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Coosemans, and J. Van Mierlo, \u201cLithium iron phosphate based battery-Assessment of the aging parameters and development of cycle life model,\u201d Applied Energy, vol.113, pp.1575-1585, 2014. 10.1016\/j.apenergy.2013.09.003","DOI":"10.1016\/j.apenergy.2013.09.003"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] C. Weng, X. Feng, J. Sun, and H. Peng, \u201cState-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking,\u201d Applied Energy, vol.180, pp.360-368, 2016. 10.1016\/j.apenergy.2016.07.126","DOI":"10.1016\/j.apenergy.2016.07.126"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] D. Liu, Y. Luo, J. Liu, Y. Peng, L. Guo, and M. 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