The Experts below are selected from a list of 234 Experts worldwide ranked by ideXlab platform
Shu Ting Goh - One of the best experts on this subject based on the ideXlab platform.
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state of charge estimation of lithium ion battery using square root spherical unscented kalman filter sqrt ukfst in nanosatellite
IEEE Transactions on Power Electronics, 2015Co-Authors: Htet Aung, Kay Soon Low, Shu Ting GohAbstract:State-of-charge (SOC) estimation is an important aspect for modern battery management system. Dynamic and closed loop model-based methods such as extended Kalman filter (EKF) have been extensively used in SOC estimation. However, the EKF suffers from drawbacks such as Jacobian matrix derivation and linearization accuracy. In this paper, a new SOC estimation method based on square root unscented Kalman filter using spherical transform (Sqrt-UKFST) with unit hyper sphere is proposed. The Sqrt-UKFST does not require the linearization for nonlinear model and uses fewer sigma points with spherical transform, which reduces the computational requirement of traditional unscented transform. The square root characteristics improve the numerical properties of state covariance. The proposed method has been experimentally validated. The results are compared with existing SOC estimation methods such as Coulomb Counting, portable fuel gauge, and EKF. The proposed method has an absolute root mean square error (RMSE) of 1.42% and an absolute maximum error of 4.96%. These errors are lower than the other three methods. When compared with EKF, it represents 37% and 44% improvement in RMSE and maximum error respectively. Furthermore, the Sqrt-UKFST is less sensitive to parameter variation than EKF and it requires 32% less computational requirement than the regular UKF.
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modeling and state of charge estimation of a lithium ion battery using unscented kalman filter in a nanosatellite
Conference on Industrial Electronics and Applications, 2014Co-Authors: Htet Aung, Kay Soon Low, Shu Ting GohAbstract:State of charge (SOC) estimation is an essential part of battery management system. Dynamic and closed loop model-based methods such as extended Kalman filter (EKF) have been extensively used in SOC estimation. However, the EKF suffers from drawbacks such as requiring Jacobian matrix derivation and linearization accuracy. In this paper, a new SOC estimation method based on square root unscented Kalman filter (Sqrt-UKF) is proposed. With the proposed method, Jacobian matrix calculation is not needed and higher linearization order (2nd order) can be achieved. The proposed approach has been validated with the experimental data and has been benchmarked with the Coulomb Counting method in terms of accuracy and performance. The experimental results have shown that the proposed method has a mean error of 1.19% and a maximum error of 4.96% and has performed better than the Coulomb Counting method.
Htet Aung - One of the best experts on this subject based on the ideXlab platform.
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state of charge estimation of lithium ion battery using square root spherical unscented kalman filter sqrt ukfst in nanosatellite
IEEE Transactions on Power Electronics, 2015Co-Authors: Htet Aung, Kay Soon Low, Shu Ting GohAbstract:State-of-charge (SOC) estimation is an important aspect for modern battery management system. Dynamic and closed loop model-based methods such as extended Kalman filter (EKF) have been extensively used in SOC estimation. However, the EKF suffers from drawbacks such as Jacobian matrix derivation and linearization accuracy. In this paper, a new SOC estimation method based on square root unscented Kalman filter using spherical transform (Sqrt-UKFST) with unit hyper sphere is proposed. The Sqrt-UKFST does not require the linearization for nonlinear model and uses fewer sigma points with spherical transform, which reduces the computational requirement of traditional unscented transform. The square root characteristics improve the numerical properties of state covariance. The proposed method has been experimentally validated. The results are compared with existing SOC estimation methods such as Coulomb Counting, portable fuel gauge, and EKF. The proposed method has an absolute root mean square error (RMSE) of 1.42% and an absolute maximum error of 4.96%. These errors are lower than the other three methods. When compared with EKF, it represents 37% and 44% improvement in RMSE and maximum error respectively. Furthermore, the Sqrt-UKFST is less sensitive to parameter variation than EKF and it requires 32% less computational requirement than the regular UKF.
