Article ; Online: Stress factor identification and Risk Probabilistic Number (RPN) analysis of Li-ion batteries based on worldwide electric vehicle usage
Applied Energy. 2023 Aug., v. 343 p.121250-
2023
Abstract: Having clear insights of the stress factors that the electric vehicle (EV) batteries encounter during their service lifetime is crucial for more reliable ageing testing and modelling. Since the first deployment of Li-ion battery based EV, numerous ... ...
| Abstract | Having clear insights of the stress factors that the electric vehicle (EV) batteries encounter during their service lifetime is crucial for more reliable ageing testing and modelling. Since the first deployment of Li-ion battery based EV, numerous driving campaigns with field data were published. The goal of this article is to gather, assess and analyse them in order to quantify the stress factors depending on the EV type. The targeted stress factors are the temperature of the cells, the discharging and charging rates, as well as the SOC ranges. 228 million km of driving and 7.8 million trips worth of data for over 37,000 EV were investigated. Along with this literature enquiry, data from an EV in which cells’ temperature was monitored for driving, charging and parking conditions, complemented the analysis. For each stress factor, results were collected, homogenised and compared with each other in order to draw conclusions. Finally, a Risk Probabilistic Number (RPN) was used to evaluate the stress factors with respect to their impact on the ageing of Li-ion batteries, considering a central European weather. The most critical stress factors for BEV cells are cycling at high mid-SOC regions 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 idle times. PHEV cells’ most critical stress factors are large DOD cycling and high charge/discharge power. Mild and low temperatures are found to be the most common in such weathers. The RPN analysis serves as a guide for parametrizing and designing reliable accelerated ageing testing on Li-ion batteries depending on their application. |
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| Keywords | electric vehicles ; energy ; lithium batteries ; risk ; temperature ; weather ; BEV ; BTMS ; C-rate ; CD ; CS ; D ; DCFC ; DOD ; EOL ; EV ; HEV ; ICE ; L1 ; L2 ; LIB ; NMC ; P ; P-rate ; PHEV ; RPN ; S ; SOC ; Tamb ; Tcells ; WLTP ; Electric vehicle ; Li-ion battery ; Stress factors ; Ageing ; Usage analysis ; Risk Probabilistic Number (RPN) |
| Language | English |
| Dates of publication | 2023-08 |
| Publishing place | Elsevier Ltd |
| Document type | Article ; Online |
| ZDB-ID | 2000772-3 |
| ISSN | 0306-2619 |
| ISSN | 0306-2619 |
| DOI | 10.1016/j.apenergy.2023.121250 |
| Database | NAL-Catalogue (AGRICOLA) |
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