Abstract
Hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) are indispensable tools in reducing greenhouse gas emissions to fight the twin evils of pollution and climate change. In these vehicles, battery replacement and fuel costs are the major recurring costs over a lifetime. Hence, there is a growing attempt to develop strategies that reduce the long-run expenditure in these vehicles without compromising on performance levels. Further, an increase in the fuel economy is also required for the effective penetration of these vehicles in society. Here, the authors attempt to identify the optimal operating values for battery state of charge (SoC), power ratings of motor, and fuel converter to increase the battery life and fuel economy without degrading the vehicle performance. The simulations have been carried out on Ford C-Max Energi (2016) as a representative for PHEVs based on the Urban Dynamometer Driving Schedule (UDDS) and Highway (HWY) driving cycles. The software used for these simulations is the future automotive systems technology simulator (FASTSim), developed by the National Renewable Energy Laboratory (NREL). In this paper, firstly, the effect of important parameters like battery SoC, fuel converter power, and motor power on HEVs’ driving range, battery life, fuel economy, cost, and charge-depleting range has been analyzed. Based on this analysis, the optimal values of the parameters have been estimated. These parameters have resulted in improvements of driving range by 4.3% and battery life by 18% at a minute cost of a 1% decrease in the charge-sustaining battery life and a 0.4-s increase in the time the car takes to hit 60 mph from the rest. This paper presents a simple, effective, and new approach that explores the effect of altering the existing design parameters on vehicle performance, without manipulating, adding, or deleting any component or controller. This can further be extended to study the impact of various other parameters in the proposed work and opens a way to explore other parameters that exist in various other components of XEVs (where X can be H/PH//F). This study will help in achieving optimal cost reduction in these vehicles.
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Abbreviations
- HEV:
-
Hybrid electric vehicle
- EV:
-
Electric vehicle
- PHEV:
-
Plug-in hybrid electric vehicle
- SoC:
-
State of charge
- NREL:
-
National renewable energy laboratory
- FASTSim:
-
The future automotive systems technology simulator
- UDDS:
-
Urban Dynamometer Driving Schedule
- HWY:
-
Highway
- ICE:
-
Internal combustion engine
- FCP:
-
Fuel converter power
- DOE:
-
Depth of discharge
- EPA:
-
Environmental protection agency
- PMP:
-
Pontryagin’s minimum principle
- CHTS:
-
California household travel survey
- ESS:
-
Energy storage system
- mpgge:
-
Miles per gallon gasoline equivalent
- FCV:
-
Fuel cell vehicle
- GHG:
-
Greenhouse gasses
- IPT:
-
Intermediate public transport
- LPG:
-
Autogas/liquified petroleum gasses
- TCO:
-
Total cost of ownership
- BAU:
-
Business as usual
- EOL:
-
End of life
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Commentary/notes
It can be seen from the results analysis that by enhancing the EM power by 15%, the driving range has increased from 21 miles to 34 miles without changing the kWh capacity of the battery. The higher rating of motor would allow longer operation on the electric fuel and thus would lead to increase in driving range. However, similar to many other studies, the present work is also not free from limitation as authors have used an open-source software which may have its own shortcomings in terms of accuracy. Therefore, in order to validate and generalize the outcome of the present study further, future studies may use more sophisticated high-end software and hardware.
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Krishna Veer Singh has designed the content and logic of this paper. Rajat Khandelwal finished the first-hand manuscript. Prof. Hari Om Bansal with Dr. Dheerendra Singh has supervised the work. All authors read and approved the final manuscript.
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Singh, K.V., Khandelwal, R., Bansal, H.O. et al. The efficient operating parameter estimation for a simulated plug-in hybrid electric vehicle. Environ Sci Pollut Res 29, 18126–18141 (2022). https://doi.org/10.1007/s11356-021-16659-4
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DOI: https://doi.org/10.1007/s11356-021-16659-4