Validity Assessment and Calibration Approach for Simulation Models of Energy Efficiency of Light-Duty Vehicles

Hamza, Karim; Chu, Kang-Ching; Favetti, Matthew; Benoliel, Peter; Karanam, Vaishnavi; Laberteaux, Kenneth P.; Tal, Gil

doi:10.4271/2020-01-1441

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…A simplified version of this power demand and expanded weight-dependent powers are shown in (2) - (5). A detailed explanation of each of the terms in (2) can be found in [24] and [25].…”

Section: Ev Energy Consumptionmentioning

confidence: 99%

Impact of additional PV weight on the energy consumption of electric vehicles with onboard PV

Patel¹,

Bittkau²,

Pieters³

et al. 2023

Preprint

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<p>Photovoltaics (PV) in onboard vehicle applications adds weight to an electric vehicle (EV), increasing the overall energy consumption. Although the added PV system weight (1.5– 40 kg) is small compared to the vehicle weight (1500–2200 kg), the power generated by PV (55–700 W) is also very small com- pared to the power needed (up to 80–285 kW) to propel an EV, making the effect of additional PV system weight on energy con- sumption a non-trivial topic to analyze. We present a method to study the impact of vehicle onboard PV weight on the energy balance of EVs for different Vehicle-added PV (VAPV) and vehi- cle-integrated PV (VIPV) configurations with eight different PV technologies, using data from vehicle onboard measurement campaigns and simulations. Simulations are carried out for the driving phase of two electric cars (medium and large passenger cars). Our method calculates the energy consumption attributa- ble to the added PV system weight (0.05–1.4 Wh/km) and PV energy yield (0.12–3.12 Wh/km) for a selection of trips. The re- sults of these simulations are expressed through a newly intro- duced parameter called “onboard PV yield factor”, where posi- tive values indicate a net energy gain and negative values indicate a net energy loss of the onboard PV system. Our results show that the onboard PV yield factor for a VAPV configuration can range between -69.1 and 86.9 %, and for a VIPV configuration, between 77.2 and 89.7 %.</p>

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“…A simplified version of this power demand and expanded weight-dependent powers are shown in (2) - (5). A detailed explanation of each of the terms in (2) can be found in [24] and [25].…”

Section: Ev Energy Consumptionmentioning

confidence: 99%

Impact of additional PV weight on the energy consumption of electric vehicles with onboard PV

Patel¹,

Bittkau²,

Pieters³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…A calibration approach using three tuning parameters for physics-based simulation models (applied to FASTSim) was previously introduced by the authors. For details (including mathematical derivation), readers are referred to [11]. An overview of the three parameters is explained as follows: α T is a scaling factor that adjusts the total traction power at every time instant (calculated by FASTSim to account for acceleration, wind drag, rotational inertia, road slope, and tire rolling resistance).…”

Section: Model Calibrationmentioning

confidence: 99%

“…While detailed physics-based models such as Autonomie are becoming popular for future transportation systems simulation, such as autonomous vehicles [8], data-based approaches also appear to remain popular in recent literature [9,10]. Previous work by the authors in [11] aimed to strike a balance between the attractive features of a computationally light physics-based model (FASTSim) along with the calibration capability of data-based approaches. The previous work by the authors in [11] proposed three tunable parameters that constitute correction terms for passenger and cargo weight, as well as auxiliary and traction power.…”

Section: Introductionmentioning

confidence: 99%

“…Previous work by the authors in [11] aimed to strike a balance between the attractive features of a computationally light physics-based model (FASTSim) along with the calibration capability of data-based approaches. The previous work by the authors in [11] proposed three tunable parameters that constitute correction terms for passenger and cargo weight, as well as auxiliary and traction power. The results of the approach in [11] were shown for three battery-electric vehicle (BEV) models (Leaf, Bolt, Model S) and three plug-in hybrid electric vehicle (PHEV) models (C-Max Energi, Prius Prime, Volt).…”

Section: Introductionmentioning

confidence: 99%

“…The previous work by the authors in [11] proposed three tunable parameters that constitute correction terms for passenger and cargo weight, as well as auxiliary and traction power. The results of the approach in [11] were shown for three battery-electric vehicle (BEV) models (Leaf, Bolt, Model S) and three plug-in hybrid electric vehicle (PHEV) models (C-Max Energi, Prius Prime, Volt).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparisons of Real-World Vehicle Energy Efficiency with Dynamometer-Based Ratings and Simulation Models

Hamza

Chu

Favetti

et al. 2021

WEVJ

Self Cite

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Software tools for fuel economy simulations play an important role during design stages of advanced powertrains. However, calibration of vehicle models versus real-world driving data faces challenges owing to inherent variations in vehicle energy efficiency across different driving conditions and different vehicle owners. This work utilizes datasets of vehicles equipped with OBD/GPS loggers to validate and calibrate FASTSim (software originally developed by NREL) vehicle models. The results show that window-sticker ratings (derived from dynamometer tests) can be reasonably accurate when averaged across many trips by different vehicle owners, but successfully calibrated FASTSim models can have better fidelity. The results in this paper are shown for nine vehicle models, including the following: three battery-electric vehicles (BEVs), four plug-in hybrid electric vehicles (PHEVs), one hybrid electric vehicle (HEV), and one conventional internal combustion engine (CICE) vehicle. The calibrated vehicle models are able to successfully predict the average trip energy intensity within ±3% for an aggregate of trips across multiple vehicle owners, as opposed to within ±10% via window-sticker ratings or baseline FASTSim.

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Impact of Additional PV Weight on the Energy Consumption of Electric Vehicles With Onboard PV

Patel,

Bittkau,

Pieters

et al. 2024

IEEE J. Photovoltaics

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Validity Assessment and Calibration Approach for Simulation Models of Energy Efficiency of Light-Duty Vehicles

Cited by 5 publications

References 11 publications

Impact of additional PV weight on the energy consumption of electric vehicles with onboard PV

Impact of additional PV weight on the energy consumption of electric vehicles with onboard PV

Comparisons of Real-World Vehicle Energy Efficiency with Dynamometer-Based Ratings and Simulation Models

Impact of Additional PV Weight on the Energy Consumption of Electric Vehicles With Onboard PV

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