# Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP
###### Abstract  
Over the last 20 years, lithium-ion batteries have become widely used in many fields due to their advantages such as ease of use and low cost. However, there are concerns about the lifetime and reliability of these batteries. These concerns can be addressed by obtaining accurate capacity and health information. This paper proposes a method to predict the capacity of lithium-ion batteries with high accuracy. Four key features were extracted from current and voltage data obtained during charge and discharge cycles. To enhance prediction accuracy, the Pearson correlation coefficient between these features and battery capacities was analyzed and eliminations were made for some batteries. Using a genetic algorithm (GA), the parameter optimization of Convolutional Neural Network (CNN), Backpropagation (BP), and Recurrent Neural Network (RNN) algorithms was performed. The parameters that provide the best performance were determined in a shorter time using GA, which includes natural selection and genetic processes instead of a trial-and-error method. The study employed five metrics--Mean Square Error (MSE), Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), and Squared Correlation (R\({}^{2}\))--to evaluate prediction accuracy. Predictions based on NASA experimental data were compared with the existing literature, demonstrating superior accuracy. Using 100 training data, 68 data predictions were made with a Root Mean Square Error (RMSE) of 0.1176%. This error rate represents an accuracy level 2.5 times higher than similarly accurate studies in the literature. capacity; convolutional neural network; genetic algorithm; lithium-ion battery; state of health +
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performance and then build a cell degradation model [8]. Although some progress has been made in model-based predictions, the presence of complex chemical reactions inside lithium-ion batteries makes it difficult to build an accurate aging model [9]. Furthermore, the condition of lithium-ion batteries is highly dependent on environmental factors such as operating temperature, anode materials, cathode materials, and others [10]. Therefore, there is no general and accurate battery degradation model to determine the key parameters of battery life for accurate capacity prediction [11]. Data-driven methods have recently attracted more attention in capacity estimation due to their flexibility and efficiency in nonlinear observability. These methods use statistical or machine learning models to predict the capacity or health status of a battery. An adaptive SOH estimation method using Feed-Forward Neural Network (FNN)-, online AC complex impedance [12]-, and simple Recurrent Neural Network (RNN)-based approaches have been proposed to estimate the SOH of lithium-ion batteries using dynamically operating RNNs [13]. Support Vector Machines (SVMs) [14; 15], Gaussian Regression Processes (GPRs) [16; 17], Autoregressive Integrated Moving Average (ARIMA) [18], Extreme Learning Machines (ELMs) [19; 20], Long- and Short-Term Memory Networks (LSTM) [11; 21], Gated Recurrent Units (GRUs) [22; 23], Savitzky-Golay filters with gated recurrent units (SG-GRU) [24], and CNN algorithm [25], which make predictions using current and voltage measurement data, have been widely used for lithium-ion battery capacity prediction due to their nonlinear mapping and self-learning capabilities. Data-driven methods can predict the SOH of a battery without requiring electrochemical information about the internal structure of the battery and aging mechanisms. Therefore, these methods can be easily applied without the need for knowledge about the electrochemical properties of a battery and environmental factors. In previous studies, various models have been proposed to enhance prediction accuracy, typically utilized through trial and error or with standard values. While these approaches have yielded satisfactory results, they often do not achieve optimal performance due to the lack of systematic parameter optimization.