Fast Open Circuit Voltage Estimation of Lithium-Ion Batteries Using a Relaxation Model and Genetic Algorithm
Battery Open Circuit Voltage (OCV) is of fundamental characteristic for enabling battery modeling and states estimation. However, the traditional OCV measurement method takes a very long time to make the battery reaches its equilibrium, which is rather inconvenient and cannot be performed online for...
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| Veröffentlicht in: | IEEE access 2022, Vol.10, p.1-1 |
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| creator | Qian, Yimin Zheng, Jian Ding, Kai Zhang, Hui Chen, Qiao Wang, Bei Wang, Yi Huang, Zengrui |
| description | Battery Open Circuit Voltage (OCV) is of fundamental characteristic for enabling battery modeling and states estimation. However, the traditional OCV measurement method takes a very long time to make the battery reaches its equilibrium, which is rather inconvenient and cannot be performed online for battery energy storage application. Motived by this, this paper proposes an effective method for fast OCV estimation in the relaxation process. In this work, a novel relaxation model is designed for capturing the voltage response of a battery during relaxation time and the Genetic Algorithm (GA) is further applied for optimizing the model parameters and acquiring accurate OCV estimation results. Experimental results confirm the validity of the proposed method under different State of Charges (SOCs), current rates, ambient temperatures, and aging conditions. The results suggest that the proposed method can accurately and quickly estimate battery OCV, which only takes 10 minutes of measurement data (more than 2 hours for the traditional method) and the maximum estimation error is limited to merely 1.8 mV. |
| doi_str_mv | 10.1109/ACCESS.2022.3203178 |
| format | Article |
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However, the traditional OCV measurement method takes a very long time to make the battery reaches its equilibrium, which is rather inconvenient and cannot be performed online for battery energy storage application. Motived by this, this paper proposes an effective method for fast OCV estimation in the relaxation process. In this work, a novel relaxation model is designed for capturing the voltage response of a battery during relaxation time and the Genetic Algorithm (GA) is further applied for optimizing the model parameters and acquiring accurate OCV estimation results. Experimental results confirm the validity of the proposed method under different State of Charges (SOCs), current rates, ambient temperatures, and aging conditions. The results suggest that the proposed method can accurately and quickly estimate battery OCV, which only takes 10 minutes of measurement data (more than 2 hours for the traditional method) and the maximum estimation error is limited to merely 1.8 mV.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2022.3203178</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Batteries ; Battery charge measurement ; Energy storage ; Estimation ; Genetic algorithms ; Integrated circuit modeling ; Lithium-ion batteries ; Lithium-ion battery ; Measurement methods ; Open circuit voltage ; Rechargeable batteries ; relaxation model ; Relaxation time ; State of charge ; Temperature measurement ; Voltage control ; Voltage measurement</subject><ispartof>IEEE access, 2022, Vol.10, p.1-1</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-cd6d4549dd8a6853718c2fa1e8d5cf30a3f6d38c687372426448b5fe5d5fa9a13</citedby><cites>FETCH-LOGICAL-c408t-cd6d4549dd8a6853718c2fa1e8d5cf30a3f6d38c687372426448b5fe5d5fa9a13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9870815$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2101,4023,27632,27922,27923,27924,54932</link.rule.ids></links><search><creatorcontrib>Qian, Yimin</creatorcontrib><creatorcontrib>Zheng, Jian</creatorcontrib><creatorcontrib>Ding, Kai</creatorcontrib><creatorcontrib>Zhang, Hui</creatorcontrib><creatorcontrib>Chen, Qiao</creatorcontrib><creatorcontrib>Wang, Bei</creatorcontrib><creatorcontrib>Wang, Yi</creatorcontrib><creatorcontrib>Huang, Zengrui</creatorcontrib><title>Fast Open Circuit Voltage Estimation of Lithium-Ion Batteries Using a Relaxation Model