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Transactions of the Institute of Systems, Control and Information Engineers Vol. 29 (2016), No. 10

ISIJ International
belloff
ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668
Publisher: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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  1. Vol. 37 (2024)

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  2. Vol. 36 (2023)

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  3. Vol. 35 (2022)

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  4. Vol. 34 (2021)

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  5. Vol. 33 (2020)

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  6. Vol. 32 (2019)

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  7. Vol. 31 (2018)

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  8. Vol. 30 (2017)

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  9. Vol. 29 (2016)

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  11. Vol. 27 (2014)

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  12. Vol. 26 (2013)

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  14. Vol. 24 (2011)

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  15. Vol. 23 (2010)

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  16. Vol. 22 (2009)

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  17. Vol. 21 (2008)

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  18. Vol. 20 (2007)

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  19. Vol. 19 (2006)

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  23. Vol. 15 (2002)

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  24. Vol. 14 (2001)

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  25. Vol. 13 (2000)

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  26. Vol. 12 (1999)

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  27. Vol. 11 (1998)

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  28. Vol. 10 (1997)

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  29. Vol. 9 (1996)

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  30. Vol. 8 (1995)

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  31. Vol. 7 (1994)

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  32. Vol. 6 (1993)

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  33. Vol. 5 (1992)

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  34. Vol. 4 (1991)

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  35. Vol. 3 (1990)

    1. No.12
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  36. Vol. 2 (1989)

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  37. Vol. 1 (1988)

    1. No.7
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    3. No.5
    4. No.4
    5. No.3
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Transactions of the Institute of Systems, Control and Information Engineers Vol. 29 (2016), No. 10

SOC Estimation of Lithium-Ion Batteries Based on Parameter-Dependent State-Space Model

Masaki Oya, Kiyotsugu Takaba, Lei Lin, Ryu Ishizaki, Naoki Kawarabayasi, Masahiro Fukui

pp. 433-440

DOI:

10.5687/iscie.29.433

Abstract

This paper is concerned with the State-Of-Charge (SOC) estimation for lithium-ion batteries by using an equivalent circuit model and the extended Kalman filtering (EKF) technique. The physical parameters in the equivalent circuit are dependent on both the temperature and the SOC of the battery. We propose a new method for improving the estimation accuracy based on a parameter-dependent state-state space model. To be more specific, we derive a parameter-dependent state-space model by viewing these parameters as time-varying parameters，and then apply the extended Kalman filter to estimate the SOC. Experimental results demonstrate the effectiveness of the proposed method.

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SOC Estimation of Lithium-Ion Batteries Based on Parameter-Dependent State-Space Model

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Ris

Functional Verification Method Considering Ways of Use for Design of Products with Sensors

Eiji Morinaga, Hidefumi Wakamatsu, Masachika Mega, Eiji Arai

pp. 441-447

DOI:

10.5687/iscie.29.441

Abstract

Recent globalization in industries has increased the number of product failures and troubles caused by using them in unexpected ways. To avoid such troubles, it is necessary to verify whether the design plan can fulfill required functions when the product is utilized in various ways. From this point of view, the authors proposed a functional verification framework considering ways of use based on Petri net modeling and analysis of behavior of the product. However, it cannot be applied to products with sensors, since sensor faults could not be dealt with by the modeling method. This paper describes enhancement of the framework considering this problem. In the behavior modeling,some Petri net elements were introduced for representing difference between the information obtained by the sensors and the real information. This enabled the behavior model to represent sensor faults. In addition, another Petri net element that stands for a state in which the product should not be was also introduced. This made it possible to judge automatically whether an irregular phenomenon on the integrated model of behavior and physical phenomena results in a functional trouble. Application of the proposed method to an example of home cleaning robot proved its potential.

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Functional Verification Method Considering Ways of Use for Design of Products with Sensors

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Ris

On Ensemble Kalman Filter, Particle Filter, and Gaussian Particle Filter

Masaya Murata, Kaoru Hiramatsu

pp. 448-462

DOI:

10.5687/iscie.29.448

Abstract

In this paper, we clarify theoretical aspects of the representative non-Gaussian filters: the ensemble Kalman filter (EnKF) and the particle filter (PF). We first show that the EnKF is a realization algorithm of the linear optimal filter for nonlinear problems. We also show that under the Gaussian assumption for the predicted state, the EnKF provides a realization algorithm of the Gaussian filter. We next propose the multiple distribution estimation approach which is a novel framework for designing non-Gaussian filters and show that the PFs are special cases. We then propose a new PF algorithm to address the particle impoverishment problem inherent in the standard PF algorithms. We also show that by applying the proposed algorithm, we can improve the filtering accuracy of the Gaussian particle filter. We finally confirm the performance of each filter using two benchmark simulation models.

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Article Title

On Ensemble Kalman Filter, Particle Filter, and Gaussian Particle Filter

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