Real-Time Control Strategy of Elman Neural Network for the Parallel Hybrid Electric Vehicle

Ruijun Liu,Dapai Shi,Chao Ma

doi:10.1155/2014/596326

Ruijun Liu, Dapai Shi + Show 1 more

Open Access

https://doi.org/10.1155/2014/596326

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Abstract

Through researching the instantaneous control strategy and Elman neural network, the paper established equivalent fuel consumption functions under the charging and discharging conditions of power batteries, deduced the optimal control objective function of instantaneous equivalent consumption, established the instantaneous optimal control model, and designs the Elman neural network controller. Based on the ADVISOR 2002 platform, the instantaneous optimal control strategy and the Elman neural network control strategy were simulated on a parallel HEV. The simulation results were analyzed in the end. The contribution of the paper is that the trained Elman neural network control strategy can reduce the simulation time by 96% and improve the real-time performance of energy control, which also ensures the good performance of power and fuel economy.

Highlights

Under the dual pressure of environmental pollution and energy crisis, hybrid vehicles have advantages of both conventional vehicles and electric vehicles, which have characteristics of energy conservation, environmental protection, diverse shapes, and strong implementation
The results show that the trained Elman neural network control strategy can replace the instantaneous optimal control strategy, optimize power distribution, and make the simulation time reduced by 60%
Through the research on the instantaneous optimal strategy and Elman neural network control strategy, we deduce the objective functions of instantaneous optimal control and establish the instantaneous control model and design the Elman controller

Summary

Introduction

Under the dual pressure of environmental pollution and energy crisis, hybrid vehicles have advantages of both conventional vehicles and electric vehicles, which have characteristics of energy conservation, environmental protection, diverse shapes, and strong implementation. The static logic threshold control strategy cannot guarantee the optimal fuel economy of PHEV, does not adapt to dynamic conditions, and cannot make the whole system to achieve maximum efficiency. Neural network energy control strategy can adapt to diverse conditions with good robustness and obtain global fuel consumption optimum by engineering experience [9, 10]. Instantaneous optimal control strategy has good fuel economy at any time and bad real-time performance. The hybrid vehicles possess good power performance and fuel economy and obtain rapid allocation energy by finding a new energy control strategy. In order to solve bad real-time defects of instantaneous control strategy, instantaneous optimal control rules are used to train the Elman neural network control strategy and improve the real-time performance of the trained Elman energy control strategy on the premise that it can guarantee advantages of the instantaneous optimal control strategy. Optimal control strategy, optimize power distribution, and make the simulation time reduced by 60%

Research Energy Control Strategy

Calculate the Equivalent Fuel Consumption of Battery

Elman Neural Network Control Strategy

Modeling of Energy Control Strategy

Simulation Results and Analysis

Conclusion