An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell

With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capabilit...
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Gespeichert in:

Autor*in:	Ghosh, Nitika [verfasserIn] Garg, Akhil [verfasserIn] Panigrahi, B.K. [verfasserIn] Kim, Jonghoon [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Evolving Quantum Fuzzy Neural Network Online estimation Neural Networks Extended Kalman filter

Übergeordnetes Werk:	Enthalten in: Applied soft computing - Amsterdam [u.a.] : Elsevier Science, 2001, 143
Übergeordnetes Werk:	volume:143

DOI / URN:	10.1016/j.asoc.2023.110263

Katalog-ID:	ELV01044145X

Internformat


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245	1	0	\|a An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell
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520			\|a With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capability of a lithium-ion battery (LIB), in terms of capacity or internal resistance. Theoretically, the cell capacity is obtained by complete charge and discharge of the cell but in practical scenario, complete charge or discharge is never the case. To address this issue of dynamic discharge, this paper presents an evolving model-based SOH estimation, predicting the capacity fade of the cell extracted from the incomplete discharge conditions as in the case of dynamic driving scenarios. The evolving algorithm uses Neural Network, which features an interval fuzzy set, with conjectural jump positions. For better identification of overlaps between the classes, the quantum fuzzy set uses graded membership function. The number of rules are automatically adjusted and evolved, in the quantum fuzzy set using Decoupled Extended Kalman Filter (DEKF) for parameter estimation. The proposed method uses voltage, current and sampling time data to estimate the capacity, over a period of 600 charging-discharging cycles of Nickel Manganese Cobalt Oxide (NMC) chemistry batteries. The dynamic discharge voltage data is obtained from the periodic characterization tests and is used for predict the complete discharge voltage. The full voltage profile has been forecasted using Long-Short Term Memory (LSTM) network and the subsequent capacity has been estimated using evolving Quantum Fuzzy Neural Network (eQFNN) with an RMSE of less than 5% making it suitable for on-board applications. The results are simulated in MATLAB 2020b and are validated using experimental verification in Battery Testing Lab (BTL), IIT Delhi.
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650		4	\|a Online estimation
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700	1		\|a Panigrahi, B.K. \|e verfasserin \|4 aut
700	1		\|a Kim, Jonghoon \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.asoc.2023.110263 doi (DE-627)ELV01044145X (ELSEVIER)S1568-4946(23)00281-8 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Ghosh, Nitika verfasserin aut An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capability of a lithium-ion battery (LIB), in terms of capacity or internal resistance. Theoretically, the cell capacity is obtained by complete charge and discharge of the cell but in practical scenario, complete charge or discharge is never the case. To address this issue of dynamic discharge, this paper presents an evolving model-based SOH estimation, predicting the capacity fade of the cell extracted from the incomplete discharge conditions as in the case of dynamic driving scenarios. The evolving algorithm uses Neural Network, which features an interval fuzzy set, with conjectural jump positions. For better identification of overlaps between the classes, the quantum fuzzy set uses graded membership function. The number of rules are automatically adjusted and evolved, in the quantum fuzzy set using Decoupled Extended Kalman Filter (DEKF) for parameter estimation. The proposed method uses voltage, current and sampling time data to estimate the capacity, over a period of 600 charging-discharging cycles of Nickel Manganese Cobalt Oxide (NMC) chemistry batteries. The dynamic discharge voltage data is obtained from the periodic characterization tests and is used for predict the complete discharge voltage. The full voltage profile has been forecasted using Long-Short Term Memory (LSTM) network and the subsequent capacity has been estimated using evolving Quantum Fuzzy Neural Network (eQFNN) with an RMSE of less than 5% making it suitable for on-board applications. The results are simulated in MATLAB 2020b and are validated using experimental verification in Battery Testing Lab (BTL), IIT Delhi. Evolving Quantum Fuzzy Neural Network Online estimation Neural Networks Extended Kalman filter Garg, Akhil verfasserin aut Panigrahi, B.K. verfasserin aut Kim, Jonghoon verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 143 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:143 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ AR 143
