Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries

Reniers, Jorn M.; Mulder, G.; Howey, David A.

doi:10.1149/2.0281914jes

Cited by 312 publications

(187 citation statements)

References 86 publications

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“…LIB's capability to store energy decreases over time resulting in capacity fade, power fade or both. This is as a result of complex electrochemical and mechanical processes inside the battery that can take place simultaneously [40]. These degradation mechanisms are influenced by the operating conditions.…”

Section: Degradation Modelsmentioning

confidence: 99%

“…Second, the lifetime of the battery strongly depends on its usage and there are many degradation mechanisms with each influenced by different usage patterns which may affect the overall economic assessment [36]. Beyond the scope of this work is a physics based battery degradation models where capacity fade is modelled due to a side reaction that leads to solid electrolyte interphase layer growth, crack growth in the electrodes, active material loss, and lithium plating amongst few others [37][38][39][40]. These models, therefore, offer an extensive understanding of the concurrent aging mechanisms that could enable testing a wide range of operating conditions and inform control strategies which leads to better battery design.…”

Section: Introductionmentioning

confidence: 99%

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Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models

et al. 2020

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Increased deployment of intermittent renewable energy plants raises concerns about energy security and energy affordability. Capacity markets (CMs) have been implemented to provide investment stability to generators and secure energy generation by reducing the number of shortage hours. The research presented in this paper contributes to answering the question of whether batteries can provide cost effective back up services for one year in this market. The analysis uses an equivalent circuit lithium ion battery model coupled with two degradation models (empirical and semi-empirical) to account for capacity fade during battery lifetime. Depending on the battery’s output power, four de-rating factors of 0.5 h, 1 h, 2 h and 4 h are considered to study which de-rating strategy can result in best economic profit. Two scenarios for the number of shortage hours per year in the CM are predicted based on the energy demand data of Great Britain and recent research. Results show that the estimated battery profit is maximum with 2 h and 1 h de-rating factors and minimum with 4 h and 0.5 h. Depending on the battery degradation model used, battery degradation cost can considerably impact the potential profit if the battery’s temperature is not controlled with adequate thermal management system. The empirical and semi-empirical models predict that the degradation cost is minimum at 5 °C and 25 °C respectively. Moreover, both models predict degradation is minimum at lower battery charge levels. While the battery’s capacity fade can be minimized to make some profits from the CM service, the increased shortage hours can make providing this service not economically viable.

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Section: Degradation Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models

et al. 2020

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“…The rates of theses side reactions are greatly influenced by the operation conditions of the battery, such as the temperature and charge/discharge current, and are time-varying when the battery EV runs on the road. There are various aging estimation models for LIBs, such as physics-based (electrochemical) models [9,24], semi-empirical models [13,25], and equivalent circuit based models (mainly for online estimation) [26].…”

Section: Battery Capacity Fade Modelmentioning

confidence: 99%

Impacts of Driving Conditions on EV Battery Pack Life Cycle

Liu

Chen

Tong

et al. 2020

WEVJ

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The aging of lithium-ion batteries (LIBs) is a crucial issue and must be investigated. The aging rate of LIBs depends not only on the material and electrochemical performance but also on the working conditions. In order to assess the impact of vehicle driving conditions, including the driving cycle, ambient temperature, charging mode, and trip distance on the battery life cycle, this paper first establishes an electric vehicle (EV) energy flow model to solve the operating parameters of the battery pack while working. Then, a powertrain test is carried out to verify the simulation model. Based on the simulated data under different conditions, the battery capacity fade process is estimated by using a semi-empirical aging model. The mileage (Ф) traveled by the vehicle before the end of life (EOL) of the battery pack is then calculated and taken as the evaluation index. The results indicate that the Ф is higher when the vehicle drives the Japanese chassis dynamometer test cycle JC08 than in the New European Driving Cycle (NEDC) and the Federal Test Procedure (FTP-75). The Ф will be dramatically reduced at both low and high ambient temperatures. Fast charging can increase the Ф at low ambient temperatures, whereas long trip driving can always increase Ф to varying degrees.

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“…For these applications and others, physics-based models that are simpler than the DFN model are desired. The simplest of these, the SPM, has been employed in several settings in recent years [10,11,12,13,31]. There has also been a number of papers that provide justification for the SPM and suggest correction terms that may increase the accuracy of the predicted voltage [14,15,16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

An Asymptotic Derivation of a Single Particle Model with Electrolyte

Marquis

Sulzer

Timms

et al. 2019

J. Electrochem. Soc.

151

166

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The standard model of a lithium-ion battery, the Doyle-Fuller-Newman (DFN) model, is computationally expensive to solve. Typically, simpler models, such as the single particle model (SPM), are used to provide insight. Recently, there has been a move to extend the SPM to include electrolyte effects to increase the accuracy and range of applicability. However, these extended models are derived in an ad-hoc manner, which leaves open the possibility that important terms may have been neglected so that these models are not as accurate as possible. In this paper, we provide a systematic asymptotic derivation of both the SPM and a correction term that accounts for the electrolyte behavior. Firstly, this allows us to quantify the error in the reduced model in terms of ratios of key parameters, from which the range of applicable operating conditions can be determined. Secondly, by comparing our model with ad-hoc models from the literature, we show that existing models neglect a key set of terms. In particular, we make the crucial distinction between writing the terminal voltage in pointwise and electrode-averaged form, which allows us to gain additional accuracy over existing models whilst maintaining the same degree of computational complexity.

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Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries

Cited by 312 publications

References 86 publications

Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models

Degradation Cost Analysis of Li-Ion Batteries in the Capacity Market with Different Degradation Models

Impacts of Driving Conditions on EV Battery Pack Life Cycle

An Asymptotic Derivation of a Single Particle Model with Electrolyte

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