Trip-oriented Energy Management Control strategy for plug-in hybrid electric vehicles

2011 ◽  
Author(s):  
Hai Yu ◽  
Ming Kuang ◽  
Ryan McGee
Keyword(s):  
Energy Management ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Management Control ◽  
Hybrid Electric ◽  
Oriented Energy

Energy management control strategy to improve the FC/SC dynamic behavior on hybrid electric vehicles: A frequency based distribution

2017 ◽  
Vol 105 ◽  
pp. 407-418 ◽  
Author(s):  
Cinda Sandoval ◽  
Victor M. Alvarado ◽  
Jean-Claude Carmona ◽  
Guadalupe Lopez Lopez ◽  
J.F. Gomez-Aguilar
Keyword(s):  
Dynamic Behavior ◽  
Energy Management ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Management Control ◽  
Hybrid Electric

scholarly journals Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging

Energies ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 202 ◽  
Author(s):  
Lu Han ◽  
Xiaohong Jiao ◽  
Zhao Zhang
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Electric Vehicles ◽  
Recurrent Neural Network ◽  
Hybrid Electric Vehicles ◽  
Management Control ◽  
Weight Factor ◽  
Traffic Data ◽  
Hybrid Electric ◽  
The Cost

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.

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