spelling	10.1016/j.asoc.2023.110263 doi (DE-627)ELV01044145X (ELSEVIER)S1568-4946(23)00281-8 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Ghosh, Nitika verfasserin aut An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capability of a lithium-ion battery (LIB), in terms of capacity or internal resistance. Theoretically, the cell capacity is obtained by complete charge and discharge of the cell but in practical scenario, complete charge or discharge is never the case. To address this issue of dynamic discharge, this paper presents an evolving model-based SOH estimation, predicting the capacity fade of the cell extracted from the incomplete discharge conditions as in the case of dynamic driving scenarios. The evolving algorithm uses Neural Network, which features an interval fuzzy set, with conjectural jump positions. For better identification of overlaps between the classes, the quantum fuzzy set uses graded membership function. The number of rules are automatically adjusted and evolved, in the quantum fuzzy set using Decoupled Extended Kalman Filter (DEKF) for parameter estimation. The proposed method uses voltage, current and sampling time data to estimate the capacity, over a period of 600 charging-discharging cycles of Nickel Manganese Cobalt Oxide (NMC) chemistry batteries. The dynamic discharge voltage data is obtained from the periodic characterization tests and is used for predict the complete discharge voltage. The full voltage profile has been forecasted using Long-Short Term Memory (LSTM) network and the subsequent capacity has been estimated using evolving Quantum Fuzzy Neural Network (eQFNN) with an RMSE of less than 5% making it suitable for on-board applications. The results are simulated in MATLAB 2020b and are validated using experimental verification in Battery Testing Lab (BTL), IIT Delhi. Evolving Quantum Fuzzy Neural Network Online estimation Neural Networks Extended Kalman filter Garg, Akhil verfasserin aut Panigrahi, B.K. verfasserin aut Kim, Jonghoon verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 143 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:143 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ AR 143
allfields_unstemmed	10.1016/j.asoc.2023.110263 doi (DE-627)ELV01044145X (ELSEVIER)S1568-4946(23)00281-8 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Ghosh, Nitika verfasserin aut An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capability of a lithium-ion battery (LIB), in terms of capacity or internal resistance. Theoretically, the cell capacity is obtained by complete charge and discharge of the cell but in practical scenario, complete charge or discharge is never the case. To address this issue of dynamic discharge, this paper presents an evolving model-based SOH estimation, predicting the capacity fade of the cell extracted from the incomplete discharge conditions as in the case of dynamic driving scenarios. The evolving algorithm uses Neural Network, which features an interval fuzzy set, with conjectural jump positions. For better identification of overlaps between the classes, the quantum fuzzy set uses graded membership function. The number of rules are automatically adjusted and evolved, in the quantum fuzzy set using Decoupled Extended Kalman Filter (DEKF) for parameter estimation. The proposed method uses voltage, current and sampling time data to estimate the capacity, over a period of 600 charging-discharging cycles of Nickel Manganese Cobalt Oxide (NMC) chemistry batteries. The dynamic discharge voltage data is obtained from the periodic characterization tests and is used for predict the complete discharge voltage. The full voltage profile has been forecasted using Long-Short Term Memory (LSTM) network and the subsequent capacity has been estimated using evolving Quantum Fuzzy Neural Network (eQFNN) with an RMSE of less than 5% making it suitable for on-board applications. The results are simulated in MATLAB 2020b and are validated using experimental verification in Battery Testing Lab (BTL), IIT Delhi. Evolving Quantum Fuzzy Neural Network Online estimation Neural Networks Extended Kalman filter Garg, Akhil verfasserin aut Panigrahi, B.K. verfasserin aut Kim, Jonghoon verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 143 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:143 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ AR 143
allfieldsGer	10.1016/j.asoc.2023.110263 doi (DE-627)ELV01044145X (ELSEVIER)S1568-4946(23)00281-8 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Ghosh, Nitika verfasserin aut An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capability of a lithium-ion battery (LIB), in terms of capacity or internal resistance. Theoretically, the cell capacity is obtained by complete charge and discharge of the cell but in practical scenario, complete charge or discharge is never the case. To address this issue of dynamic discharge, this paper presents an evolving model-based SOH estimation, predicting the capacity fade of the cell extracted from the incomplete discharge conditions as in the case of dynamic driving scenarios. The evolving algorithm uses Neural Network, which features an interval fuzzy set, with conjectural jump positions. For better identification of overlaps between the classes, the quantum fuzzy set uses graded membership function. The number of rules are automatically adjusted and evolved, in the quantum fuzzy set using Decoupled Extended Kalman Filter (DEKF) for parameter estimation. The proposed method uses voltage, current and sampling time data