Abstract
AbstractLithium‐ion technologies have become the most attractive and selected choice for battery electric vehicles. However, the understanding of battery aging is still a complex and nonlinear experience which is critical to the modeling methodologies. In this work, a comprehensive lifetime modeling twin framework following semi‐empirical methodology has been developed to predict the crucial degradation outputs accurately in terms of capacity fade and resistance increase. The constructed model considers all the relevant aging influential factors for commercial nickel manganese cobalt (NMC) Li‐ion cells based on long‐term laboratory‐level investigation and combines both the cycle life and the calendar life aspects. To demonstrate robustness, the model is validated with a real‐life worldwide harmonized light‐duty test cycle (WLTC). The model can precisely predict the capacity fade and the internal resistance growth with a root‐mean‐squared error (RMSE) of 1.31% and 0.56%, respectively. The developed model can be used as an advanced online tool forecasting the lifetime based on dynamic profiles.
Highlights
Lithium-ion (Li-ion) technology has supported mankind for almost 30 years achieving remarkable advancements since its first commercialization.[1]
The influential parameters contribute to the degradation at a different scale and the magnitude is different in the case of cycle and calendar life and for capacity and power fade
The lifetime characterization is continued for at least 18 months before slowly stopping the running tests. This results in a partial degradation info that are processed
Summary
Lithium-ion (Li-ion) technology has supported mankind for almost 30 years achieving remarkable advancements since its first commercialization.[1]. . |2 battery degradation can resemble human aging behavior where a commercial product can be compared to a fully grown body. Whereas different biological factors contribute to the decline of the lifetime, multiple environmental and operating conditions degrade the battery performance. Prognostics and diagnostics of battery cells draw special attention which can prolong the longevity just like medical care improves the health of the human body. This is why accurate prediction models are requisite tools yet challenging to explain the highly nonlinear and complex systems like batteries.[4]
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