Graphite//LFP synthetic duty cycle dataset
Description
This training dataset was calculated using the mechanistic modeling approach. See “Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis“ (Journal of Power Sources, Volume 479, 15 December 2020, 228806) and "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Diagnosis and Prognosis" (Energies, under review) for more details For this proof of concept work, we considered eight parameters to scan. For each degradation mode, degradation was chosen to follow equation (1). %degradation=a × cycle+ (exp^(b×cycle)-1) (1) Considering the three degradation modes, this accounts for six parameters to scan. In addition, two other parameters were added, a delay for the exponential factor for LLI, and a parameter for the reversibility of lithium plating. The delay was introduced to reflect degradation paths where plating cannot be explained by an increase of LAMs or resistance [55]. The chosen parameters and their values are summarized in Table S1 and their evolution is represented in Figure S1. Figure S1(a,b) presents the evolution of parameters p1 to p7. At the worst, the cells endured 100% of one of the degradation modes in around 1,500 cycles. Minimal LLI was chosen to be 20% after 3,000 cycles. This is to guarantee at least 20% capacity loss for all the simulations. For the LAMs, conditions were less restrictive, and, after 3,000 cycles, the lowest degradation is of 3%. The reversibility factor p8 was calculated with equation (2) when LAMNE > PT. %LLI=%LLI+p8 (LAM_PE-PT) (2) Where PT was calculated with equation (3) from [60]. PT=100-((100-LAMPE)/(100×LRini-LAMPE ))×(100-OFSini-LLI) (3) Varying all those parameters accounted for close to 130,000 individual duty cycles with one voltage curve for every 200 cycles (one every 10 cycles for cycles 1-200) This dataset necessitate the associated V vs. Q dataset to be functional (10.17632/bs2j56pn7y.2) See read me for details on variable and examples.