A Combined Multiple Factor Degradation Model and Online Verification for Electric Vehicle Batteries

Battery state of health (SOH) is related to the reduction of total capacity due to complicated aging mechanisms known as calendar aging and cycle aging. In this study, a combined multiple factor degradation model was established to predict total capacity fade considering both calendar aging and cycle aging. Multiple factors including temperature, state of charge (SOC), and depth of discharge (DOD) were introduced into the general empirical model to predict capacity fade for electric vehicle batteries. Experiments were carried out under different aging conditions. By fitting the data between multiple factors and model parameters, battery degradation equations related to temperature, SOC, and DOD could be formulated. The combined multiple factor model could be formed based on the battery degradation equations. An online state of health estimation based on the multiple factor model was proposed to verify the correctness of the model. Predictions were in good agreement with experimental data for over 270 days, as the margin of error between the prediction data and the experimental data never exceeded 1%.

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Capacity Fade Estimation of a Lithium-Ion Battery Through an Integrated Electrochemical Battery Model and Empirical Cycle Aging Model

Volume 7A: Dynamics, Vibration, and Control ◽

10.1115/imece2020-24146 ◽

2020 ◽

Author(s):

Sohel Anwar

Keyword(s):

Electric Vehicle ◽

Constant Current ◽

Capacity Fade ◽

State Of Health ◽

Current Profile ◽

Combined Model ◽

Battery Model ◽

Electric Vehicle Battery ◽

Electrochemical Battery ◽

Constant Current Charge

Abstract An electrochemical model based capacity fade estimation method for a Li-Ion battery is investigated in this paper. An empirical capacity fade model for estimating the state of health of a LiFePO4 electric vehicle battery was integrated with electrochemical battery model in Matlab/Simulink platform. This combined model was then validated against experimental data reported in the literature for constant current charge / discharge cycling. An HPPC current profile was then applied to the validated electrochemical-empirical battery prognosis model which reflected a real-time operating condition for charge and discharge current fluctuations in an electric vehicle battery. The combined model was simulated under the two different HPPC current inputs for three different cycle times. Additionally temperature was taken in account in estimating the cycle aging under the applied current profile to assess the present capacity remaining in the battery. The simulation results provided the state of health (SOH) of the battery for these cycling times which were comparable to the published experimental SOH values for constant current charge/discharge profiles. Thus this model can potentially be used to predict the capacity fade status of an electric vehicle battery.

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What influences the referential properties of reflexives and pronouns in Finnish

ZAS Papers in Linguistics ◽

10.21248/zaspil.44.2006.307 ◽

2006 ◽

Vol 44 (1) ◽

pp. 155-169

Author(s):

Elsi Kaiser ◽

Jeffrey T. Runner ◽

Rachel S. Sussman ◽

Michael K. Tanenhaus

Keyword(s):

Factor Model ◽

Binding Theory ◽

Sources Of Information ◽

Reference Resolution ◽

Multiple Factors ◽

Complementary Distribution ◽

Morphological Differences ◽

Multiple Factor ◽

Perceiver Effects

According to standard Binding Theory, pronouns and reflexives are in (nearly) complementary distribution. However, representational NPs (e.g. 'picture of her/herself') allow both. It has been suggested that in English, reflexives in representational NPs (RNPs) have a preference for 'sources of information' and that pronouns prefer 'perceivers of information.' We conducted two experiments investigating the effects of structural and non-structural (source/perceiver) factors on the interpretation of two kinds of RNP structures in a typologically different language, namely Finnish. Our results reveal source/perceiver effects for postnominal but not for prenominal RNPs in Finnish, with a difference in the degree of sensitivity that pronouns and reflexives exhibit to the source/perceiver manipulation, and our results also suggest that morphological differences in Finnish reflexives correspond to interpretation differences. As a whole, these results support a multiple-factor model of reference resolution, which assumes that multiple factors can play a role in reference resolution and that the relative contributions of these factors can be different for different anaphoric forms (Kaiser 2003b, Kaiser & Trueswell in press).

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Evaluation of the End-of-Life of Electric Vehicle Batteries According to the State-of-Health

World Electric Vehicle Journal ◽

10.3390/wevj10040063 ◽

2019 ◽

Vol 10 (4) ◽

pp. 63 ◽

Cited By ~ 3

Author(s):

Casals ◽

Rodríguez ◽

Corchero ◽

Carrillo

Keyword(s):

End Of Life ◽

Electric Vehicle ◽

Statistical Methods ◽

The State ◽

Statistical Distributions ◽

State Of Health ◽

Battery Degradation ◽

Daily Trip

As a result of monitoring thousands of electric vehicle charges around Europe, this study builds statistical distributions that model the amount of energy necessary for trips between charges, showing that most of trips are within the range of electric vehicle even when the battery degradation reaches the end-of-life, commonly accepted to be 80% State of Health. According to these results, this study analyses how far this End-of-Life can be pushed forward using statistical methods and indicating the provability of failing to fulfill the electric vehicle (EV) owners’ daily trip needs.

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Stochastic energy management of an electricity retailer with a novel plug-in electric vehicle-based demand response program and energy storage system: A linearized battery degradation cost model

Sustainable Cities and Society ◽

10.1016/j.scs.2021.103154 ◽

2021 ◽

pp. 103154

Author(s):

Saeed Zeynali ◽

Naghi Rostami ◽

Ali Ahmadian ◽

Ali Elkamel

Keyword(s):

Energy Storage ◽

Electric Vehicle ◽

Energy Management ◽

Demand Response ◽

Cost Model ◽

Storage System ◽

Energy Storage System ◽

System A ◽

Demand Response Program ◽

Battery Degradation

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Performance prediction of transonic axial multistage compressor based on one-dimensional meanline method

Proceedings of the Institution of Mechanical Engineers Part A Journal of Power and Energy ◽

10.1177/0957650921998819 ◽

2021 ◽

pp. 095765092199881

Author(s):

Yingying Zhang ◽

Shijie Zhang

Keyword(s):

Experimental Data ◽

Mass Flow ◽

Flow Separation ◽

Performance Prediction ◽

Factor Model ◽

Separation Method ◽

Prediction Program ◽

Blockage Factor ◽

Reliability And Robustness ◽

Work Done

This study proposes a 1D meanline program for the modeling of modern transonic axial multistage compressors. In this method, an improved blockage factor model is proposed. Work-done factor that varies with the compressor performance conditions is added in this program, and at the same time a notional blockage factor is kept. The coefficient of deviation angle model is tuned according to experimental data. In addition, two surge methods that originated from different sources are chosen to add in and compare with the new method called mass flow separation method. The salient issues presented here deal first with the construction of the compressor program. Three well-documented National Aerodynamics and Space Administration (NASA) axial transonic compressors are calculated, and the speedlines and aerodynamic parameters are compared with the experimental data to verify the reliability and robustness of the proposed method. Results show that consistent agreement can be obtained with such a performance prediction program. It was also apparent that the two common methods of surge prediction, which rely upon either stage or overall characteristic gradients, gave less agreement than the method called mass flow separation method.

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