# Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network
## 3 Problem Statement and Solving Method
### Level 2: Reference for on Board Electronic Circuit  
The level 2 hierarchical control structure performs power demand prediction of EV and the obtained predictions become a reference input to the fuzzy logic controller at level 1 hierarchical control structure. This paper utilizes an artificial neural network (ANN) as a method to predict the power demand. ANN has been increasingly used and has become a commonly used learning algorithm for detecting relationships in a dataset [25]. In addition,  
Figure 5: Fuzzy logic controller model. ANN can provide a satisfactory outcome for complex pattern recognition problems due to its processing ability [26]. The principle of ANN or artificial neuron is based on the organic brain. A significant number of neurons are connected, forming a network to process inputs from receptors. Similar to the neuron an organic brain, each of neuron in ANN is connected and each of them is associated with a layer. The ANN generic architecture used for this work is shown in Figure 6, which is composed of three layers: input, hidden, and output. Each of the neurons is connected with all neurons from subsequent layers, and the connections between them are weighted. The process starts when the neurons in the input layer obtain some inputs value and deliver them to the neurons in the middle layer. The middle layer is called the hidden layer, and the number of hidden layers is known as a value to measure the depth of the ANN. The last layer is the output layer, which presents the final output network to the user. During the process, a functional operation to combine the weighted sum of the input values within the neuron is applied. This operation causes the neural network to perform a nonlinear function operation [27]. In the application for this work, ANN is an excellent method to perform power prediction that requires a nonlinear function. ANN has its hidden layer to map in and map out the relationship between the input and output. Therefore, it can perform linear or nonlinear functions. In [28], multiple linear regression (MLR) is discussed as a statistical analysis method. Fundamentally, MLR is a quick method to generate a function between input and output. It can perform effectively to represent a linear relationship. However, MLR is not possible to represent any nonlinearity. Thus, ANN is a better choice for this work due to its ability to perform nonlinearity functions better than MLR. Furthermore, ANN is simple to use and direct. It is not required to fully understand the algorithm and calculation in its hidden layer, since it is complicated to direct a math function to connect input and output. There are two typical steps to apply ANN [29]. The first step is a training process. During this process, the parameter, input values and target values are determined. Once the ANN has been trained, the testing phase to minimize error is performed. A vehicular network is a typical form of IoT [30], where information from other EV can be obtained and used as input data for power prediction calculation. For this work, the power prediction is calculated based on EV's status conditions: speed and acceleration. The study in [31] stated that the vehicle speed and acceleration are dynamic and difficult to predict. Which leads to the relation between the EV state and future power demand as a complex nonlinear function. Therefore, this work adopts ANN, which has the ability to process nonlinear functions to find the relation between the EV condition and future power demand. A brief ANN prediction model for this work is shown in Figure 7. The prediction process begins with three inputs based on other EV information data in which two of them correspond to the previous speed at T-1 and T-2 while the other one is the  
Figure 6: ANN architecture. vehicle acceleration data. All of the obtained inputs data will be calculated by ANN to perform power prediction. Thus, the output would be the predicted future power demand at time T and more details about the ANN configuration set up will be covered in Section 4.