Application of a modified thermostatic control strategy to parallel mild HEV for improving fuel economy in urban driving conditions

2016 ◽  
Vol 17 (2) ◽  
pp. 339-346 ◽  
Author(s):  
D. B. Jung ◽  
S. W. Cho ◽  
S. J. Park ◽  
K. D. Min
Keyword(s):  
Fuel Economy ◽  
Control Strategy ◽  
Thermostatic Control ◽  
Driving Conditions

Optimal adaptation equivalent factor of energy management strategy for plug-in CVT HEV

2018 ◽  
Vol 233 (4) ◽  
pp. 877-889 ◽  
Author(s):  
Xinyou Lin ◽  
Qigao Feng ◽  
Liping Mo ◽  
Hailin Li
Keyword(s):  
Energy Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Adaptation Strategy ◽  
Management Control ◽  
Energy Management Strategy ◽  
Driving Mode ◽  
Driving Cycles ◽  
Equivalent Factor

This study presents an adaptive energy management control strategy developed by optimally adjusting the equivalent factor (EF) in real-time based on driving pattern recognition (DPR), to guarantee the plug-in hybrid electric vehicle (PHEV) can adapt to various driving cycles and different expected trip distances and to further improve the fuel economy performance. First, the optimization model for the EF with the battery state of charge (SOC) and trip distance were developed based on the equivalent consumption minimization strategy (ECMS). Furthermore, a methodology of extracting the globally optimal EF model from genetic algorithm (GA) solution is proposed for the design of the EF adaptation strategy. The EF as the function of trip distances and SOC in various driving cycles is expressed in the form of map that can be applied directly in the corresponding driving cycle. Finally, the algorithm of DPR based on learning vector quantization (LVQ) is established to identify the driving mode and update the optimal EF. Simulation and hardware-in-loop experiments are conducted on synthesis driving cycles to validate the proposed strategy. The results indicate that the optimal adaption EF control strategy will be able to adapt to different expected trip distances and improve the fuel economy performance by up to 13.8% compared to the ECMS with constant EF.

scholarly journals Design and Optimization of the Power Management Strategy of an Electric Drive Tracked Vehicle

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Qunzhang Tu ◽  
Xiaochen Zhang ◽  
Ming Pan ◽  
Chengming Jiang ◽  
Jinhong Xue
Keyword(s):  
Power Management ◽  
Electric Drive ◽  
Fuel Economy ◽  
Control Strategy ◽  
Fuzzy Controller ◽  
Management Control ◽  
Drive System ◽  
Tracked Vehicles ◽  
Hybrid Particle Swarm Optimization ◽  
Electric Drive System

This article studies the power management control strategy of electric drive system and, in particular, improves the fuel economy for electric drive tracked vehicles. Combined with theoretical analysis and experimental data, real-time control oriented models of electric drive system are established. Taking into account the workloads of engine and the SOC (state of charge) of battery, a fuzzy logic based power management control strategy is proposed. In order to achieve a further improvement in fuel economic, a DEHPSO algorithm (differential evolution based hybrid particle swarm optimization) is adopted to optimize the membership functions of fuzzy controller. Finally, to verify the validity of control strategy, a HILS (hardware-in-the-loop simulation) platform is built based on dSPACE and related experiments are carried out. The results indicate that the proposed strategy obtained good effects on power management, which achieves high working efficiency and power output capacity. Optimized by DEHPSO algorithm, fuel consumption of the system is decreased by 4.88% and the fuel economy is obviously improved, which will offer an effective way to improve integrated performance of electric drive tracked vehicles.

Depleting Mode Control Strategies for Plug-in Hybrid Electric Vehicles

2011 ◽  
Vol 130-134 ◽  
pp. 2211-2215
Author(s):  
Bing Zhan Zhang ◽  
Han Zhao ◽  
An Dong Yin
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Control Strategies ◽  
Great Influence ◽  
Torque Control ◽  
Drive Cycle ◽  
Vehicle Simulation ◽  
Hybrid Electric

Control strategy is the most important issue in the Plug-in Hybrid electric vehicles (PHEV) design, which has two modes: charge depleting mode (CD) and charge sustaining mode (CS). The different control strategies in depleting mode will have a great influence on PHEV dynamic performance and fuel economy. The engine optimal torque control strategy was proposed in the paper. The vehicle simulation model in Powertrain Systems Analysis Toolkit (PSAT) was adopted to evaluate the proposed control strategy. The aggressive highway drive cycle Artemis_hwy and a random drive cycle generated by Markov Process were used. The simulation results indicate the proposed control strategy has great improvement in fuel economy.

Simulink on Hydraulic System of Engineering Vehicle

2012 ◽  
Vol 201-202 ◽  
pp. 499-502
Author(s):  
Zhong Yun Qiao ◽  
Fu Zhou Zhao
Keyword(s):  
Hybrid System ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hydraulic System ◽  
Large Scale ◽  
Energy Savings ◽  
Operating Mode ◽  
Mode Switching ◽  
Energy Control ◽  
Simulation Results

Traditional energy saving methods for engineering vehicle cannot raise the effect on a large scale if there are no major technology breakthrough. Hybrid system has the potential of improving fuel economy by operating the engine in an optimum efficiency range and it has been successfully applied in engineering vehicles. So equipping engineering vehicle with the hybrid system provides a new way to achieve energy savings. Simulation results of vehicles based on backward modelling shows that the energy control strategy can achieve a variety of reasonable operating mode switching and meet the vehicle at power.

Real-Time Optimal Power Management for Hybrid Electric Vehicle Based on Prediction of Trip Information

2013 ◽  
Vol 321-324 ◽  
pp. 1539-1547 ◽  
Author(s):  
Li Cun Fang ◽  
Gang Xu ◽  
Tian Li Li ◽  
Ke Min Zhu
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Power Management ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Moving Window ◽  
Optimal Control Strategy ◽  
Time Optimal

Power management of hybrid electric vehicle (HEV) is an important operational factor for HEV to enhance fuel economy and reduce emissions. Optimal control for HEV requires the knowledge of entire driving cycle and elevation profile to obtain the optimal control strategy over fixed driving cycle. In this paper, the traffic knowledge extracted from intelligent transportation systems (ITSs),global positioning systems (GPSs) and geographical information systems (GISs) is used for predicting the knowledge of the future driving cycle, and the real-time optimal control strategy based on dynamic programming in a moving window is investigated in order to minimize fuel consumption. A simulation study was conducted for two driving cycles, and the results showed significant improvement in fuel economy compared with a rule-based control. Furthermore, the results showed that the distance of the moving window has obvious effect on the fuel economy.

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