CONSTRUCTION OF AN ACCELERATED AGING TEST SYSTEM FOR SERIES CONNECTION BATTERY PACK
Keywords:Accelerated Aging Test, Lithium-Ion Battery Pack, Battery Internal Resistance, Test System Design, State Of Health (Soh)
Nowadays, Lithium-ion batteries are widely used in various aspects, such as mobile electronic devices, mobility, EVs, and so on. Exactly to estimate State of Health (SoH) and Remaining Useful Life (RUL) becomes more and more necessary for realistic applications. Accelerated aging test can provide reliable experimental data for research of SoH estimation. An accelerated aging test system for a battery pack was designed in the research, which included hardware design and programming of test system control and monitoring. After establishment of the test system, several test cycles were implemented and the acquired data indicated that the developed aging test system worked very well and can be used for degradation experiment of the Lithium-ion battery pack in future work.
Chen, Y., & Huang, M. (2016). A Method of Battery State of Health Prediction based on AR-Particle Filter. SAE Technical Paper 2016-01-1212. Retrieved from https://doi.org/10.4271/2016-01-1212
Gregory, L. P. (2015). Battery Management Systems Volume I Battery Modeling. Artech House.
Gregory, L. P. (2016). Battery Management Systems Volume II Equivalent-Circuit Methods. Artech House
Hu, X. S., Xu, L., Lin, X. K., & Pecht, M. (2020). Battery Lifetime Prognostics. Joule, 4(2), 310-346. Retrieved from https://doi.org/10.1016/j.joule.2019.11.018
Jiang, J. C., Zhang, C. P. (2015). Fundamentals and Applications of Lithium‐ion Batteries in Electric Drive Vehicles. John Wiley & Sons. Retrieved from https://doi.org/10.1002/9781118414798
Kai, G., Bhaskar, S., Abhinav, S., et al. (2008). Prognostics in Battery Health Management. IEEE Instrumentation & Measurement Magazine, 11(4), 33-40. Retrieved from https://doi.org/10.1109/MIM.2008.4579269
Lin, C., Tang, A. H., & Wang, W. W. (2015). A review of SOH estimation methods in Lithium-ion batteries for electric vehicle applications. Energy Procedia, 75, 1920-1925. Retrieved from https://doi.org/10.1016/j.egypro.2015.07.199
Rezvanizanian, S. M., Huang, Y. X., Chuan, J., & Lee, J. (2012). A Mobility Performance Assessment on Plug-in EV Battery. International Journal of Prognostics and Health Management, 3(2), Retrieved from https://doi.org/10.36001/ijphm.2012.v3i2.1363
Saha, B., & Goebel, K. (2007). Battery Data Set, NASA Ames Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA. Retrieved from https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/
Verena, K., Mårten, B., Göran, L. (2014). A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation. Journal of Power Sources, 270(15), 262-272. Retrieved from https://doi.org/10.1016/j.jpowsour.2014.07.116
Vetter, J., Novak, P., Wagner, M. R., et al. (2005). Ageing mechanisms in lithium-ion batteries. Journal of Power Sources, 147(1-2), 269-281. Retrieved from https://doi.org/10.1016/j.jpowsour.2005.01.006
Vasan, A. S. S., Mahadeo, D. M., Doraiswam, R., Huang, Y., and Pecht, M. (2013) Point-of-care biosensor system. Center for Advanced Life Cycle Engineering (CALCE). 39-71 january 1
Xu, J., Mi, C. C., Cao, B. G., Cao, J. Y. (2013). A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model. Journal of Power Sources, 233(1), 277-284. Retrieved from https://doi.org/10.1016/j.jpowsour.2013.01.094