Luenberger Observer for Lithium Battery State-of-Charge Estimation

Barsali, Stefano; Ceraolo, Massimo; Li, Jiajing; Lutzemberger, Giovanni; Scarpelli, Claudio

doi:10.1007/978-3-030-37161-6_49

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…Therefore, it is difficult to observe after batteries are manufactured. Many studies and algorithms have been proposed for battery SoC estimation, from the non-model-based methods (e.g., the coulomb counting method [2] and the open-circuit voltage method [3,4]) and machine learning approaches (e.g., artificial neural networks (ANN) [5], fuzzy logic [6,7], and support vector regression (SVR) [8]) to model-based methods (e.g., Kalman filter (KF) algorithms and its related methods [9][10][11], the Luenberger observer [12][13][14], and sliding mode observer (SMO) [15][16][17][18]).…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

et al. 2022

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Lithium-ion battery devices are essential for energy storage and supply in distributed energy generation systems. Robust battery management systems (BMSs) must guarantee that batteries work within a safe range and avoid the damage caused by overcharge and overdischarge. The state-of-charge (SoC) of Li-ion batteries is difficult to observe after batteries are manufactured. The hysteresis phenomenon influences the existing battery modeling and SoC estimation accuracy. This research applies a terminal sliding mode observer (TSMO) algorithm based on a hysteresis resistor-capacitor (RC) equivalent circuit model to enable accurate SoC estimation. The proposed method is evaluated using two dynamic battery tests: the dynamic street test (DST) and the federal urban driving schedule (FUDS) test. The simulation results show that the proposed method achieved high estimation accuracy and fast response speed. Additionally, real-time battery information, including battery output voltage and SoC, was acquired and displayed by an automatic monitoring system. The designed system is valuable for all battery application cases.

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Section: Introductionmentioning

confidence: 99%

Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

et al. 2022

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“…On the other hand, many indirect estimation methods have been developed in the recent research literature. They are based on state observers such as the Kalman filter (KF), the Extended KF (EKF), [14, 15], the Unscembled KF (UKF) [16], the Sigma‐Point KF (SPKF) [17–19], the Splice KF (SKF) [20], the Lunenberger observer [21], the Sliding Mode Observer (SMO) [22], the nonlinear model based

{\mathrm{H}}_{\infty }

[23], the Smooth Variable Structure Filter (SVSF) [24–26] etc. These indirect methods have proven to be stable and accurate for tracking and estimating the online or offline battery's SoC.…”

Section: Introductionmentioning

confidence: 99%

An efficient field programmable gate arrays based real‐time implementation of smooth variable structure filter to estimate the state of charge of Li‐ion battery in electric vehicle application

Jemmali

Hamouda

Manaï

2022

IET Electrical Syst in Trans

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The computational efficiency is a challenging task in the real-time implementation of battery State of Charge (SoC) estimation algorithms in Electric Vehicle (EV) application. This study proposes for the first time a pure hardware architecture of a Smooth Variable Structure Filter (SVSF) to estimate the SoC of a lithium-ion battery in an EV. The proposed embedded architecture is implemented on a Xilinx Zynq-7000 field programmable gate arrays board without using any soft-core, and validated with real-time tests carried out on a 20 Ah NMC battery with nominal voltage about 3.65 V. The whole embedded architecture of the SVSF needs a reduced execution time in the range of 921 ns, and it consumes only 466 mW. The average estimation errors of the SoC and battery voltage at different temperatures are kept within 4.95% and 2.35% respectively. The successful convergence of the algorithm and the accurate results obtained in different circumstances, prove the practicability and computational efficiency of the proposed hardware architecture.

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“…The model-based methods utilize the battery model, e.g. electrochemical model or Equivalent Circuit Model (ECM) [6], together with a state estimator such as Kalman Filter and its derivations [7]- [8], Luenberger observer [9], sliding mode observer [10], particle filter [11], etc. Among different model-based methods, the techniques based on ECMs and filters achieve an acceptable performance level at a relatively low computational cost.…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter

Naseri

Schaltz

Stroe

et al. 2021

2021 12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC)

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Lithium-ion (Li) battery based on nickelmanganese-cobalt (NMC) cathode has emerged as one of the most successful battery types for powertrain of Electric Vehicles (EVs). The effective management of the NMC-based battery relies on accurate estimation of its State-of-Charge (SoC) in the Battery Management System (BMS). In this paper, an effective system identification approach is applied to establish the battery model using a Continuous Transfer Function (CTF) model. The Akaike information criterion (AIC) is applied to obtain the suitable model structure considering the accuracy and real-time efficiency of the model. Then, the SoC Estimation is fulfilled based on the developed model and the Extended Kalman Filter (EKF) algorithm. The correct performance of the proposed method is evaluated and confirmed using experimental data of 3.4 Ah 3.7 V NMC-based battery cells. Likewise, the feasibility of embedded implementation is proven through some Hardware-in-the-Loop (HiL) tests.

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Luenberger Observer for Lithium Battery State-of-Charge Estimation

Cited by 7 publications

References 10 publications

Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

Sliding Mode Observer for State-of-Charge Estimation Using Hysteresis-Based Li-Ion Battery Model

An efficient field programmable gate arrays based real‐time implementation of smooth variable structure filter to estimate the state of charge of Li‐ion battery in electric vehicle application

State-of-Charge Estimation of NMC-based Li-ion Battery Based on Continuous Transfer Function Model and Extended Kalman Filter

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