Stress factors identification and Risk Probabilistic Number (RPN) analysis of Li-ionbatteries based on worldwide electric vehicles usage

Autor: Marc Haber, Philippe Azaïs, Sylvie Genies, Olivier Raccurt
Přispěvatelé: Département de l'électricité et de l'hydrogène dans les transports (DEHT), Laboratoire d'Innovation pour les Technologies des Energies Nouvelles et les nanomatériaux (LITEN), Institut National de L'Energie Solaire (INES), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de L'Energie Solaire (INES), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), European Project: 800945,NUMERICS
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Applied Energy
Applied Energy, 2023, 343, 121250, DOI:10.1016/j.apenergy.2023.121250. ⟨10.1016/j.apenergy.2023.121250⟩
ISSN: 0306-2619
DOI: 10.1016/j.apenergy.2023.121250.
Popis: International audience; Having clear insights of the stress factors that the electric vehicle (EV) batteriesencounter during their service lifetime is crucial for more reliable ageing testing andmodelling. Since the first deployment of Li-ion battery based EV, numerous drivingcampaigns with field data were published. The goal of this article is to gather, assessand analyse them in order to quantify the stress factors depending on the EV type. Thetargeted stress factors are the temperature of the cells, the discharging and chargingrates, as well as the SOC ranges. 228 million km of driving and 7.8 million trips worthof data for over 37,000 EV were investigated. Along with this literature enquiry, datafrom an EV in which cells' temperature was monitored for driving, charging andparking conditions, complemented the analysis. For each stress factor, results werecollected, homogenised and compared with each other in order to draw conclusions.Finally, a Risk Probabilistic Number (RPN) was used to evaluate the stress factors withrespect to their impact on the ageing of Li-ion batteries, considering a central Europeanweather. The most critical stress factors for BEV cells are cycling at high mid-SOCregions and high SOC idle times. Concerning HEV cells, high power cycling at mid-SOC regions is the most critical stress, and no stresses were identified during idletimes. PHEV cells' most critical stress factors are large DOD cycling and highcharge/discharge power. Mild and low temperatures are found to be the most commonin such weathers. The RPN analysis serves as a guide for parametrizing and designingreliable accelerated ageing testing on Li-ion batteries depending on their application.
Databáze: OpenAIRE
Externí odkaz: