A Control Strategy for Driver Specific Driver Assistant System to Improve Fuel Economy of Connected Vehicles in Urban Roads

2019 ◽  
Author(s):  
Baisravan HomChaudhuri ◽  
Pierluigi Pisu
Keyword(s):  
Fuel Economy ◽  
Control Strategy ◽  
Connected Vehicles ◽  
Improve Fuel Economy

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.

OPTIMIZATION OF ICE START-STOP BASED ON MPC FOR AN HEV TO IMPROVE FUEL ECONOMY

2017 ◽  
Vol 41 (3) ◽  
pp. 355-374 ◽  
Author(s):  
Aiyun Gao ◽  
Xiaozhong Deng ◽  
Zhumu Fu ◽  
Mingzhu Zhang
Keyword(s):  
Fuel Economy ◽  
Improve Fuel Economy

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.

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