Simulation Based Hybrid Electric Vehicle Components Sizing and Fuel Economy Prediction by Using Design of Experiments and Stochastic Process Model

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.

• Record URL:
  https://doi.org/10.4271/2019-01-0357
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Bao, Ran
  • Baxter, James
  • Revereault, Pascal
• Conference:
  • WCX SAE World Congress Experience
  • Location: Detroit Michigan, United States
  • Date: 2019-4-9 to 2019-4-11
• Publication Date: 2019-4-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Dynamic models; Electric batteries; Electric vehicles; Fuel conservation; Hybrid vehicles; Power train components; Simulation; Transmissions; Vehicle design
• Subject Areas: Design; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01719587
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2019-01-0357
• Files: TRIS, SAE
• Created Date: Oct 21 2019 9:46AM