scholarly journals A Novel Torque Distribution Strategy Based on Deep Recurrent Neural Network for Parallel Hybrid Electric Vehicle

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 65174-65185 ◽  
Author(s):  
Huifang Kong ◽  
Yao Fang ◽  
Lei Fan ◽  
Hai Wang ◽  
Xiaoxue Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Electric Vehicle ◽  
Recurrent Neural Network ◽  
Hybrid Electric Vehicle ◽  
Torque Distribution ◽  
Distribution Strategy ◽  
Parallel Hybrid Electric Vehicle ◽  
Deep Recurrent Neural Network ◽  
Parallel Hybrid ◽  
Hybrid Electric

Optimization of Economy Shift Schedule for Automated Mechanical Transmission in a Parallel Hybrid Electric Vehicle

2012 ◽  
Vol 260-261 ◽  
pp. 331-336
Author(s):  
Zhen Tong Liu ◽  
Hong Wen He ◽  
Wei Qing Li
Keyword(s):  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Mechanical Transmission ◽  
Torque Distribution ◽  
Power Train ◽  
Distribution Strategy ◽  
Parallel Hybrid Electric Vehicle ◽  
Shift Schedule ◽  
Parallel Hybrid ◽  
Hybrid Electric

Power train of hybrid electric vehicle (HEV) equipped with automated mechanical transmission (AMT) is made up of engine, electric motor, batteries and propulsion system. Shift schedule can’t be worked out with the same way of conventional AMT vehicle. Based on the optimal torque distribution strategy and analysis of the driving efficiency for parallel hybrid electric vehicle (PHEV), a new economy shift schedule for PHEVs equipped with AMT is proposed to maximize the driving efficiency. The MATLAB/CRUISE co-simulation results show that the proposed shift schedule can more efficiently improve the fuel economy performance.

Fuzzy Torque Distribution Control for a Parallel Hybrid Electric Vehicle

2001 ◽  
Author(s):  
Jong-Seob Won ◽  
Reza Langari
Keyword(s):  
Electric Vehicle ◽  
Energy Management ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Torque Distribution ◽  
Parallel Hybrid Electric Vehicle ◽  
Cycle Simulation ◽  
Mode Of Operation ◽  
Parallel Hybrid ◽  
Hybrid Electric

Abstract A fuzzy torque distribution controller for energy management (and emission control) of a parallel-hybrid electric vehicle is proposed. The proposed controller is implemented in terms of a hierarchical architecture which incorporates the mode of operation of the vehicle as well as empirical knowledge of energy flow in each mode. Moreover, the rule set for each mode of operation of the vehicle is designed in view of an overall energy management strategy that ranges from maximal emphasis on battery charge sustenance to complete reliance on the electrical power source. The proposed control system is evaluated via computational simulations under the FTP75 urban drive cycle. Simulation results reveal that the proposed fuzzy torque distribution strategy is effective over the entire operating range of the vehicle in terms of performance, fuel economy as well as emissions.

scholarly journals Design of artificial neural network based power split controller for hybrid electric vehicle

2018 ◽  
Vol 1 (1) ◽  
pp. 72-80
Author(s):  
Aqsa Kk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dynamic Programming ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Design Flow ◽  
Parallel Hybrid Electric Vehicle ◽  
Parallel Hybrid ◽  
Artificial Neural ◽  
Hybrid Electric

 In this paper the work represent the design flow of artificial neural network (ANN) for the parallel hybrid electric vehicle using the dynamic programming strategy, for the better fuel economy and power for the real time driving condition. In this paper the artificial neural network for the parallel hybrid electric vehicle is first trained from the input/output data generated by a dynamic programming. The power spilt between electric motor (EM) and  internal combustion engine (ICE) an is prescribe by using this artificial neural network controller. One input layer is used and one output layer is used with 2 hidden layers. For the training of the data the numpy-library is used and matlab-simulink is used for the implementation. The trained data is used. The data is tasted on three driving cycle named NEDC, US06 and FTP-75 for both the thermal and hybrid vehicles.

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