scholarly journals Simulation Research on Fuel Consumption Reduction Strategy of 48V Micro Hybrid Electric Vehicles

2019 ◽  
Author(s):  
Huihui Zo ◽  
Jinrui Nan ◽  
Fangxiang Peng
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Simulation Research ◽  
Reduction Strategy ◽  
Fuel Consumption Reduction ◽  
Consumption Reduction ◽  
Hybrid Electric

Fuel consumption reduction by introducing best-mode controller for hybrid electric vehicles

2019 ◽  
Vol 234 (2-3) ◽  
pp. 810-822
Author(s):  
Behrooz Mashadi ◽  
Mahdi Khadem Nahvi
Keyword(s):  
Dynamic Programming ◽  
Fuel Consumption ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Optimization Strategy ◽  
Consumption Reduction ◽  
Wide Range ◽  
Battery State Of Charge ◽  
Hybrid Electric

In this paper, a power management system for hybrid electric vehicles is developed and shown to improve the vehicle fuel consumption in various working conditions. A best-mode concept is defined based on the results of the dynamic programming global optimization strategy. It is shown that the use of exclusive control relations for each working mode improves fuel saving. The working state of the engine and one electric motor is used to determine the best-mode of powertrain operation. The best-mode classification also considers the battery state of charge. This enables the controller to specify near optimal working points in a wide range of state of charge. The control rule for each different work-mode is developed based on the dynamic programming results and applying the particle swarm optimization algorithm. The results show that the best-mode controller is capable of achieving fuel consumptions around 97% of that of the offline dynamic programming.

scholarly journals Real-Time Control Strategy for CVT-Based Hybrid Electric Vehicles Considering Drivability Constraints

2019 ◽  
Vol 9 (10) ◽  
pp. 2074 ◽  
Author(s):  
Hangyang Li ◽  
Yunshan Zhou ◽  
Huanjian Xiong ◽  
Bing Fu ◽  
Zhiliang Huang
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Continuously Variable Transmission ◽  
Speed Ratio ◽  
Driving Pattern ◽  
Variable Transmission ◽  
Equivalent Factor ◽  
Hybrid Electric

The energy management strategy has a great influence on the fuel economy of hybrid electric vehicles, and the equivalent consumption minimization strategy (ECMS) has proved to be a useful tool for the real-time optimal control of Hybrid Electric Vehicles (HEVs). However, the adaptation of the equivalent factor poses a major challenge in order to obtain optimal fuel consumption as well as robustness to varying driving cycles. In this paper, an adaptive-ECMS based on driving pattern recognition (DPR) is established for hybrid electric vehicles with continuously variable transmission. The learning vector quantization (LVQ) neural network model was adopted for the on-line DPR algorithm. The influence of the battery state of charge (SOC) on the optimal equivalent factor was studied under different driving patterns. On this basis, a method of adaptation of the equivalent factor was proposed by considering the type of driving pattern and the battery SOC. Besides that, in order to enhance drivability, penalty terms were introduced to constrain frequent engine on/off events and large variations of the continuously variable transmission (CVT) speed ratio. Simulation results showed that the proposed method efficiently improved the equivalent fuel consumption with charge-sustaining operations and also took into account driving comfort.

Research on Modeling and Control Strategy of Parallel Hybrid Electric Back-Loading Compression Sanitation Vehicle

2013 ◽  
Vol 420 ◽  
pp. 355-362
Author(s):  
Rong Yang ◽  
Di Ming Lou ◽  
Pi Qiang Tan ◽  
Zhi Yuan Hu
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Stable Operation ◽  
Parallel Hybrid Electric Vehicle ◽  
Fuel Consumption Reduction ◽  
Consumption Reduction ◽  
Parallel Hybrid ◽  
Hybrid Electric

Establish simulation models of the conventional and parallel hybrid electric back-loading compression sanitation vehicle by AVL CRUISE and MATLAB/Simulink software. Study on control strategy of parallel hybrid electric vehicle based on the work characteristics of back-loading compression sanitation. Results show that: about 24.5% fuel consumption reduction in hybrid modeling compared to the conventional sanitation vehicle under heavy commercial vehicle standard test cycle (C-WTVC, Adapted World Transient Vehicle Cycle), and battery SOC was little changed at 50%. About 32% fuel consumption reduction in hybrid compared to the conventional vehicle under the actual road testing spectrum, and SOC increased about 21.6% relative to the initial state. It controls the engine to work in more stable operation region and reduces engine idle time, but increases engine start-stop times. It also could provide some references for specific engine development of parallel hybrid electric vehicle

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