Main characteristic parameters to describe driving patterns and construct driving cycles

Quirama, Luis F.; Giraldo, Michael; Huertas, José Ignacio; Tibaquirá, Juan Esteban; Cordero-Moreno, Daniel

doi:10.1016/j.trd.2021.102959

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…For this method, the real cycle is divided into so-called microtrips, which start and end in vehicle stops. Then, the synthetic cycles are built by selecting microtrips from the generated database (Quirama et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Estimation of electric vehicle battery capacity requirements based on synthetic cycles

Etxandi-Santolaya

Casals

Corchero

2023

Transportation Research Part D: Transport and Environment

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Section: Introductionmentioning

confidence: 99%

Estimation of electric vehicle battery capacity requirements based on synthetic cycles

Etxandi-Santolaya

Casals

Corchero

2023

Transportation Research Part D: Transport and Environment

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“…Next, the collected data were used to determine the driving pattern of the drivers that operate in the routes of CircuitoTec using diesel-fueled HDVs. In this work, we described their driving pattern using 14 characteristic parameters (CPs), which, as stated by several authors [38,39], implicitly describe the driving patterns that drivers follow in a region of interest. CPs are metrics based on any derivation of speed and time data, such as average speed, average positive acceleration, and idling time.…”

Section: Phase 1: Current Operation Of the Case Studymentioning

confidence: 99%

Configuration of Electric Vehicles for Specific Applications from a Holistic Perspective

Huertas

Mogro

Jiménez

2022

WEVJ

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Electrification of heavy-duty vehicles (HDVs) used for passengers and goods transportation is a key strategy to reduce the high levels of air pollution in large urban centers. However, the high investment cost of the commercially available electrified HDVs has limited their adoption. We hypothesized that there are applications where the operation with tailored electrified HDVs results in a lower total cost of ownership and lower well-to-wheel emissions of air pollutants, with higher acceleration capacity and energy efficiency than the fossil-fueled counterparts. The road transportation services running on fixed routes with short span distances (<50 km), such as the last mile cargo distribution and the passenger shuttle services, is a clear example with a high possibility of cost reduction through tailored electric HDVs. In this work, we present a methodology to define the most appropriate configuration of the powertrain of an electric vehicle for any given application. As a case study, this work aimed to define an electric powertrain configuration tailored for a university shuttle service application. A multi-objective weighted-sum optimization was performed to define the best geometrical gearbox ratios, energy management strategy, size of the motor, and batteries required. Based on three different driving profiles and five battery technologies, the results showed that, based on a 50 km autonomy, the obtained powertrain configuration satisfies the current vehicle operation with a reduced cost in every driving profile and battery technology compared. Furthermore, by using lithium-based batteries, the vehicle’s acceleration capacity is improved by 33% while reducing energy consumption by 37%, CO2 emissions by 31%, and the total cost of ownership by 29% when compared to the current diesel-fueled buses.

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“…Within this method, only 3 CP i are considered, and they are referred to as assessment criteria. In this work, we used as assessment criteria average speed, percentage of idling time, and specific fuel consumption (SFC), following up the results reported in Quirama et al [21], who identified the main characteristic parameters to construct DC. They also found that by including SFC in the assessment criteria, the resulting DC reproduce other CP i and emissions.…”

Section: Construction Of Representative Dcsmentioning

confidence: 99%

The Effect of Driving Cycle Duration on Its Representativeness

Giraldo

Quirama

Huertas

et al. 2021

WEVJ

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There is an increasing interest in properly representing local driving patterns. The most frequent alternative to describe driving patterns is through a representative time series of speed, denominated driving cycle (DC). However, the DC duration is an important factor in achieving DC representativeness. Long DCs involve high testing costs, while short DCs tend to increase the uncertainty of the fuel consumption and tailpipe emissions results. There is not a defined methodology to establish the DC duration. This study aims to study the effect of different durations of the DCs on their representativeness. We used data of speed, time, fuel consumption, and emissions obtained by monitoring for two months the regular operation of a fleet of 15 buses running in two flat urban regions with different traffic conditions. Using the micro-trips method, we constructed DCs with a duration of 5, 10, 15, 20, 25, 30, 45, 60, and 120 min for each region. For each duration, we repeated the process 500 times in order to establish the trend and dispersion of the DC characteristic parameters. The results indicate that to obtain driving pattern representativeness, the DCs must last at least 25 min. This duration also guarantees the DC representativeness in terms of energy consumption and tailpipe emissions.

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Main characteristic parameters to describe driving patterns and construct driving cycles

Cited by 11 publications

References 23 publications

Estimation of electric vehicle battery capacity requirements based on synthetic cycles

Estimation of electric vehicle battery capacity requirements based on synthetic cycles

Configuration of Electric Vehicles for Specific Applications from a Holistic Perspective

The Effect of Driving Cycle Duration on Its Representativeness

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