Optimal dispatch approach for second-life batteries considering degradation with online SoH estimation

Cheng, Ming; Zhang, Xuan; Ran, Aihua; Wei, Guodan; Sun, Hongbin

doi:10.1016/j.rser.2022.113053

Cited by 21 publications

(6 citation statements)

References 41 publications

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“…For instance, Cheng et al have reported an optimal dispatch approach for online SOH estimation of second-life batteries, achieving impressive accuracy by a combination of frequent short-term capacity estimation method and infrequent long-term data-driven prediction model. [90] Lastly, data-driven approaches can be applied to the recycling of batteries incapable to continue servicing as well. Conventionally, mechanical process is adopted to discover valuable electrode materials from these cells, where subsequent hydrometallurgical and pyrometallurgical processes usually require extensive energy input and yield low material recovery rate.…”

Section: Battery Recyclingmentioning

confidence: 99%

“…Most batteries nowadays are retired when their capacity retention drops to 70 %–80 %, which leads to a waste of the remaining capacity. Battery recycling in this case can be useful by endowing them with second service life in lower‐request energy storage systems, which contributes to the reduction of resource waste, economic cost, as well as environmental pollution [90] . In the traditional battery recycling process, intensive time and labor costs are required for cell screening, classification, and disassembly, while online battery prediction provides a nondestructive alternative to assist all these processes [91] …”

Section: Real‐world Application Scenarios Of Online Prognosismentioning

confidence: 99%

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Data‐Driven Online Prognosis of Rechargeable Batteries: Prospect and Perspective

Liu,

Wang,

Liu

et al. 2024

Batteries & Supercaps

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Along with the growing popularity of electric vehicles (EVs) and smart grids, rechargeable batteries are playing an increasingly important role in the field of energy storage. To ensure a safe and stable operation, it remains essential to estimate the states of batteries accurately and efficiently in advance. Herein, we provide a perspective on data‐driven online prognosis of rechargeable batteries, where its scope and superiorities are first introduced. Four promising application scenarios in real world including battery manufacture, EVs, smart grid, and battery recycling are then discussed, followed by the challenges that require further investigation. We anticipate this perspective to attract more interest to this field, to illuminate potential directions for future researches, and to broaden the context of data‐driven physical and engineering sciences.

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Section: Battery Recyclingmentioning

confidence: 99%

Section: Real‐world Application Scenarios Of Online Prognosismentioning

confidence: 99%

Data‐Driven Online Prognosis of Rechargeable Batteries: Prospect and Perspective

Liu,

Wang,

Liu

et al. 2024

Batteries & Supercaps

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“…Among the various published techniques relevant to the storage devices in the MGs, the hybrid energy storage systems (HESS) gained much attention due to their efficiency over the other techniques. The degradation of the battery's lifetime can be well improved by integrating the battery and the super‐capacitor into a HESS 34 . Due to the wind power and SPV's lower voltage rating, boosting voltages are required.…”

Section: Introductionmentioning

confidence: 99%

“…The degradation of the battery's lifetime can be well improved by integrating the battery and the super-capacitor into a HESS. 34 Due to the wind power and SPV's lower voltage rating, boosting voltages are required. Thus, appropriate converter configurations and control structures suitable to integrate the HESS with the RER are still in demand.…”

Section: Introductionmentioning

confidence: 99%

Supply energy management methods for a direct current microgrid: A comprehensive review

Varshnry,

Lather,

Dahiya

2024

Energy Storage

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The revolution of microgrids (MGs) in the modern world has impacted the power systems to deal with the loads with optimal use of renewable energy resources (RERs). In the models consisting of energy management systems (EMS) and generation sources, there will be power fluctuations between the source and the load under certain conditions due to the unpredictable behavior of RERs. Therefore, a reliable scheme has to be implemented between the source and the load to ensure optimal performance of direct current (DC)‐MGs. This article has presented a review of the EMS models introduced by various researchers to enable optimal power supply and efficient operation of the DC‐MGs. A thorough illustration of the schemes adopted to provide efficient operations under grid‐connected and islanded mode of operations are provided. The effects and advantages of centralized and decentralized EMSs are discussed. The role of a battery energy storage system (BESS) in microgrid scheduling is presented and compared with a few other energy storage systems. The scheduling strategies implemented for optimal EMS using different algorithms and techniques are elaborated. In addition, the effects of battery degradation and the formulated solutions to improve the degraded models with improved battery life are also provided with cycle life and calendar life. Then, the lithium ion and lead acid BESS effect on microgrid EMS is presented. Finally, the energy management methods for DC‐MGs end with challenges, research gaps, and future direction.

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“…[ 6 ] However, lithium‐ion batteries (LiBs) performance is bound to degrade gradually due to calendar aging and cyclic aging effects. [ 7 ] The degradation of the state of health (SOH) is a typical manifestation of battery aging. When SOH degradation accumulates to a critical threshold, it can lead to catastrophic consequences such as battery failure, combustion, or even explosion.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network‐Long Short‐Term Memory‐Based State of Health Estimation for Li‐Ion Batteries under Multiple Working Conditions

Feng,

Song,

Lin

et al. 2023

Energy Tech

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The state of health (SOH) for lithium‐ion batteries is an important indicator to ensure the safety and reliability of battery energy storage systems. Aiming at the difficulty of accurately estimating the SOH of lithium‐ion batteries under different working conditions, this article proposes a method based on a hybrid convolutional neural network‐long short‐term memory (CNN‐LSTM) model. First, the battery health indicators and capacity data under different operating conditions are extracted from the public dataset to form a new dataset. Second, the CNN has multiple one‐dimensional convolutional layers to improve the efficiency of feature extraction from new datasets, and the resulting features are used as inputs to the LSTM to predict SOH. Finally, the CNN‐LSTM model integrates a fully connected layer that outputs the estimation of SOH for different operating conditions. The results show that the mean absolute error of the SOH estimation results is within 2.33% and 3.01% for the same and different working conditions, respectively.

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Optimal dispatch approach for second-life batteries considering degradation with online SoH estimation

Cited by 21 publications

References 41 publications

Data‐Driven Online Prognosis of Rechargeable Batteries: Prospect and Perspective

Data‐Driven Online Prognosis of Rechargeable Batteries: Prospect and Perspective

Supply energy management methods for a direct current microgrid: A comprehensive review

Convolutional Neural Network‐Long Short‐Term Memory‐Based State of Health Estimation for Li‐Ion Batteries under Multiple Working Conditions

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