Lithium battery SOC estimation based on whale optimization algorithm and unscented Kalman filter

Cao

et al. 2022

Circuit Theory & Apps

The state of charge (SOC) of lithium-ion batteries is the main parameter of the battery management system. To improve the accuracy of lithium-ion battery SOC estimation, this paper uses a double RC physical characteristic circuit network to model the polarization reaction inside the battery and realizes the full parameter identification of the model based on the double-exponential fitting strategy. Then, using the spherical unscented transform (SUT) to realize the selection of sigma points and the calculation of weight coefficients, at the same time, the adaptive factor is introduced to correct the error covariance matrix in real time and an adaptive spherical unscented Kalman filter (AS-UKF) algorithm. Finally, the algorithm is compared with the unscented Kalman filter (UKF) and adaptive unscented Kalman filter (AUKF) algorithms through simulation. The results show that the average error of the AS-UKF algorithm is reduced by 0.5% and 1.18% under the Hybrid Pulse Power Characterization (HPPC) and the Beijing Bus Dynamic Street Test (BBDST) conditions. The AS-UKF algorithm not only improves the accuracy but also is more stable.

Section: Introductionmentioning

confidence: 99%

An improved adaptive spherical unscented Kalman filtering method for the accurate state‐of‐charge estimation of lithium‐ion batteries

Cao

et al. 2022

Circuit Theory & Apps

“…23 In addition, the discharge current and internal temperature are variable under complex working conditions 24 ; the battery self-discharge and the battery aging caused by repeated recycling of materials make it difficult for the traditional algorithm to estimate the SOC of lithium-ion batteries to obtain real-time and accurate SOC of lithium-ion batteries. 25,26 Therefore, in recent years, researchers have put forward some improved algorithms based on the traditional algorithms to solve the problems of online estimation of lithium-ion batteries and low estimation accuracy. [27][28][29] Fang et al 30 proposed a model-based SOC estimation method for lithium-ion batteries based on the state estimation error caused by inaccurate parameter estimation of the battery model.…”

Section: Introductionmentioning

confidence: 99%

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

Fan

et al. 2022

Circuit Theory & Apps

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.

“…Among existing model‐based methods, commonly used battery SOC estimation methods are extended Kalman filter (EKF) 30 and unscented Kalman filter (UKF) 31 . However, EKF is a linearization treatment of nonlinear problems that possesses large truncation errors, affecting the accuracy of state estimation.…”

Section: Introductionmentioning

confidence: 99%

“…Among existing model-based methods, commonly used battery SOC estimation methods are extended Kalman filter (EKF) 30 and unscented Kalman filter (UKF). 31 However, EKF is a linearization treatment of nonlinear problems that possesses large truncation errors, affecting the accuracy of state estimation. UKF utilizes UT transformation in order to avoid solving differentiation, while its filtering depends on the selection of parameters, resulting in insufficient reliability.…”

Section: Introductionmentioning

confidence: 99%

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

Intl J of Energy Research

Meng

Chang

et al. 2021

Summary Under complex working conditions in variable temperatures, the accuracy of SOC is reduced due to the low robustness of the lithium‐ion battery model online parameter identification method as well as the SOC estimation approach. Given this problem, a parameter identification method called FF‐AGLS (alternative generalized least squares with forgetting factor) is proposed. The proposed method was combined with the robust H∞‐CKF (cubature Kalman filter) based on singular value decomposition (SVD) in order to achieve an accurate estimation of lithium‐ion battery SOC. FF‐AGLS, which adopts unbiased estimation, has strong parameter tracking ability in low temperatures and low SOC regions, as well as high model parameter identification accuracy. As a result, combining the H∞ filter with SVD‐CKF can maintain the robustness of the algorithm when the model parameters are uncertain, which may solve issues related to the decrease in SOC estimation accuracy caused by temperature changes. Finally, a series of experiments were conducted on the proposed method at different temperatures, while its performance was verified with the current under different working conditions. Accordingly, the joint algorithm based on FF‐AGLS and H∞‐SVD‐CKF was able to accurately track model parameters and SOC with a strong degree of robustness.