Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine. Feng, X., Weng, C., He, X., Han, X., Lu, L., Ren, D., & Ouyang, M. IEEE Transactions on Vehicular Technology, 68(9):8583–8592, September, 2019. Conference Name: IEEE Transactions on Vehicular Technologydoi abstract bibtex The online estimation of battery state-of-health (SOH) is an ever significant issue for the intelligent energy management of the autonomous electric vehicles. Machine-learning based approaches are promising for the online SOH estimation. This paper proposes a machine-learning based algorithm for the online SOH estimation of Li-ion battery. A predictive diagnosis model used in the algorithm is established based on support vector machine (SVM). The support vectors, which reflects the intrinsic characteristics of the Li-ion battery, are determined from the charging data of fresh cells. Furthermore, the coefficients of the SVMs for cells at different SOH are identified once the support vectors are determined. The algorithm functions by comparing partial charging curves with the stored SVMs. Similarity factor is defined after comparison to quantify the SOH of the data under evaluation. The operation of the algorithm only requires partial charging curves, e.g., 15 min charging curves, making fast on-board diagnosis of battery SOH into reality. The partial charging curves can be intercepted from a wide range of voltage section, thereby relieving the pain that there is little chance that the driver charges the battery pack from a predefined state-of-charge. Train, validation, and test are conducted for two commercial Li-ion batteries with Li(NiCoMn)1/3O2 cathode and graphite anode, indicating that the algorithm can estimate the battery SOH with less than 2% error for 80% of all the cases, and less than 3% error for 95% of all the cases.
@article{feng_online_2019,
title = {Online {State}-of-{Health} {Estimation} for {Li}-{Ion} {Battery} {Using} {Partial} {Charging} {Segment} {Based} on {Support} {Vector} {Machine}},
volume = {68},
issn = {1939-9359},
doi = {10.1109/TVT.2019.2927120},
abstract = {The online estimation of battery state-of-health (SOH) is an ever significant issue for the intelligent energy management of the autonomous electric vehicles. Machine-learning based approaches are promising for the online SOH estimation. This paper proposes a machine-learning based algorithm for the online SOH estimation of Li-ion battery. A predictive diagnosis model used in the algorithm is established based on support vector machine (SVM). The support vectors, which reflects the intrinsic characteristics of the Li-ion battery, are determined from the charging data of fresh cells. Furthermore, the coefficients of the SVMs for cells at different SOH are identified once the support vectors are determined. The algorithm functions by comparing partial charging curves with the stored SVMs. Similarity factor is defined after comparison to quantify the SOH of the data under evaluation. The operation of the algorithm only requires partial charging curves, e.g., 15 min charging curves, making fast on-board diagnosis of battery SOH into reality. The partial charging curves can be intercepted from a wide range of voltage section, thereby relieving the pain that there is little chance that the driver charges the battery pack from a predefined state-of-charge. Train, validation, and test are conducted for two commercial Li-ion batteries with Li(NiCoMn)1/3O2 cathode and graphite anode, indicating that the algorithm can estimate the battery SOH with less than 2\% error for 80\% of all the cases, and less than 3\% error for 95\% of all the cases.},
number = {9},
journal = {IEEE Transactions on Vehicular Technology},
author = {Feng, Xuning and Weng, Caihao and He, Xiangming and Han, Xuebing and Lu, Languang and Ren, Dongsheng and Ouyang, Minggao},
month = sep,
year = {2019},
note = {Conference Name: IEEE Transactions on Vehicular Technology},
keywords = {Calibration, Electric vehicle, Estimation, Lithium-ion batteries, State of charge, Support vector machines, Voltage measurement, batteries, ecml, energy storage, state estimation, state-of-health},
pages = {8583--8592},
}
Downloads: 0
{"_id":"qaHp75efMam7xeqtj","bibbaseid":"feng-weng-he-han-lu-ren-ouyang-onlinestateofhealthestimationforliionbatteryusingpartialchargingsegmentbasedonsupportvectormachine-2019","author_short":["Feng, X.","Weng, C.","He, X.","Han, X.","Lu, L.","Ren, D.","Ouyang, M."],"bibdata":{"bibtype":"article","type":"article","title":"Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine","volume":"68","issn":"1939-9359","doi":"10.1109/TVT.2019.2927120","abstract":"The online estimation of battery state-of-health (SOH) is an ever significant issue for the intelligent energy management of the autonomous electric vehicles. Machine-learning based approaches are promising for the online SOH estimation. This paper proposes a machine-learning based algorithm for the online SOH estimation of Li-ion battery. A predictive diagnosis model used in the algorithm is established based on support vector machine (SVM). The support vectors, which reflects the intrinsic characteristics of the Li-ion battery, are determined from the charging data of fresh cells. Furthermore, the coefficients of the SVMs for cells at different SOH are identified once the support vectors are