Attractive Ellipsoid Sliding Mode Observer Design for State of Charge Estimation of Lithium-Ion Cells

Nath, Anirudh; Gupta, Raghvendra; Mehta, Rohit; Bahga, Supreet Singh; Gupta, Amit; Bhasin, Shubhendu

doi:10.1109/tvt.2020.3035681

Cited by 28 publications

(3 citation statements)

References 47 publications

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“…With these enhancements, the method can mitigate model errors, address the output jitter problem, and exhibit strong robustness. Nath et al [28] established a second-order RC equivalent circuit model and introduced an ellipsoid-based improved sliding film observer. This observer is capable of achieving higher accuracy in state-of-charge estimation even in the presence of boundary uncertainties and external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

Chang,

Li,

Sun

et al. 2024

Batteries

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This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles.

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Section: Introductionmentioning

confidence: 99%

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

Chang,

Li,

Sun

et al. 2024

Batteries

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“…The algorithms used to estimate the SOC include the open-circuit voltage method, the Ampere-hour (Ah) integration method, the Kalman filter algorithm [23], and the neural network algorithm [24,25]. Due to the complex working conditions and the complex electrochemical reaction inside the battery, the ohmic effect, the self-discharge effect, etc., the results of the traditional state of charge estimation have a large error [26,27]. Therefore, researchers have proposed an improved algorithm based on the traditional one [28][29][30]; there are also data-driven methods for estimating the state of charge of lithium-ion batteries [31][32][33][34], and some of the proposed algorithms by the latter are effective in state of charge estimation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive Back Propagation Neural Network-Unscented Kalman Filtering Algorithm for Accurate Lithium-Ion Battery State of Charge Estimation

Wang

Wang²,

Fan³

et al. 2022

Metals

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Accurate State of Charge (SOC) estimation for lithium-ion batteries has great significance with respect to the correct decision-making and safety control. In this research, an improved second-order-polarization equivalent circuit (SO-PEC) modelling method is proposed. In the process of estimating the SOC, a joint estimation algorithm, the Adaptive Back Propagation Neural Network and Unscented Kalman Filtering algorithm (ABP-UKF), is proposed. It combines the advantages of the robust learning rate in the Back Propagation (BP) neural network and the linearization error reduction in the Unscented Kalman Filtering (UKF) algorithm. In the BP neural network part, the self-adjustment of the learning factor accompanies the whole estimation process, and the improvement of the self-adjustment algorithm corrects the shortcomings of the UKF algorithm. In the verification part, the model is validated using a segmented double-exponential fit. Using the Ampere-hour integration method as the reference value, the estimation results of the UKF algorithm and the Back Propagation Neural Network and Unscented Kalman Filtering (BP-UKF) algorithm are compared, and the estimation accuracy of the proposed method is improved by 1.29% under the Hybrid Pulse Power Characterization (HPPC) working conditions, 1.28% under the Beijing Bus Dynamic Stress Test (BBDST) working conditions, and 2.24% under the Dynamic Stress Test (DST) working conditions. The proposed ABP-UKF algorithm has good results in estimating the SOC of lithium-ion batteries and will play an important role in the high-precision energy management process.

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“…In addition, to obtain a reliable battery model, the SOC estimation also requires a high precision algorithm. Recently, effective estimation methods have been presented, such as the open circuit voltage (OCV) method [6], amperehour integration method [7,8], Kalman filter algorithm, neural network method [9,10], sliding mode observer [11,12], H ∞ filter [13,14], adaptive particle filter [15] and others. Each of these methods presents advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter

Chen

Lopes

et al. 2021

Fractal Fract

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The covariance matrix of measurement noise is fixed in the Kalman filter algorithm. However, in the process of battery operation, the measurement noise is affected by different charging and discharging conditions and the external environment. Consequently, obtaining the noise statistical characteristics is difficult, which affects the accuracy of the Kalman filter algorithm. In order to improve the estimation accuracy of the state of charge (SOC) of lithium-ion batteries under actual working conditions, a fuzzy fractional-order unscented Kalman filter (FFUKF) is proposed. The algorithm combines fuzzy inference with fractional-order unscented Kalman filter (FUKF) to infer the measurement noise in real time and take advantage of fractional calculus in describing the dynamic behavior of the lithium batteries. The accuracy of the SOC estimation under different working conditions at three different temperatures is verified. The results show that the accuracy of the proposed algorithm is superior to those of the FUKF and extended Kalman filter (EKF) algorithms.

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Attractive Ellipsoid Sliding Mode Observer Design for State of Charge Estimation of Lithium-Ion Cells

Cited by 28 publications

References 47 publications

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

A Novel Adaptive Back Propagation Neural Network-Unscented Kalman Filtering Algorithm for Accurate Lithium-Ion Battery State of Charge Estimation

State of Charge Estimation of Lithium-Ion Batteries Based on Fuzzy Fractional-Order Unscented Kalman Filter

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