## 3 Optimization-Based Strategies
### Data-Driven Strategies
#### 3.3.1 Neural Network-Dynamic Programming (NN-DP)  
An artificial neural network is composed of a considerable number of artificial neurons, which are widely interconnected. It can be seen as a directed graph with artificial neuron nodes connected by directed weighted arcs. Artificial neural networks can mainly be divided into feed-forward networks and feedback networks, which can be used to solve optimization problems by the minimum point of energy functions. The back propagation neural network (BP-NN) trains the neural network by using error back propagation; this is the simplest neural network and is widely used in multiple fields at present. Moreover, the radial basis function neural network (RBF-NN) has a better performance for nonlinear function approximation, generalization ability, and faster learning convergence speed, and is also utilized to construct the model. Since dynamic programming cannot achieve on-line optimization, neural networks (NN) can be utilized to construct the road environment prediction model and the driving condition prediction model due to their self-learning ability and adaptivity. They can be back propagation neural networks (BP-NN), radial basis function neural networks (RBF-NN) or deep learning neural networks. Based on the simplified neural network module designed to represent trip information, the DP results of standard driving cycle and actual driving cycle can be used to train the neural network module. There are two ways to effectively combine neural networks with DP. On the one hand, the vehicle speed can be predicted based on current vehicle speed, acceleration, average vehicle speed and so forth, and then the optimal results obtained offline by DP are extracted for the training of neural network, which can select power demand, vehicle speed and SOC as the inputs. On the other hand, it can predict the road environment (including the roadway type and the traffic-congestion level) and the driver's instantaneous reactions according to the vehicle speed, acceleration, trip information, and so forth. The DP method is applied to a set of standard driving cycles to obtain the optimal control sequences and the corresponding driving trend, which are used as inputs of the neural network to obtain battery power and engine torque or motor speed in real time. Based on the above methods, the main research areas for the NN-DP strategy for HEVs are presented in Figure 9.  
As for the predetermined bus route, [27; 92; 93; 94] proposed a length ratio-based neural network energy management strategy for online control of PHEB to reduce the computational time and storage capacity of the micro-controller and to achieve approximate optimal control performance. The length ratio representing the space domain was chosen as the input variable of the neural network module to represent trip information, which consists of four parameters: trip length, trip duration, current driving length and current driving time. Based on the idea of transforming the global optimization problem into a local optimization problem, the trip condition information at the \(kth(s)\) can be used to predict the future trip condition information according to the prediction transfer relation. The process is from the \((k+1)th(s)\) to \((k+l)th(s)\), where \(l\) represents the length of a preview window. Therefore, the vehicle-speed trajectory can be acquired online through the trip condition prediction model, which can be constructed by BP-NN. To improve the prediction accuracy of the prediction model, the initial weights and thresholds  
Figure 9: Description of the main research areas for the NN-DP-based strategy. can be optimized by some intelligent optimization algorithm. In [95], the focus was to provide an on-line energy management control strategy based on trip condition prediction to minimize fuel consumption for PHEVs. It predicts the vehicle speeds on-line by establishing the trip condition prediction model based on GA/PSOA-BPNN.