Application of Machine Learning in Battery: State of Charge Estimation Using Feed Forward Neural Network for Na-Ion Batteries

Abstract

Estimating accurate State-of-Charge (SOC) of a battery is important to avoid the over/under charging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equation in Extended Kalman Filter (EKF) and equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate SOC value accurately where battery parameters such current, voltage and charge are mapped directly to SOC value at the output. FNN has the ability to self-learn the weights with each training data points and update the model parameters like weights and bias using a combination of two gradient descent (Adam). This model comprises Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and bias. Our FNN model was trained with data comprising different current rates and tested for different cycling data for example 5th, 10th, 20th, 50th cycle and at different cut-off voltage (4.5 V). The battery used for estimating the SOC value is Na-ion, which is highly non-linear and it was fabricated in house using Na0.67Fe0.5Mn0.5O2 (NFM) and Na metal. FNN successfully estimated the SOC value for highly non-linear nature of Na-ion battery at different current rates- 0.05C, 0.1C, 0.5C, 1C, 2C, having the R2 value of ~0.97-0.99 and ~0.99 for different cycling data and at higher cut-off voltage of -4.5 VNa+.