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.

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.

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

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4376 ◽  
Author(s):  
Yuan Chen ◽  
Yigang He ◽  
Zhong Li ◽  
Liping Chen
Keyword(s):  
Experimental Data ◽  
Electric Vehicle ◽  
Factor Model ◽  
Capacity Fade ◽  
State Of Health ◽  
Degradation Model ◽  
Multiple Factors ◽  
Battery Degradation ◽  
Total Capacity ◽  
Multiple Factor

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%.

scholarly journals Electric vehicle battery reuse: Preparing for a second life

2017 ◽  
Vol 10 (2) ◽  
pp. 266 ◽  
Author(s):  
Lluc Canals Casals ◽  
Beatriz Amante García ◽  
Lázaro V. Cremades
Keyword(s):  
End Of Life ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Second Life ◽  
High Capacity ◽  
State Of Health ◽  
Battery Capacity ◽  
Strong Relation ◽  
Battery Technology ◽  
Vehicle Market

Purpose: On pursue of economic revenue, the second life of electric vehicle batteries is closer to reality. Common electric vehicles reach the end of life when batteries loss between a 20 or 30% of its capacity. However, battery technology is evolving fast and the next generation of electric vehicles will have between 300 and 400 km range. This study will analyze different End of Life scenarios according to battery capacity and their possible second life’s opportunities. Additionally, an analysis of the electric vehicle market will define possible locations for battery repurposing or remanufacturing plants.Design/methodology/approach: Calculating the barycenter of the electric vehicle market offers an optimal location to settle the battery repurposing plant from a logistic and environmental perspective.This paper presents several possible applications and remanufacture processes of EV batteries according to the state of health after their collection, analyzing both the direct reuse of the battery and the module dismantling strategy.Findings: The study presents that Netherlands is the best location for installing a battery repurposing plant because of its closeness to EV manufacturers and the potential European EV markets, observing a strong relation between the EV market share and the income per capita.15% of the batteries may be send back to the an EV as a reposition battery, 60% will be prepared for stationary or high capacity installations such as grid services, residential use, Hybrid trucks or electric boats, and finally, the remaining 25% is to be dismantled into modules or cells for smaller applications, such as bicycles or assisting robots.Originality/value: Most of studies related to the EV battery reuse take for granted that they will all have an 80% of its capacity. This study analyzes and proposes a distribution of battery reception and presents different 2nd life alternatives according to their state of health.

Value of psychotherapeutic self-control in energy saving at experts of dangerous professions

2018 ◽  
pp. 56-65
Author(s):  
Е.Ю. Соколов ◽  
А.И. Адаев ◽  
А.А. Фомин ◽  
Л.Г. Магурдумова
Keyword(s):  
Energy Saving ◽  
Military Personnel ◽  
The State ◽  
Self Control ◽  
Preliminary Training ◽  
Control Methods ◽  
State Of Health ◽  
Emergency Situations ◽  
The Military ◽  
Business Trip

In article the importance of use of psychotherapeutic actions of self-control by employees of a dangerous profession is stated during the work in emergency situations. The state of health of fighters who before the direction in business trip were trained previously in self-control methods at different stages of performance of a fighting task, with a condition of group of the military personnel who didn’t pass preliminary training in energy saving methods is compared.

scholarly journals Health Status of Healthcare Professionals Working in an Institute for Chronically Ill Psychiatric Patients

2015 ◽  
Vol 66 (1) ◽  
pp. 43-52
Author(s):  
Katalin Nagyváradi ◽  
Zsuzsa Mátrai
Keyword(s):  
Health Status ◽  
Working Conditions ◽  
Healthcare Professionals ◽  
Psychiatric Patients ◽  
Chronically Ill ◽  
Subjective Health ◽  
The State ◽  
State Of Health ◽  
Subjective Health Status ◽  
Objective Health

AbstractSeveral research works in the related international literature on sociology and health sciences deal with the state of health in one selected population. In these studies, the chosen sample is often connected with special jobs, especially with healthcare professionals and their working conditions. These studies predominantly examine the self-rated subjective health status using questionnaires. There are others that assess the state of health based not only on self-rated subjective indicators, but also using objective data gained by measuring. Considering the international experiences, we chose a special population in our research – healthcare professionals working in an institute for chronically ill psychiatric patients. Our choice was influenced by the fact that we wanted to include their unique working conditions when exploring and assessing their health status. Moreover, our approach was to assess the objective state of health alongside the subjective factors, as our hypothesis was that the majority of the indicators presumably coincided. The data were collected with the help of three questionnaires and some indicators of the objective health statuses were measured. The findings were processed using the SPSS 17.0 mathematical-statistical software package. Following the descriptive statistics, we applied hierarchic cluster-analysis based on results of the WHOQOLD-BREF26 life-quality questionnaire, the WHO WBI-5 Well Being Index, and on the body composition analysis. The results show the objective and subjective health status of population and the factors that influenced it; the working conditions and the interpersonal contacts in the workplace. The conclusion was that in the examined population the subjective and objective health status doesn’t coincide.

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