Lithium Ion batteries have been extensively used for many applications such as laptops, mobile phones and electric vehicles due its long cycle life, high power and high energy densities. The life of battery is affected by many different factors including cycles, discharge current, charge current, charge voltage, temperature and state of charge ranges (depth of discharge). In this project, the remaining useful life of the Lithium Ion battery has been analysed with Deep Neural Network (DNN), based on the Voltage, Current and Temperature of the charging cycles [1]. The MATLAB Implementation of the same paper has been analyzed in [2]. Lithium ion battery data has been taken from NASA Ames Prognostics Data Repository [3]. Few of the programming concepts have been taken from [4]
[1] Choi, Yohwan, et al. "Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles." IEEE Access 7 (2019): 75143-75152.
[2] Wanbin Song (2020). Machine Learning Lithium-Ion Battery Capacity Estimation (https://github.com/wanbin-song/BatteryMachineLearning), GitHub. Retrieved August 16, 2020.
[3] B. Saha and K. Goebel (2007). "Battery Data Set", NASA Ames Prognostics Data Repository (https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#battery), NASA Ames Research Center, Moffett Field, CA