A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter

Sepasi, Saeed; Ghorbani, Reza; Liaw, Bor Yann

doi:10.1016/j.jpowsour.2013.06.108

Cited by 193 publications

(79 citation statements)

References 10 publications

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“…The extended Kalman filtering algorithm includes a highly efficient observer, which also presents high robustness against non-linear systems [8,9]. To apply the EKF in the insulation monitoring system, the system should be described by a state space model [10], which can be expressed as: Equation (6) can be acquired via the following steps:…”

Section: Design Of the Discrete Extended Kalman Filtermentioning

confidence: 99%

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

et al. 2017

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Abstract:To improve the accuracy of insulation monitoring between the battery pack and chassis of electric vehicles, we established a serial battery pack model composed of first-order resistor-capacitor (RC) circuit battery cells. We then designed a low-voltage, low-frequency insulation monitoring model based on this serial battery pack model. An extended Kalman filter (EKF) was designed for this non-linear system to filter the measured results, thus mitigating the influence of noise. Experimental and simulation results show that the proposed monitoring model and extended Kalman filtering algorithm for insulation resistance monitoring present satisfactory estimation accuracy and robustness.

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Section: Design Of the Discrete Extended Kalman Filtermentioning

confidence: 99%

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

et al. 2017

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“…then we obtain Equation (25) to represent the relationship between the standard EKF feedback gain and the intermittent EKF feedback gain when EKF converges:…”

Section: Derivation Of the Lazy-extended Kalman Filtermentioning

confidence: 99%

“…Open loop approaches, such as the coulomb counting (CC) [6,7] and open circuit voltage (OCV) methods [8,9], are simple, but their drawbacks are significant: the CC method directly integrates the current over time to obtain the battery capacity change, leading to accumulated sensing error [23,24]. It is unlikely to provide an accurate SOC estimation with uncertain initial SOC value [25]. According to [4], OCV can reflect SOC in an accurate way, whereas it may take hours for the terminal voltage of a LiFePO4 battery to become stable and close to the OCV at low temperatures.…”

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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“…Thus, modified KF algorithms have to be used in order to extend the application of KF in the nonlinear battery systems. Two commonly used types are extended Kalman filter (EKF) [15][16][17][18][19][20][21][22][23][24][25][26][27][28] and unscented Kalman filter (UKF) [29][30][31][32][33][34][35][36]. The EKF transforms a nonlinear system into a linear system by linearizing the nonlinear function on the basis of the first-order Taylor series expansion.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

et al. 2015

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Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms. OPEN ACCESSEnergies 2015, 8 5917

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A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter

Cited by 193 publications

References 10 publications

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

Insulation Resistance Monitoring Algorithm for Battery Pack in Electric Vehicle Based on Extended Kalman Filtering

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

State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

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