Dynamic Modeling and Fuel Consumption Potential of an Intercooled Regenerative Reheat Gas Turbine Auxiliary Power Unit on Series Hybrid Electric Vehicle

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Wissam Bou Nader ◽  
Florent Breque ◽  
Youssef Mazloum ◽  
Clément Dumand ◽  
Maroun Nemer
Keyword(s):  
Gas Turbine ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Power Unit ◽  
Hybrid Electric Vehicle ◽  
Energy Management Strategy ◽  
Auxiliary Power ◽  
Start Up ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Electric

Abstract Gas turbine systems are among potential energy converters to substitute the internal combustion engine in future series hybrid electric vehicle. Fuel consumption of these powertrains strongly relies on the energy converter efficiency, the energy management strategy deployed on-board as well as on the transient operation during start-up phase. This paper presents a dynamic modeling and the fuel consumption calculation of an intercooled regenerative reheat gas turbine system used as an auxiliary power unit on a series hybrid electric vehicle. A vehicle model is developed and an optimization method is proposed to optimize the powertrain operation. It consists of using the dynamic programing as an energy management strategy in order to minimize the fuel consumption and the number of switching On/Off of the power unit. Fuel consumption simulations are performed on the worldwide-harmonized light vehicles test cycle while considering the electric and the thermal comfort vehicle energetic needs. Then, a gas turbine dynamic model is developed, where turbomachinery and heat exchanger components are modeled by taking into account their dynamic inertias. The efficiency, the power, and the fuel consumption are calculated during transient operations. Based on the optimization results of switching ON and OFF the system, the fuel consumption dynamic simulation results are considered instead of the dynamic programming results. A constant power start-up strategy and a constant fuel strategy were investigated. Results show an increase in fuel consumption between 2.4% and 3.8% with the first start-up scenario and between 5.7% and 6.4% with the second scenario, compared with static model.

Technological analysis and fuel consumption saving potential of different gas turbine thermodynamic configurations for series hybrid electric vehicles

2019 ◽  
Vol 234 (6) ◽  
pp. 1544-1562
Author(s):  
Wissam Bou Nader ◽  
Yuan Cheng ◽  
Emmanuel Nault ◽  
Alexandre Reine ◽  
Samer Wakim ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Power Unit ◽  
Hybrid Electric Vehicle ◽  
Auxiliary Power Unit ◽  
Auxiliary Power ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Electric ◽  
Technological Analysis

Gas turbine systems are among potential energy converters to substitute the internal combustion engine as auxiliary power unit in future series hybrid electric vehicle powertrains. Fuel consumption of these auxiliary power units in the series hybrid electric vehicle strongly relies on the energy converter efficiency and power-to-weight ratio as well as on the energy management strategy deployed on-board. This paper presents a technological analysis and investigates the potential of fuel consumption savings of a series hybrid electric vehicle using different gas turbine–system thermodynamic configurations. These include a simple gas turbine, a regenerative gas turbine, an intercooler regenerative gas turbine, and an intercooler regenerative reheat gas turbine. An energetic and technological analysis is conducted to identify the systems’ efficiency and power-to-weight ratio for different operating temperatures. A series hybrid electric vehicle model is developed and the different gas turbine–system configurations are integrated as auxiliary power units. A bi-level optimization method is proposed to optimize the powertrain. It consists of coupling the non-dominated sorting genetic algorithm to the dynamic programming to minimize the fuel consumption and the number of switching ON/OFF of the auxiliary power unit, which impacts its durability. Fuel consumption simulations are performed on the worldwide-harmonized light vehicles test cycle while considering the electric and thermal comfort vehicle energetic needs. Results show that the intercooler regenerative reheat gas turbine–auxiliary power unit presents an improved fuel consumption compared with the other investigated gas turbine systems and a good potential for implementation in series hybrid electric vehicles.

Exergo-technological explicit methodology for gas-turbine system optimization of series hybrid electric vehicles

2017 ◽  
Vol 232 (10) ◽  
pp. 1323-1338 ◽  
Author(s):  
Wissam S Bou Nader ◽  
Charbel J Mansour ◽  
Maroun G Nemer ◽  
Olivier M Guezet
Keyword(s):  
Gas Turbine ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Energy Management Strategy ◽  
Auxiliary Power Unit ◽  
Auxiliary Power ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Electric

Significant research efforts have been invested in the automotive industry on hybrid electrified powertrains in order to reduce the dependence of passenger cars on oil. Electrification of powertrains resulted in a wide range of hybrid vehicle architectures. The fuel consumption of these powertrains strongly relies on the energy converter performance, as well as on the energy management strategy deployed on board. This paper investigates the potential of fuel consumption savings of a series hybrid electric vehicle using a gas turbine as an energy converter instead of the conventional internal-combustion engine. An exergo-technological explicit analysis is conducted to identify the best configuration of the gas-turbine system. An intercooled regenerative reheat cycle is prioritized, offering higher efficiency and higher power density than those of other investigated gas-turbine systems. A series hybrid electric vehicle model is developed and powertrain components are sized by considering the vehicle performance criteria. Energy consumption simulations are performed over the Worldwide Harmonized Light Vehicles Test Procedure driving cycle using dynamic programming as the global optimal energy management strategy. A sensitivity analysis is also carried out in order to evaluate the impact of the battery size on the fuel consumption, for self-sustaining and plug-in series hybrid electric vehicle configurations. The results show an improvement in the fuel consumption of 22–25% with the gas turbine as the auxiliary power unit in comparison with that of the internal-combustion engine. Consequently, the studied auxiliary power unit for the gas turbine presents a potential for implementation on series hybrid electric vehicles.