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modeling and state of charge estimation of a lithium ion battery using unscented kalman filter in a nanosatellite
Conference on Industrial Electronics and Applications, 2014Co-Authors: Htet Aung, Kay Soon Low, Shu Ting GohAbstract:State of charge (SOC) estimation is an essential part of battery management system. Dynamic and closed loop model-based methods such as extended Kalman filter (EKF) have been extensively used in SOC estimation. However, the EKF suffers from drawbacks such as requiring Jacobian matrix derivation and linearization accuracy. In this paper, a new SOC estimation method based on square root unscented Kalman filter (Sqrt-UKF) is proposed. With the proposed method, Jacobian matrix calculation is not needed and higher linearization order (2nd order) can be achieved. The proposed approach has been validated with the experimental data and has been benchmarked with the Coulomb Counting method in terms of accuracy and performance. The experimental results have shown that the proposed method has a mean error of 1.19% and a maximum error of 4.96% and has performed better than the Coulomb Counting method.
Chris Bingham - One of the best experts on this subject based on the ideXlab platform.
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New battery model and state-of-health determination through subspace parameter estimation and state-observer techniques
IEEE Transactions on Vehicular Technology, 2009Co-Authors: C. R. Gould, D A Stone, Chris Bingham, Phil BentleyAbstract:This paper describes a novel adaptive battery model based on a remapped variant of the well-known Randles' lead-acid model. Remapping of the model is shown to allow improved modeling capabilities and accurate estimates of dynamic circuit parameters when used with subspace parameter-estimation techniques. The performance of the proposed methodology is demonstrated by application to batteries for an all-electric personal rapid transit vehicle from the urban light transport (ULTRA) program, which is designated for use at Heathrow Airport, U.K. The advantages of the proposed model over the Randles' circuit are demonstrated by comparisons with alternative observer/estimator techniques, such as the basic Utkin observer and the Kalman estimator. These techniques correctly identify and converge on voltages associated with the battery state-of-charge (SoC), despite erroneous initial conditions, thereby overcoming problems attributed to SoC drift (incurred by Coulomb-Counting methods due to overcharging or ambient temperature fluctuations). Observation of these voltages, as well as online monitoring of the degradation of the estimated dynamic model parameters, allows battery aging (state-of-health) to also be assessed and, thereby, cell failure to be predicted. Due to the adaptive nature of the proposed algorithms, the techniques are suitable for applications over a wide range of operating environments, including large ambient temperature variations. Moreover, alternative battery topologies may also be accommodated by the automatic adjustment of the underlying state-space models used in both the parameter-estimation and observer/estimator stages.
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Novel battery model of an all-electric personal rapid transit vehicle to determine state-of-health through subspace parameter estimation and a Kalman Estimator
2008 International Symposium on Power Electronics Electrical Drives Automation and Motion, 2008Co-Authors: C. R. Gould, D A Stone, Chris Bingham, Phil BentleyAbstract:The paper describes a real-time adaptive battery model for use in an all-electric personal rapid transit vehicle. Whilst traditionally, circuit-based models for lead-acid batteries centre on the well-known Randles' model, here the Randles' model is mapped to an equivalent circuit, demonstrating improved modelling capabilities and more accurate estimates of circuit parameters when used in Subspace parameter estimation techniques. Combined with Kalman Estimator algorithms, these techniques are demonstrated to correctly identify and converge on voltages associated with the battery State-of-Charge, overcoming problems such as SoC drift (incurred by Coulomb-Counting methods due to over-charging or ambient temperature fluctuations). Online monitoring of the degradation of these estimated parameters allows battery ageing (State-of-Health) to be assessed and, in safety-critical systems, cell failure may be predicted in time to avoid inconvenience to passenger networks. Due to the adaptive nature of the proposed methodology, this system can be implemented over a wide range of operating environments, applications and battery topologies.
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nonlinear observers for predicting state of charge and state of health of lead acid batteries for hybrid electric vehicles
IEEE Transactions on Vehicular Technology, 2005Co-Authors: B S Bhangu, D A Stone, P Bentley, Chris BinghamAbstract:This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence-an unfortunate feature of more traditional Coulomb-Counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by acCounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack.
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Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles
IEEE Transactions on Vehicular Technology, 2005Co-Authors: B S Bhangu, P Bentley, David A. Stone, Chris BinghamAbstract:This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence-an unfortunate feature of more traditional Coulomb-Counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by acCounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack.
Najoua Essoukri Ben Amara - One of the best experts on this subject based on the ideXlab platform.
