Nature Energy intl_tech D1

发布：2024-12-09 · 事件：2024-12-09

Download PDF Subjects Batteries Abstract Laboratory ageing campaigns elucidate the complex degradation behaviour of most technologies. In lithium-ion batteries, such studies aim to capture realistic a...

Download PDF Subjects Batteries Abstract Laboratory ageing campaigns elucidate the complex degradation behaviour of most technologies. In lithium-ion batteries, such studies aim to capture realistic ageing mechanisms to optimize cell chemistries and designs as well as to engineer reliable battery management systems. In this study, we systematically compared dynamic discharge profiles representative of electric vehicle driving to the well-accepted constant current profiles. Surprisingly, we found that dynamic discharge enhances lifetime substantially compared with constant current discharge. Specifically, for the same average current and voltage window, varying the dynamic discharge profile led to an increase of up to 38% in equivalent full cycles at end of life. Explainable machine learning revealed the importance of both low-frequency current pulses and time-induced ageing under these realistic discharge conditions. This work quantifies the importance of evaluating new battery chemistries and designs with realistic load profiles, highlighting the opportunities to revisit our understanding of ageing mechanisms at the chemistry, material and cell levels. Similar content being viewed by others An ageing study of twenty 18650 lithium-ion Graphite/LFP cells in first and second life use Article Open access 06 March 2025 Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning Article Open access 15 January 2025 Comprehensive battery aging dataset: capacity and impedance fade measurements of a lithium-ion NMC/C-SiO cell Article Open access 16 September 2024 Main Lithium-ion batteries (LIBs) age through intertwined mechanisms that depend critically on conditions of use, as do solar cells, polymeric materials, biomedical devices and so on. Understanding how degradation occurs across realistic use cases is essential to accelerate material design and improve battery management systems 1 . As a well-accepted practice, the vast majority of laboratory battery studies are conducted under constant current discharge profiles 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 . In actual use cases, however, LIBs are subjected to dynamic current profiles during discharge 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 . In electric vehicles (EVs), load profiles consist of oscillations, pulses and rests 24 , 25 , 26 , 27 , 28 . On the one hand, several studies have investigated current profiles with alternating current frequencies, typically well above 1–10 Hz (ref. 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ). Above such frequencies, limited degradation has been observed as electrochemical processes such as charge transfer and diffusion are only partially activated 30 , 39 , 40 . On the other hand, regenerative braking, driving in stop-and-go traffic and so on occur at lower frequencies (<1 Hz) 41 , but are not well understood 42 , 43 . In addition, time-induced ageing (including calendar ageing at zero current 15 , 16 , 44 , 45 , 46 , 47 , 48 ) is another critical component of realistic usage 49 but requires several years of experiments before being observed. Therefore, a gap exists at the intersection of data-driven approaches and battery ageing experiments with realistic discharge protocols. We aimed to fill this gap by generating and analysing a non-accelerated and dynamically cycled battery dataset that represents realistic EV driving. Thus, in this study, we compared 47 different dynamic discharge profiles with realistic average discharge currents ranging from C/16 to C/2, cycled over 24 months (where 1C corresponds to the nominal current that discharges the battery in 1 h) on 92 commercial silicon oxide–graphite/nickel cobalt aluminium lithium-ion EV energy cells. We elucidated the effect of dynamic, non-constant current discharge profiles while holding the average C-rate and voltage window constant. We found that dynamic cycling enhances battery lifetime by up to 38%. Moreover, we determined the window for the tip-over C-rate that balances time-induced ageing and cycling ageing for this commercially relevant chemistry to be approximately between 0.3C and 0.5C, in the range of realistic average C-rates. Finally, we applied explainable machine learning (ML) to deconvolute the impacts of dynamic discharge profiles on battery degradation. Specifically, we discovered the importance of low-frequency current pulses (8.2 mHz on average) in the discharge profile signal for lifetime metrics. This work illustrates the importance of testing batteries under realistic conditions of use and challenges the broadly adopted convention of constant current discharge in the laboratory. Evaluating batteries with realistic cycling profiles is necessary to properly understand ageing mechanisms at the chemistry, material and cell levels. Dynamic discharge profiles We designed four different types of discharge duty cycle to simulate different operating conditions (Fig. 1 ). These consisted of (1) baseline