# Application of Deep Learning Techniques for the State of Charge Prediction of Lithium-Ion Batteries
## 3 Structural Design of LSTM Model  
This section provides a detailed description of the characteristics of the battery and covers data preprocessing and implementation methods for the LSTM network model. First, we compare the impact of the shape of the model input, and then compare and analyze the learning ability and performance according to the LSTM structure and model parameter settings. In order to write the internal structure of the LSTM cell shown in Figure 5 in Section 2 of this paper as a Python program, a library such as TensorFlow or keras is used to perform calculations that update each gate and cell state. Figure 4: Process of model training and performance evaluation. Figure 3: Comparison between SOC and OCV in (**a–d**) for CS2 battery cells 35, 36, 37, and 38, respectively. Figure 4 is a flow chart showing the process of constructing an LSTM model using the Python code and keras library. The LSTM model accepts input data, processes it through LSTM cells, and finally produces output data. Inside an LSTM cell, information is processed through several gates. Since the basic structure of the LSTM model was designed by substituting the equations in Section 2, the pseudocode in Figure 3 also shows the structure of the LSTM model. The pseudocode in Algorithm 1 defines the process of calculating each gate and cell state of an LSTM cell as a function. Sigmoid and hyperbolic tangent stand for the sigmoid activation function and hyperbolic tangent activation function, respectively, which combine weights and inputs to output values between 0 and 1, or \(-1\) and 1. ```
Function LSTM_Cell(input \(x_{t}\), previous_hidden_state \(h_{t-1}\), previous_cell_state C\({}_{t-1}\)): # Calculate the forget gate \(f_{t}=\text{Sigmoid}(\text{forget\_weights}\ W_{f}\ ^{*}[h_{t-1},x_{t}]+\text{forget\_bias}\ b_{f})\) # Calculate the input gate \(i_{t}=\text{Sigmoid}(\text{input\_weights}\ W_{i}\ ^{*}[h_{t-1},x_{t}]+\text{input\_bias}\ b_{i})\) \(\overset{\sim}{C}_{t}=\text{HyperbolicTangent}(\text{cell\_state\_weights}\ W_{C}\ ^{*}[h_{t-1},x_{t}]+\text{cell\_state\_bias}\ b_{C})\) # Update the cell state \(C_{t}=f_{t}\ ^{*}C_{-1}+i_{t}\ ^{*}\overset{\sim}{C}_{t}\) # Calculate the output gate \(o_{t}=\text{Sigmoid}(\text{output\_weights}\ W_{o}\ ^{*}[h_{t-1},x_{t}]+\text{output\_bias}\ b_{o})\) \(h_{t}=o_{t}\ ^{*}\text{HyperbolicTangent}(C_{t})\)  return \(h_{t},C_{t}\) # Weights and biases are optimized during the model training process. # This pseudocode represents the operations within an LSTM cell at a single time step. # These operations are repeated sequentially over the entire sequence. [\(h_{t-1},x_{t}\)] means a vector that concatenates the previous hidden state and the current input. * indicates element-wise multiplication, and in actual implementation matrix operations are used. These operations are designed to enable LSTM networks to learn long-term dependencies in sequence data. Deep learning libraries abstract these complex calculations from the user, making it easier to construct and experiment with models at a higher level. Algorithm 1 shows Python code for constructing an LSTM model using the keras library. Each part of the code is directly related to the internal workings of the LSTM cell. As an explanation of Algorithm 2, where input_time_steps refers to the time steps of the input data, input_features refers to the number of features in the input data, and output_features refers to the number of features in the output data. x_train, y_train, x_val, y_val, x_test, and y_test represent training, validation, and test data and labels, respectively. Rather than directly implementing the actual LSTM internal calculations

Figure 5: Flowchart for LSTM structure design. and gate update logic, this code demonstrates how to leverage the LSTM layer provided by TensorFlow to create a model that processes sequence data. The loss function and optimization algorithm used when compiling the model are used to learn the weights and biases inside the LSTM cells. ```
Define the model. model = Sequential() # Add an LSTM layer.