Diagnostic: NMC/Graphite synthetic dataset

If you make use of our data, please cite our dataset directly using its DOI, as well as the corresponding paper: "Generalised diagnostic framework for rapid battery degradation quantification with deep learning, Energy and AI, Haijun Ruan, Jingyi Chen, Weilong Ai, Billy Wu". In Syn_NMC_4dm file, there are the degradation modes (DMs) and the corresponding pseudo OCVs (discharge at 0.3C); The four DMs are LLI, LAMdeNE, LAMdePE, and RI, respectively; This data is used for the CNN training.

In Syn_NMC_path1 file, there are the DMs for the aging path I, and the corresponding pseudo OCVs (discharge at 0.3C). The six DMs are LLI, LAMliNE, LAMdeNE, LAMdePE, LAMliPE, and RI, respectively; This data is used for the validation of the proposed diagnostic method. Please read the corresponding paper to find the details about the training data generation and the assumed aging paths.