Abstract
In this paper, an online optimal energy distribution method is proposed for composite power vehicles using BP neural network velocity prediction. Firstly, the predicted vehicle speed in the future period is obtained via the output of a BP neural network, where the current vehicle driving state and elapsed vehicle speed information is used as the input. Then, according to the predicted vehicle speed, an energy management method based on model predictive control is proposed, and online real-time power distribution is carried out through rolling optimization and feedback correction. Cosimulation results under urban drive cycle show that the proposed method can effectively improve the energy efficiency of composite power sources compared with the commonly used method with the assumption of prior known driving conditions.
Highlights
Zero-emission pure electric clean vehicles, which can effectively solve the problems of environmental pollution and resource shortage in the automotive field, are the most valuable and significant research direction at present [1,2,3,4].e current problems of pure electric vehicles are as follows
Pure electric vehicle with single power source cannot recover regenerative braking energy, which leads to partial energy loss and reduces energy utilization. erefore, it is necessary to find an auxiliary energy source with high specific power to improve the power performance and energy utilization of pure electric vehicles
Unlike electric vehicles with a single power source, the composite power source involves the power distribution of two power sources, so it is necessary to design an optimal control strategy for energy management. e current research on energy management strategies of composite power supplies can be roughly divided into two categories: rule-based and optimization-based energy management control strategies. e rule-based energy management control strategy is mainly divided into logic threshold control based on deterministic rules [6] and fuzzy control based on fuzzy rules [7]
Summary
Zero-emission pure electric clean vehicles, which can effectively solve the problems of environmental pollution and resource shortage in the automotive field, are the most valuable and significant research direction at present [1,2,3,4]. Unlike electric vehicles with a single power source, the composite power source involves the power distribution of two power sources, so it is necessary to design an optimal control strategy for energy management. Model predictive control [16] searches for the optimal control sequence in the finite time domain at each sampling instant, which makes it possible to achieve the online optimal solution This method largely relies on effective prediction of future vehicle speed. En, based on the principle of model predictive control, the optimal control quantity at each moment is obtained through rolling optimization and feedback regulation, so as to achieve the instantaneous optimal distribution of composite power system. Considering the weight, initial cost, energy efficiency, and control strategy implementation, the composite power topological structure, as shown, is selected in this paper. Considering the weight, initial cost, energy efficiency, and control strategy implementation, the composite power topological structure, as shown in Figure 1, is selected in this paper. e lithium battery pack as the main energy source of Lithium battery pack
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