to estimate the capacity, over a period of 600 charging-discharging cycles of Nickel Manganese Cobalt Oxide (NMC) chemistry batteries. The dynamic discharge voltage data is obtained from the periodic characterization tests and is used for predict the complete discharge voltage. The full voltage profile has been forecasted using Long-Short Term Memory (LSTM) network and the subsequent capacity has been estimated using evolving Quantum Fuzzy Neural Network (eQFNN) with an RMSE of less than 5% making it suitable for on-board applications. The results are simulated in MATLAB 2020b and are validated using experimental verification in Battery Testing Lab (BTL), IIT Delhi. Evolving Quantum Fuzzy Neural Network Online estimation Neural Networks Extended Kalman filter Garg, Akhil verfasserin aut Panigrahi, B.K. verfasserin aut Kim, Jonghoon verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 143 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:143 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ AR 143
allfieldsSound	10.1016/j.asoc.2023.110263 doi (DE-627)ELV01044145X (ELSEVIER)S1568-4946(23)00281-8 DE-627 ger DE-627 rda eng 004 VZ 54.00 bkl Ghosh, Nitika verfasserin aut An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capability of a lithium-ion battery (LIB), in terms of capacity or internal resistance. Theoretically, the cell capacity is obtained by complete charge and discharge of the cell but in practical scenario, complete charge or discharge is never the case. To address this issue of dynamic discharge, this paper presents an evolving model-based SOH estimation, predicting the capacity fade of the cell extracted from the incomplete discharge conditions as in the case of dynamic driving scenarios. The evolving algorithm uses Neural Network, which features an interval fuzzy set, with conjectural jump positions. For better identification of overlaps between the classes, the quantum fuzzy set uses graded membership function. The number of rules are automatically adjusted and evolved, in the quantum fuzzy set using Decoupled Extended Kalman Filter (DEKF) for parameter estimation. The proposed method uses voltage, current and sampling time data to estimate the capacity, over a period of 600 charging-discharging cycles of Nickel Manganese Cobalt Oxide (NMC) chemistry batteries. The dynamic discharge voltage data is obtained from the periodic characterization tests and is used for predict the complete discharge voltage. The full voltage profile has been forecasted using Long-Short Term Memory (LSTM) network and the subsequent capacity has been estimated using evolving Quantum Fuzzy Neural Network (eQFNN) with an RMSE of less than 5% making it suitable for on-board applications. The results are simulated in MATLAB 2020b and are validated using experimental verification in Battery Testing Lab (BTL), IIT Delhi. Evolving Quantum Fuzzy Neural Network Online estimation Neural Networks Extended Kalman filter Garg, Akhil verfasserin aut Panigrahi, B.K. verfasserin aut Kim, Jonghoon verfasserin aut Enthalten in Applied soft computing Amsterdam [u.a.] : Elsevier Science, 2001 143 Online-Ressource (DE-627)334375754 (DE-600)2057709-6 (DE-576)256145733 1568-4946 nnns volume:143 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.00 Informatik: Allgemeines VZ AR 143
language	English
source	Enthalten in Applied soft computing 143 volume:143
sourceStr	Enthalten in Applied soft computing 143 volume:143
format_phy_str_mv	Article
bklname	Informatik: Allgemeines
institution	findex.gbv.de
topic_facet	Evolving Quantum Fuzzy Neural Network Online estimation Neural Networks Extended Kalman filter
dewey-raw	004
isfreeaccess_bool	false
container_title	Applied soft computing
authorswithroles_txt_mv	Ghosh, Nitika @@aut@@ Garg, Akhil @@aut@@ Panigrahi, B.K. @@aut@@ Kim, Jonghoon @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	334375754
dewey-sort	14
id	ELV01044145X
language_de	englisch
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author	Ghosh, Nitika
spellingShingle	Ghosh, Nitika ddc 004 bkl 54.00 misc Evolving Quantum Fuzzy Neural Network misc Online estimation misc Neural Networks misc Extended Kalman filter An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell
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topic_title	004 VZ 54.00 bkl An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell Evolving Quantum Fuzzy Neural Network Online estimation Neural Networks Extended Kalman filter
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title	An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell
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title_full	An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell
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title_sort	an evolving quantum fuzzy neural network for online state-of-health estimation of li-ion cell
title_auth	An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell
