Battery SOC estimation device based on immune algorithm optimization BP neural network

The invention discloses a battery SOC estimation device based on an immune algorithm optimization BP neural network. The device comprises a sensor group and a microprocessor loaded with a BP neural network model. The sensor group collects the data of the battery, including charging and discharging c...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: TAO RENJIAN, CHEN WANSHUN, XIA YUEWU, WANG YONG, QIAN FENG, HU XIANG
Format: Patent
Sprache:chi ; eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator TAO RENJIAN
CHEN WANSHUN
XIA YUEWU
WANG YONG
QIAN FENG
HU XIANG
description The invention discloses a battery SOC estimation device based on an immune algorithm optimization BP neural network. The device comprises a sensor group and a microprocessor loaded with a BP neural network model. The sensor group collects the data of the battery, including charging and discharging current, terminal voltage, ambient temperature, the number of times of charging and discharging, anda previous measurement value of the charged state of the battery. The microprocessor estimates the value of the charged state of the battery at the current time based on the collected data. The battery SOC estimation device based on the immune algorithm optimization BP neural network can establish an accurate mathematical model for the power battery to accurately estimate the charged state of thepower battery. 本发明公开基于免疫算法优化BP神经网络的电池SOC估计装置,包括:传感器组和装载BP神经网络模型的微处理器;其中,所述传感器组采集电池的以下数据:充放电电流、端电压、环境温度、充放电次数和前次的电池荷电状态测量值,所述微处理器根据所采集的数据估计当前时刻电池荷电状态的值。该基于免疫算法优化BP神经网络的电池SOC估计装置可以对动力电池建立精确数学模型,实现对动力电池核电状态的精确估计。
format Patent
fullrecord <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN108572324A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN108572324A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN108572324A3</originalsourceid><addsrcrecordid>eNrjZAhzSiwpSS2qVAj2d1ZILS7JzE0syczPU0hJLctMTlVISixOTVEA8jNzc0vzUhUSc9LzizJLMnIV8guAajOrIKqdAhTyUkuLEnOAVEl5flE2DwNrWmJOcSovlOZmUHRzDXH20E0tyI9PLS5ITE4Fqox39jM0sDA1NzI2MnE0JkYNAAYXOYI</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Battery SOC estimation device based on immune algorithm optimization BP neural network</title><source>esp@cenet</source><creator>TAO RENJIAN ; CHEN WANSHUN ; XIA YUEWU ; WANG YONG ; QIAN FENG ; HU XIANG</creator><creatorcontrib>TAO RENJIAN ; CHEN WANSHUN ; XIA YUEWU ; WANG YONG ; QIAN FENG ; HU XIANG</creatorcontrib><description>The invention discloses a battery SOC estimation device based on an immune algorithm optimization BP neural network. The device comprises a sensor group and a microprocessor loaded with a BP neural network model. The sensor group collects the data of the battery, including charging and discharging current, terminal voltage, ambient temperature, the number of times of charging and discharging, anda previous measurement value of the charged state of the battery. The microprocessor estimates the value of the charged state of the battery at the current time based on the collected data. The battery SOC estimation device based on the immune algorithm optimization BP neural network can establish an accurate mathematical model for the power battery to accurately estimate the charged state of thepower battery. 本发明公开基于免疫算法优化BP神经网络的电池SOC估计装置,包括:传感器组和装载BP神经网络模型的微处理器;其中,所述传感器组采集电池的以下数据:充放电电流、端电压、环境温度、充放电次数和前次的电池荷电状态测量值,所述微处理器根据所采集的数据估计当前时刻电池荷电状态的值。该基于免疫算法优化BP神经网络的电池SOC估计装置可以对动力电池建立精确数学模型,实现对动力电池核电状态的精确估计。</description><language>chi ; eng</language><subject>MEASURING ; MEASURING ELECTRIC VARIABLES ; MEASURING MAGNETIC VARIABLES ; PHYSICS ; TESTING</subject><creationdate>2018</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20180925&amp;DB=EPODOC&amp;CC=CN&amp;NR=108572324A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76290</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&amp;date=20180925&amp;DB=EPODOC&amp;CC=CN&amp;NR=108572324A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>TAO RENJIAN</creatorcontrib><creatorcontrib>CHEN WANSHUN</creatorcontrib><creatorcontrib>XIA YUEWU</creatorcontrib><creatorcontrib>WANG YONG</creatorcontrib><creatorcontrib>QIAN FENG</creatorcontrib><creatorcontrib>HU XIANG</creatorcontrib><title>Battery SOC estimation device based on immune algorithm optimization BP neural network</title><description>The invention discloses a battery SOC estimation device based on an immune