapacitive contribution for enhanced sodium-ion storage

FeSe₂nanocrystalline aggregated microspheres with ultrahigh pseudocapacitive contribution for enhanced sodium-ion storage

Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

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Lithium-ion battery lifetime extension: A review of derating methods

Cited by 33 publications

References 99 publications

FeSe2nanocrystalline aggregated microspheres with ultrahigh pseudocapacitive contribution for enhanced sodium-ion storage

FeSe2nanocrystalline aggregated microspheres with ultrahigh pseudocapacitive contribution for enhanced sodium-ion storage

Real-Time Lithium Battery Aging Prediction Based on Capacity Estimation and Deep Learning Methods

Multiscale modeling for enhanced battery health analysis: Pathways to longevity

Contact Info

Product

Resources

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FeSe₂nanocrystalline aggregated microspheres with ultrahigh pseudocapacitive contribution for enhanced sodium-ion storage

FeSe₂nanocrystalline aggregated microspheres with ultrahigh pseudocapacitive contribution for enhanced sodium-ion storage