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Yi-Ping Chen

Bio: Yi-Ping Chen is an academic researcher from Industrial Technology Research Institute. The author has contributed to research in topics: State of charge & Voltage. The author has an hindex of 2, co-authored 2 publications receiving 981 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, a smart estimation method based on coulomb counting is proposed to improve the estimation accuracy for state-of-charge (SOC) estimation of lithium-ion batteries with high charging and discharging efficiencies.

1,172 citations

Proceedings ArticleDOI
01 Dec 2008
TL;DR: In this article, the authors investigated the relationship between the state-of-charge (SOC) and the dynamically changed open-circuit voltage of lead-acid batteries, and proposed a dynamic open circuit voltage method by taking account of the open circuit time and the previous charging/discharging rate.
Abstract: The charging and discharging characteristics of lead-acid batteries are investigated to learn the relationship between the state-of-charge (SOC) and the dynamically changed open-circuit voltage Experimental results indicate that the open-circuit voltage of the lead-acid battery varies methodically with the charging or discharging rates and the duration since they have been disconnected from the load or charger Accordingly, a dynamic open-circuit voltage method by taking account of the open-circuit time and the previous charging/discharging rate is capable of more precisely estimating the SOC in a shorter time Experiments that emulate practical operations are carried out to demonstrate the effectiveness and accuracy of the proposed method

69 citations


Cited by
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Journal ArticleDOI
Languang Lu1, Xuebing Han1, Jianqiu Li1, Jianfeng Hua, Minggao Ouyang1 
TL;DR: In this article, a brief introduction to the composition of the battery management system (BMS) and its key issues such as battery cell voltage measurement, battery states estimation, battery uniformity and equalization, battery fault diagnosis and so on, is given.

3,650 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a summary of techniques, models, and algorithms used for battery ageing estimation, going from a detailed electrochemical approach to statistical methods based on data, and their respective characteristics are discussed.

1,224 citations

Journal ArticleDOI
TL;DR: In this article, a comprehensive review of the battery state of charge estimation and its management system for the sustainable future electric vehicles (EVs) applications is presented, which can guarantee a reliable and safe operation and assess the battery SOC.
Abstract: Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

1,150 citations

Journal ArticleDOI
TL;DR: In this paper, a battery management system (BMS) for the smart grid and electric vehicles (EVs) has been proposed to improve the performance of Li-ion batteries.
Abstract: With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.

721 citations

Journal ArticleDOI
TL;DR: In this paper, the effects of cold temperatures on the capacity/power fade of Li-ion battery technology are discussed, along with thermal strategies and the ideal approach to cold-temperature operation.

711 citations