scholarly journals Cloud Model-Based Energy Management Strategy for Parallel Hybrid Vehicles

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaolan Wu ◽  
Guifang Guo ◽  
Zhifeng Bai
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Combustion Engine ◽  
Cloud Model ◽  
Hybrid Vehicles ◽  
State Of Charge ◽  
Energy Management Strategy ◽  
Control Scheme ◽  
Parallel Hybrid ◽  
Simulation Results

Using the uncertain conversion capacity between the expressions of quantitative and qualitative concept in the cloud model, an energy management strategy based on cloud model is developed for parallel hybrid vehicles (PHVs). By the driver input and the state of charge (SOC) of the energy storage, a set of rules are developed to effectively determine the torque split between the internal combustion engine (ICE) and the electric motor. An analysis of the simulation results is conducted using ADVISOR in order to verify the effectiveness of the proposed control strategy. It is confirmed that the control scheme can be used to improve fuel economy and emission of the hybrid vehicles.

scholarly journals Energy Management Strategy Implementation for Hybrid Electric Vehicles Using Genetic Algorithm Tuned Pontryagin’s Minimum Principle Controller

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Aishwarya Panday ◽  
Hari Om Bansal
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Management Strategy ◽  
Minimum Principle ◽  
Hybrid Vehicles ◽  
State Of Charge ◽  
Energy Management Strategy ◽  
Power Split ◽  
Hybrid Electric ◽  
State Of Charge Estimation

To reduce apace extraction of natural resources, to plummet the toxic emissions, and to increase the fuel economy for road transportation, hybrid vehicles are found to be promising. Hybrid vehicles use batteries and engine to propel the vehicle which minimizes dependence on liquid fuels. Battery is an important component of hybrid vehicles and is mainly characterized by its state of charge level. Here a modified state of charge estimation algorithm is applied, which includes not only coulomb counting but also open circuit voltage, weighting factor, and correction factor to track the run time state of charge efficiently. Further, presence of battery and engine together needs a prevailing power split scheme for their efficient utilization. In this paper, a fuel efficient energy management strategy for power-split hybrid electric vehicle using modified state of charge estimation method is developed. Here, the optimal values of various governing parameters are firstly computed with genetic algorithm and then fed to Pontryagin’s minimum principle to decide the threshold power at which engine is turned on. This process makes the proposed method robust and provides better chance to improve the fuel efficiency. Engine efficient operating region is identified to operate vehicle in efficient regions and reduce fuel consumption.

Energy management strategy to reduce pollutant emissions during the catalyst light-off of parallel hybrid vehicles

2020 ◽  
Vol 266 ◽  
pp. 114866 ◽  
Author(s):  
Alice Guille des Buttes ◽  
Bruno Jeanneret ◽  
Alan Kéromnès ◽  
Luis Le Moyne ◽  
Serge Pélissier
Keyword(s):  
Energy Management ◽  
Management Strategy ◽  
Hybrid Vehicles ◽  
Pollutant Emissions ◽  
Energy Management Strategy ◽  
Parallel Hybrid

Receding Horizon Real-Time Energy Management Strategy for Hybrid Vehicles

2013 ◽  
Author(s):  
Bram de Jager ◽  
Thijs van Keulen
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Vehicles ◽  
State Control ◽  
Energy Management Strategy ◽  
Receding Horizon ◽  
Fuel Use ◽  
Commodity Hardware ◽  
The Future

Indirect optimal control and dynamic programming are combined in a receding horizon controller to obtain an energy management strategy for hybrid vehicles. This combination permits the use of inaccurate predictions of the future, instead of requiring exact knowledge, and allows the use of mixed state-control constraints, like voltage constraints for batteries. The controller can run in real-time on commodity hardware and, using a prediction of the future based on geographic information only, obtains a fuel use within 0.2% of the optimal fuel use computed with the exact speed and power trajectory of the vehicle known in advance. All this for a planned distance of more than 500 [km].

An online energy management strategy for fuel cell hybrid vehicles

2021 ◽  
Author(s):  
Yu Zhang ◽  
Ming Chen ◽  
Shuo Cai ◽  
Shengyan Hou ◽  
Hai Yin ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Energy Management ◽  
Cell Hybrid ◽  
Management Strategy ◽  
Hybrid Vehicles ◽  
Energy Management Strategy ◽  
Fuel Cell Hybrid
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