hardware in the loop
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Elyas Zamiri ◽  
Alberto Sanchez ◽  
María Sofía Martínez-García ◽  
Angel de Castro

2022 ◽  
Vol 166 ◽  
pp. 108455
Pengfei Liu ◽  
Minyi Zheng ◽  
Donghong Ning ◽  
Nong Zhang ◽  
Haiping Du

2022 ◽  
Yasaman Haj Norouz Ali ◽  
Maryam Malekzadeh ◽  
Mohammad Ataei

2022 ◽  
Thanakorn Khamvilai ◽  
Medrdad Pakmehr ◽  
George Lu ◽  
Yaojung Yang ◽  
Eric M. Feron ◽  

2022 ◽  
Ahmed A. Elgohary ◽  
Ahmed M. Ashry ◽  
Ahmed M. Kaoud ◽  
Marwan M. Gomaa ◽  
Mohamed H. Darwish ◽  

2022 ◽  
Vol 51 ◽  
pp. 101476
Semiha Ergan ◽  
Zhengbo Zou ◽  
Suzana Duran Bernardes ◽  
Fan Zuo ◽  
Kaan Ozbay

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 261
Mario Picerno ◽  
Sung-Yong Lee ◽  
Michal Pasternak ◽  
Reddy Siddareddy ◽  
Tim Franken ◽  

The increasing requirements to further reduce pollutant emissions, particularly with regard to the upcoming Euro 7 (EU7) legislation, cause further technical and economic challenges for the development of internal combustion engines. All the emission reduction technologies lead to an increasing complexity not only of the hardware, but also of the control functions to be deployed in engine control units (ECUs). Virtualization has become a necessity in the development process in order to be able to handle the increasing complexity. The virtual development and calibration of ECUs using hardware-in-the-loop (HiL) systems with accurate engine models is an effective method to achieve cost and quality targets. In particular, the selection of the best-practice engine model to fulfil accuracy and time targets is essential to success. In this context, this paper presents a physically- and chemically-based stochastic reactor model (SRM) with tabulated chemistry for the prediction of engine raw emissions for real-time (RT) applications. First, an efficient approach for a time-optimal parametrization of the models in steady-state conditions is developed. The co-simulation of both engine model domains is then established via a functional mock-up interface (FMI) and deployed to a simulation platform. Finally, the proposed RT platform demonstrates its prediction and extrapolation capabilities in transient driving scenarios. A comparative evaluation with engine test dynamometer and vehicle measurement data from worldwide harmonized light vehicles test cycle (WLTC) and real driving emissions (RDE) tests depicts the accuracy of the platform in terms of fuel consumption (within 4% deviation in the WLTC cycle) as well as NOx and soot emissions (both within 20%).

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