4.8 Article

State of health estimation of second-life lithium-ion batteries under real profile operation

Journal

APPLIED ENERGY
Volume 326, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.119992

Keywords

Lithium-ion battery; Second-life batteries; State of health estimation; Residential storage; Fast charge station

Funding

  1. MCIN/AEI, Spain [PID2019-111262RB-I00]
  2. European Union [774094]
  3. Government of Navarre, Spain [0011-1411-2022-000039]
  4. Universidad Publica de Navarra, Spain
  5. Government of Navarre

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This study focuses on the estimation of State of Health (SOH) in second-life (SL) Li-ion batteries from electric vehicles (EVs). Through experimental assessment, the feasibility and accuracy of SOH estimation in different SL scenarios are demonstrated. The method can effectively reduce testing time without interrupting battery operation.
The economic viability of second-life (SL) Li-ion batteries from electric vehicles (EVs) is still uncertain nowadays. Assessing the internal state of reused cells is key not only at the repurposing stage but also during their SL operation. As an alternative of the traditional capacity tests used to this end, the estimation of State of Health (SOH) allows to reduce the testing time and the need of equipment, thereby reinforcing the economic success of SL batteries. However, the estimation of SOH in real SL operation has been rarely analysed in literature. This contribution aims thus to cover this gap, by focusing on the experimental assessment of SOH estimation in reused modules from Nissan Leaf EVs under two SL scenarios: a residential household with self -consumption and a fast charge station for EVs. By means of partial charge and experimental data from cycling and calendar ageing tests, accuracy and robustness of health indicators is firstly assessed. Then, SOH estimation is carried out using real profiles, covering a SOH range from 91.3 to 31%. Offline assessment led to RMSE values of 0.6% in the residential profile and 0.8% in the fast charge station, with a reduction in testing times of 85% compared to a full capacity test. In order to avoid the interruption of battery operation, online assessment in profiles was also analysed, obtaining RMSE values below 1.3% and 3.6% in the residential and charging station scenarios, respectively. Therefore, the feasibility of SOH estimation in SL profiles is highlighted, as it allows to get accurate results reducing testing times or even without interrupting normal operation.

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