Stirling system optimization for series hybrid electric vehicles

2021 ◽  
pp. 095440702110180
Author(s):  
Charbel R Ghanem ◽  
Elio N Gereige ◽  
Wissam S Bou Nader ◽  
Charbel J Mansour
Keyword(s):  
Fuel Consumption ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Optimization Technique ◽  
System Optimization ◽  
Energy Management Strategy ◽  
Fuel Savings ◽  
Auxiliary Power ◽  
Hybrid Electric ◽  
External Combustion

There have been many studies conducted to replace the conventional internal combustion engine (ICE) with a more efficient engine, due to increasing regulations over vehicles’ emissions. Throughout the years, several external combustion engines were considered as alternatives to these traditional ICEs for their intrinsic benefits, among which are Stirling machines. These were formerly utilized in conventional powertrains; however, they were not implemented in hybrid vehicles. The purpose of this study is to investigate the possibility of implementing a Stirling engine in a series hybrid electric vehicle (SHEV) to substitute the ICE. Exergy analysis was conducted on a mathematical model, which was developed based on a real simple Stirling, to pinpoint the room for improvements. Then, based on this analysis, other configurations were retrieved to reduce exergy losses. Consequently, a Stirling-SHEV was modeled, to be integrated as auxiliary power unit (APU). Hereafter, through an exergo-technological detailed selection, the best configuration was found to be the Regenerative Reheat two stages serial Stirling (RRe-n2-S), offering the best efficiency and power combination. Then, this configuration was compared with the Regenerative Stirling (R-S) and the ICE in terms of fuel consumption, in the developed SHEV on the WLTC. This was performed using an Energy Management Strategy (EMS) consisting of a bi-level optimization technique, combining the Non-dominated Sorting Genetic Algorithm (NSGA) with the Dynamic Programming (DP). This arrangement is used to diminish the fuel consumption, while considering the reduction of the APU’s ON/OFF switching times, avoiding technical issues. Results prioritized the RRe-n2-S presenting 12.1% fuel savings compared to the ICE and 14.1% savings compared to the R-S.

scholarly journals Research on energy management strategy considering battery life for plug-in hybrid electric vehicle

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879776 ◽  
Author(s):  
Jianjun Hu ◽  
Zhihua Hu ◽  
Xiyuan Niu ◽  
Qin Bai
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Energy Management ◽  
Management Strategy ◽  
Hybrid Electric Vehicle ◽  
Battery Life ◽  
Energy Management Strategy ◽  
Driving Cycles ◽  
Comprehensive Test ◽  
Hybrid Electric

To improve the fuel efficiency and battery life-span of plug-in hybrid electric vehicle, the energy management strategy considering battery life decay is proposed. This strategy is optimized by genetic algorithm, aiming to reduce the fuel consumption and battery life decay of plug-in hybrid electric vehicle. Besides, to acquire better drive-cycle adaptability, driving patterns are recognized with probabilistic neural network. The standard driving cycles are divided into urban congestion cycle, highway cycle, and urban suburban cycle; the optimized energy management strategies in three representative driving cycles are established; meanwhile, a comprehensive test driving cycle is constructed to verify the proposed strategies. The results show that adopting the optimized control strategies, fuel consumption, and battery’s life decay drop by 1.9% and 3.2%, respectively. While using the drive-cycle recognition, the features of different driving cycles can be identified, and based on it, the vehicle can choose appropriate control strategy in different driving conditions. In the comprehensive test driving cycle, after recognizing driving cycles, fuel consumption and battery’s life decay drop by 8.6% and 0.3%, respectively.

scholarly journals Multiobjective optimisation of a series hybrid electric vehicle using DIRECT algorithm

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Abdelmoula Rihab ◽  
◽  
Ben Hadj Naourez ◽  
Chaieb Mohamed ◽  
Neji Rafik ◽  
...  
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicle ◽  
Hybrid Electric Vehicle ◽  
Combustion Engine ◽  
Vehicle Performance ◽  
Performance Requirements ◽  
Toxic Emissions ◽  
Series Hybrid Electric Vehicle ◽  
Hybrid Electric ◽  
Fuel Consumption And Emissions

With the economic development, transportation in the city becomes more crowded. Furthermore, fuel consumption is causing a serious problem of pollution in the urban environment. Hybrid electric vehicles are considered as a good solution compared to conventional internal combustion engine vehicles. In order to solve those problems, the components parameters of a series hybrid electric vehicle are selected and tested with the ADvanced VehIcle SimulatOR (ADVISOR) simulation tool, which is a software-based on Matlab_simulink. Then, an optimisation was done to minimise simultaneous fuel consumption and emissions (HC, CO, and NOx) of the vehicle engine. In addition, the driving performance requirements are also examined during the urban dynamometer driving schedule (UDDS) to fix their optimal control parameters. Finally, the results show that those steps help reduce fuel consumption and emissions while guaranteeing vehicle performance. Hence, the series hybrid electric vehicle greatly improves fuel economy and reduces toxic emissions.

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