B. Saha, S. Poll, K. Goebel, and J. Christophersen, "An integrated approach to battery health monitoring using Bayesian regression and state estimation," in 2007 IEEE Autotestcon, 2007, pp. 646-653.
B. Saha and K. Goebel, "Modeling Li-ion battery capacity depletion in a particle filtering framework," in Proceedings of the annual conference of the prognostics and health management society, 2009, pp. 2909-2924.
I do not think there is a set of well validated data for your practice in the public domain. Using capacity based estimation might be not truly useful to derive SOC and SOH. I published a review article recently in J Power Sources which may help explain why:Article On state-of-charge determination for lithium-ion batteries
Soumyaranjan Sabat If you are looking for Lithium Ion , you can find a dataset for LiFePO4 here https://data.matr.io/1/projects/5c48dd2bc625d700019f3204
Maybe this dataset will be useful https://data.mendeley.com/datasets/wykht8y7tg/1
" The tests can be used to test Neural Network and Kalman Filter State of Charge algorithms, or to develop battery models, and are intended to be a reference so researchers can compare their algorithm and model performance for a standard data set. "
Wang, Y., Liu, C., Pan, R., Chen, Z., Experimental data of lithium-ion battery and ultracapacitor under DST and UDDS profiles at room temperature, Data in Brief, 12, pp. 161-163, 2017, DOI: 10.1016/j.dib.2017.01.019.
Zhang, X., Wang, Y., Yang, D., Chen, Z., Behavior data of battery and battery pack SOC estimation under different working conditions, Data in Brief, 9, pp.737-740, 2016, DOI:10.1016/j.dib.2016.10.012.