For the broad commercial success of electric vehicles (EVs), it is essential to deeply understand how batteries behave in this challenging application. This thesis has therefore been focused on studying automotive lithium-ion batteries in respect of their performance under EV operation. Particularly, the need for simple methods estimating the state-of-health (SOH) of batteries during EV operation has been addressed in order to ensure safe, reliable, and cost-effective EV operation. Within the scope of this thesis, a method has been developed that can estimate the SOH indicators capacity and internal resistance. The method is solely based on signals that are available on-board during ordinary EV operation such as the measured current, voltage, temperature, and the battery management system’s state-of-charge estimate. The approach is based on data-driven battery models (support vector machines (SVM) or system identification) and virtual tests in correspondence to standard performance tests as established in laboratory testing for capacity and resistance determination. The proposed method has been demonstrated for battery data collected in field tests and has also been verified in laboratory. After a first proof-of-concept of the method idea with battery pack data from a plug-in hybrid electric vehicle (PHEV) field test, the method was improved with the help of a laboratory study where battery electric vehicle (BEV) operation of a battery cell was emulated under controlled conditions providing a thorough validation possibility. Precise partial capacity and instantaneous resistance estimations could be derived and an accurate diffusion resistance estimation was achieved by including a current history variable in the SVM-based model. The dynamic system identification battery model gave precise total resistance estimates as well. The SOH estimation method was also applied to a data set from emulated hybrid electric vehicle (HEV) operation of a battery cell on board a heavy-duty vehicle, where on-board standard test validation revealed accurate dynamic voltage estimation performance of the applied model even during high-current situations. In order to exhibit the method’s intended implementation, up-to-date SOH indicators have been estimated from driving data during a one-year time period. / <p>QC 20150914</p>
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-173544 |
| Date | January 2015 |
| Creators | Klass, Verena |
| Publisher | KTH, Tillämpad elektrokemi, Stockholm |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Doctoral thesis, comprehensive summary, info:eu-repo/semantics/doctoralThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | TRITA-CHE-Report, 1654-1081 ; 2015:45 |
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