Abstract
Neural networks are widely used in the learning of offline global optimization rules to reduce the fuel consumption and real-time performance of hybrid electric vehicles. Considering that the torque and transmission ratio are direct control variables, online recognition by a neural network of these two parameters is insufficiently accurate. In the meanwhile, the dynamic program (DP) algorithm requires huge computing costs. Based on these problems, a fusion algorithm combining a dynamic programming algorithm and an approximate equivalent fuel consumption minimum control strategy (A-ECMS) is proposed in this paper. Taking the equivalent factor as the control variable, the global optimal sequence of the factor is obtained offline. The back propagation (BP) neural network is used to extract the sequence to form an online control strategy. The simulation results illustrate that, compared with the traditional dynamic programming algorithm, although the fuel consumption increases slightly, the computational cost of the fusion algorithm proposed in this paper is significantly reduced. Moreover, because the optimal sequence of the equivalent factors is within a particular range, the online control strategy based on DP-A-ECMS has a high robustness. Compared with an online control strategy based on the torque and transmission ratio, the fuel economy is improved by 2.46%.
Highlights
As a product of the transition from traditional fuel to electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs) are currently a hot topic in research on new energy vehicles
Compared with an online control strategy based on the torque and transmission ratio, the fuel economy is improved by 2.46%
In a traditional dynamic programming algorithm used in the energy management strategy of a PHEV, the battery state of charge (SOC) and the transmission speed ratio are usually taken as state variables, and the motor torque is taken as a control variable
Summary
As a product of the transition from traditional fuel to electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs) are currently a hot topic in research on new energy vehicles. A large number of studies have realized the online application of a global optimization algorithm in a hybrid electric vehicle energy management strategy through a neural network. In [11,12], the authors used the battery power and engine speed as control variables, and applied dynamic programming to calculate the global optimal fuel consumption. To obtain the best battery power of the hybrid electric vehicles (HEV), as well as the best engine speed, the neural network is trained using the vehicle speed, required power, battery state of charge (SOC), and driving style of the offline calculation results Another neural network module based on 11 variables has been used to predict the operating mode.
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