abstract	With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capability of a lithium-ion battery (LIB), in terms of capacity or internal resistance. Theoretically, the cell capacity is obtained by complete charge and discharge of the cell but in practical scenario, complete charge or discharge is never the case. To address this issue of dynamic discharge, this paper presents an evolving model-based SOH estimation, predicting the capacity fade of the cell extracted from the incomplete discharge conditions as in the case of dynamic driving scenarios. The evolving algorithm uses Neural Network, which features an interval fuzzy set, with conjectural jump positions. For better identification of overlaps between the classes, the quantum fuzzy set uses graded membership function. The number of rules are automatically adjusted and evolved, in the quantum fuzzy set using Decoupled Extended Kalman Filter (DEKF) for parameter estimation. The proposed method uses voltage, current and sampling time data to estimate the capacity, over a period of 600 charging-discharging cycles of Nickel Manganese Cobalt Oxide (NMC) chemistry batteries. The dynamic discharge voltage data is obtained from the periodic characterization tests and is used for predict the complete discharge voltage. The full voltage profile has been forecasted using Long-Short Term Memory (LSTM) network and the subsequent capacity has been estimated using evolving Quantum Fuzzy Neural Network (eQFNN) with an RMSE of less than 5% making it suitable for on-board applications. The results are simulated in MATLAB 2020b and are validated using experimental verification in Battery Testing Lab (BTL), IIT Delhi.
abstractGer	With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capability of a lithium-ion battery (LIB), in terms of capacity or internal resistance. Theoretically, the cell capacity is obtained by complete charge and discharge of the cell but in practical scenario, complete charge or discharge is never the case. To address this issue of dynamic discharge, this paper presents an evolving model-based SOH estimation, predicting the capacity fade of the cell extracted from the incomplete discharge conditions as in the case of dynamic driving scenarios. The evolving algorithm uses Neural Network, which features an interval fuzzy set, with conjectural jump positions. For better identification of overlaps between the classes, the quantum fuzzy set uses graded membership function. The number of rules are automatically adjusted and evolved, in the quantum fuzzy set using Decoupled Extended Kalman Filter (DEKF) for parameter estimation. The proposed method uses voltage, current and sampling time data to estimate the capacity, over a period of 600 charging-discharging cycles of Nickel Manganese Cobalt Oxide (NMC) chemistry batteries. The dynamic discharge voltage data is obtained from the periodic characterization tests and is used for predict the complete discharge voltage. The full voltage profile has been forecasted using Long-Short Term Memory (LSTM) network and the subsequent capacity has been estimated using evolving Quantum Fuzzy Neural Network (eQFNN) with an RMSE of less than 5% making it suitable for on-board applications. The results are simulated in MATLAB 2020b and are validated using experimental verification in Battery Testing Lab (BTL), IIT Delhi.
abstract_unstemmed	With the rapid advancement in the battery industry, more accurate and advanced state estimation methods are required to meet the performance requirements. The State of Health (SOH) estimation is performed in the battery management system (BMS), which provides the qualitative measure of the capability of a lithium-ion battery (LIB), in terms of capacity or internal resistance. Theoretically, the cell capacity is obtained by complete charge and discharge of the cell but in practical scenario, complete charge or discharge is never the case. To address this issue of dynamic discharge, this paper presents an evolving model-based SOH estimation, predicting the capacity fade of the cell extracted from the incomplete discharge conditions as in the case of dynamic driving scenarios. The evolving algorithm uses Neural Network, which features an interval fuzzy set, with conjectural jump positions. For better identification of overlaps between the classes, the quantum fuzzy set uses graded membership function. The number of rules are automatically adjusted and evolved, in the quantum fuzzy set using Decoupled Extended Kalman Filter (DEKF) for parameter estimation. The proposed method uses voltage, current and sampling time data to estimate the capacity, over a period of 600 charging-discharging cycles of Nickel Manganese Cobalt Oxide (NMC) chemistry batteries. The dynamic discharge voltage data is obtained from the periodic characterization tests and is used for predict the complete discharge voltage. The full voltage profile has been forecasted using Long-Short Term Memory (LSTM) network and the subsequent capacity has been estimated using evolving Quantum Fuzzy Neural Network (eQFNN) with an RMSE of less than 5% making it suitable for on-board applications. The results are simulated in MATLAB 2020b and are validated using experimental verification in Battery Testing Lab (BTL), IIT Delhi.
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title_short	An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell
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An Evolving Quantum Fuzzy Neural Network for online State-of-Health estimation of Li-ion cell

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