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

Abstract

Estimating the accurate State of Charge (SOC) of a battery is important to avoid the over/undercharging and protect the battery pack from low cycle life. Current methods of SOC estimation use complex equations in the Extended Kalman Filter (EKF) and the equivalent circuit model. In this paper, we used a Feed Forward Neural Network (FNN) to estimate the SOC value accurately where battery parameters such as current, voltage, and charge are mapped directly to the SOC value at the output. A FNN could self-learn the weights with each training data point and update the model parameters such as weights and bias using a combination of two gradient descents (Adam). This model comprises the Dropout technique, which can have many neural network architectures by dropping the neuron/mode at each epoch/training cycle using the same weights and biases. Our FNN model was trained with data comprising different current rates and tested for different cycling data, for example, 5th, 10th, 20th, and 50th cycles and at a different cutoff voltage (4.5 V). The battery used for estimating the SOC value was a Na-ion based battery, which is highly non-linear, and it was fabricated in a house using Na0.67Fe0.5Mn0.5O2 (NFM) as a cathode and Na metal as a reference electrode. The FNN successfully estimated the SOC value for the highly non-linear nature of the Na-ion battery at different current rates (0.05 C, 0.1 C, 0.5 C, 1 C, 2 C), for different cycling data, and at higher cut-off voltage of –4.5 V Na+, reaching the R2 value of ~0.97–~0.99, ~0.99, and ~0.98, respectively.

Emerging Chemistry for Wide-Temperature Sodium-Ion Batteries

“…With the rapid developments of computer science and big data technology, machine learning has shown its value in effectively guiding the design and preparation of high-performance battery materials. − As mentioned in the previous section, the construction of WT-SIBs strongly relies on the design and optimization of each battery component. Based on the accumulated research data, combining the density functional theory (DFT) calculations with the machine learning methods to screen for suitable materials and predict the electrochemical performances can be expected to accelerate the development of WT-SIBs.…”

Measurement of State of Charge of Lithium-Nickel Manganese Cobalt Battery using Artificial Neural Network and NARX Algorithm

“…Many neurons make up a FNN, which is also the fundamental unit of information processing [31]. Weights connect each neuron, resulting in probability-weighted correlations among source and result [32]. Fig.…”

State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence

“…However, the individual impact of the inpu output is unknown. For example, in this SOC estimation model, if five current, temperature, average voltage, and average current) are prov (10) where r k is the reset gate and z k is the update gate.…”

“…The AI model is then applicable to an unknown data set to estimate the SOC. Feedforward neural network (FNN) models for predicting the SOC, were presented by Darbar and Bhattacharya [10], using voltage, current, and temperature measurements as the input variables. Once confronted with a variety of driving conditions at varying temperatures throughout training and testing, the proposed scheme was adequate in estimating the SOC.…”