algorithm optimization BP neural network. The device comprises a sensor group and a microprocessor loaded with a BP neural network model. The sensor group collects the data of the battery, including charging and discharging current, terminal voltage, ambient temperature, the number of times of charging and discharging, anda previous measurement value of the charged state of the battery. The microprocessor estimates the value of the charged state of the battery at the current time based on the collected data. The battery SOC estimation device based on the immune algorithm optimization BP neural network can establish an accurate mathematical model for the power battery to accurately estimate the charged state of thepower battery. 本发明公开基于免疫算法优化BP神经网络的电池SOC估计装置,包括:传感器组和装载BP神经网络模型的微处理器;其中,所述传感器组采集电池的以下数据:充放电电流、端电压、环境温度、充放电次数和前次的电池荷电状态测量值,所述微处理器根据所采集的数据估计当前时刻电池荷电状态的值。该基于免疫算法优化BP神经网络的电池SOC估计装置可以对动力电池建立精确数学模型,实现对动力电池核电状态的精确估计。</description><subject>MEASURING</subject><subject>MEASURING ELECTRIC VARIABLES</subject><subject>MEASURING MAGNETIC VARIABLES</subject><subject>PHYSICS</subject><subject>TESTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2018</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZAhzSiwpSS2qVAj2d1ZILS7JzE0syczPU0hJLctMTlVISixOTVEA8jNzc0vzUhUSc9LzizJLMnIV8guAajOrIKqdAhTyUkuLEnOAVEl5flE2DwNrWmJOcSovlOZmUHRzDXH20E0tyI9PLS5ITE4Fqox39jM0sDA1NzI2MnE0JkYNAAYXOYI</recordid><startdate>20180925</startdate><enddate>20180925</enddate><creator>TAO RENJIAN</creator><creator>CHEN WANSHUN</creator><creator>XIA YUEWU</creator><creator>WANG YONG</creator><creator>QIAN FENG</creator><creator>HU XIANG</creator><scope>EVB</scope></search><sort><creationdate>20180925</creationdate><title>Battery SOC estimation device based on immune algorithm optimization BP neural network</title><author>TAO RENJIAN ; CHEN WANSHUN ; XIA YUEWU ; WANG YONG ; QIAN FENG ; HU XIANG</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN108572324A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2018</creationdate><topic>MEASURING</topic><topic>MEASURING ELECTRIC VARIABLES</topic><topic>MEASURING MAGNETIC VARIABLES</topic><topic>PHYSICS</topic><topic>TESTING</topic><toplevel>online_resources</toplevel><creatorcontrib>TAO RENJIAN</creatorcontrib><creatorcontrib>CHEN WANSHUN</creatorcontrib><creatorcontrib>XIA YUEWU</creatorcontrib><creatorcontrib>WANG YONG</creatorcontrib><creatorcontrib>QIAN FENG</creatorcontrib><creatorcontrib>HU XIANG</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>TAO RENJIAN</au><au>CHEN WANSHUN</au><au>XIA YUEWU</au><au>WANG YONG</au><au>QIAN FENG</au><au>HU XIANG</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Battery SOC estimation device based on immune algorithm optimization BP neural network</title><date>2018-09-25</date><risdate>2018</risdate><abstract>The invention discloses a battery SOC estimation device based on an immune algorithm optimization BP neural network. The device comprises a sensor group and a microprocessor loaded with a BP neural network model. The sensor group collects the data of the battery, including charging and discharging current, terminal voltage, ambient temperature, the number of times of charging and discharging, anda previous measurement value of the charged state of the battery. The microprocessor estimates the value of the charged state of the battery at the current time based on the collected data. The battery SOC estimation device based on the immune algorithm optimization BP neural network can establish an accurate mathematical model for the power battery to accurately estimate the charged state of thepower battery. 本发明公开基于免疫算法优化BP神经网络的电池SOC估计装置,包括:传感器组和装载BP神经网络模型的微处理器;其中,所述传感器组采集电池的以下数据:充放电电流、端电压、环境温度、充放电次数和前次的电池荷电状态测量值,所述微处理器根据所采集的数据估计当前时刻电池荷电状态的值。该基于免疫算法优化BP神经网络的电池SOC估计装置可以对动力电池建立精确数学模型,实现对动力电池核电状态的精确估计。</abstract><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier
ispartof
issn
language chi ; eng
recordid cdi_epo_espacenet_CN108572324A
source esp@cenet
subjects MEASURING
MEASURING ELECTRIC VARIABLES
MEASURING MAGNETIC VARIABLES
PHYSICS
TESTING
title Battery SOC estimation device based on immune algorithm optimization BP neural network
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T02%3A11%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=TAO%20RENJIAN&rft.date=2018-09-25&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN108572324A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true