An Enhanced Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction

The particle filter (PF) has been used for the analysis of nonlinear, non-Gaussian dynamical systems with hidden state variables. However, PF has some limitations in real-world applications, for instant, the sample degeneracy and the impoverishment, which are considered as long-standing challenges in this research and development field. Although several techniques have been proposed in the literature for this purpose, they have some limitations: for example, they cannot represent the entire probability density function (pdf) effectively and are usually problem dependent. In this paper, an enhanced mutated PF (EMPF) technique is proposed to improve the performance of PFs. In the EMPF technique, first, a novel enhanced mutation approach is proposed to actively explore the posterior pdf to locate the high-likelihood area. Second, a new selection scheme is suggested to process low-weight particles for optimizing the posterior distribution and tackling sample degeneracy. Third, an outlier assessment method is adopted to monitor the overall pattern of the posterior distribution based on the interquartile range statistical analysis. The effectiveness of the proposed EMPF technique is verified by simulation tests. It is also implemented for the remaining useful life prediction of lithium-ion batteries. Test results show that the developed EMPF technique can capture a system’s dynamic behavior and track system characteristics effectively.

[1]  A. Jazwinski.  Stochastic Processes and Filtering Theory , 1970 .

[2]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[3]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[4]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[5]  Jay Lee,et al.  A review on prognostics and health monitoring of Li-ion battery , 2011 .

[6]  Shen Yin,et al.  Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System , 2015, IEEE Transactions on Industrial Electronics.

[7]  Guangzhong Dong,et al.  Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression , 2018, IEEE Transactions on Industrial Electronics.

[8]  Wilson Wang,et al.  A Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction , 2014, IEEE Transactions on Instrumentation and Measurement.

[9]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[10]  M. Pecht,et al.  Prognostics of lithium-ion batteries using model-based and data-driven methods , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).

[11]  Yu Peng,et al.  Uncertainty quantification of fusion prognostics for lithium-ion battery remaining useful life estimation , 2013, 2013 IEEE Conference on Prognostics and Health Management (PHM).

[12]  Chen Lu,et al.  Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction , 2014 .

[13]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[14]  Mia Hubert,et al.  Robust statistics for outlier detection , 2011, WIREs Data Mining Knowl. Discov..

[15]  Juan M. Corchado,et al.  Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches , 2013, Expert Syst. Appl..

[16]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[17]  Jie Liu,et al.  A regularized auxiliary particle filtering approach for system state estimation and battery life prediction , 2011 .

[18]  Jun S. Liu,et al.  Sequential Monte Carlo methods for dynamic systems , 1997 .

[19]  Thia Kirubarajan,et al.  An Optimization-Based Parallel Particle Filter for Multitarget Tracking , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[20]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[21]  G. Casella,et al.  Rao-Blackwellisation of sampling schemes , 1996 .

[22]  Jinsong Yu,et al.  Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter , 2017 .

[23]  K. Goebel,et al.  Prognostics in Battery Health Management , 2008, IEEE Instrumentation & Measurement Magazine.

[24]  Kusum Deep,et al.  A new crossover operator for real coded genetic algorithms , 2007, Appl. Math. Comput..

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

[26]  Niclas Bergman,et al.  Recursive Bayesian Estimation : Navigation and Tracking Applications , 1999 .

[27]  Christian Musso,et al.  Improving Regularised Particle Filters , 2001, Sequential Monte Carlo Methods in Practice.

[28]  Eric Moulines,et al.  Comparison of resampling schemes for particle filtering , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

[29]  Wei Jiang,et al.  On-line outlier detection and data cleaning , 2004, Comput. Chem. Eng..

[30]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[31]  W. Marsden.  I and J , 2012 .

[32]  W. Wang,et al.  A data-model-fusion prognostic framework for dynamic system state forecasting , 2012, Eng. Appl. Artif. Intell..

[33]  Mohan M. Trivedi,et al.  A General Active-Learning Framework for On-Road Vehicle Recognition and Tracking , 2010, IEEE Transactions on Intelligent Transportation Systems.

[34]  Min Li,et al.  Prognostics of Lithium-Ion Batteries Based on the Verhulst Model, Particle Swarm Optimization and Particle Filter , 2014, IEEE Transactions on Instrumentation and Measurement.

[35]  Michael Osterman,et al.  Remaining useful performance analysis of batteries , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[36]  L. Devroye.  A Course in Density Estimation , 1987 .