Longitudinal Dynamics Simulation Tool for Hybrid APU and Full Electric Vehicle

Due to problems related to environmental pollution and fossil fuels consumption that have not infinite availability, the automotive sector is increasingly moving towards electric powertrains. The most limiting aspect of this category of vehicles is certainly the battery pack, regarding the difficulty in obtaining high range with good performance and low weights. The aim of this work is to provide a simulation tool, which allows for the analysis of the performance of different types of electric and hybrid powertrains, concerning both mechanical and electrical aspects. Through this model it is possible to test different vehicle configurations before prototype realization or to investigate the impact that subsystems’ modifications may have on a vehicle under development. This will allow to speed-up the model-based design process typical for fully electric and hybrid vehicles. The model aims to be at the same time complete but simple enough to lower the simulation time and computational burden so that it can be used in real-time applications, such as driving simulators. All this reduces the time and costs of vehicle design. Validation is also provided, based on a real vehicle and comparison with another consolidated simulation tool. Maximum error on mechanical quantities is proved to be within 5% while on electrical quantities it is always lower than 10%.

[1]  Kevin Knowles,et al.  A neurofuzzy-controlled power management strategy for a series hybrid electric vehicle , 2014 .

[2]  Wei Zhou,et al.  Predictive energy management for a plug-in hybrid electric vehicle using driving profile segmentation and energy-based analytical SoC planning , 2021 .

[3]  Lei Wang,et al.  Hardware-in-the-loop simulation for the design and verification of the control system of a series-parallel hybrid electric city-bus , 2012, Simul. Model. Pract. Theory.

[4]  L.-A. Dessaint,et al.  A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles , 2007, 2007 IEEE Vehicle Power and Propulsion Conference.

[5]  K. B. Wipke,et al.  ADVISOR 2.1: a user-friendly advanced powertrain simulation using a combined backward/forward approach , 1999 .

[6]  Marco Gadola,et al.  Simulation tool for optimization and performance prediction of a generic hybrid electric series powertrain , 2014 .

[7]  Rochdi Trigui,et al.  HYBRID LIGHT DUTY VEHICLES EVALUATION PROGRAM , 2003 .

[8]  M. Gadola,et al.  Experiential learning in engineering education: The role of student design competitions and a case study , 2019 .

[9]  Marco Gadola,et al.  Integrated Design Tools for Model-Based Development of Innovative Vehicle Chassis and Powertrain Systems , 2019, DSMIE-2019.

[10]  Marco Gadola,et al.  What is the most representative Standard Driving Cycle to estimate Diesel emissions of a Light Commercial Vehicle , 2018 .

[11]  A. Flammini,et al.  Smartphone-based system for the monitoring of vital parameters and stress conditions of amatorial racecar drivers , 2015, 2015 IEEE SENSORS.

[12]  Fernando A. Silva Modern Electric, Hybrid Electric, and Fuel Cell Vehicles, Third Edition [Book News] , 2018, IEEE Industrial Electronics Magazine.

[13]  Tirunagaru V. Sarathkumar,et al.  Modelling and Simulation of Electric Vehicle Drive Through SAEJ227 & EUDC Cycles , 2020, 2020 IEEE Students Conference on Engineering & Systems (SCES).

[14]  Emanuele Bonera,et al.  On the Influence of Suspension Geometry on Steering Feedback , 2020 .

[15]  Zeng Xiaohua,et al.  Analysis and Simulation of Conventional Transit Bus Energy Loss and Hybrid Transit Bus Energy Saving , 2005 .

[16]  Valerie H. Johnson,et al.  Battery performance models in ADVISOR , 2002 .