scholarly journals The effect of cycling on the state of health of the electric vehicle battery

2013 ◽  
Author(s):  
Gillian Lacey ◽  
Tianxiang Jiang ◽  
Ghanim Putrus ◽  
Richard Kotter
Keyword(s):  
Electric Vehicle ◽  
The State ◽  
State Of Health ◽  
Electric Vehicle Battery

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

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.

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

2019 ◽  
Vol 10 (4) ◽  
pp. 63 ◽  
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.

A Review of Research on the State-of-Charge Estimation Methods for Electric Vehicle Battery

2019 ◽  
Vol 13 ◽  
Author(s):  
Mingyue Zhang ◽  
Xiaobin Fan ◽  
Jing Gan ◽  
Zeng Song ◽  
Bin Zhao
Keyword(s):  
Control Theory ◽  
Electric Vehicle ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
The State ◽  
State Of Charge ◽  
Estimation Methods ◽  
Advantages And Disadvantages ◽  
Electric Vehicle Battery ◽  
State Of Charge Estimation

Background: Battery technology has been one of the bottlenecks in electric cars. Whether it is in theory or in practice, the research on battery management is extremely important, especially for battery state-of-charge estimation. In fact, the battery has a strong time change and non-linear properties, which are extremely complex systems. Therefore, accurate estimating the state of charge is a challenging thing. Objective: The study aims to report the latest progress in the studies of the state-of-charge estimation methods for electric vehicle battery. Methods: This paper reviews various representative patents and papers related to the state of charge estimation methods for electric vehicle battery. According to their theoretical and experimental characteristics, the estimation methods were classified into three groups: the traditional estimation algorithm based on the battery experiment, the estimation algorithm based on modern control theory and other estimation algorithm based on the innovative ideas, especially focusing on the algorithms based on control theory. Results: The advantages and disadvantages, current and future developments of the state-of-charge estimation methods are finally provided and discussed. Conclusion: Each kind of state of charge estimation method has its own characteristics, suitable for different occasions. At present, algorithms based on control theory, especially intelligent algorithms, are the focus of research in this field. The future development direction is to establish rich database, improve hardware technology, put up with more perfect battery model, and give full play to the advantages of each algorithm.

scholarly journals A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

2020 ◽  
pp. 095965182095325
Author(s):  
Alireza Rastegarpanah ◽  
Jamie Hathaway ◽  
Mohamed Ahmeid ◽  
Simon Lambert ◽  
Allan Walton ◽  
...  
Keyword(s):  
Neural Network ◽  
End Of Life ◽  
Equivalent Circuit ◽  
Electric Vehicle ◽  
Root Mean Square ◽  
Equivalent Circuit Model ◽  
The State ◽  
Mean Square ◽  
State Of Health ◽  
Estimation Scheme

There is growing interest in recycling and re-use of electric vehicle batteries owing to their growing market share and use of high-value materials such as cobalt and nickel. To inform the subsequent applications at battery end of life, it is necessary to quantify their state of health. This study proposes an estimation scheme for the state of health of high-power lithium-ion batteries based on extraction of parameters from impedance data of 13 Nissan Leaf 2011 battery modules modelled by a modified Randles equivalent circuit model. Using the extracted parameters as predictors for the state of health, a baseline single hidden layer neural network was evaluated by root mean square and peak state of health prediction errors and refined using a Gaussian process optimisation procedure. The optimised neural network predicted state of health with a root mean square error of (1.729 ± 0.147)%, which is shown to be competitive with some of the most performant existing neural network–based state of health estimation schemes, and is expected to outperform the baseline model with ∼50 training samples. The use of equivalent circuit model parameters enables more in-depth analysis of the battery degradation state than many similar neural network–based schemes while maintaining similar accuracy despite a reduced dataset, while there is demonstrated potential for measurement times to be reduced to as little as 30 s with frequency targeting of the impedance measurements.

Hybrid/Electric Vehicle Battery Manufacturing: The State-of-the-Art

2015 ◽  
pp. 795-815
Author(s):  
Claudia P. Arenas Guerrero ◽  
Feng Ju ◽  
Jingshan Li ◽  
Guoxian Xiao ◽  
Stephan Biller
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
State Of The Art ◽  
The State ◽  
Electric Vehicle Battery ◽  
Hybrid Electric
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