A review of state-of-charge indication of batteries by means of a.c. impedance measurements

Rodrigues, Shalini; Munichandraiah, N.; Shukla, A. K.

doi:10.1016/s0378-7753(99)00351-1

Cited by 397 publications

(163 citation statements)

References 17 publications

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“…Then, processing units do not interfere with each other, impedance spectra and their final results are possible to monitor. This new design is expected to provide development guides for monitoring systems of electric vehicle batteries, 15) fuel cell power plants, 16,17) and so on.…”

Section: Resultsmentioning

confidence: 99%

A New Algorithm Design for the Real-time Electrochemical Impedance Monitoring System

Chang

2012

Journal of Electrochemical Science and Technology

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:It is generally known that electrochemical impedance spectroscopy is a powerful technique and its real-time application has been demanded for prompt observations on instantaneous electrochemical changes. Nevertheless, long measurement time and laborious analysis procedures have hindered development of it. Solving the problems, here I report of a new algorithm design for development of a real-time electrochemical impedance monitoring system, which potentially provides a guideline in developing monitoring systems of electric vehicles batteries and other electrochemical power plants. The significant progress in this report is employment of the parallel processing protocol which connects independent sub functions to successfully operate with avoiding mutual interruptions. Therefore, all the processes required to monitor electrochemical impedance changes in realtime are properly operated. To realize the conceptual scheme, a Labview program was coded with sub functions units which conduct their processes individually and only data are transferred between them through the parallel pipelines. Finally, measured impedance spectra and analysis results are displayed, which are synchronized according to the time of change.

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Section: Resultsmentioning

confidence: 99%

A New Algorithm Design for the Real-time Electrochemical Impedance Monitoring System

Chang

2012

Journal of Electrochemical Science and Technology

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“…Currently, four main equivalent circuit models, i.e., the Rint model, Thevenin model, PNGV model, and a multi-order RC circuit model, are widely used in electric vehicle simulation [9][10][11][12][13].…”

Section: Building the Battery Modelmentioning

confidence: 99%

Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

et al. 2016

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An estimation of the power battery state of charge (SOC) is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS) with a forgetting factor and the adaptive unscented Kalman filter (AUKF) algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.

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“…An accurate SOC estimation can not only provide the information of the remaining mileage, but also give assistance to active balancing and protection of the battery pack [5]. There are many different kinds of methods that focus on SOC estimation [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. However, due to the limited computational ability of the embedded processor and the large amount of batteries used in EVs, the SOC estimation algorithm needs to be simple and reasonably accurate.…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer

et al. 2016

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A battery's state-of-charge (SOC) can be used to estimate the mileage an electric vehicle (EV) can travel. It is desirable to make such an estimation not only accurate, but also economical in computation, so that the battery management system (BMS) can be cost-effective in its implementation. Existing computationally-efficient SOC estimation algorithms, such as the Luenberger observer, suffer from low accuracy and require tuning of the feedback gain by trial-and-error. In this study, an algorithm named lazy-extended Kalman filter (LEKF) is proposed, to allow the Luenberger observer to learn periodically from the extended Kalman filter (EKF) and solve the problems, while maintaining computational efficiency. We demonstrated the effectiveness and high performance of LEKF by both numerical simulation and experiments under different load conditions. The results show that LEKF can have 50% less computational complexity than the conventional EKF and a near-optimal estimation error of less than 2%.

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A review of state-of-charge indication of batteries by means of a.c. impedance measurements

Cited by 397 publications

References 17 publications

A New Algorithm Design for the Real-time Electrochemical Impedance Monitoring System

A New Algorithm Design for the Real-time Electrochemical Impedance Monitoring System

Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm

State-of-Charge Estimation for Li-Ion Power Batteries Based on a Tuning Free Observer

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