State of charge estimation based on a new dual-polarization-resistance model for electric vehicles

Zhao, Xiaowei; Cai, Yunze; Yang, Lin; Deng, Zhongwei; Qiang, Jiaxi

doi:10.1016/j.energy.2017.06.094

Cited by 32 publications

(16 citation statements)

References 30 publications

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“…Hence, it has become an important topic of the research of lithium ion battery. To indicate the performance of battery residual energy and power output capability for a battery pack, the state of charge (SOC) is a commonly used index [90]. To avoid the abuse of batteries and improve the driving safety, the SOC is required to be estimated accurately [91].…”

Section: Analysis Of Abstractmentioning

confidence: 99%

Characteristics and Trends of Research on New Energy Vehicle Reliability Based on the Web of Science

Zhao

Wang

2018

Sustainability

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In order to satisfy the increasing energy demand and deal with the environmental problem caused by the conventional energy vehicle; the new energy vehicle (NEV), especially the electric vehicle (EV), has attracted increasing attention and the corresponding research has developed rapidly in recent years. The electric vehicle requires a battery with high energy density and frequent charging. In order to ensure high performance of the electric vehicle; the reliability of its charging system is extremely important. In this paper; an overview of the research on electric vehicle charging system reliability from 1998 to 2017 is presented from a bibliometric perspective. This study provides a comprehensive analysis of the current research climate and the emerging trends from the following four aspects: basic characteristics of publication outputs; including annual publication outputs and document types; collaboration analysis of countries/territories; institutions and authors; co-citation analysis of cited authors and cited references; co-occurrence analysis of subjects and keywords. By using CiteSpace; the collaboration relationship; co-citation and co-occurrence networks are shown clearly. According to the analysis results; studies in this research field will keep developing rapidly in the near future and several future research directions are proposed in the conclusions.

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Section: Analysis Of Abstractmentioning

confidence: 99%

Characteristics and Trends of Research on New Energy Vehicle Reliability Based on the Web of Science

Zhao

Wang

2018

Sustainability

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“…The state-space representation of battery model is must to implement EKF. The state-space model is formulated and expressed using (14) and (15). Let us consider, state vector, x = [s, V 1 , V 2 ] T Output vector, y = V and input vector, u = I then,…”

Section: B Soc Estimation Algorithmmentioning

confidence: 99%

“…To realize (14), (15) in FPGA, a discrete state-space model is needed which is expressed in (16) and (17).…”

Section: B Soc Estimation Algorithmmentioning

confidence: 99%

FPGA Based SoC Estimator and Constant Current Charging/Discharging Controller for Lead–Acid Battery

Dinesh

Teja

Singh

et al. 2018

2018 15th IEEE India Council International Conference (INDICON)

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The state of charge (SoC) and the rate of charging/discharging current are the vital parameters associated with a battery by which its accurate runtime can be estimated. This paper aims to design a controller which comprises of a fieldprogrammable gate array, back to back connected dc-dc converters, and a resistive touch display based graphical user interface (GUI). The controller estimates SoC and performs constant current charging or discharging of the battery. The implementation of GUI is to input the reference charging/discharging current from the user and to display the SoC. This research delves into battery and obtains its internal parameters by conducting hybrid pulse power characterization test. Furthermore, the obtained internal parameters are processed through an extended Kalman filter which yields the SoC. This controller has large applications in the renewable energy system, battery testing system, smart residential energy management systems, and micro-grids.

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“…There has been an increasing interest in ECM, which is flexible to account the effect of temperature, aging, and other factors on battery operation. 13 Secondly, the nonlinear state observers are introduced to provide the robust performance for model identification, such as Lyapunov-Based observer, 14 extended Kalman filter (EKF), 15,16 unscented Kalman filter, 17 particle filter (PF), 18 H infinity filter (HIF), 19 and some other extensions 20,21 . Among these methods, the Bayesian-based algorithms, such as the family of Kalman filter, HIF, and PF, are widely applied.…”

Section: Introductionmentioning

confidence: 99%

A method for remaining discharge time prediction of lithium-ion batteries under dynamic uncertainty

Zhang

Dong

et al. 2019

Int J Energy Res

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Summary Remaining discharge time of the battery system in electric vehicles relates strongly to the decision‐making of driving. Subjected to the various uncertainties, such as modeling uncertainty, state estimation uncertainty, and future load uncertainty, the accuracy and reliability of the remaining discharge time prediction reduce, which will lead to range anxiety. A stochastic framework based on the state‐of‐charge estimation and prediction strategy is proposed to predict remaining discharge time against the uncertainty. Firstly, the equivalent circuit model is established to model the dynamic behavior of the battery. Through the analysis of different fitting functions of open circuit voltage and state‐of‐charge, the Akaike information criterion is introduced to select the best function. Secondly, the coestimator is employed to bound the influence of the model parameter uncertainty and noise uncertainty. Finally, the uncertainties are quantified by a probabilistic method, and a Monte Carlo–based stochastic prediction strategy is proposed to predict the probability distribution of remaining discharge time under dynamic uncertainty. Experimental results demonstrate that the proposed prediction framework is of great effectiveness as it can provide an accurate remaining discharge time interval under dynamic uncertainty, which helps to promote the energy‐saving driving and overcome the range anxiety.

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State of charge estimation based on a new dual-polarization-resistance model for electric vehicles

Cited by 32 publications

References 30 publications

Characteristics and Trends of Research on New Energy Vehicle Reliability Based on the Web of Science

Characteristics and Trends of Research on New Energy Vehicle Reliability Based on the Web of Science

FPGA Based SoC Estimator and Constant Current Charging/Discharging Controller for Lead–Acid Battery

A method for remaining discharge time prediction of lithium-ion batteries under dynamic uncertainty

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