Unifying Model-Based Prognosis With Learning-Based Time-Series Prediction Methods: Application to Li-Ion Battery

Aggab, Toufik; Avila, Manuel; Vrignat, Pascal; Kratz, Frédéric

doi:10.1109/jsyst.2021.3080125

Cited by 20 publications

(6 citation statements)

References 51 publications

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“…In this reference, modelbased PID controller used to provide damping performance through some specific operating cases. In [10], the change of system operating point used to update the PID parameters to reach proper damping performances. Since, power system is a nonlinear and complex system, therefore the conventional linear PID controllers cannot provide proper damping performances which considering the system complexity, PID presents different performances for damping the oscillations [11].…”

Section: B1 Recent Research Evaluationsmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Damping Controller of Grid-Connected Microgrid Hybrid System Integrating Wind Farms and Batteries

Habibi,

Yousefi,

Shirazi

et al. 2024

IEEE Access

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Renewable energies equipped with LIRs as wind turbines and photovoltaic arrays provide negative impacts on power system dynamic securities. For this issue, developing adaptive controlling strategies play important role on controlling the system dynamic oscillations. In this paper, based on HVDC link, an ANFDC damping controller is proposed for controlling the system dynamic behavior through different LIRs. To do this, a fuzzy linguistic role proposed for tuning ANFDC parameters which the input dynamic signals are transferred into linguistic variables through offline working mode. Then, considering microgrid consists of different LIRs as offshore and onshore WTs, PVs and SSSGs integrated with together, ANFDC is trained which then in online working mode, the system damping performances through different operational and technological structures are evaluated. The proposed scheme is an online and non-modelbased controller which uses the advantages of both neural and fuzzy logics together for providing a fast and secure structure of damping controller through online evaluations. The merit and effectiveness of the proposed approach investigated on a grid-connected microgrid consisting of PV, WT and SSSG connected in an incorporated HVDC link which considering different short circuit faults, damping performances are evaluated. Results indicate effectiveness of the proposed scheme for damping dynamic oscillations of LIRs with high damping ratios subject against severe fault events.

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Section: B1 Recent Research Evaluationsmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Damping Controller of Grid-Connected Microgrid Hybrid System Integrating Wind Farms and Batteries

Habibi,

Yousefi,

Shirazi

et al. 2024

IEEE Access

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“…Here, a PSO is used as the identification algorithm, but other methods are possible to estimate the parameters, such as, for example, the observer method [37,38]. The PSO originated in the study of the behavior of birds.…”

Section: Model Parameter Identification and Validationmentioning

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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“…[16] Specifically, in the view of the expected widespread of electric vehicles, several methodologies for LIB SoH estimation and remaining useful life (RUL) prediction have been widely investigated through suitable model-based approaches. [17][18][19] With regards to data-driven methods, several studies are available in literature. Interesting works present approaches for battery performance monitoring [20] and RUL estimation.…”

Section: Introductionmentioning

confidence: 99%

“…In general, such methods aim to deeply predict the processes involving Li‐ion battery cycle and calendar aging [16] . Specifically, in the view of the expected widespread of electric vehicles, several methodologies for LIB SoH estimation and remaining useful life (RUL) prediction have been widely investigated through suitable model‐based approaches [17–19] …”

Section: Introductionmentioning

confidence: 99%

A Data‐Driven Method based on Discrete Wavelet Transform for online Li‐Ion Battery State‐of‐Health Prediction and Monitoring

Pelosi,

Gallorini,

Ottaviano

et al. 2024

Batteries & Supercaps

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Transportation electrification is accelerating the clean energy transition. Due to high efficiencies and energy density, Li‐ion batteries (LIBs) are used as on‐board energy carrier for battery electric vehicles (BEVs). Nevertheless, LIBs are subject to rapid degradation due to fast‐charging, mechanical, electrical and thermal factors. Thus, state‐of‐health (SoH) prediction is of utmost importance for extending LIB lifespan. In this paper, an online accurate and easy‐of‐implementation battery SoH prediction and monitoring method is presented for BEV applications. The method is based on the application of discrete wavelet transform (DWT) analysis to voltage profiles, measured while driving. Specifically, an extensive cycle aging experimental campaign on NCR 18650 cells was performed, applying two typical US test drives (urban and extra‐urban drive cycle, respectively) to the cells at different SoH. Moreover, tests carried out on LIBs at different temperatures demonstrated that temperature effect on the implemented DWT‐based method can be distinguished and separated from cycle aging effect. The proposed method allows a real‐time SoH estimation showing a good accuracy (MAE, ME and RMSE respectively result in 0.917, 2.897 and 1.32) without requiring high computational efforts. This allows to predict LIB aging also during the driving, continuously providing SoH information to avoid LIB fast degradation.

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Unifying Model-Based Prognosis With Learning-Based Time-Series Prediction Methods: Application to Li-Ion Battery

Cited by 20 publications

References 51 publications

Adaptive Neuro-Fuzzy Damping Controller of Grid-Connected Microgrid Hybrid System Integrating Wind Farms and Batteries

Adaptive Neuro-Fuzzy Damping Controller of Grid-Connected Microgrid Hybrid System Integrating Wind Farms and Batteries

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

A Data‐Driven Method based on Discrete Wavelet Transform for online Li‐Ion Battery State‐of‐Health Prediction and Monitoring

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