Hardware implementation of an algorithm based on kalman filtrer for monitoring low capacity Li-ion batteries

Baccouche, Ines; Jemmali, Sabeur; Manaï, Bilal; Chaibi, Rania; Amara, Najoua Essoukri Ben

doi:10.1109/irec.2016.7478930

Cited by 14 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be approximated to a linear curve in part, thus giving the instinctive idea that it tends to be broken down into eight linear parts [23]. As appeared in Fig.2 and every piece can be stated as a linear relation as in Eq.…”

Section: Fig2 Voc-soc Curve Part Wise Linear Mappingmentioning

confidence: 99%

SOC Estimation of Non-Linear Lithium-Ion Battery using Modified Coulomb Counting Method with RUL

Dongre¹

2019

IJEAT

View full text Add to dashboard Cite

Batteries are preferred source of energy in recent few decades, the development is vast. New development comes with new challenges, the coulomb counting method used to study battery behavior with an accurate measurement mechanism like estimating battery state of charge (SOC), battery's remaining useful life (RUL), working condition and changing tendency. To accomplish this experiment and algorithm efficiency, we used PIC18F MCU with IoT device to send data to the web server using GPRS, and can be utilized on an Electronic Vehicles (EV) and portable devices in real time to show what's really happening within the battery. The significant challenge of this method is, we might receive accumulative errors associated with initial SOC also the faults of quantities are undefined, hence to get over these restrictions, a part wise linear imprecise used with inconsistent constants to define the integrally non-linear relation amongst the SOC and open circuit voltage (VOC). In past few decades, Li-ion batteries has developed much consideration in EV applications as well as for mobile phone market due to its many advantages like, quick charging ability, more durable, light weight, good energy density, low rate of self-discharge etc. The SOC is key principle to find, control of Li-ion battery performance which is current area of interest in this publication. This paper will demonstrate how the SOC is controlled, observed with the help of Coulomb-counting algorithm.

show abstract

Section: Fig2 Voc-soc Curve Part Wise Linear Mappingmentioning

confidence: 99%

SOC Estimation of Non-Linear Lithium-Ion Battery using Modified Coulomb Counting Method with RUL

Dongre¹

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…Several OCV models were proposed in the literature. Some of these models are very simple and do not take into account the nonlinearity, such as the linear approximation, which was upgraded to a piecewise linear approach that increases the accuracy, but still not considering the phenomenological aspect, even if it is easy to implement [15,38].…”

Section: Overview Of Li-ion Ocv Modelsmentioning

confidence: 99%

“…It actually uses the dynamics of the system defining its evolution over time to obtain better data, thus eliminating the effect of noise. This estimation technique has been widely used for monitoring Li-ion batteries because they exhibit nonlinear behavior [38,49]. Different variants of Kalman filtering techniques like the linear Kalman filter (KF) [38], the extended Kalman filter (EKF) [35], the unscented Kalman filter (UKF) [50] and the sigma-point Kalman filter (SPKF) [51] have been used to estimate the SOC based on different battery equivalent models.…”

Section: The Principle Of Ekfmentioning

confidence: 99%

“…This estimation technique has been widely used for monitoring Li-ion batteries because they exhibit nonlinear behavior [38,49]. Different variants of Kalman filtering techniques like the linear Kalman filter (KF) [38], the extended Kalman filter (EKF) [35], the unscented Kalman filter (UKF) [50] and the sigma-point Kalman filter (SPKF) [51] have been used to estimate the SOC based on different battery equivalent models. Unlike KF where the system is linearly formulated, the EKF requires a local linearization that consists of using the Jacobian function to bring the system to the conventional form, with interdependent variables rather than a constant matrix [52].…”

Section: The Principle Of Ekfmentioning

confidence: 99%

“…In fact, thanks to the prediction and the correction processes of the Kalman filtering algorithm, the latter allows converging to the real value of SOC even if the initial value is very far from the real one, and this according to many works [38,46,49].…”

mentioning

confidence: 98%

See 2 more Smart Citations

Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

et al. 2017

Self Cite

View full text Add to dashboard Cite

Accurate modeling of the nonlinear relationship between the open circuit voltage (OCV) and the state of charge (SOC) is required for adaptive SOC estimation during the lithium-ion (Li-ion) battery operation. Online SOC estimation should meet several constraints, such as the computational cost, the number of parameters, as well as the accuracy of the model. In this paper, these challenges are considered by proposing an improved simplified and accurate OCV model of a nickel manganese cobalt (NMC) Li-ion battery, based on an empirical analytical characterization approach. In fact, composed of double exponential and simple quadratic functions containing only five parameters, the proposed model accurately follows the experimental curve with a minor fitting error of 1 mV. The model is also valid at a wide temperature range and takes into account the voltage hysteresis of the OCV. Using this model in SOC estimation by the extended Kalman filter (EKF) contributes to minimizing the execution time and to reducing the SOC estimation error to only 3% compared to other existing models where the estimation error is about 5%. Experiments are also performed to prove that the proposed OCV model incorporated in the EKF estimator exhibits good reliability and precision under various loading profiles and temperatures.

show abstract

Pure hardware design and implementation on FPGA of an EKF based accelerated SoC estimator for a lithium‐ion battery in electric vehicles

Jemmali

Manaï

Hamouda

2022

IET Power Electronics

Self Cite

View full text Add to dashboard Cite

Hardware implementation of an algorithm based on kalman filtrer for monitoring low capacity Li-ion batteries

Cited by 14 publications

References 15 publications

SOC Estimation of Non-Linear Lithium-Ion Battery using Modified Coulomb Counting Method with RUL

SOC Estimation of Non-Linear Lithium-Ion Battery using Modified Coulomb Counting Method with RUL

Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter

Pure hardware design and implementation on FPGA of an EKF based accelerated SoC estimator for a lithium‐ion battery in electric vehicles

Contact Info

Product

Resources

About