Chemo-economic analysis of battery aging and capacity fade in lithium-ion battery

Sarkar, Abhishek; Shrotriya, Pranav; Chandra, Abhijit; Hu, Chao

doi:10.1016/j.est.2019.100911

Cited by 20 publications

(11 citation statements)

References 31 publications

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“…Aging models based on SEI formation and Li‐plating have been integrated into P2D models to simulate the battery capacity losses. [ 181,208,211,239–248 ] The additionally required equations are summarized in Table 4 . The SEI formation (Equation (43d)) and Li‐plating (Equation (44)) are frequently described by cathodic Tafel expressions in order to reduce the computational complexity of the simulations.…”

Section: Applications To Libsmentioning

confidence: 99%

“…[232,233] Aging models based on SEI formation and Li-plating have been integrated into P2D models to simulate the battery capacity losses. [181,208,211,[239][240][241][242][243][244][245][246][247][248] The additionally required Table 4. Governing equations of losses in Li inventory models.…”

Section: Wwwadvancedsciencenewscommentioning

confidence: 99%

“…The electrolyte porosity change has been described by Equation (50). In Refs., [211,[239][240][241][242] the SEI growth is implemented into the framework of a P2D model to simulate battery capacity losses. For example, Frank et al [211] modeled the capacity and power losses during battery cycling and storage.…”

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confidence: 99%

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Porous Electrode Modeling and its Applications to Li‐Ion Batteries

Chen

Danilov

Eichel

et al. 2022

Advanced Energy Materials

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Figure 12. a-c) Schematic representation of the electrode potential at electrode/electrolyte interfaces. [145] Red areas indicate the electrode region and blue the electrolyte region. a) The Butler-Volmer approach does not consider charges at the interface. b) More detailed representation of the electrode/ electrolyte interface, considering specifically adsorbed species (green area) and screening counter charges at the surface of the electrode (dark red) and in the electrolyte (dark blue). c) Reduced electrode/electrolyte interface considering the surface charge in the electrode and screening charge in the electrolyte. d) Typical examples of impedance spectra for a porous electrode with insertion-type reactions. [151] Impedance simulations of porous graphite electrodes in contact with electrolyte with e) various particle sizes and f) electrode porosities. [152] a-c) Reproduced with permission. [145]

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Section: Applications To Libsmentioning

confidence: 99%

Section: Wwwadvancedsciencenewscommentioning

confidence: 99%

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Porous Electrode Modeling and its Applications to Li‐Ion Batteries

Chen

Danilov

Eichel

et al. 2022

Advanced Energy Materials

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“…This approach is necessary both for short and long term performance analysis based on the State of Charge (SoC) [68][69][70][71][72] and State of Health [73][74][75][76][77] estimation using different modeling technique and specific testing activities [78][79][80][81]. If proper care of the battery pack working conditions is guaranteed, chemical aging mechanisms can be stemmed achieving a total life of thousands of cycles depending on the specific battery chemistry [82][83][84].…”

Section: Battery Electric Vehiclesmentioning

confidence: 99%

A Review of Hybrid Electric Architectures in Construction, Handling and Agriculture Machines

Mocera¹,

Somà²

2022

New Perspectives on Electric Vehicles

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Recent regulations on pollutant emissions have pushed working machines manufacturers towards research and development efforts to meet the strict limits imposed. For a long time, the use of gas aftertreatment systems have been the most widely accepted solution to reduce the amount of pollutants produced per unit of work done. However, lower emissions limits lead to larger systems and consequently higher difficulties in vehicle integration. Thus, alternative solutions have been studied in the last years to solve the emissions problem using wisely the on-board space. Hybrid electric technologies represent a valuable alternative in this direction. In this work, a review of the current state of the art in the adoption of hybrid and electric technologies on working vehicles is proposed. Due to the high amount of application fields and concepts for special applications, the analysis focused on the three major fields which however includes most of the working machines: Construction, Handling and Agriculture. This work highlights how the requirements of each specific field, strongly affects the design of an optimal hybrid electric architectures.

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“…According to cell size, capacity, cell design, and types of active material in the battery, thermal abuse tolerance of the battery can be varied from the SEI formation [23]. Even though it has a strong relationship with the capacity loss, it is difficult to identify the multi-physical behavior and thermodynamics of the SEI layer in real-time applications because of the model's complexity and a large number of parameters [24]. The SEI growth for the prediction of the SOH can be simplified by using electrochemical impedance spectroscopy (EIS) and an equivalent circuit model (ECM).…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

et al. 2020

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To enhance the efficiency of an energy storage system, it is important to predict and estimate the battery state, including the state of charge (SOC) and state of health (SOH). In general, the statistical approaches for predicting the battery state depend on historical data measured via experiments. The statistical methods based on experimental data may not be suitable for practical applications. After reviewing the various methodologies for predicting the battery capacity without measured data, it is found that a joint estimator that estimates the SOC and SOH is needed to compensate for the data shortage. Therefore, this study proposes an integrated model in which the dual extended Kalman filter (DEKF) and autoregressive (AR) model are combined for predicting the SOH via a statistical model in cases where the amount of measured data is insufficient. The DEKF is advantageous for estimating the battery state in real-time and the AR model performs better for predicting the battery state using previous data. Because the DEKF has limited performance for capacity estimation, the multivariate AR model is employed and a health indicator is used to enhance the performance of the prediction model. The results of the multivariate AR model are significantly better than those obtained using a single variable. The mean absolute percentage errors are 1.45% and 0.5183%, respectively.

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Chemo-economic analysis of battery aging and capacity fade in lithium-ion battery

Cited by 20 publications

References 31 publications

Porous Electrode Modeling and its Applications to Li‐Ion Batteries

Porous Electrode Modeling and its Applications to Li‐Ion Batteries

A Review of Hybrid Electric Architectures in Construction, Handling and Agriculture Machines

Integrated Approach Based on Dual Extended Kalman Filter and Multivariate Autoregressive Model for Predicting Battery Capacity Using Health Indicator and SOC/SOH

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