Battery Health Estimation in Electric Vehicles

University dissertation from Stockholm : KTH Royal Institute of Technology

Abstract: 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.

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