Hierarchical Energy Management Control of Connected Hybrid Electric Vehicles on Urban Roads With Efficiencies Feedback

Author(s):  
Zhiyuan Du ◽  
Lihong Qiu ◽  
Pierluigi Pisu

In this paper, we present a fuel efficient control strategy for a group of connected hybrid electric vehicles (HEVs) in urban road conditions. A hierarchical control architecture is proposed where the higher level controller is located at traffic signal light while the lower level controllers are equipped on each HEV. The higher level controller utilizes Signal Phase and Timing (SPAT) information from the traffic lights to generate target velocities for every HEV, which allows a maximum number of vehicles pass the intersection at given green light window. Model Predictive Control (MPC) is used to track the target velocity and evaluate the energy efficient velocity profile for every vehicle for a given horizon. Each lower level controller then follows the velocity profile (from the higher level controller) in a fuel efficient fashion using adaptive equivalent consumption minimization strategy (A-ECMS). The lower level controller also feeds the average recuperation efficiency in a certain time window back to the higher level controller, thus affects the future velocity profile evaluation from the higher level controller, which is the major contribution of this paper. In this paper, the HEV model is developed based on Autonomie software and the simulation results show the effectiveness of our proposed approach.

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 202 ◽  
Author(s):  
Lu Han ◽  
Xiaohong Jiao ◽  
Zhao Zhang

A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research on the multi-objective energy management control problem, which aims to not only minimize the gasoline consumption and consumed electricity but also prolong battery life, is necessary and challenging for HEV. This paper presents an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. As the determined EF is dependent on the optimal co-state of the Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. In addition to the mappings of the weight factor and the major component of the EF linked to the optimal co-state of the PMP, the real-time performance of the energy management control is also guaranteed by the tuning component of the EF of A-ECMS resulting from the Proportional plus Integral (PI) control on the deviation between the battery SOC and the optimal trajectory of the SOC obtained by the Recurrent Neural Network (RNN). The RNN is trained offline by the SOC trajectory optimized by dynamic programming (DP) utilizing the historical traffic data. Finally, the effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of plug-in hybrid electric vehicles based on GT-SUITE (a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies of US company) compared with the existing strategy.


2017 ◽  
Vol 105 ◽  
pp. 407-418 ◽  
Author(s):  
Cinda Sandoval ◽  
Victor M. Alvarado ◽  
Jean-Claude Carmona ◽  
Guadalupe Lopez Lopez ◽  
J.F. Gomez-Aguilar

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