Improved battery SOC prediction method for RBF neural network
The invention discloses an improved battery SOC prediction method for an RBF neural network. First a power battery SOC prediction model is built through an RBF neural network method, then an output battery SOC of the neural network is used as an evaluation index to build an optimization model, and a...
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| creator | Wang Yong Tian Li Li Congfei Feng Zhimin Lou Jie Cao Anzhao Wu Daolin |
| description | The invention discloses an improved battery SOC prediction method for an RBF neural network. First a power battery SOC prediction model is built through an RBF neural network method, then an output battery SOC of the neural network is used as an evaluation index to build an optimization model, and an artificial fish swarm algorithm is adopted to perform optimization calculation on a width vector [delta]i and a center vector v of the neural network and a weight w of an output neuron. The neural network prediction method has the characteristics of high efficiency and low cost.
本发明公开了种改进的RBF神经网络的电池SOC预测方法，首先通过RBF神经网络方法建立动力电池SOC预测模型，然后以神经网络的输出电池SOC作为评价指标，建立个优化模型，采用人工鱼群算法分别对神经网络的宽度向量δ、中心向量v和输出神经元的权值w进行优化计算。神经网络预测方法具有效率高、成本低的特点。 |
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本发明公开了种改进的RBF神经网络的电池SOC预测方法，首先通过RBF神经网络方法建立动力电池SOC预测模型，然后以神经网络的输出电池SOC作为评价指标，建立个优化模型，采用人工鱼群算法分别对神经网络的宽度向量δ、中心向量v和输出神经元的权值w进行优化计算。神经网络预测方法具有效率高、成本低的特点。</description><language>chi ; eng</language><subject>CALCULATING ; CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TOTRANSPORTATION ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC ; GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS ; MEASURING ; MEASURING ELECTRIC VARIABLES ; MEASURING MAGNETIC VARIABLES ; PHYSICS ; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS ; TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINSTCLIMATE CHANGE ; TESTING</subject><creationdate>2016</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&date=20160803&DB=EPODOC&CC=CN&NR=105823989A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,309,781,886,25569,76552</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20160803&DB=EPODOC&CC=CN&NR=105823989A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Wang Yong</creatorcontrib><creatorcontrib>Tian Li</creatorcontrib><creatorcontrib>Li Congfei</creatorcontrib><creatorcontrib>Feng Zhimin</creatorcontrib><creatorcontrib>Lou Jie</creatorcontrib><creatorcontrib>Cao Anzhao</creatorcontrib><creatorcontrib>Wu Daolin</creatorcontrib><title>Improved battery SOC prediction method for RBF neural network</title><description>The invention discloses an improved battery SOC prediction method for an RBF neural network. First a power battery SOC prediction model is built through an RBF neural network method, then an output battery SOC of the neural network is used as an evaluation index to build an optimization model, and an artificial fish swarm algorithm is adopted to perform optimization calculation on a width vector [delta]i and a center vector v of the neural network and a weight w of an output neuron. The neural network prediction method has the characteristics of high efficiency and low cost.
本发明公开了种改进的RBF神经网络的电池SOC预测方法，首先通过RBF神经网络方法建立动力电池SOC预测模型，然后以神经网络的输出电池SOC作为评价指标，建立个优化模型，采用人工鱼群算法分别对神经网络的宽度向量δ、中心向量v和输出神经元的权值w进行优化计算。神经网络预测方法具有效率高、成本低的特点。</description><subject>CALCULATING</subject><subject>CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TOTRANSPORTATION</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC</subject><subject>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS</subject><subject>MEASURING</subject><subject>MEASURING ELECTRIC VARIABLES</subject><subject>MEASURING MAGNETIC VARIABLES</subject><subject>PHYSICS</subject><subject>TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS</subject><subject>TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINSTCLIMATE CHANGE</subject><subject>TESTING</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2016</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLD1zC0oyi9LTVFISiwpSS2qVAj2d1YoKEpNyUwuyczPU8hNLcnIT1FIyy9SCHJyU8hLLS1KzAFSJeX5Rdk8DKxpiTnFqbxQmptB0c01xNlDN7UgPz61uCAxORWoMt7Zz9DA1MLI2NLC0tGYGDUA6gkvyA</recordid><startdate>20160803</startdate><enddate>20160803</enddate><creator>Wang Yong</creator><creator>Tian Li</creator><creator>Li Congfei</creator><creator>Feng Zhimin</creator><creator>Lou Jie</creator><creator>Cao Anzhao</creator><creator>Wu Daolin</creator><scope>EVB</scope></search><sort><creationdate>20160803</creationdate><title>Improved battery SOC prediction method for RBF neural network</title><author>Wang Yong ; Tian Li ; Li Congfei ; Feng Zhimin ; Lou Jie ; Cao Anzhao ; Wu Daolin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN105823989A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2016</creationdate><topic>CALCULATING</topic><topic>CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TOTRANSPORTATION</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC</topic><topic>GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS</topic><topic>MEASURING</topic><topic>MEASURING ELECTRIC VARIABLES</topic><topic>MEASURING MAGNETIC VARIABLES</topic><topic>PHYSICS</topic><topic>TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS</topic><topic>TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINSTCLIMATE CHANGE</topic><topic>TESTING</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang Yong</creatorcontrib><creatorcontrib>Tian Li</creatorcontrib><creatorcontrib>Li Congfei</creatorcontrib><creatorcontrib>Feng Zhimin</creatorcontrib><creatorcontrib>Lou Jie</creatorcontrib><creatorcontrib>Cao Anzhao</creatorcontrib><creatorcontrib>Wu Daolin</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang Yong</au><au>Tian Li</au><au>Li Congfei</au><au>Feng Zhimin</au><au>Lou Jie</au><au>Cao Anzhao</au><au>Wu Daolin</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Improved battery SOC prediction method for RBF neural network</title><date>2016-08-03</date><risdate>2016</risdate><abstract>The invention discloses an improved battery SOC prediction method for an RBF neural network. First a power battery SOC prediction model is built through an RBF neural network method, then an output battery SOC of the neural network is used as an evaluation index to build an optimization model, and an artificial fish swarm algorithm is adopted to perform optimization calculation on a width vector [delta]i and a center vector v of the neural network and a weight w of an output neuron. The neural network prediction method has the characteristics of high efficiency and low cost.
本发明公开了种改进的RBF神经网络的电池SOC预测方法，首先通过RBF神经网络方法建立动力电池SOC预测模型，然后以神经网络的输出电池SOC作为评价指标，建立个优化模型，采用人工鱼群算法分别对神经网络的宽度向量δ、中心向量v和输出神经元的权值w进行优化计算。神经网络预测方法具有效率高、成本低的特点。</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TOTRANSPORTATION COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS MEASURING MEASURING ELECTRIC VARIABLES MEASURING MAGNETIC VARIABLES PHYSICS TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINSTCLIMATE CHANGE TESTING |
| title | Improved battery SOC prediction method for RBF neural network |
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