Adaptive Sliding Mode Observer for Estimation of State of Charge

Belhani, Ahmed; M'Sirdi, Kouider; Naamane, Aziz

doi:10.1016/j.egypro.2013.11.038

Cited by 13 publications

(13 citation statements)

References 25 publications

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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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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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“…[14][15][16][17][18][19] Control theory had been introduced in the estimation of SoC; one can find a Luenberger observer, 20 an adaptive observer, 2,8,12,21 and the sliding mode observer. 22,23 In this paper, we propose a field programmable gate array (FPGA) solution of an adaptive sliding mode estimation (ASME) observer for the online SoC estimation of the battery. The main objective is to go to the system-onchip (SoC) notion by using an FPGA device.…”

Section: Introductionmentioning

confidence: 99%

Adaptive sliding mode estimation of state of charge implemented on a field programmable gate array Spartan‐6 Xilinx device

2021

Self Cite

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This paper presents the implementation of adaptive sliding mode estimation (ASME) of the state of charge of a battery on a field programmable gate array (FPGA). The study is conducted in two steps: in the first step, we design an adaptive sliding mode observer by taking into account an electrical equivalent circuit describing the battery's behavior and identify the parameters of this equivalent circuit by using the recursive least squares estimation method. In the second step, we study the implementation of this ASME on an FPGA card.

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“…The proposed adaptive law is based on the feedback term, and additional calculations as the methods in [28,29] to obtain the gains are avoided. It also differs from the sliding mode observer with adaptive switching gains in [4,27] due to the chattering phenomena of sliding mode observer, which can be avoided in the proposed observer. Furthermore, the proposed observer has only 3 gains to be adjusted, and is suitable for implementation on embedded hardware for EVs.…”

Section: Observer Designmentioning

confidence: 99%

“…Nevertheless, the significant nonlinearity of SOC-OCV function means the sensitivity of the output with respect to the state varies greatly and constant gain is not suitable [26]. More recently, the SOC estimation methods based on sliding mode observer with gains adaption has been proposed to overcome the limitation [4,27]. They are able to reduce the chattering magnitudes and improve the SOC estimation accuracy by dynamically adjusting the switching gains of the observer.…”

Section: Introductionmentioning

confidence: 99%

Design and Experiment of Nonlinear Observer with Adaptive Gains for Battery State of Charge Estimation

Zhao

Liu

2017

Energies

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State of charge (SOC) is an important evaluation index for lithium-ion batteries (LIBs) in electric vehicles (EVs). This paper proposes a nonlinear observer with a new adaptive gain structure for SOC estimation based on a second-order RC model. It is able to dynamically adjust the gains and obtain a better balance between convergence speed and estimation accuracy with less computational time. A sufficient condition is derived to guarantee the uniform asymptotic stability of the observer, and its robustness with respect to disturbances and uncertainties is analyzed with the help of input-to-state stability (ISS) theory. A selection guide of the observer gains in practical application is presented. The estimation accuracy and convergence rate of the observer are evaluated and compared with those of extended Kalman filter (EKF) based on multi-temperature datasets from two different types of LIB cells. The robustness against different disturbances and uncertainties that may appear in a real vehicle is validated and discussed in detail. The experimental results show that the proposed observer is capable of achieving better performance with less computational time in comparison to EKF for different types of LIB cells under various working conditions. The observer is also capable of estimating SOC accurately for real life conditions according to the validation results of datasets from a battery management system (BMS) in an EV battery pack. Furthermore, the observer is simple enough, and is suitable for implementation on embedded hardware for LIB cells of EVs.

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Adaptive Sliding Mode Observer for Estimation of State of Charge

Cited by 13 publications

References 25 publications

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

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

Adaptive sliding mode estimation of state of charge implemented on a field programmable gate array Spartan‐6 Xilinx device

Design and Experiment of Nonlinear Observer with Adaptive Gains for Battery State of Charge Estimation

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