Patent application EP16172548A (EP), filed 2016-06-01
Publication number: EP3252616A1
Title: ESTIMATING VARIABLES THAT ARE NOT DIRECTLY MEASURABLE
Abstract: Devices, methods, and systems for estimating variables that are not directly measurable are described herein. One device includes a memory, and a processor configured to execute executable instructions stored in the memory to estimate a number of variables that are not directly measurable using an inferential sensor with a model having a number of uncertain parameters associated therewith, wherein the model describes a relationship between the number of variables to be estimated and a number of directly measurable variables, and a number of constraints on the number of variables to be estimated.
IPC codes: G01R  31/    36            A I
G06F  17/    18            A I
Claims: number: 1
text: A computing device (100) for estimating variables that are not directly measurable, comprising:
number: 2
text: The computing device (100) of claim 1, wherein the number of uncertain parameters are described by probability distributions and are not updated by new measurements of the directly measurable variables.
number: 3
text: The computing device (100) of claim 1, wherein the number of constraints include a number of physical constraints on the number of variables to be estimated.
number: 4
text: The computing device (100) of claim 1, wherein the number of constraints include a number of constraints on a validity range of the model (214, 216).
number: 5
text: The computing device (100) of claim 1, wherein the processor (102) is configured to execute the instructions to estimate the number of variables that are not directly measurable based on:
number: 6
text: The computing device (100) of claim 1, wherein the estimation of the number of variables that are not directly measurable includes:
number: 7
text: The computing device (100) of claim 1, wherein the processor (102) is configured to execute the instructions to use a non-diagonal weighting matrix (220) to minimize errors associated with the model (214, 216).
number: 8
text: The computing device (100) of claim 7, wherein the non-diagonal weighting matrix (220) minimizes a mean square error of an estimated state sequence for the model (214, 216).
number: 9
text: The computing device (100) of claim 1, wherein:
number: 10
text: The computing (100) device of claim 1, wherein the processor (102) is configured to execute the instructions to store the estimate of the number of variables in the memory (104).
number: 11
text: A method for estimating variables that are not directly measurable, comprising:
number: 12
text: The method of claim 11, wherein the number of variables that are not directly measurable include an internal state of a battery (334-1, 334-2, ..., 334-N).
number: 13
text: The method of claim 11, wherein the number of directly measured variables include a number of characteristics associated with a battery (334-1, 334-2, ..., 334-N).
number: 14
text: The method of claim 11, wherein the number of uncertain parameters associated with the model (214, 216) include parameters associated with a battery (334-1, 334-2, ..., 334-N) that have manufacturing variability.
number: 15
text: The method of claim 11, wherein the method includes receiving a direct measurement of the number of directly measured variables.
Office action: search_report, dated 2017-12-06
Rejection reasons: novelty_epc_54
Prior art cited: ref_id: US2005110498A1
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severity: background
ref_id: CN104242393A
ref_type: patent
severity: background
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severity: background
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severity: background
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ref_id: - GREGORY L. PLETT: "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs", JOURNAL OF POWER SOURCES, vol. 134, no. 2, 1 August 2004 (2004-08-01), pages 277 - 292, XP055218097, ISSN: 0378-7753, DOI: 10.1016/j.jpowsour.2004.02.033
ref_type: npl
severity: background
ref_id: US2005110498
ref_type: patent
severity: background
ref_id: CN104242393
ref_type: patent
severity: background
ref_id: - Murali R Rajamani, "Data-based techniques to improve state estimation in model predictive control", PhD. Thesis, (2007), pages 1 - 257, URL: http://jbrwww.che.wisc.edu/theses/rajamani.pdf, (20170202), XP055341547 [X] 1-11,15 * pages 2, 6, 23, 31 and 174 section 2.1.1 * [I] 12-14
ref_type: npl
severity: novelty_destroying
ref_id: - MA YAN ET AL, "Lithium-ion battery state of charge estimation based on moving horizon", PROCEEDING OF THE 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, IEEE, (20140629), doi:10.1109/WCICA.2014.7053563, pages 5002 - 5007, XP032742560 [I] 1-15 * section III *
ref_type: npl
severity: background
ref_id: - ZHANG CAIPING ET AL, "Robust and Adaptive Estimation of State of Charge for Lithium-Ion Batteries", IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, IEEE SERVICE CENTER, PISCATAWAY, NJ, USA, vol. 62, no. 8, doi:10.1109/TIE.2015.2403796, ISSN 0278-0046, (201508), pages 4948 - 4957, (20150626), XP011585857 [I] 1-15 * section VI *
ref_type: npl
severity: background
ref_id: - HUIMIN CHEN, "Adaptive cubature Kalman filter for nonlinear state and parameter estimation", INFORMATION FUSION (FUSION), 2012 15TH INTERNATIONAL CONFERENCE ON, IEEE, (20120709), ISBN 978-1-4673-0417-7, pages 1413 - 1420, XP032228582 [A] 1-15 * sections II, III and IV *
ref_type: npl
severity: background
ref_id: - NACHI GUPTA ET AL, "Kalman Filtering with Equality and Inequality State Constraints", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, (20070918), XP080302311 [A] 1-15 * sections 3 and 4 *
ref_type: npl
severity: background
ref_id: - ERIC L. HASELTINE ET AL, "Critical Evaluation of Extended Kalman Filtering and Moving-Horizon Estimation", INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH., US, (20030312), vol. 44, no. 8, doi:10.1021/ie034308l, ISSN 0888-5885, pages 2451 - 2460, XP055340103 [A] 1-15 * the whole document *
ref_type: npl
severity: background
Final disposition: pending (2017-12-06)