Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework

The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for inventing an alternative eco-friendly transportation fleet. Plug-in electrical vehicles (PEVs) are introduced to cut the continuing increase in energy use and carbon emission of the urban mobility. However, the increased demand for mobility, and therefore energy, can create constraints on the power network which can reduce the benefits of electrification as a certain and reliable source. Thus, the rise in the use of electric vehicles needs electric grids to be able to feed the increased energy demand while the current infrastructure supports it. In this paper, we introduce a methodological framework for scheduling smart PEVs charging by considering the uncertainties and battery degradation. This framework includes an economic model for charging and discharging of PEVs which has been implemented in a 21-node sample distribution network with a wind turbine as a distributed generation (DG) unit. Our proposed approach indicates that the optimal charging of the PEVs has a high impact on the distribution network operation, particularly under the high market penetration of PEVs. Thus, the smart grid to vehicle (G2V) charging mode is a potential solution to maximize the PEV’s owner profit, while considering the battery degradation cost of the PEVs. The simulation result indicates that smart charging effectuation is economical.

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“…In Equation (10), battery degradation cost has been calculated based on the PEVs total battery degradation at each time for each scenario [21].…”

Section: Problem Statementmentioning

confidence: 99%

“…Stem from the United States Advanced Battery Consortium (USABC) PEVs' battery should be changed if the battery capacity is reduced to 80% of its total capacity [21], thus ρ = 0.8.…”

Section: Problem Statementmentioning

confidence: 99%

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

et al. 2019

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“…Most of the battery degradation literature consists of empirical-based studies with results extracted from experimental tests in laboratories [93]. Ahmadian et al in [93] present the most prominent degradation models and the effects of degradation factors on LiBs' performance.…”

Section: Model-based Estimation Methodsmentioning

confidence: 99%

“…Ahmadian et al in [93] present the most prominent degradation models and the effects of degradation factors on LiBs' performance. Bashash et al in [94] state that there is an exponential relation between calendar aging and SoC.…”

Section: Model-based Estimation Methodsmentioning

confidence: 99%

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SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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Battery degradation: Impact on economic dispatch

Schade,

Egging‐Bratseth

2024

Energy Storage

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Batteries are crucial to manage the rising share of intermittent energy sources and variability in demand. Most technoeconomic models in the literature oversimplify battery degradation representation. Accounting properly for battery degradation allows for better cost tradeoffs and optimal battery usage, especially in dynamic settings. We propose a highly accurate and scalable formulation for battery degradation that considers the combined impact of cycle depth (CD) and state of charge on calendar and cycle aging. This includes a novel way to track charge‐discharge cycles. We test the consequences of battery degradation in a stylized price arbitrage model on battery operation and solution times. When ignoring battery degradation, ex post calculations reveal hidden degradation costs that exceed revenues and hence turn seemingly profitable trades into losing trades. Considering battery degradation leads to smaller CDs and lower average states of charge. Overall, we show that a much‐improved representation of battery degradation is possible at modest computational cost, allowing better decisions and higher profits.

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Plug-in electric vehicle batteries degradation modeling for smart grid studies: Review, assessment and conceptual framework

Cited by 143 publications

References 91 publications

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

Battery degradation: Impact on economic dispatch

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