Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

Sun, Xiaodong; Ji, Jingrun; Ren, Biying; Xie, Chenxue; Yan, Dan Lei

doi:10.3390/en12122242

Cited by 93 publications

(54 citation statements)

References 18 publications

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“…The upper limits of internal ohmic resistance R e , internal resistance R l of electrochemical polarization and internal resistance R s of the concentration polarization are determined in terms of the specifications of batteries and the technical parameters supplied by the manufacturers. Their low limits are all determined to be 0.005 Ω, based on the parameter calculation of ECM introduced in [32], as well as the experimental analysis. Meanwhile, the range of C l and C s can be deduced to be 100 F to 10 4 F. In addition, τ l and τ s are time constants, where τ l = R l C l and τ s = R s C s .…”

Section: The Capacity Estimation Algorithmmentioning

confidence: 99%

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

et al. 2020

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In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium-ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available capacity in the scope of whole lifespan, is constructed to describe the capacity attenuation, and the battery available capacity is identified by a genetic algorithm (GA), together with the parameters related to SOC. The square root cubature Kalman filter (SRCKF) is employed to estimate the SOC with the consideration of capacity degradation. The experimental results demonstrate the effectiveness and feasibility of the co-estimation scheme.Energies 2020, 13, 1410 2 of 15 initial values, and the latter is not suitable for online estimation, as it usually costs long shelving time to acquire the OCV value. With the development of computation technologies and machine learning, a variety of artificial intelligence-based, data-driven methods, such as neural networks [6] and support vector machines [7], are proposed for SOC estimation by establishing black-box models. Data-driven methods feature a strong nonlinear mapping capability with high accuracy; however, these approaches show high complexity, and require a considerable amount of training data. Alternatively, model-based methods have been widely investigated and applied for SOC estimation, thanks to the capabilities of online application, high precision and the independence of initial values. Conventional modeling manners mainly include electrochemical models and the equivalent circuit models (ECMs). Compared with complicated electrochemical models, ECM is commonly used to describe the electrical behavior of batteries, and subsequently to estimate the SOC due to its simplification and preferable precision. Yanwen Li et al. proposed a multi-model probability fusion algorithm to describe the battery's electrical characteristics, and subsequently estimate the SOC [8]. In model-based methods, the combination of the battery model and the intelligent filtering algorithm is a hotspot in SOC estimation research. The frequently used filtering algorithms include Kalman filtering (KF) [9], the H-infinity filter (HIF) [10], particle filter (PF) [11], and their various extensions. In particular, the extended KF (EKF) is widely employed to execute SOC estimation using a first-order Taylor expansion on the basis of the battery's nonlinear model [12]. Nonetheless, the second and higher order expansion is usually neglected, thus leading to slow a convergence rate, and even divergence. The unscented KF (UKF) is exploited to estimate battery SOC, based on the recursive unscented transformation to approximate the nonlinear observation without Taylor polynomial expansions [13]. The UKF shows better estimation precision and robustness than the EKF in strong, nonlinear systems [14]. On the basis of the radial-spherical cubature criterion, the cubature Kalman filter (CKF) leverages a set of volume points to appro...

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Section: The Capacity Estimation Algorithmmentioning

confidence: 99%

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

et al. 2020

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“…However, it is expected from the forgetting factor to vary adaptively with the identification parameter error. especially when the online identification parameter error is very large to make the online identification have faster convergence speed and reduce the identification error [15].…”

Section: Adaptive Forgetting Factormentioning

confidence: 99%

“…The crucial part of the variable forgetting factor least squares algorithm is how to make the forgetting factor adaptively change. An adjusted equation inspired from [15] to calculate the adaptive forgetting factor is proposed to achieve the above goal, it is expressed by: (13) λmin is the minimum value of the forgetting factor fixed at 0.98 to give better balance between accuracy and speed [15]. Figure. 2 Schematic of the recursive least square method for battery model update [16] International h is a constant coefficient, it indicates the sensitivity of the forgetting factor with respect to errors.…”

Section: Adaptive Forgetting Factormentioning

confidence: 99%

“…When it is close to 0 itchanges sharply, which speeds up the parameter identification response, but reduces the accuracy. We used the same value 0.9 as in [15]. e(k) is the error at time k, and ebase is chosen based on the magnitude of the expected error.…”

Section: Adaptive Forgetting Factormentioning

confidence: 99%

“…The problem with RLS is the fact that it is affected by old data, this means that the generation of new data will be affected by old data, which will lead to large errors. This is adjusted by introducing a forgetting factor into the RLS, so that the proportion of old data is reduced when new data is available, and the algorithm can converge to the actual value quickly [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Parameter Estimation of an Electric Vehicle Lithium-Ion Battery Using Adaptive Forgetting Factor Recursive Least Squares

Elmarghichi¹,

Bouzi²,

Ettalabi³

2020

IJIES

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In this paper, a battery model suitable for electric vehicle application is analyzed. Open circuit voltage is described by an adaptation of Nernst equation. Thevenin circuit is used to depict the instantaneous and transient regime. Hysteresis effect is outlined by a zero-state correction term. We propose a new algorithm AFFRLS (adaptive forgetting factor recursive least squares) to extract the parameter of the battery model, then to predict the output voltage, and compare it to the original FFRLS (forgetting factor recursive least squares). To evaluate these algorithms, we used experimental data conducted by CALCE Battery Research Group on the Samsung INR 18650-20R battery cell. We fed the data to the algorithms and compared the estimated output voltage for two dynamic tests on MATLAB. Results show that AFFRLS has low distribution in high error range up to 4% less than FFRLS, this means that AFFRLS has a better parameter identification than FFRLS.

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A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

Liu

Wang

Fan

et al. 2022

Circuit Theory & Apps

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Aiming at the problem that it is difficult to accurately estimate the state of charge (SOC) of lithium‐ion batteries in the strongly nonlinear interval, a novel algorithm based on a fuzzy control strategy is proposed. It integrates extended Kalman filter (EKF) and ampere‐hour (Ah) integration accurately estimate the SOC of lithium‐ion batteries. First, the algorithm uses the advantage that the EKF algorithm has high estimation accuracy in the nonlinear interval and can solve the problem of the large error caused by the inaccurate initial value of the Ah integral algorithm. Then the fuzzy‐EKF‐Ah (F‐EKF‐Ah) is used to fuse the two algorithms of EKF and Ah integral. The fused algorithm can effectively solve the problems of the cumulative error caused by the sampling accuracy of the Ah integral algorithm and the large estimation error of the EKF algorithm in the strong nonlinear interval. Finally, the equivalent circuit model is used for analysis. The experimental results show that the improved algorithm can achieve high estimation accuracy under three experimental conditions.

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Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

Cited by 93 publications

References 18 publications

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan

Robust Parameter Estimation of an Electric Vehicle Lithium-Ion Battery Using Adaptive Forgetting Factor Recursive Least Squares

A novel fuzzy‐extended Kalman filter‐ampere‐hour (F‐EKF‐Ah) algorithm based on improved second‐order PNGV model to estimate state of charge of lithium‐ion batteries

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