This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Simulation Based Hybrid Electric Vehicle Components Sizing and Fuel Economy Prediction by Using Design of Experiments and Stochastic Process Model
Technical Paper
2019-01-0357
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
The aim of this study is to evaluate the Fuel Economy (FE) over the driving cycle for a 48 Volt P2 technology vehicle with different component ratings (battery and electric machine) in the hybrid powertrain, using simulation and Design of Experiments (DoE) tools. The P2 architecture was selected for this study based on an initial assessment of a wide number of possibilities, using the Ricardo “Architecture Independent Modelling (AIM)” toolset. This allows rapid evaluation of different powertrain options independently of a defined hybrid control strategy. For the vehicle with P2 architecture, a DoE test matrix of battery capacity and electric machine power rating was created. The test matrix was then imported into the simulation environment to perform the driving cycle FE simulations. Then, a 48 V P2 Hybrid Electric Vehicle (HEV) FE emulator model was created and interrogated using model visualisation and optimisation methods. For the HEV without an on-board charger (i.e. no Plug-in capability), legislation strictly requires the HEV to complete the driving cycle with a balanced battery State-Of-Charge (SOC) when doing the FE test. Therefore, the paper also compares two methods, optimisation and DoE, for calibrating the HEV control strategy to achieve charge neutrality, and discusses the pros and cons of these methods.
Recommended Content
Authors
Citation
Bao, R., Baxter, J., and Revereault, P., "Simulation Based Hybrid Electric Vehicle Components Sizing and Fuel Economy Prediction by Using Design of Experiments and Stochastic Process Model," SAE Technical Paper 2019-01-0357, 2019, https://doi.org/10.4271/2019-01-0357.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 | ||
Unnamed Dataset 3 | ||
Unnamed Dataset 4 |
Also In
References
- Ehsani , M. , Gao , Y. , and Emadi , A. Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory, and Design Second Boca Raton CRC Press 2009 1420054007
- Tate , E.D. , Harpster , M.O. , and Savagian , P.J. The Electrification of the Automobile: From Conventional Hybrid, to Plug-In Hybrids, to Extended-Range Electric Vehicles SAE Int. J. Passeng. Cars - Electron. Electr. Syst. 1 1 156 166 2008 10.4271/2008-01-0458
- Bao , R. , Avila , V. , and Baxter , J. Effect of 48 V Mild Hybrid System Layout on Powertrain System Efficiency and Its Potential of Fuel Economy Improvement SAE Technical Paper 2017-01-1175 2017 https://doi.org/10.4271/2017-01-1175
- Fisher , R.A. The Design of Experiments Edinburgh Oliver and Boyd 1935
- Yates , F. Complex Experiments Supplement to Journal Royal Statistical Society 2 181 247 1935
- Box , G.E.P. and Wilson , K.B. On the Experimental Attainment of Optimum Conditions Journal of the Royal Statistical Society. Series B (Methodological) 13 1 1 45 1951
- Grove , D.M. and Davis , T.P. Engineering, Quality, and Experimental Design UK Longman Scientific & Technical 1992
- Taguchi , G. System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Costs 1 New York UNIPUB/Kraus International Publications 1987
- Sacks , J. , Welch , W.J. , Mitchell , T.J. , and Wynn , H.P. Design and Analysis of Computer Experiments Statist. Sci 4 4 409 423 1989
- Seabrook J. , Revereault P. , Preston M. , Grahn M. , et al. Application of Emulator Models in Hybrid Vehicle Development Automotive Data Analytics, Methods, Design of Experiments (DoE) Berlin 2017
- Mohd Azmin , F. , Mortimer , P. , and Seabrook , J. Accurate Cycle Predictions and Calibration Optimization Using a Two-Stage Global Dynamic Model SAE Int. J. Commer. Veh 10 1 2017 10.4271/2017-01-0583
- https://www.unece.org/fileadmin/DAM/trans/main/wp29/wp29regs/r101r2e.pdf
- http://www.gb688.cn/bzgk/gb/newGbInfo?hcno=DAC4255B8828F157BB997A3FB90C0327
- Bao , R. and Stobart , R. Design and Optimisation of the Propulsion Control Strategy for a Pneumatic Hybrid City Bus SAE Int. J. Alt. Power 5 1 2016