A Comparison between State of Charge Estimation Methods: Extended Kalman Filter and Unscented Kalman Filter

Ilies, Adelina Ioana; Chindris, G.; Pitica, Dan

doi:10.1109/siitme50350.2020.9292232

Cited by 10 publications

(9 citation statements)

References 7 publications

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“…The battery model parameters used in this work are determined using Simulink Design Optimization, which tunes the model's parameters to meet the desired measured data based on measurement data. Similar results were obtained for the state of charge estimation using the EKF method and UKF method reported in Ilieş et al 51 The battery operating temperature is fixed at 20°C while calculating the values of parameters. The 2RC circuit parameters are assumed to be modeled and dependent only on SOC.…”

Section: Methodssupporting

confidence: 81%

“…The UKF chooses a limited number of sample points, also known as sigma points, around the average using a deterministic sampling approach; this is called the unscented transformation (UT). Following that, the sigma points are transmitted through the nonlinear functions, yielding a new average and covariance estimate 51 . When compared to the EKF, the UKF can provide considerable advantages.…”

Section: Methodsmentioning

confidence: 99%

“…To approximate the nonlinear system to the linear system at each time step linearization technique is used, then it is used in the KF, which resulting an EKF system. 51 In this research Extended Kalman filter block from MATLAB Simulink library is used for state estimation of LIB using discrete-time EKF algorithm. Discrete-time EKF algorithm is used because EKF is usually implemented in digital computers even though the system dynamics are continuous-time.…”

Section: Soc Estimation Using Ekfmentioning

confidence: 99%

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State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

2022

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Lithium‐ion batteries (LIBs) are currently the most widely used energy storage technologies in electric vehicles (EVs) due to their higher power density, greater energy density, longer life, fast charging capabilities, and low discharge rate. The brain of rechargeable batteries is referred to be the battery management system (BMS). The operation of the battery is monitored & regulated by an electronic system called BMS. Most significantly, it prevents the battery from exceeding its safety limits. One of the important parameters of BMS is the state of charge (SOC); it determines the battery's remaining capacity of accessible stored energy, battery lifespan, and cell balancing. As a result, an accurate SOC calculation is essential for BMS, and it is used for safer operations that avoid overcharging and discharging for the optimum battery life. The primary ambition of this article is to find an accurate and robust SOC estimation based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques. RMS error, average error, minimum error, and maximum error are used to compare the EKF method and UKF method. Two resistor‐capacitor (2RC) equivalent circuit model has been considered for the dynamic performance of the battery. The waveforms are displayed, and errors are simulated for SOC estimate using the EKF and UKF algorithms, taking the same initial SOC into consideration at a time. The UKF algorithm outperforms the EKF approach in terms of dynamic results when comparing the two methods.

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Section: Methodssupporting

confidence: 81%

Section: Methodsmentioning

confidence: 99%

Section: Soc Estimation Using Ekfmentioning

confidence: 99%

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State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

2022

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“…Using typical methods such as unscented Kalman filter (UKF) and Extended Kalman filter (EKF), both rely on the cell unit to predict the voltage [28]. This results in voltage stimulus estimating the internal battery state (SOC) to compare this forecast against the terminal voltage measurement [29]. Another approach is to convert the readings of the battery voltages to equivalent SOC using a known discharge curve by the indication of voltage versus SOC.…”

Section: Filtering Methodsmentioning

confidence: 99%

Memristive Equivalent Circuit Model for Battery

Mogadem

2021

Sustainability

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The design of mathematical models is based on conservation laws and also on the fundamental principles of modeling: structure, parameters, and physical meaning. Those kinds of modeling should have specific capabilities to deal with different working conditions and environments coping with challenges that include but are not limited to battery capacity, life-cycle, or the attempts to manipulate the current profiles during operation. Introducing memristive elements in batteries will be ideal to satisfy these fundamentals and goals of modeling, whereas addressing the recycling and sustainability concerns on the environmental impact by the placement of TiO2 memristor into this model can promote a recovery hierarchy via recycling and dispatching a slight amount to disposal as the previous focus was mainly concentrated on availability. As for battery materials, modeling, performing, and manufacturing all have proliferated to grasp the possible sustainability challenges inherited in these systems. This paper investigated electrochemical impedance spectroscopy to study this model and the dynamic behavior inside the battery. We found a solution to address the existing battery limitations that elucidate the battery degradation without affecting the performance, correspondingly by employing the dampest least-squares combination with nonlinear autoregressive exogenous for identifying such model and its associated parameters because of its embedded memory and fast convergence to diminish the influence of the vanishing gradient. Lastly, we found that this model is proven to be efficient and accurate compared to actual experimented data to validate our theory and show the value of the proposed model in real life while assuming Normal Gaussian distribution of data error with outstanding results; the auto-correlations were within the 95% confidence limit, the best validation was 2.7877, and an overall regression of 0.99993 was achieved.

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“…Since only linear processing is performed on the signal, the system is a linear system. Through the analysis of the error source, it can be concluded that the main noise of the system is a stationary random signal, which is Gaussian white noise [5]. Meet the two conditions of use of Kalman filtering.…”

Section: Kalman Filter Algorithmmentioning

confidence: 99%

Design of high precision angle measuring instrument based on Kalman filter

Guo

Wang

2022

J. Phys.: Conf. Ser.

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Angle parameters are common parameters in production and life. The main process is converting the inclination signal into other signals for measurement. In order to overcome the problems of low-cost, long-term, and high-precision angle detection that traditional angle measurement systems cannot solve, a high-precision, low-cost, and small-size angle measurement system is designed. The high-precision angle sensor signal used in this paper is based on MEMS angle sensor. The angle sensor has the characteristics of extremely low temperature dependence, low noise and high resolution. In terms of hardware, various methods are used on the circuit board to eliminate noise during signal transmission. These methods are used to improve the accuracy of the angle measurement system. The Kalman filter algorithm is used to filter out noise to reduce the influence of noise on high-precision angle signals in terms of software. Experiments on the system show that the accuracy of the angle measurement system reaches 0.0014°, which achieve the goal of system design, can solve the problem of long-term, high-precision angle measurement, and completes the system design.

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A Comparison between State of Charge Estimation Methods: Extended Kalman Filter and Unscented Kalman Filter

Cited by 10 publications

References 7 publications

State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

Memristive Equivalent Circuit Model for Battery

Design of high precision angle measuring instrument based on Kalman filter

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