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implementation of an improved Coulomb Counting algorithm based on a piecewise soc ocv relationship for soc estimation of li ionbattery
arXiv: Systems and Control, 2018Co-Authors: Ines Baccouche, Sabeur Jemmali, Asma Mlayah, Bilal Manai, Najoua Essoukri Ben AmaraAbstract:Considering the expanding useofembedded devices equipped with rechargeable batteries, especially Li-ionbatteries that have higher power and energy density, the battery management systemis becomingincreasingly important. Infact, theestimationaccuracy of the amount of the remaining charges is critical as it affects the device operational autonomy.Therefore, the battery State-Of-Charge (SOC) is defined to indicate its estimated available charge. In this paper, a solution isproposed for Li-ion battery SOC estimation based on an enhanced Coulomb-Counting algorithm to be implemented formultimedia applications.However,the Coulomb-Counting algorithm suffers from cumulative errors due to the initial SOC andtheerrors ofmeasurements uncertainties,thereforeto overcome these limitations,we use the Open-CircuitVoltage (OCV),thushavinga piecewise linear SOC-OCV relationship andperformingperiodic re-calibration of the battery capacity. Thissolutionis implementedand validated on a hardware platform based onthePIC18F MCU family. The measured resultsarecorrelated withthetheoretical ones; they have shown a reliable estimation since accuracy is less than 2%.
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implementation of an improved Coulomb Counting algorithm based on a piecewise soc ocv relationship for soc estimation of li ion battery
International Journal of Renewable Energy Research, 2018Co-Authors: Ines Baccouche, Sabeur Jemmali, Asma Mlayah, Bilal Manai, Najoua Essoukri Ben AmaraAbstract:Considering the expanding use of mobile devices equipped with rechargeable batteries, especially Li-ion batteries that have higher power and energy density, the battery management function becomes increasingly important. In fact, the accuracy of the amount of remaining charges estimation is critical as it affects the device autonomy. Therefore, the battery State-Of-Charge (SOC) is defined to indicate its estimated available capacity. In this paper, a method for Li-ion battery SOC estimation based on an enhanced Coulomb-Counting is proposed to be implemented for multimedia applications. Assuming that Coulomb-Counting suffers from cumulative errors due to the initial SOC and the measurements uncertainties errors, we used a piece-wise linear SOC-OCV relationship and periodic re-calibration to overcome these limitations. This solution has been implemented and validated on a hardware platform based on PIC18F MCU Family. The measurement results were correlated with theoretical ones and the method has shown a reliable estimation since accuracy is less than 2%.
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implementation of a Coulomb Counting algorithm for soc estimation of li ion battery for multimedia applications
International Multi-Conference on Systems Signals and Devices, 2015Co-Authors: Ines Baccouche, Sabeur Jemmali, Asma Mlayah, Bilal Manai, Najoua Essoukri Ben AmaraAbstract:Lithium-Ion based batteries are quite p opular thanks to their good electrical characteristics but they risk to be damaged when they are overcharged or deeply discharged. In order to avoid these problems, Lithium-Ion batteries require an accurate state of charge determination to extend their lifetime and hence protect the equipment they supply. In this paper we propose a solution based on an enhanced Coulomb Counting method which we have implemented on a hardware platform based on the PIC18F MCU Family. The results are promising. The proposed system is supported by IntelliBatteries and integrated on its products.
Danielioan Stroe - One of the best experts on this subject based on the ideXlab platform.
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recursive state of charge and state of health estimation method for lithium ion batteries based on Coulomb Counting and open circuit voltage
Energies, 2020Co-Authors: Alejandro Gismero, Erik Schaltz, Danielioan StroeAbstract:The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb Counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be implemented for all kinds of applications, its accuracy is strongly dependent on the operation conditions. In this work, the behavior of the batteries at different current and temperature conditions is analyzed in order to adjust the charge measurement according to the battery efficiency at the specific operating conditions. The open-circuit voltage (OCV) is used to reset the SOC estimation and prevent the error accumulation. Furthermore, the SOH is estimated by evaluating the accumulated charge between two different SOC using a recursive least squares (RLS) method. The SOC and SOH estimations are verified through an extensive test in which the battery is subjected to a dynamic load profile at different temperatures.
