Lithium Ion batteries have been extensively used for many applications such as laptops, mobile phones and electric vehicles due its long cycle lie, 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). Convolutional Neural Network (CNN) are used for Image data but can be used for numeric data as well. This project predicts the life of the Lithium Ion battery with Convolutional Neural Network based on the Voltage, Current and Temperature of the charging cycles [1]. Keras deep learning library has been utilized to implement Conv2Ds. The MATLAB Implementation has been analyzed in [2]. Lithium ion battery data has been taken from NASA Ames Prognostics Data Repository [3]. SVR implementation has been elaborately studied in [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

[4] https://github.com/PotatoSpudowski/RUL-and-SOH-estimation-of-Lithium-ion-satellite-power-systems-using-support-vector-regression