Author
Yuejiu Zheng
Other affiliations: Tsinghua University
Bio: Yuejiu Zheng is an academic researcher from University of Shanghai for Science and Technology. The author has contributed to research in topics: Battery (electricity) & Battery pack. The author has an hindex of 27, co-authored 91 publications receiving 2889 citations. Previous affiliations of Yuejiu Zheng include Tsinghua University.
Papers
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01 Aug 2019
TL;DR: A comprehensive review on the key issues of the battery degradation among the whole life cycle is provided in this paper, where the battery internal aging mechanisms are reviewed considering different anode and cathode materials for better understanding the battery fade characteristic.
Abstract: The lithium ion battery is widely used in electric vehicles (EV) The battery degradation is the key scientific problem in battery research The battery aging limits its energy storage and power output capability, as well as the performance of the EV including the cost and life span Therefore, a comprehensive review on the key issues of the battery degradation among the whole life cycle is provided in this paper Firstly, the battery internal aging mechanisms are reviewed considering different anode and cathode materials for better understanding the battery fade characteristic Then, to get better life performance, the influence factors affecting battery life are discussed in detail from the perspectives of design, production and application Finally, considering the difference between the cell and system, the battery system degradation mechanism is discussed
695 citations
TL;DR: In this article, the aging mechanism of different types of lithium-ion batteries differs and the loss of lithium inventory, loss of active material, and the increase in resistance may result in battery aging.
540 citations
TL;DR: A novel perspective focusing on the error analysis of the SOC estimation methods is proposed and the error flow charts are proposed to analyze the error sources from the signal measurement to the models and algorithms for the widely used online SOC estimation Methods in new energy vehicles.
287 citations
TL;DR: The results indicate that the model accuracy does not always improve by increasing the order of the RC network, and the higher-order RC model has better robustness considering the variation in model parameters and sensor errors.
247 citations
TL;DR: In this article, the electrical characteristics of the internal short circuit (ISCr) of a large format lithium ion battery are analyzed using the equivalent circuit model (ECM), and an ISCr detection method is developed based on battery consistency within the battery pack.
185 citations
Cited by
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TL;DR: In this article, a review of the key technological developments and scientific challenges for a broad range of Li-ion battery electrodes is presented, and the potential/capacity plots are used to compare many families of suitable materials.
5,057 citations
TL;DR: In this article, the authors provided a comprehensive review on the thermal runaway mechanism of the commercial lithium ion battery for electric vehicles, and a three-level protection concept was proposed to help reduce thermal runaway hazard.
1,604 citations
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
TL;DR: In this paper, the authors present experimental evidence supporting the widely reported degradation modes by means of tests conducted on coin cells, engineered to include different, known amounts of lithium inventory and active electrode material.
829 citations
TL;DR: In this paper, the methods for monitoring the battery state of charge, capacity, impedance parameters, available power, state of health, and remaining useful life are reviewed with the focus on elaboration of their strengths and weaknesses for the use in on-line BMS applications.
827 citations