Remaining useful life prediction of lithium‐ion battery based on an improved particle filter algorithm

[1]  Yuan Cao,et al.  Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system. , 2019, Chaos.

[2]  Peng Li,et al.  Parallel processing algorithm for railway signal fault diagnosis data based on cloud computing , 2018, Future Gener. Comput. Syst..

[3]  Huajing Fang,et al.  A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery , 2017 .

[4]  Xin Zhang,et al.  Remaining useful life prediction of lithium-ion battery using an improved UPF method based on MCMC , 2017, Microelectron. Reliab..

[5]  Peng Shi,et al.  Distributed Hybrid Particle/FIR Filtering for Mitigating NLOS Effects in TOA-Based Localization Using Wireless Sensor Networks , 2017, IEEE Transactions on Industrial Electronics.

[6]  Dong Wang,et al.  Prognostics of Li(NiMnCo)O2-based lithium-ion batteries using a novel battery degradation model , 2017, Microelectron. Reliab..

[7]  Junwei Han,et al.  Particle Learning Framework for Estimating the Remaining Useful Life of Lithium-Ion Batteries , 2017, IEEE Transactions on Instrumentation and Measurement.

[8]  Lixin Wang,et al.  A lead-acid battery's remaining useful life prediction by using electrochemical model in the Particle Filtering framework , 2017 .

[9]  Jihong Wang,et al.  Capacity fade modelling of lithium-ion battery under cyclic loading conditions , 2016 .

[10]  Linxia Liao,et al.  A hybrid framework combining data-driven and model-based methods for system remaining useful life prediction , 2016, Appl. Soft Comput..

[11]  Zonghai Chen,et al.  An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks , 2016 .

[12]  Dong Wang,et al.  Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter , 2016, IEEE Transactions on Instrumentation and Measurement.

[13]  Huajing Fang,et al.  An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction , 2015, Reliab. Eng. Syst. Saf..

[14]  Myo-Taeg Lim,et al.  Improving Reliability of Particle Filter-Based Localization in Wireless Sensor Networks via Hybrid Particle/FIR Filtering , 2015, IEEE Transactions on Industrial Informatics.

[15]  Myo Taeg Lim,et al.  Horizon group shift FIR filter: Alternative nonlinear filter using finite recent measurements , 2014 .

[16]  M. Pecht,et al.  Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm , 2014, Neural Computing and Applications.

[17]  Kwok-Leung Tsui,et al.  An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..

[18]  Yuriy S. Shmaliy,et al.  Suboptimal FIR Filtering of Nonlinear Models in Additive White Gaussian Noise , 2012, IEEE Transactions on Signal Processing.

[19]  Hongwen He,et al.  State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model , 2011, IEEE Transactions on Vehicular Technology.

[20]  Changqing Liu,et al.  Minimum-Variance Unbiased Unknown Input and State Estimation for Multi-Agent Systems by Distributed Cooperative Filters , 2018, IEEE Access.