and Genetic Algorithm</title><title>IEEE access</title><addtitle>Access</addtitle><description>Battery Open Circuit Voltage (OCV) is of fundamental characteristic for enabling battery modeling and states estimation. However, the traditional OCV measurement method takes a very long time to make the battery reaches its equilibrium, which is rather inconvenient and cannot be performed online for battery energy storage application. Motived by this, this paper proposes an effective method for fast OCV estimation in the relaxation process. In this work, a novel relaxation model is designed for capturing the voltage response of a battery during relaxation time and the Genetic Algorithm (GA) is further applied for optimizing the model parameters and acquiring accurate OCV estimation results. Experimental results confirm the validity of the proposed method under different State of Charges (SOCs), current rates, ambient temperatures, and aging conditions. The results suggest that the proposed method can accurately and quickly estimate battery OCV, which only takes 10 minutes of measurement data (more than 2 hours for the traditional method) and the maximum estimation error is limited to merely 1.8 mV.</description><subject>Batteries</subject><subject>Battery charge measurement</subject><subject>Energy storage</subject><subject>Estimation</subject><subject>Genetic algorithms</subject><subject>Integrated circuit modeling</subject><subject>Lithium-ion batteries</subject><subject>Lithium-ion battery</subject><subject>Measurement methods</subject><subject>Open circuit voltage</subject><subject>Rechargeable batteries</subject><subject>relaxation model</subject><subject>Relaxation time</subject><subject>State of charge</subject><subject>Temperature measurement</subject><subject>Voltage control</subject><subject>Voltage measurement</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkVtrGzEQhZfQQkOaX5AXQZ_X1X21j-7ipAaXQNL0VYx1cWTWK0eSIf33lbshRC8aDeecGfE1zQ3BC0Jw_305DKvHxwXFlC4YxYx06qK5pET2LRNMfvpQf2muc97jelRtie6yGW8hF3R_dBMaQjKnUNCfOBbYObTKJRyghDih6NEmlOdwOrTr-vwBpbgUXEZPOUw7BOjBjfA6a39F60YEk0V3bnIlGLQcdzFV--Fr89nDmN31233VPN2ufg8_28393XpYblrDsSqtsdJywXtrFUglWEeUoR6IU1YYzzAwLy1TRqqOdZRTybnaCu-EFR56IOyqWc-5NsJeH1P9RvqrIwT9vxHTTkOqm41Omy0H76xQssccuN9600lssAVhzlXN-jZnHVN8Oblc9D6e0lTX17QjkvEOC1ZVbFaZFHNOzr9PJVifKemZkj5T0m-UqutmdgXn3LujV13FI9g_PryOMA</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Qian, Yimin</creator><creator>Zheng, Jian</creator><creator>Ding, Kai</creator><creator>Zhang, Hui</creator><creator>Chen, Qiao</creator><creator>Wang, Bei</creator><creator>Wang, Yi</creator><creator>Huang, Zengrui</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, the traditional OCV measurement method takes a very long time to make the battery reaches its equilibrium, which is rather inconvenient and cannot be performed online for battery energy storage application. Motived by this, this paper proposes an effective method for fast OCV estimation in the relaxation process. In this work, a novel relaxation model is designed for capturing the voltage response of a battery during relaxation time and the Genetic Algorithm (GA) is further applied for optimizing the model parameters and acquiring accurate OCV estimation results. Experimental results confirm the validity of the proposed method under different State of Charges (SOCs), current rates, ambient temperatures, and aging conditions. The results suggest that the proposed method can accurately and quickly estimate battery OCV, which only takes 10 minutes of measurement data (more than 2 hours for the traditional method) and the maximum estimation error is limited to merely 1.8 mV.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2022.3203178</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Batteries Battery charge measurement Energy storage Estimation Genetic algorithms Integrated circuit modeling Lithium-ion batteries Lithium-ion battery Measurement methods Open circuit voltage Rechargeable batteries relaxation model Relaxation time State of charge Temperature measurement Voltage control Voltage measurement |
| title | Fast Open Circuit Voltage Estimation of Lithium-Ion Batteries Using a Relaxation Model and Genetic Algorithm |
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