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用于多故障检测的高能量密度锂离子电池建模与仿真

Modeling and simulation of high energy density lithium-ion battery for multiple fault detection.

作者信息

Sadhukhan Chandrani, Mitra Swarup Kumar, Bhattacharyya Suvanjan, Almatrafi Eydhah, Saleh Bahaa, Naskar Mrinal Kanti

机构信息

Electrical Engineering Department, MCKV Institute of Engineering, Liluah, Howrah, West Bengal, 712104, India.

Electronic & Telecommunication Engineering Department, MCKV Institute of Engineering, Liluah, Howrah, West Bengal, 712104, India.

出版信息

Sci Rep. 2022 Jun 13;12(1):9800. doi: 10.1038/s41598-022-13771-4.

DOI:10.1038/s41598-022-13771-4
PMID:35697718
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9192780/
Abstract

Lithium-ion battery, a high energy density storage device has extensive applications in electrical and electronic gadgets, computers, hybrid electric vehicles, and electric vehicles. This paper presents multiple fault detection of lithium-ion battery using two non-linear Kalman filters. A discrete non-linear mathematical model of lithium ion battery has been developed and Unscented Kalman filter (UKF) is employed to estimate the model parameter. Occurrences of multiple faults such as over-charge, over-discharge and short circuit faults between inter cell power batteries, affects the parameter variation of system model. Parallel combinations of some UKF (bank of filters) compare the model parameter variation between the normal and faulty situation and generates residual signal indicating different fault. Simulation results of multiple numbers of statistical tests have been performed for residual based fault diagnosis and threshold calculation. The performance of UKF is then compared with Extended Kalman filter (EKF) with same battery model and fault scenario. The simulation result proves that UKF model responses better and quicker than that of EKF for fault diagnosis.

摘要

锂离子电池作为一种高能量密度存储设备，在电气和电子设备、计算机、混合动力电动汽车以及电动汽车中有着广泛的应用。本文提出了一种使用两个非线性卡尔曼滤波器对锂离子电池进行多重故障检测的方法。建立了锂离子电池的离散非线性数学模型，并采用无迹卡尔曼滤波器（UKF）来估计模型参数。诸如过充电、过放电以及电池组间的短路故障等多重故障的出现，会影响系统模型的参数变化。一些UKF（滤波器组）的并行组合比较正常情况和故障情况下的模型参数变化，并生成指示不同故障的残差信号。针对基于残差的故障诊断和阈值计算，进行了多个统计测试的仿真结果。然后将UKF的性能与具有相同电池模型和故障场景的扩展卡尔曼滤波器（EKF）进行比较。仿真结果证明，在故障诊断方面，UKF模型的响应比EKF更好、更快。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/f125d7e83243/41598_2022_13771_Fig12_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/f125d7e83243/41598_2022_13771_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/fa42b8dd64ff/41598_2022_13771_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/d27e38f1d665/41598_2022_13771_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/072d141d1a50/41598_2022_13771_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/32eeef00336c/41598_2022_13771_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/6611791cde13/41598_2022_13771_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/ac56c4dd35ce/41598_2022_13771_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/f2500e11e33d/41598_2022_13771_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/251e45d6b3fc/41598_2022_13771_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/0bf2b3b1a9b9/41598_2022_13771_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/ada43eaf356c/41598_2022_13771_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/0a088d3970db/41598_2022_13771_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d826/9192780/f125d7e83243/41598_2022_13771_Fig12_HTML.jpg

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本文引用的文献

1
Issues and challenges facing rechargeable lithium batteries.可充电锂电池面临的问题与挑战。
Nature. 2001 Nov 15;414(6861):359-67. doi: 10.1038/35104644.