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State-of-health estimation of lithium-ion battery under emulated HEV operation on board heavy-duty truck
KTH, Skolan för kemivetenskap (CHE), Kemiteknik, Tillämpad elektrokemi.ORCID-id: 0000-0001-5748-0226
Scania CV AB.ORCID-id: 0000-0001-9559-0004
KTH, Skolan för kemivetenskap (CHE), Kemiteknik, Tillämpad elektrokemi.ORCID-id: 0000-0002-9392-9059
KTH, Skolan för kemivetenskap (CHE), Kemiteknik, Tillämpad elektrokemi.ORCID-id: 0000-0001-9203-9313
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
Abstract [en]

This paper addresses the need for simple and cost-effective methods that detect the state-of-health (SOH) of batteries in vehicle applications solely based on data readily available from the battery management system without any knowledge of battery properties, prior laboratory measurements or additional equipment.

A power-optimized lithium-ion battery cell is operated in an emulated hybrid electric vehicle (HEV) environment on board a conventional heavy-duty truck. The HEV operation of the battery cell depends on the driving pattern of the truck within set limits. Beyond the HEV operation, the performance of the battery cell is periodically measured with on-board standard pulse and capacity tests. On basis of the battery operating data collected in the field test, support vector machine-based battery models are built. From a model input of current, temperature, state-of-charge, and current history, the model accurately estimates the battery voltage despite the tough HEV operating conditions with high current pulses. This data-driven battery model is used to estimate the battery cell’s charge and discharge resistance as well as capacity, i.e. the performance measures verified with the standard tests. These SOH indicators can be predicted by the model with adequate accuracy for on-board SOH detection and are followed throughout the one-year field test period.

HSV kategori
Forskningsprogram
Kemiteknik
Identifikatorer
URN: urn:nbn:se:kth:diva-173537OAI: oai:DiVA.org:kth-173537DiVA, id: diva2:853530
Merknad

QS 2015

Tilgjengelig fra: 2015-09-14 Laget: 2015-09-14 Sist oppdatert: 2015-09-14bibliografisk kontrollert
Inngår i avhandling
1. Battery Health Estimation in Electric Vehicles
Åpne denne publikasjonen i ny fane eller vindu >>Battery Health Estimation in Electric Vehicles
2015 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

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.

sted, utgiver, år, opplag, sider
Stockholm: KTH Royal Institute of Technology, 2015. s. 59
Serie
TRITA-CHE-Report, ISSN 1654-1081 ; 2015:45
Emneord
Lithium-ion battery, state-of-health, electric vehicle, support vector machine, resistance, capacity
HSV kategori
Forskningsprogram
Kemiteknik
Identifikatorer
urn:nbn:se:kth:diva-173544 (URN)978-91-7595-671-8 (ISBN)
Disputas
2015-10-09, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 09:30 (engelsk)
Opponent
Veileder
Merknad

QC 20150914

Tilgjengelig fra: 2015-09-14 Laget: 2015-09-14 Sist oppdatert: 2015-09-14bibliografisk kontrollert

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