Optimal Scheduling and Cost-Benefit Analysis of Lithium-Ion Batteries Based on Battery State of Health

Amini, Mohammad; Nazari, Mohammad Hassan; Hoseinian, Seyyed Hossein

doi:10.1109/access.2022.3232282

Cited by 6 publications

(2 citation statements)

References 46 publications

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“…In [32], the superiority of Lithium-ion-Batteries (LiBs) and the Vanadium Redox Flow Battery (VRFB) technologies have been proved for both CES ownership scenarios (individual ownership and shared ownership between DSO and aggregator). Also, [4,33] confirms this result by comparing the performance of Li-ion batteries, lead-acid batteries, nickel-cadmium batteries, sodium-sulfur, and redox flow batteries for CES architecture. To attract all benefits from CES investment, like any ESS, proper subsidy policies and investment decision-making mechanism is a must and can be ensured for the investment value of the microgrid and reduce the initial cost of such a capitalintensive component as reported in [34].…”

Section: B Literature Reviewsupporting

confidence: 61%

Cloud Energy Storage Investment by Collaboration of Microgrids for Profit and Reliability Enhancement Considering a TSO-DSO Yearly Reward

Haghighi

Jalalzad²,

Salehizadeh

et al. 2023

IEEE Access

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A shared pool of grid-scale storage resources called Cloud Energy Storage (CES) can bring substantial benefits to the economical and reliable operation of MGs. However, the investment cost of CES may not be affordable for a single microgrid (MG). As a solution, we propose an approach in which neighboring microgrids in a distribution network collaborate and form a multi-microgrid (multi-MG) to install a shared CES to increase their profit and improve their reliability. Different investment scenarios are evaluated by considering the yearly reward from TSO and DSO. For each of the investment scenarios, TSO and DSO give a yearly reward based on the contribution of CES in peak-shaving and distribution network operation yearly cost reduction. Afterward, a decision table is provided in which, for all investment scenarios, profit, reliability index based on expected energy not supplied (ENS), and TSO-DSO yearly reward are determined. Finally, the microgrids select one of the investment scenarios using a multi-attribute decisionmaking approach. Simulation results of a case study validate the effectiveness of the proposed collaborative decision-making framework in increasing the economic value of CES investment, reliability enhancement in multi-MG, and peak-shaving.

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Section: B Literature Reviewsupporting

confidence: 61%

Cloud Energy Storage Investment by Collaboration of Microgrids for Profit and Reliability Enhancement Considering a TSO-DSO Yearly Reward

Haghighi

Jalalzad²,

Salehizadeh

et al. 2023

IEEE Access

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“…In the aging analysis of Li-ion batteries, different degradation mechanisms have been linked to degradation modes that contribute to power and capacity fading. Some researchers have identified the loss of lithium inventory (LLI) and loss of active materials (LAM) in the anode and cathode as degradation modes [75], [76], whereas others have extended this further to include an increase in the internal resistance (IR) [77], [78], which is inversely referred to as conductivity loss (CL) in [79], [80]. The most prevalent mode of degradation in cell capacity fading is LLI.…”

Section: B Degradation Modes and Effectsmentioning

confidence: 99%

Battery Reliability Assessment in Electric Vehicles: A State-of-the-Art

Omakor,

Miah,

Chaoui

2024

IEEE Access

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Lithium-ion (Li-ion) batteries are being used in electric vehicles to reduce the reliance on fossil fuels due to their high energy density, design flexibility, and efficiency compared to other battery technologies. However, they undergo complex nonlinear degradation and performance declines when abused, making their reliability crucial for effective electric vehicle performance. This survey paper presents a comprehensive review of state-of-the-art battery reliability assessments for electric vehicles. First, the operating principle of Li-ion batteries, their degradation patterns, and degradation models are briefly discussed. Afterwards, the reliability assessments of Li-ion batteries are detailed in qualitative and quantitative approaches. The qualitative approach encompasses failure modes mechanisms and effects analysis, X-ray computed tomography, and scanning electron microscopy. In contrast, quantitative approaches involve multiphysics modelling, electrochemical impedance spectroscopy, incremental capacity and differential voltage analysis, machine learning, and transfer learning. Each technique is examined in terms of its principles, advantages, limitations, and applicability in Li-ion batteries for electric vehicles. Comparative analysis reveals that qualitative methods are primarily used in early design stages to assess potential risks and in post-mortem battery analysis in the laboratory, while quantitative techniques such as machine learning and transfer learning offer real-time prognostic health management and anomaly prevention. Also, the quantitative techniques tend to be more cost-effective compared to their counterparts. The potential for consolidating reliability methods through standardization of testing protocols, real-world data integration, controller area network use, and policy regulation are highlighted to guide further research.INDEX TERMS Capacity fade, causes of failure, data-driven, degradation trajectory, electric vehicle, electrochemical impedance spectroscopy, failure modes mechanisms and effects analysis, incremental capacity and differential voltage analysis, Li-ion batteries, machine learning, model-based, power fade, qualitative analysis, quantitative analysis, remaining useful life, reliability, state of health, state of charge, transfer learning, X-ray computed tomography.

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