An Investigation on the Effect of Drivetrain Hybridization in a Transit Bus, Series Configuration

2006 ◽  
Author(s):  
Ahmad Khanipour ◽  
Mohsen Esfahanian ◽  
Farhad Sangtarash ◽  
Meisam Amiri
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Control Strategies ◽  
Drive Cycle ◽  
Conventional Vehicle ◽  
Vehicle Engine ◽  
Electric Bus ◽  
Series Configuration ◽  
Hybrid Electric

To achieve higher fuel economy and lower emissions hybridization of conventional vehicles seems to be an effective solution and an important step. In this paper, after a short introduction about the hybrid electric vehicles a brief design of series electric vehicles is introduced. Then one of the Iran-Khodro city buses named O457 is chosen to change to a series hybrid electric bus. After choosing the proper hybrid components the bus performance is investigated to assure whether it can satisfy the required performance or not. Then the conventional O457 and its series configurations for two different control strategies are defined and evaluated using the ADvanced VehIcle SimulatOR, ADVISOR. Simulations are carried out in a combined urban drive cycle because of the lack of a real drive cycle for Tehran city. The fuel consumption and the amount of produced emissions are compared together for three mentioned cases. The validity of simulation has been proved by the close conformity between the value of fuel consumption of the conventional vehicle reported by the company to what has been achieved from the simulation. It is observed that compared to the conventional vehicle, a reduction in fuel consumption about 32% in the maximum SOC control strategy and about 27% in the thermostat control strategy are possible to achieve. In addition, simulation results indicate that air pollution caused by vehicle engine can be greatly reduced through hybridization using each of the mentioned control strategies.

Depleting Mode Control Strategies for Plug-in Hybrid Electric Vehicles

2011 ◽  
Vol 130-134 ◽  
pp. 2211-2215
Author(s):  
Bing Zhan Zhang ◽  
Han Zhao ◽  
An Dong Yin
Keyword(s):  
Electric Vehicles ◽  
Fuel Economy ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Control Strategies ◽  
Great Influence ◽  
Torque Control ◽  
Drive Cycle ◽  
Vehicle Simulation ◽  
Hybrid Electric

Control strategy is the most important issue in the Plug-in Hybrid electric vehicles (PHEV) design, which has two modes: charge depleting mode (CD) and charge sustaining mode (CS). The different control strategies in depleting mode will have a great influence on PHEV dynamic performance and fuel economy. The engine optimal torque control strategy was proposed in the paper. The vehicle simulation model in Powertrain Systems Analysis Toolkit (PSAT) was adopted to evaluate the proposed control strategy. The aggressive highway drive cycle Artemis_hwy and a random drive cycle generated by Markov Process were used. The simulation results indicate the proposed control strategy has great improvement in fuel economy.

scholarly journals Powertrain Control for Hybrid-Electric Vehicles Using Supervised Machine Learning

Vehicles ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 267-286 ◽  
Author(s):  
Craig K. D. Harold ◽  
Suraj Prakash ◽  
Theo Hofman
Keyword(s):  
Machine Learning ◽  
Dynamic Programming ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Supervised Machine Learning ◽  
Test Case ◽  
Drive Cycle ◽  
Powertrain Control ◽  
Hybrid Electric

This paper presents a novel framework to enable automatic re-training of the supervisory powertrain control strategy for hybrid electric vehicles using supervised machine learning. The aim of re-training is to customize the control strategy to a user-specific driving behavior without human intervention. The framework is designed to update the control strategy at the end of a driving task. A combination of dynamic programming and supervised machine learning is used to train the control strategy. The trained control strategy denoted as SML is compared to an online-implementable strategy based on the combination of the optimal operation line and Pontryagin’s minimum principle denoted as OOL-PMP, on the basis of fuel consumption. SML consistently performed better than OOL-PMP, evaluated over five standard drive cycles. The EUDC performance was almost identical while on FTP75 the OOL-PMP consumed 14.7% more fuel than SML. Moreover, the deviation from the global benchmark obtained from dynamic programming was between 1.8% and 5.4% for SML and between 5.8% and 16.8% for OOL-PMP. Furthermore, a test-case was conducted to emulate a real-world driving scenario wherein a trained controller is exposed to a new drive cycle. It is found that the performance on the new drive cycle deviates significantly from the optimal policy; however, this performance gap is bridged with a single re-training episode for the respective test-case.

Fuel Efficient Control Strategies for Connected Hybrid Electric Vehicles in Urban Roads

2015 ◽  
Author(s):  
Runing Lin ◽  
Baisravan HomChaudhuri ◽  
Pierluigi Pisu
Keyword(s):  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Control Strategies ◽  
Hierarchical Control ◽  
Control Architecture ◽  
Urban Road ◽  
Hierarchical Control Architecture ◽  
Efficient Control ◽  
Hybrid Electric

This paper presents a fuel efficient control strategy for a group of connected hybrid electric vehicles (HEVs) in urban road conditions. A hierarchical control architecture is proposed in this paper where the higher level controller is considered to be a part of the transportation infrastructure while the lower level controllers are considered to be present in every HEV. The higher level controller uses model predictive control strategy to evaluate the energy efficient velocity profiles for every vehicle for a given horizon. Each lower level controller then tracks its velocity profile (obtained from the higher level controller) in a fuel efficient fashion using equivalent consumption minimization strategy (ECMS). In this paper, the vehicles are modeled in Autonomie software and the simulation results provided in the paper shows the effectiveness of our proposed control architecture.

scholarly journals Intelligent Control of Parallel Hybrid Electric Vehicles

1999 ◽  
Author(s):  
Bradley Glenn ◽  
Gregory Washington ◽  
Giorgio Rizzoni
Keyword(s):  
Fuzzy Logic ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Logic Controller ◽  
Hybrid Electric Vehicles ◽  
Conventional Vehicle ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Reduce Emissions

Abstract Currently Hybrid Electric Vehicles (HEV) are being considered as an alternative to conventional automobiles in order to improve efficiency and reduce emissions. To demonstrate the potential of an advanced control strategy for HEV’s, a fuzzy logic control strategy has been developed and implemented in simulation in the National Renewable Energy Laboratory’s simulator Advisor (version 2.0.2). The Fuzzy Logic Controller (FLC) utilizes the electric motor in a parallel hybrid electric vehicle (HEV) to force the ICE (66KW Volkswagen TDI) to operate at or near its peak point of efficiency or at or near its best fuel economy. Results with advisor show that the vehicle with the Fuzzy Logic Controller can achieve (56) mpg in the city, while maintaining a state of charge of .68 for the battery pack, compared to (43) mpg for a conventional vehicle. This scheme has also brought to light various rules of thumb for the design and operation of HEV’s.

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