determined. The algorithm functions by comparing partial charging curves with the stored SVMs. Similarity factor is defined after comparison to quantify the SOH of the data under evaluation. The operation of the algorithm only requires partial charging curves, e.g., 15 min charging curves, making fast on-board diagnosis of battery SOH into reality. The partial charging curves can be intercepted from a wide range of voltage section, thereby relieving the pain that there is little chance that the driver charges the battery pack from a predefined state-of-charge. Train, validation, and test are conducted for two commercial Li-ion batteries with Li(NiCoMn)1/3O2 cathode and graphite anode, indicating that the algorithm can estimate the battery SOH with less than 2% error for 80% of all the cases, and less than 3% error for 95% of all the cases.","number":"9","journal":"IEEE Transactions on Vehicular Technology","author":[{"propositions":[],"lastnames":["Feng"],"firstnames":["Xuning"],"suffixes":[]},{"propositions":[],"lastnames":["Weng"],"firstnames":["Caihao"],"suffixes":[]},{"propositions":[],"lastnames":["He"],"firstnames":["Xiangming"],"suffixes":[]},{"propositions":[],"lastnames":["Han"],"firstnames":["Xuebing"],"suffixes":[]},{"propositions":[],"lastnames":["Lu"],"firstnames":["Languang"],"suffixes":[]},{"propositions":[],"lastnames":["Ren"],"firstnames":["Dongsheng"],"suffixes":[]},{"propositions":[],"lastnames":["Ouyang"],"firstnames":["Minggao"],"suffixes":[]}],"month":"September","year":"2019","note":"Conference Name: IEEE Transactions on Vehicular Technology","keywords":"Calibration, Electric vehicle, Estimation, Lithium-ion batteries, State of charge, Support vector machines, Voltage measurement, batteries, ecml, energy storage, state estimation, state-of-health","pages":"8583–8592","bibtex":"@article{feng_online_2019,\n\ttitle = {Online {State}-of-{Health} {Estimation} for {Li}-{Ion} {Battery} {Using} {Partial} {Charging} {Segment} {Based} on {Support} {Vector} {Machine}},\n\tvolume = {68},\n\tissn = {1939-9359},\n\tdoi = {10.1109/TVT.2019.2927120},\n\tabstract = {The online estimation of battery state-of-health (SOH) is an ever significant issue for the intelligent energy management of the autonomous electric vehicles. Machine-learning based approaches are promising for the online SOH estimation. This paper proposes a machine-learning based algorithm for the online SOH estimation of Li-ion battery. A predictive diagnosis model used in the algorithm is established based on support vector machine (SVM). The support vectors, which reflects the intrinsic characteristics of the Li-ion battery, are determined from the charging data of fresh cells. Furthermore, the coefficients of the SVMs for cells at different SOH are identified once the support vectors are determined. The algorithm functions by comparing partial charging curves with the stored SVMs. Similarity factor is defined after comparison to quantify the SOH of the data under evaluation. The operation of the algorithm only requires partial charging curves, e.g., 15 min charging curves, making fast on-board diagnosis of battery SOH into reality. The partial charging curves can be intercepted from a wide range of voltage section, thereby relieving the pain that there is little chance that the driver charges the battery pack from a predefined state-of-charge. Train, validation, and test are conducted for two commercial Li-ion batteries with Li(NiCoMn)1/3O2 cathode and graphite anode, indicating that the algorithm can estimate the battery SOH with less than 2\\% error for 80\\% of all the cases, and less than 3\\% error for 95\\% of all the cases.},\n\tnumber = {9},\n\tjournal = {IEEE Transactions on Vehicular Technology},\n\tauthor = {Feng, Xuning and Weng, Caihao and He, Xiangming and Han, Xuebing and Lu, Languang and Ren, Dongsheng and Ouyang, Minggao},\n\tmonth = sep,\n\tyear = {2019},\n\tnote = {Conference Name: IEEE Transactions on Vehicular Technology},\n\tkeywords = {Calibration, Electric vehicle, Estimation, Lithium-ion batteries, State of charge, Support vector machines, Voltage measurement, batteries, ecml, energy storage, state estimation, state-of-health},\n\tpages = {8583--8592},\n}\n\n\n\n","author_short":["Feng, X.","Weng, C.","He, X.","Han, X.","Lu, L.","Ren, D.","Ouyang, M."],"key":"feng_online_2019","id":"feng_online_2019","bibbaseid":"feng-weng-he-han-lu-ren-ouyang-onlinestateofhealthestimationforliionbatteryusingpartialchargingsegmentbasedonsupportvectormachine-2019","role":"author","urls":{},"keyword":["Calibration","Electric vehicle","Estimation","Lithium-ion batteries","State of charge","Support vector machines","Voltage measurement","batteries","ecml","energy storage","state estimation","state-of-health"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["calibration","electric vehicle","estimation","lithium-ion batteries","state of charge","support vector machines","voltage measurement","batteries","ecml","energy storage","state estimation","state-of-health"],"search_terms":["online","state","health","estimation","ion","battery","using","partial","charging","segment","based","support","vector","machine","feng","weng","he","han","lu","ren","ouyang"],"title":"Online State-of-Health Estimation for Li-Ion Battery Using Partial Charging Segment Based on Support Vector Machine","year":2019}