## 3 Neural Network Fuzzy EMS Based on DCR
### Neural Network Fuzzy EMS (NNF-EMS) Design Based on DCR  
In this paper, the backpropagation (BP) neural network is used for DCR. The BP neural network is a multi-layer feed-forward network trained based on the error backpropagation algorithm. It can store and learn a large number of input-output mappings without having to mathematically describe these mappings. It embodies a nonlinear functional relationship between the input and output of the network. The BP neural network generally consists of an input layer, a hidden layer, and an output layer. The basic structure of the network is shown in Figure 9, which has \(M\) input nodes, \(L\) output nodes, and \(q\) neurons, wherein the actual input of the network is \(x_{1},x_{2},\ldots,x_{M}\), and the actual output is \(y_{1},y_{2},\ldots,y_{M}\). In the calculation process, learning and training are continuously performed according to the input sample network, and the response values of the neurons are transmitted from the input layer to the output layer through the hidden layer. According to the obtained network response, the output layer continuously adjusts the connection weights and thresholds of the network in the direction of the greatest reduction of error; the square of the error of the network is minimized by backpropagation, thereby improving the accuracy of the network output [27; 28]. BP neural networks are used for pattern recognition and classification, but are not suitable for expressing rule-based knowledge. In the fuzzy system, knowledge extraction and rule expression are more convenient and suitable for fuzzy or qualitative knowledge, but lack the capacity for self-learning and self-adaptation. It can be seen that the neural network fuzzy EMS (NNF-EMS) of HEV combines the neural network-based cycle recognition and F-EMS, and their advantages complement each other to realize the DCR-based fuzzy control reasoning process. Although the actual driving conditions are random and undeterminable, if there are sufficient circulating conditions, the driving cycle blocks with similar characteristics can be found, and the control parameters can be adjusted or the corresponding control strategies can be found to improve control performance through real-time analysis. Figure 8: Division of “driving cycle blocks” in NEDC. The main advantage of a neural network for DCR is that the sample data can adapt to the driving conditions, and the neural network can also function normally even when the acquired driving condition data contain noise. In addition, the neural network provides simple tools for automatic feature selection, generates useful data representations, and can also serve as a front-end preprocessor for fuzzy controllers. Therefore, this paper uses a neural network to realize DCR, and the recognition result is combined with the input signals such as SOC and demand torque, and then used as input to the fuzzy controller to optimize the fuzzy membership function and fuzzy rules in overcoming the poorly targeted shortcomings of the F-EMS of HEVs. The BP neural network algorithm is used as the training algorithm, and the number of training times is 50. The input node of the neural network selects the relevant characteristic parameters of the current driving velocity, average vehicle velocity, average acceleration, average deceleration, maximum vehicle velocity, maximum acceleration, maximum deceleration, traveled distance, and the travel time as the input nodes of the BP network. By adjusting the connection weighting coefficient, the role of different characteristic parameters in the identification of the working cycle can be adjusted, and the output value of the network node can be obtained by the weighted sum [16]. In this paper, the graphical interface editor (Anfis editor) of the adaptive neuro-fuzzy inference system was used provided by MATLAB Fuzzy Logic Toolbox to train and test the driving conditions of NEDC, and we selected parameters such as BP algorithm, error accuracy, and training times to train the neural network system.