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). This project predicts the capacity of the Lithium Ion battery with LSTM based on the Voltage, Current and Temperature of the charging cycles [1]. Keras deep learning library has been utilized to implement LSTM. Lithium ion battery data has been taken from NASA Ames Prognostics Data Repository [2].
[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] 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