Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384283287> ?p ?o ?g. }
Showing items 1 to 95 of
95
with 100 items per page.
- W4384283287 endingPage "128445" @default.
- W4384283287 startingPage "128445" @default.
- W4384283287 abstract "A concise and accurate method for estimating the state of health (SOH) of lithium-ion batteries in the on-board energy management system is critical. However, SOH cannot be directly measured by on-board equipment. To improve the accuracy of SOH estimation for Li-ion batteries, this work proposes an SOH estimation model based on multi-source health indicators (HIs) extraction and sparse Bayesian learning. First, four direct HIs are extracted from the voltage and temperature curves of the batteries during charging and discharging, and two indirect HIs are extracted from the incremental capacity curves in combination with a Gaussian filtering algorithm. Then, the datasets are divided into three different training and test sets, which are used to simulate online SOH estimation under different situations. Finally, the six extracted HIs are selected using the Pearson correlation coefficient method, and the experiment is repeated for one of the situations using the three higher correlation features, and the results before and after selection are compared. The experimental results show that the proposed model can achieve satisfactory results in various simulated online estimation situations on the NASA and Oxford datasets." @default.
- W4384283287 created "2023-07-15" @default.
- W4384283287 creator A5008407879 @default.
- W4384283287 creator A5050151730 @default.
- W4384283287 creator A5069596903 @default.
- W4384283287 creator A5075437021 @default.
- W4384283287 creator A5082020327 @default.
- W4384283287 creator A5087103114 @default.
- W4384283287 date "2023-11-01" @default.
- W4384283287 modified "2023-09-23" @default.
- W4384283287 title "Lithium-ion battery state of health estimation based on multi-source health indicators extraction and sparse Bayesian learning" @default.
- W4384283287 cites W2589106271 @default.
- W4384283287 cites W2783030034 @default.
- W4384283287 cites W2789390005 @default.
- W4384283287 cites W2901900441 @default.
- W4384283287 cites W2910740817 @default.
- W4384283287 cites W2911341021 @default.
- W4384283287 cites W2920881684 @default.
- W4384283287 cites W2967729973 @default.
- W4384283287 cites W2968446814 @default.
- W4384283287 cites W2985426613 @default.
- W4384283287 cites W2995939154 @default.
- W4384283287 cites W3010268307 @default.
- W4384283287 cites W3043676997 @default.
- W4384283287 cites W3074222438 @default.
- W4384283287 cites W3095221264 @default.
- W4384283287 cites W3107462358 @default.
- W4384283287 cites W3130543603 @default.
- W4384283287 cites W3168568235 @default.
- W4384283287 cites W3179571438 @default.
- W4384283287 cites W3194090521 @default.
- W4384283287 cites W3204812266 @default.
- W4384283287 cites W3216182831 @default.
- W4384283287 cites W4200603035 @default.
- W4384283287 cites W4205464123 @default.
- W4384283287 cites W4220715364 @default.
- W4384283287 cites W4220826887 @default.
- W4384283287 cites W4283822818 @default.
- W4384283287 cites W4295025062 @default.
- W4384283287 cites W4309197564 @default.
- W4384283287 cites W4313494465 @default.
- W4384283287 cites W4319991355 @default.
- W4384283287 cites W4321603931 @default.
- W4384283287 doi "https://doi.org/10.1016/j.energy.2023.128445" @default.
- W4384283287 hasPublicationYear "2023" @default.
- W4384283287 type Work @default.
- W4384283287 citedByCount "0" @default.
- W4384283287 crossrefType "journal-article" @default.
- W4384283287 hasAuthorship W4384283287A5008407879 @default.
- W4384283287 hasAuthorship W4384283287A5050151730 @default.
- W4384283287 hasAuthorship W4384283287A5069596903 @default.
- W4384283287 hasAuthorship W4384283287A5075437021 @default.
- W4384283287 hasAuthorship W4384283287A5082020327 @default.
- W4384283287 hasAuthorship W4384283287A5087103114 @default.
- W4384283287 hasConcept C107673813 @default.
- W4384283287 hasConcept C119857082 @default.
- W4384283287 hasConcept C121332964 @default.
- W4384283287 hasConcept C124101348 @default.
- W4384283287 hasConcept C154945302 @default.
- W4384283287 hasConcept C163258240 @default.
- W4384283287 hasConcept C2777294910 @default.
- W4384283287 hasConcept C2780092901 @default.
- W4384283287 hasConcept C41008148 @default.
- W4384283287 hasConcept C555008776 @default.
- W4384283287 hasConcept C62520636 @default.
- W4384283287 hasConceptScore W4384283287C107673813 @default.
- W4384283287 hasConceptScore W4384283287C119857082 @default.
- W4384283287 hasConceptScore W4384283287C121332964 @default.
- W4384283287 hasConceptScore W4384283287C124101348 @default.
- W4384283287 hasConceptScore W4384283287C154945302 @default.
- W4384283287 hasConceptScore W4384283287C163258240 @default.
- W4384283287 hasConceptScore W4384283287C2777294910 @default.
- W4384283287 hasConceptScore W4384283287C2780092901 @default.
- W4384283287 hasConceptScore W4384283287C41008148 @default.
- W4384283287 hasConceptScore W4384283287C555008776 @default.
- W4384283287 hasConceptScore W4384283287C62520636 @default.
- W4384283287 hasLocation W43842832871 @default.
- W4384283287 hasOpenAccess W4384283287 @default.
- W4384283287 hasPrimaryLocation W43842832871 @default.
- W4384283287 hasRelatedWork W1987291222 @default.
- W4384283287 hasRelatedWork W2961085424 @default.
- W4384283287 hasRelatedWork W2991588090 @default.
- W4384283287 hasRelatedWork W3046775127 @default.
- W4384283287 hasRelatedWork W3170094116 @default.
- W4384283287 hasRelatedWork W4285260836 @default.
- W4384283287 hasRelatedWork W4286629047 @default.
- W4384283287 hasRelatedWork W4306321456 @default.
- W4384283287 hasRelatedWork W4306674287 @default.
- W4384283287 hasRelatedWork W4224009465 @default.
- W4384283287 hasVolume "282" @default.
- W4384283287 isParatext "false" @default.
- W4384283287 isRetracted "false" @default.
- W4384283287 workType "article" @default.