This paper proposes a framework to perform design optimization of a series PHEV and investigates the impact of using real-world driving inputs on final design. Real-World driving is characterized from a database of naturalistic driving generated in Field Operational Tests. The procedure utilizes Markov chains to generate synthetic drive cycles representative of real-world driving. Subsequently, PHEV optimization is performed in two steps. First the optimal battery and motor sizes to most efficiently achieve a desired All Electric Range (AER) are determined. A synthetic cycle representative of driving over a given range is used for function evaluations. Then, the optimal engine size is obtained by considering fuel economy in the charge sustaining (CS) mode. The higher power/energy demands of real-world cycles lead to PHEV designs with substantially larger batteries and engines than those developed using repetitions of the federal urban cycle (UDDS). This is a finding of high relevance for forecasting technology diffusion, consumer acceptance, and impact of PHEVs on power grid. These differences increase progressively with desired AER due to increasing energy/mile usage of real world driving with distance.
Design Optimization of a Series Plug-in Hybrid Electric Vehicle for Real-World Driving Conditions
Sae Int. J. Engines
SAE 2010 World Congress & Exhibition ; 2010
Sae International Journal of Engines ; 3 , 1 ; 655-665
2010-04-12
11 pages
Aufsatz (Konferenz)
Englisch
British Library Conference Proceedings | 2010
|Energy Management Optimization for Plug-In Hybrid Electric Vehicles Based on Real-World Driving Data
British Library Conference Proceedings | 2019
|Energy Management Optimization for Plug-In Hybrid Electric Vehicles Based on Real-World Driving Data
SAE Technical Papers | 2019
|Design Optimization of a Plug-In Hybrid Electric Vehicle
British Library Conference Proceedings | 2007
|