Vignesh S, Hang Seng Che, Jeyraj Selvaraj, Kok Soon Tey
Incremental Capacity Analysis (ICA) is a widely used non-destructive technique to predict State of Health (SoH) of Lithium-ion Battery (LiB). Till today, the SoH is judged on ICA magnitude of peaks and valleys for defining the health indicators. This research work provides the state-of-the art in voltage and capacity profile reconstruction available for ICA through Constant Capacity (CC), Constant Voltage (CV), and hybrid sampling. Here, hybrid sampling offers flexibility for the repurposer to define voltage bands for partial charge and discharge limits according to ICA peaks. This research work aims to address the scope of reducing time and efforts on partial charging and discharging tests conducted by the repurposers for SoH and Remaining useful Life (RuL) estimation of Second Life Battery (SLB). Nissan Leaf battery 2011 model taken from EV application is tested for health indicators catering insights on SoH and RuL by reconstructing the voltage and capacity profile. The necessity of a filter can be neglected as a part of ICA post-processing. The peaks and valleys from reconstructed profile is aiding the repurposer to define voltage limits for partial charging and discharging based on SoH. This led to reduction in testing time and efforts in the range of 16% to 83% during SLB characterization. The accuracy of this technique is validated by varying the sampling frequencies ranging from 0.1 Ah, 0.01 Ah and 0.001 Ah before proceeding with reconstruction. Sandia National Laboratories open source data is considered for verifying the proof of concept. Pearson correlation coefficient is deployed to differentiate strong and weak SLB based on sampled data. The actual data obtained from battery cycler fails to do the same and hence, the sampled data aids repurposer in reducing the charging and discharging time of SLB through narrowed voltage bands. © 2024 Elsevier Ltd
Higher Institution Centre of Excellence (HiCoE), UM Power Energy Dedicated Advanced Centre (UMPEDAC), Level 4, Wisma R&D, Universiti Malaya, Jalan Pantai Baharu, Kuala Lumpur, 59990, Malaysia; Institute for Advanced Studies, Universiti Malaya, Kuala Lumpur, 50603, Malaysia; Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Padang, 25131, Indonesia; Department of Computer System & Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, 50603, Malaysia
