Enhanced Coulomb counting method with adaptive SOC reset time for estimating OCV

Jeong, Yeon Kwon; Cho, Yong-Ki; Ahn, Jung-Hoon; Ryu, Sie Jeong; Lee, Byoung-Kuk

doi:10.1109/ecce.2014.6953989

Cited by 62 publications

(29 citation statements)

References 4 publications

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“…However, the later has some advantages including computational effectiveness since no interpolation is required. Coulombcounting method is based on the battery current integration over time; thus, it is simple to implement and gives an acceptable precision, but suffers from cumulative errors due to the measurement inaccuracies [6][7]. The commonly used data driven models include neural networks and Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

System Level Modelling of Batteries in Spacecraft Power System in Multiple Source and Multiple Load Configuration

Prasad

Ananda

Mahanta

et al. 2019

INCOSE International Symp

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In present day spacecraft applications, power system uses solar arrays as the primary source of power generation and rechargeable Lithium ion batteries as secondary source of power. In normal orbits, during sunlit period, solar arrays support the power requirement from all the subsystems and payloads along with the charge power requirement of batteries. During eclipse period, entire load should be supported by batteries. In addition to the eclipse support, starting from launch pad to till deployment of solar panels, all loads will be on batteries. Contingency loads should also be taken care by batteries. So, battery sizing is very critical part in designing the power system of spacecraft. In multiple source and multiple load configuration where two or more batteries are connected to same Spacecraft power bus, current sharing between the batteries is expected, which needs to be considered for optimized sizing and designing of batteries. The scenario becomes complex with additional constraints namely some loads can come on one battery and not on others or vice versa. Even in a configuration where potential difference exists between batteries, current sharing will start as soon as this difference vanishes. A system level simulation model is developed, using network equations, to analyze in-circuit performance of batteries in multiple source and multiple load power system. The simulation model uses actual current sharing and predicts State of Charge (SOC) of each battery from which Open Circuit Voltage (OCV) is derived. This OCV of batteries is used periodically for finding actual current sharing which runs in a loop for analyzing battery performance in multisource system. Currently a configuration in which two batteries are connected to two buses, which are paralleled and having different loads, is simulated. Optimized battery sizing, actual current sharing and SOC of each battery are derived. This model can also be extended to a system where more than two batteries are present. Two different approaches are used for simulation and results are compared. The developed generic model has features to use in multiple battery configuration. He has served as Project Manager and Deputy Project Director for several spacecraft projects. He steered the development of modular design of high energy lithium ion batteries, Nickel hydrogen and Nickel cadmium batteries. He has been involved in Design, fabrication, testing and on orbit Management of Satellite Batteries and power system since Sept 1985. Sankaran M, currently Outstanding Scientist and Deputy Director, Communications and Power Systems Area, U R Rao Satellite Centre, Indian Space Research Organization. He joined ISRO in 1986 after his M.Sc., degree in Physics from RSGC, under Bharathidasan University, Tiruchirappalli. During his past 32.5 years in ISRO, he has contributed mainly in the area of solar arrays, power systems and RF communication systems for spacecraft. He has served as Project Manager and Deputy Project Director for several spacecraft projects. He steered the...

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Section: Introductionmentioning

confidence: 99%

System Level Modelling of Batteries in Spacecraft Power System in Multiple Source and Multiple Load Configuration

Prasad

Ananda

Mahanta

et al. 2019

INCOSE International Symp

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“…The conventional methods for SOC estimation include Ampere hour counting or coulomb counting [6], open circuit voltage estimation [7], artificial neural networks [8], and others [9]- [12], all of which can be adopted to complete the SOC estimation process and obtain acceptable results in different applications. Ampere hour counting and open circuit voltage estimation are the most common methods for SOC estimation.…”

Section: Introductionmentioning

confidence: 99%

Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine

Meng

Luo

Gao

2016

IEEE Trans. Power Electron.

286

122

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An accurate algorithm for lithium polymer battery SOC estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of proposed AUKF method is established by the LSSVM battery model, and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.Index Terms-Lithium polymer battery, moving window method, modeling, least square support vector machine (LSSVM), adaptive unscented Kalman filter (AUKF), state of charge (SOC).  Manuscript

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“…The Coulomb counting method [7,10] and the open circuit voltage method [7,11,12] are the two most used methods for SOC estimation. Coulomb counting method makes full use of the current measurement since the SOC is defined as the integration of current over the time, divided by the battery capacity.…”

Section: Introductionmentioning

confidence: 99%

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

et al. 2018

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State of charge (SOC) is one of the most important parameters in battery management systems, as it indicates the available battery capacity at every moment. There are numerous battery model-based methods used for SOC estimation, the accuracy of which depends on the accuracy of the model considered to describe the battery dynamics. The SOC estimation method proposed in this paper is based on an Extended Kalman Filter (EKF) and nonlinear battery model which was parameterized using extended laboratory tests performed on several 13 Ah lithium titanate oxide (LTO)-based lithium-ion batteries. The developed SOC estimation algorithm was successfully verified for a step discharge profile at five different temperatures and considering various initial SOC initialization values, showing a maximum SOC estimation error of 1.16% and a maximum voltage estimation error of 44 mV. Furthermore, by carrying out a sensitivity analysis it was showed that the SOC and voltage estimation error are only slightly dependent on the variation of the battery model parameters with the SOC.

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Enhanced Coulomb counting method with adaptive SOC reset time for estimating OCV

Cited by 62 publications

References 4 publications

System Level Modelling of Batteries in Spacecraft Power System in Multiple Source and Multiple Load Configuration

System Level Modelling of Batteries in Spacecraft Power System in Multiple Source and Multiple Load Configuration

Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine

Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries

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