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.

Characterizing Optimal Control Strategy Parameters for Improving Fuel Economy of Hybrid Electric Vehicles in Velocity Planning

2012 ◽  
Author(s):  
Jinling Wang ◽  
Wen F. Lu
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Optimal Control Strategy ◽  
Velocity Planning ◽  
Optimal Value ◽  
Polluting Emissions ◽  
Strategy Parameters

Modern traffic prediction technologies enable real-time velocity planning of vehicles for less fuel consumption and polluting emissions by reducing the frequency of acceleration/deceleration, idle time, the number of stop, and variation of vehicle speeds. The fuel economy could be further improved if the optimal control strategy parameter could be used in the real-time velocity planning. However, it is difficult to find the optimal value of the control strategy parameter in this real-time velocity planning of vehicles. This paper aims to develop an advising system for control strategy parameters of HEVs in velocity planning. With this aim, the characteristics of the optimal control strategy parameters for various velocity profiles obtained from predictive velocity planning are studied in a parallel HEV. The optimal control strategy parameters with the effect of the average speed, stop frequency, and the traveling distance are investigated. The observed characteristics of the optimal parameters are obtained and can be used in the advising system to improve fuel economy in real-time velocity planning of HEVs.

Real-Time Implementation of Optimal Energy Management in Hybrid Electric Vehicles: Globally Optimal Control of Acceleration Events

2020 ◽  
Vol 142 (8) ◽  
Author(s):  
Zachary D. Asher ◽  
David A. Trinko ◽  
Joshua D. Payne ◽  
Benjamin M. Geller ◽  
Thomas H. Bradley
Keyword(s):  
Optimal Control ◽  
Real Time ◽  
Fuel Economy ◽  
Control Strategy ◽  
Limited Information ◽  
Optimal Control Strategy ◽  
Time Optimization ◽  
Vehicle Operation ◽  
Operation Knowledge ◽  
Real Time Optimization

Abstract Widely published research shows that significant fuel economy improvements through optimal control of a vehicle powertrain are possible if the future vehicle velocity is known and real-time optimization calculations can be performed. In this research, however, we seek to advance the field of optimal powertrain control by limiting future vehicle operation knowledge and using no real-time optimization calculations. We have realized optimal control of acceleration events (AEs) in real-time by studying optimal control trends across 384 real world drive cycles and deriving an optimal control strategy for specific acceleration event categories using dynamic programming (DP). This optimal control strategy is then applied to all other acceleration events in its category, as well as separate standard and custom drive cycles using a look-up table. Fuel economy improvements of 2% average for acceleration events and 3.9% for an independent drive cycle were observed when compared to our rigorously validated 2010 Toyota Prius model. Our conclusion is that optimal control can be implemented in real-time using standard vehicle controllers assuming extremely limited information about future vehicle operation is known such as an approximate starting and ending velocity for an acceleration event.

scholarly journals Optimal Energy Control Strategy Design for a Hybrid Electric Vehicle

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yuan Zou ◽  
Hou Shi-jie ◽  
Li Dong-ge ◽  
Gao Wei ◽  
Xiao-song Hu
Keyword(s):  
Optimal Control ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Heavy Duty ◽  
Dynamic Features ◽  
Optimal Control Strategy ◽  
Energy Control ◽  
Optimal Energy ◽  
Parallel Hybrid ◽  
Hybrid Electric

A heavy-duty parallel hybrid electric truck is modeled, and its optimal energy control is studied in this paper. The fundamental architecture of the parallel hybrid electric truck is modeled feed-forwardly, together with necessary dynamic features of subsystem or components. Dynamic programming (DP) technique is adopted to find the optimal control strategy including the gear-shifting sequence and the power split between the engine and the motor subject to a battery SOC-sustaining constraint. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement on the fuel economy can be achieved in the heavy-duty vehicle cycle from the natural driving statistics.

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