Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter

Yu, Jing; Mo, Baohua; Liu, Hao; Wan, Jiuqing

doi:10.1080/08982112.2017.1322210

Cited by 32 publications

(16 citation statements)

References 17 publications

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“…Wang et al [10] estimated the battery SOH by using a state-space model of the discharge rate. Relying on an empirical model [11], Yu et al [12] applied a particle filter to estimate battery SOH. Jong [9] employed a battery equivalent circuit model and used the extended Kalman filter (EKF) method to estimate the SOH.…”

Section: Introductionmentioning

confidence: 99%

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

2021

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Section: Introductionmentioning

confidence: 99%

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

2021

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“…Nieto et al proposed a novel predictive model by integrating the PSO approach into the support vector machines and pointed out that such model can dispense with historical operation states. In the extending study, Yu et al introduced a quantum PSO approach for the RUL prediction of lithium‐ion batteries, in which a part of controlled parameters from the traditional PSO is eliminated to improve the randomness of particle movement and also the global searching ability.…”

Section: Parameter Identification and Rul Derivationmentioning

confidence: 99%

Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

Zhou

Chen

et al. 2020

Quality & Reliability Eng

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Multivariable stochastic degradation system (MSDS) is quite common in indus-tries such as blast furnace ironmaking, vehicle transportation, and aerospace manufacturing. Large-scale complex equipments may be affected by multiple factors, resulting in not just a single deteriorating performance characteristic. It is difficult to handle unknown failure structures of practical systems by using traditional univariate degradation modeling methods. A novel health index (HI) is constructed to quantitatively analyze the health state for the overall system. Considering the interaction between internal reactions and external environments, the fractional Brownian motion (FBM), a typical non-Markovian diffusion process, is added for the purpose of reflecting stochastic uncertainties and memory effects. Based on the wavelet estimators and the maximum likelihood estimation (MLE) algorithm, multi-sensor observations of degradation variables are analyzed simultaneously to identify model parameters. A closed-form distribution of system-level remaining useful life (RUL) is obtained with a mild two-layer approximation. Relevant case studies are then handled that adequately demonstrate the effectiveness and the practical utility of the proposed method.

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“…Choosing the univariate nonstationary growth model, and the process model and measurement model are given as Formulas (34) and (35), ie,…”

Section: Test Of Basic Performance Improved Filtermentioning

confidence: 99%

“…Thus, the efficiency of moving toward high‐likelihood region will be reduced. Yu et al proposed a quantum PSO‐based PF (QPSO‐PF), with fewer parameters to control, which makes QPSO‐PF easier for applications, but QPSO is obviously better than PSO in global optimization capability, which will reduce the local optimization capability and restrict the further improvement of filtering precision. Wang and Qian improved the efficiency of the resampling phase of the sequential importance resampling PF; thus, the importance between a priori density and likelihood density is balanced, but particles in this method might move toward the direction of nonoptimal regions easily, which will reduce the stability of filter.…”

Section: Introductionmentioning

confidence: 99%

Improved infrared small target detection and tracking method based on new intelligence particle filter

Chen¹,

Tian

et al. 2017

Computational Intelligence

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Track-before-detect algorithm based on the particle filter algorithm has the problems of low tracking precision, poor particles, and requiring a large amount of particles to be calculated in a low signal-to-noise ratio, which is difficult to meet the accuracy and speed required by the modern infrared search and tracking system. In this paper, an improved infrared small target detection and tracking method based on a new particle filter is proposed. This is where particles are used to represent an individual bat to imitate the hunting process of bats. By adjusting loudness, frequency, and impulse emissivity of a particle swarm, the optimal particle at that time is followed to search in the solution space. In addition, the global search and the local search can also be dynamically switched to improve the quality and distribution of the particle swarm. The performance of the proposed algorithm is tested in a simulation scene and the real scene of the infrared small target detection and tracking. Experimental results show that the proposed algorithm improves the performance of the infrared searching and tracking system.

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Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter

Cited by 32 publications

References 17 publications

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

Remaining useful life prediction for multivariable stochastic degradation systems with non‐Markovian diffusion processes

Improved infrared small target detection and tracking method based on new intelligence particle filter

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