Prognosis and Remaining Useful Life Estimation of Lithium-Ion Battery with Optimal Multi-Level Particle Filter and Genetic Algorithm

Ossai, Chinedu I.

doi:10.3390/batteries4020015

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…In general terms, the RUL is defined as the useful operative life of the supercapacitor before it attains the threshold level (70% or 80% of the initial capacity). Therefore, it is suggested that the supercapacitor is replaced when the threshold level is achieved [68]. The estimation of the RUL of a supercapacitor is important, as it provides crucial information by determining the occurrence of failure and eliminating issues [6,69].…”

Section: Progress Of Rul Estimation Techniques In Smsmentioning

confidence: 99%

SOC, SOH and RUL Estimation for Supercapacitor Management System: Methods, Implementation Factors, Limitations and Future Research Improvements

et al. 2022

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The development of a supercapacitor management system (SMS) for clean energy applications is crucial to addressing carbon emissions problems. Consequently, state of charge (SOC), state of health (SOH), and remaining useful life (RUL) for SMS must be developed to evaluate supercapacitor robustness and reliability for mitigating supercapacitor issues related to safety and economic loss. State estimation of SMS results in safe operation and eliminates undesirable event occurrences and malfunctions. However, state estimations of SMS are challenging and tedious, as SMS is subject to various internal and external factors such as internal degradation mechanism and environmental factors. This review presents a comprehensive discussion and analysis of model-based and data-driven-based techniques for SOC, SOH, and RUL estimations of SMS concerning outcomes, advantages, disadvantages, and research gaps. The work also investigates various key implementation factors such as a supercapacitor test bench platform, experiments, a supercapacitor cell, data pre-processing, data size, model operation, functions, hyperparameter adjustments, and computational capability. Several key limitations, challenges, and issues regarding SOC, SOH, and RUL estimations are outlined. Lastly, effective suggestions are outlined for future research improvements towards delivering accurate and effective SOC, SOH, and RUL estimations of SMS. Critical analysis and discussion would be useful for developing accurate SMS technology for state estimation of a supercapacitor with clean energy and high reliability, and will provide significant contributions towards reducing greenhouse gas (GHG) to achieve global collaboration and sustainable development goals (SDGs).

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Section: Progress Of Rul Estimation Techniques In Smsmentioning

confidence: 99%

SOC, SOH and RUL Estimation for Supercapacitor Management System: Methods, Implementation Factors, Limitations and Future Research Improvements

et al. 2022

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“…FIGURE 2 flow chart of the remaining useful life (RUL) prediction where f () and g() are the nonlinear transfer functions, which are going be made first-order Taylor series expansion near (x k , u k ), respectively. Then, the system state space model can be got after linearization and the final results can be calculated by a series of mathematical formulas from (15) to (20). Q and R are, respectively, the variances of w(k) and v(k).…”

Section: Introduction Of the Ekf For Importance Resamplingmentioning

confidence: 99%

“…Internal resistance, capacity, and power of the battery are used as key parameters to establish the cycle life model . And, the particle filter (PF) can be used to predict the RUL based on the experience degradation model, which is suitable for the nonlinear and non‐Gaussian system. But, the PF algorithm has poor adaptability because of the particle degradation phenomena.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction of lithium‐ion battery based on extended Kalman particle filter

et al. 2019

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Summary Scientific estimation and prediction of the state of health (SOH) of lithium‐ion battery, especially the remaining useful life (RUL), has important significance to guarantee the battery safety and reliability in the full life cycle to avoid catastrophic accidents as much as possible. In order to accurately predict the RUL of the lithium‐ion battery, this paper firstly analyzes the problems of the standard particle filter (PF). Then, a novel extended Kalman particle filter (EKPF) is proposed, in which the extended Kalman filter (EKF) is used as the sampling density function to optimize PF algorithm. The life cycle tests are designed and carried out to get accurate and reliable data for the RUL prediction. And, the aging properties of lithium‐ion battery are analyzed in detail. The RUL prediction is done based on the established capacity degradation model and the proposed EKPF method. Results show that the RUL prediction error of the proposed method is less than 5%, which has higher precision compared with the standard PF method and can be used both offline and online.

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“…The accelerated aging tests can be divided into two categories: calendar aging and cycling aging [1][2][3][4]. The behavior of an ESS during its storage is tested in calendar aging.…”

Section: Introductionmentioning

confidence: 99%

Lifetime Prediction of Lithium-Ion Capacitors Based on Accelerated Aging Tests

2019

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Lithium-ion Capacitors (LiCs) that have intermediate properties between lithium-ion batteries and supercapacitors are still considered as a new technology whose aging is not well studied in the literature. This paper presents the results of accelerated aging tests applied on 12 samples of LiCs. Two high temperatures (60 °C and 70 °C) and two voltage values were used for aging acceleration for 20 months. The maximum and the minimum voltages (3.8 V and 2.2 V respectively) had different effects on capacitance fade. Cells aging at 2.2 V encountered extreme decrease of the capacitance. After storing them for only one month at 60 °C, they lost around 22% of their initial capacitance. For this reason, an aging model was developed for cells aging at the lowest voltage value to emphasize the huge decrease of the lifetime at this voltage condition. Moreover, two measurement tools of the capacitance were compared to find the optimal method for following the evolution of the aging process. It was proved that electrochemical impedance spectroscopy is the most accurate measurement technique that can reveal the actual level of degradation inside a LiC cell.

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Prognosis and Remaining Useful Life Estimation of Lithium-Ion Battery with Optimal Multi-Level Particle Filter and Genetic Algorithm

Cited by 5 publications

References 43 publications

SOC, SOH and RUL Estimation for Supercapacitor Management System: Methods, Implementation Factors, Limitations and Future Research Improvements

SOC, SOH and RUL Estimation for Supercapacitor Management System: Methods, Implementation Factors, Limitations and Future Research Improvements

Remaining useful life prediction of lithium‐ion battery based on extended Kalman particle filter

Lifetime Prediction of Lithium-Ion Capacitors Based on Accelerated Aging Tests

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