A Review on Re-Utilization of Electric Vehicle’s Retired Batteries

Bhatt, Ankit; Tiwari, Shubham; Ongsakul, Weerakorn

doi:10.23919/icue-gesd.2018.8635717

Cited by 6 publications

(7 citation statements)

References 25 publications

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“…The battery with the largest DSOH is N (1,31), which changes by 0.9884%. The worst battery obtained using the other indexes is N (4,196). Based on the evaluation results of the state-oriented and state-change-oriented indexes, the worst 20 battery rankings could be obtained using the RI index.…”

Section: Application Of Reliability Assessment and Weak Component Ana...mentioning

confidence: 99%

“…W ITH its distinct advantages of rapid power response, high energy density, and flexible deployment, battery energy storage (BES) plays an important role in applications such as frequency regulation, peak shaving, renewable energy fluctuation suppression, and economic dispatch [1]- [3]. In addition, with the rapid development of new loads such as electric vehicles (EVs) and internet data centers, recycling has become an urgent need for numerous decommissioned lithium-ion batteries [4]. Grid applications of decommissioned BES systems represent promising solutions [5].…”

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confidence: 99%

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Operational Reliability Modeling and Assessment of Battery Energy Storage Based on Lithium-ion Battery Lifetime Degradation

Cheng

Wan

Zhou

et al. 2022

Journal of Modern Power Systems and Clean Energy

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Battery energy storage (BES) systems can effectively meet the diversified needs of power system dispatching and assist in renewable energy integration. The reliability of energy storage is essential to ensure the operational safety of the power grid. However, BES systems are composed of battery cells. This suggests that BES performance depends not only on the configuration but also on the operating state over different lifetime durations. The lack of safety and reliability is the main bottleneck preventing widespread applications of BES systems. Therefore, a reliability assessment algorithm and a weak-link analytical method for BES systems are proposed while considering battery lifetime degradation. Firstly, a novel lithium-ion battery model is proposed to identify the degradation rate of solid electrolyte interphase film formation and capacity plummeting. The impacts of different operating conditions are considered in stress factor models. Then, a reliability assessment algorithm for a BES system is introduced based on a universal generating function. An innovative weak-link analytical method based on the reliability importance index is proposed that combines the evaluation results of state-oriented and state-change-oriented indexes through an entropy weight method. The model, algorithm, indexes, and the usefulness are demonstrated in case studies based on aging test data and actual bus operating data. The results demonstrate the effects of the battery status and working conditions on BES reliability. Weak-link analysis is also used to assist BES systems in avoiding short-board batteries to achieve long lifetimes and efficient operation.

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Section: Application Of Reliability Assessment and Weak Component Ana...mentioning

confidence: 99%

mentioning

confidence: 99%

Operational Reliability Modeling and Assessment of Battery Energy Storage Based on Lithium-ion Battery Lifetime Degradation

Cheng

Wan

Zhou

et al. 2022

Journal of Modern Power Systems and Clean Energy

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“…With the increasing awareness of green environmental protection, the new energyfriendly vehicle industry has been developed rapidly in many countries [1], which has led to a large amount of residual energy from recyclable batteries for energy storage to meet other applications [2]. Therefore, the recycling and utilization of waste batteries have attracted much attention in the past several years [3].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Optimization of Synchronous Rectification Low-Inductance Current Secondary Boost Converter (SLIC-QBC)

Liu,

Kui,

Gao

et al. 2023

Energies

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For recycling waste batteries, high-gain DC-DC provides a great solution. In this article, the design and optimization of a high-gain converter–synchronous rectification low-inductance current secondary boost converter (SLIC-QBC) is studied. The optimization objective of this article is to propose an automatic design method for passive components and the switching frequency of the converter to improve efficiency and power density. A machine learning-integrated optimization method is proposed to minimize the converter mass and power loss of the converter. In this method, first, a component-based automatic design model is built with embedded SLIC-QBC simulation. Then, a series of design schemes is generated within a large parameter range, and training data for machine learning are collected. Support vector machine (SVM) and artificial neural network (ANN) are used to validate the converter design scheme, where ANN establishes the mapping relationship from design parameters to optimization objectives. After the optimization, an experimental prototype is built for experimental verification. The simulation and experimental results verify the practicability of the proposed method.

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“…12 Due to several characteristics of Li-ion batteries such as high energy density, high power density, a wide range of discharging time, high energy efficiency, low daily selfdischarge, long life cycles, smaller size, and lightweight, vehicle manufacturers give preference to these batteries. [13][14][15] The existence of a battery's second life is only when the capacity of a new battery used in an EV reaches up to 80% of its initial capacity. Batteries are usually replaced from EVs or plug-in hybrid EVs after completing their life of around 6-8 years, due to their low performance, warranty, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Among these three classifications, lithium‐ion (Li‐ion) batteries that come under secondary batteries, are considered to be the key battery technologies used in EVs 12 . Due to several characteristics of Li‐ion batteries such as high energy density, high power density, a wide range of discharging time, high energy efficiency, low daily self‐discharge, long life cycles, smaller size, and lightweight, vehicle manufacturers give preference to these batteries 13‐15 . The existence of a battery's second life is only when the capacity of a new battery used in an EV reaches up to 80% of its initial capacity.…”

Section: Introductionmentioning

confidence: 99%

Machinelearning‐basedapproach for useful capacity prediction ofsecond‐lifebatteries employing appropriate input selection

Bhatt

Ongsakul²,

Madhu

et al. 2021

Intl J of Energy Research

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Electric vehicle-discarded second-life batteries still contain 80% of usable capacity and can serve as a low-cost alternative for microgrid storage applications where the battery storage capacity is flexible against transport applications. By accurately predicting the remaining useful capacity or state of health of these batteries, using the data from their first life operation, their costeffectiveness for microgrid energy management can be analyzed. For this purpose, three machine learning models are proposed here. The input parameters for the models are selected from the charging and discharging profiles of batteries, considering both the aging and regeneration phenomenon. Eight different input cases with and without K-fold (K = 10) cross-validation are used for training the proposed models. Based on the comparative analysis it is found that all the models trained with K-fold cross-validation, show minimum error as compared to without K-fold. The forecasting results from multiple approaches showed that the long short term memory model trained with battery discharging profile outperformed other models, quantified by multiple error indices, including root mean square error (0.009147), mean absolute error (0.005841), and R 2 (0.9713). The robustness of the model is validated with multiple battery datasets. Further, the study illustrates the importance of activation functions, in machine learning models used for forecasting.

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A Review on Re-Utilization of Electric Vehicle’s Retired Batteries

Cited by 6 publications

References 25 publications

Operational Reliability Modeling and Assessment of Battery Energy Storage Based on Lithium-ion Battery Lifetime Degradation

Operational Reliability Modeling and Assessment of Battery Energy Storage Based on Lithium-ion Battery Lifetime Degradation

Machine Learning-Based Optimization of Synchronous Rectification Low-Inductance Current Secondary Boost Converter (SLIC-QBC)

Machinelearning‐basedapproach for useful capacity prediction ofsecond‐lifebatteries employing appropriate input selection

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