State of charge and health estimation of batteries for electric vehicles applications:key issues and challenges

Using electric vehicles(EVs)for transportation is considered as a necessary component for managing sustainable development and environmental issues.The present concerns regarding the environment,such as rapid fossil fuel depletion,increases in air pollution,accelerating energy demands,global warming,and climate change,have paved the way for the electrification of the transport sector.EVs can address all of the aforementioned issues.Portable power supplies have become the lifeline of the EV world,especially lithium-ion(Li-ion)batteries.Li-ion batteries have attracted considerable attention in the EV industry,owing to their high energy density,power density,lifespan,nominal voltage,and cost.One major issue with such batteries concerns providing a quick and accurate estimation of a battery’s state and health;therefore,accurate determinations of the battery’S performance and health,as well as an accurate prediction of its life,are necessary to ensure reliability and efficiency.This study conducts a review of the technological briefs of EVs and their types,as well as the corresponding battery characteristics.Various aspects of recent research and developments in Li-ion battery prognostics and health monitoring are summarized,along with the techniques,algorithms,and models used for current/voltage estimations,state-of-charge(SoC)estimations,capacity estimations,and remaining-useful-life predictions.

A model-based driving cycle test procedure of electric vehicle batteries

The battery test methods are the key issues to investigate the energy-storage characteristics and dynamic characteristics of electric vehicle(EV) batteries.In this paper,the research advances of existing battery test methods as well as driving cycles are reviewed.An electric vehicle model that consists of EV dynamics model,battery model and electric motor model is built.The dynamic characteristics of the battery in frequency domain are analyzed.Based on the EV model and the frequency domain characteristics of the battery,a driving cycle test procedure of EV battery is proposed.The battery test procedure is able to reflect the real-world characteristics of EV batteries,and can be used as a universal EV battery test method.

Single Exposure to Antidepressants during Infancy Is Associated with Delayed Behavioral Changes in C57BL/6 Mice

As serotoninergic transmission plays a crucial role in higher brain function in mammals, the disturbance of this system will likely have significant effects on emotion and cognition. Previous studies have reported that chronic treatment with Specific Serotonin Reuptake Inhibitors (SSRIs) during both late pregnancy and lactation was associated with abnormal behavior in adult rats. These data imply that disturbances in serotoninergic transmission during neurodevelopment may have negative effects on both the structure and function of the resultant adult brain. Therefore, the effect of a single exposure to an SSRI or a tricyclic antidepressant that preferentially inhibits serotonin reuptake during the pre-weaning period was examined in adult mice. An oral infusion of paroxetine (70 mg/kg), fluvoxamine (250 mg/kg), clomipramine (180 mg/kg), or saline was administered on postnatal day 14. Starting at 11 weeks of age, mice were assessed using a comprehensive behavioral test battery. Mice treated with paroxetine demonstrated altered behavior on the open field and hole-board tasks;those treated with fluvoxamine had behavioral changes on the light-dark box, hole-board, and sucrose preference tasks, while alteration in forced swimming and cued fear behavior were noted in mice treated with clomipramine. These results suggest that even a single administration of an antidepressant could have profound effects on behavior in adulthood, although the effects might differ dependent on the specific drug that was administered.

Development and Standardization of a New Cognitive Assessment Test Battery for Chinese Aphasic Patients： A Preliminary Study

Background： Nonlinguistic cognitive impairment has become an important issue for aphasic patients, but currently there are few neuropsychological cognitive assessment tests for it. To get more information on cognitive impairment of aphasic patients, this study aimed to develop a new cognitive assessment test battery for aphasic patients, the Non-language-based Cognitive Assessment （NLCA）, and evaluate its utility in Chinese-speaking patients with aphasia. Methods： The NLCA consists of five nonverbal tests, which could assess five nonlinguistic cognitive domains such as visuospatial functions, attention test, memory, reasoning, and executive functions of aphasic patients. All tests are modified from the nonverbal items of the current existed tests with some changes to the characteristics of Chinese culture. The NLCA was tested in 157 participants （including 57 aphasic patients, 50 mild cognitive impairment （MCI） patients, and 50 normal controls）, and was compared with other well-established relative neuropsychological tests on the reliability, validity, and utility. Results： The NLCA was fully applicable in the MCI patients and the normal controls, almost working in the aphasic patients （57/62 patients, 91.9%）. The NLCA scores were 66.70± 6.30, 48.67± 15.04, and 77.58±2.56 for the MCI group, the aphasic group, and the control group, respectively , and a significant difference was found among three groups （F = 118.446, P 〈 0.001）. The Cronbach＇s alpha of the NLCA as an index of internal consistency was 0.805, and the test-retest and interrater reliability was adequate （r=0.977 and r= 0.970, respectively）. The correlations of the cognitive subtests and their validation instruments were between 0.540 and 0.670 （all P 〈 0.05）. Spearman＆#39;s correlation analysis indicated that the coefficient of internal consistency of each subtest itself was higher than other subtests.When choosing the Montreal Cognitive Assessment score of 〈26 as the diagnostic criteria of cognitive impairment, the area under the curve for all participants in the control and MCI groups was 0.942 （95% confidence interval： 0.895–0.989）, and an optimal cutoff point of 75.00 seemed to provide the best balance between sensitivity and specificity. Age （r = -0.406, P 〈 0.001） was the main influence factor for the NLCA. Conclusions： The NLCA could efficiently differentiate the cognitive impairment patients from the normal controls and is a reliable and valid cognitive assessment test battery to specially find nonlinguistic cognitive function for aphasic patients.

Lithium-ion battery data and where to find it

Lithium-ion batteries are fuelling the advancing renewable-energy based world.At the core of transformational developments in battery design,modelling and management is data.In this work,the datasets associated with lithium batteries in the public domain are summarised.We review the data by mode of experimental testing,giving particular attention to test variables and data provided.Alongside highlighted tools and platforms,over 30 datasets are reviewed.

Performance metrics for the comparison of lithium ion cell aging experiments

The increasing use of lithium-ion cells in large-scale,long-term applications drives a need for design methods that considers aging and accurate state of health estimation.A common approach is to rely on an empirical or semiempirical aging model fit to experimental data to estimate the evolution of capacity and power fade.Because aging data are costly to collect,pack designers either use Design of Experiment(DOE)techniques to define a set of efficient tests,or use existing aging data to calibrate aging models.Given the increasing quantity of available aging data,the question arises:how can experimental aging campaigns be quickly compared?However,a methodology for the comparison of sets of aging experiments is not discussed in the literature.As a result,pack designers usually rely on intuition to select between multiple aging studies proposed by DOE techniques or in the literature.This work proposes metrics to quantitatively capture the alignment between a set of aging experiments and a target application.These metrics allow pack designers to quickly compare many sets of aging experiments to evaluate those which have tested conditions relevant to the application.Case studies are presented to illustrate the application of these metrics using aging campaign data from the literature.To validate these metrics,this work examines the relationship between these metric values and aging model validation error for calendar aging data for 18650 NMC battery cells.It is demonstrated that greater metric values correspond to reduced model error for an empirical capacity fade model.