Optimization based method to develop representative driving cycle for real-world fuel consumption estimation

Cui, Yuepeng; Xu, Hao; Zou, Fang; Chen, Zhihui; Gong, Kuangmin

doi:10.1016/j.energy.2021.121434

Cited by 19 publications

(11 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fuel is not injected during the fuel-cut period activated by releasing the gas pedal with gear in the drive position for better fuel efficiency. The FC for idle is about 2-3 mcc for usual passenger cars [25]. Therefore, the FC is zero for fuel-cut then it steps up to idle FC value.…”

Section: D/l Resultsmentioning

confidence: 98%

A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

Lee

Jeong³

2021

Sustainability

View full text Add to dashboard Cite

A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.

show abstract

Section: D/l Resultsmentioning

confidence: 98%

A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

Lee

Jeong³

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…Governments use standard driving cycles (SDCs) to evaluate and regulate consumption and emission levels ( 1 , 2 ). In the U.S., several driving cycles have been employed.…”

mentioning

confidence: 99%

“…Another approach to mitigate this problem is the development of a specific driving cycle for the traffic characteristics of a given location, which is called a “local driving cycle” (LDC) ( 12 ). Recent studies with driving cycles demonstrate that there is an average difference in consumption and emissions levels between SDC and LDC in cities around the world, from values below 5% up to values above 40% ( 1 , 3 , 13–15 )…”

mentioning

confidence: 99%

“…Another approach to mitigate this problem is the development of a specific driving cycle for the traffic characteristics of a given location, which is called a ''local driving cycle'' (LDC) (12). Recent studies with driving cycles demonstrate that there is an average difference in consumption and emissions levels between SDC and LDC in cities around the world, from values below 5% up to values above 40% (1,3,(13)(14)(15) In the literature, the two prevalent methods for developing driving cycles based on real-world traffic data are the micro-trip and the Markov chain method. In the micro-trip method, the speed-time data is processed to identify individual micro-trips.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of the Minimum Number of Local Driving Cycles Required to Represent the Traffic of Distinct Cities: A Case Study of Two Brazilian Metropolises

Andrade

Santos

Araújo

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

This study aimed to determine whether a single local driving cycle (LDC) can effectively represent different cities in the same country, in both urban and highway routes, and for cars and motorcycles. To achieve this, experienced drivers drove different monitored vehicles (five cars and three motorcycles) on seven selected routes in two Brazilian states (Pernambuco and São Paulo State), collecting 170 h of speed data in urban and highway routes during peak and off-peak hours. Using the micro-trip and Markov chain methods, LDCs were then developed based on the collected real-world data. The kinematic and energy parameters of different route groupings were compared, revealing that two LDCs, one for cars and one for motorcycles, could be used to represent all urban routes. However, each highway route required a unique LDC. When compared with standard driving cycles adopted in Brazil, the created LDCs presented a coefficient of variation of 13%–46% in kinematic characteristic parameters, highlighting the need for developing LDCs to better represent Brazilian traffic.

show abstract

“…With the above starting hypotheses, values are given to the velocity and acceleration functions (Figure 2), both dependent on the variable defined as independent: time. This time-velocity relationship is the one established for different driving cycles widely used for road consumption studies [42][43][44].…”

mentioning

confidence: 99%

Energy Consumption of the Urban Transport Fleet in UNESCO World Heritage Sites: A Case Study of Ávila (Spain)

et al. 2022

View full text Add to dashboard Cite

Emission reduction and energy efficiency are fundamental objectives for the sustainability of the urban transport (UT) sector. One of the actions to achieve these objectives is to replace the vehicles that make up the fleet of UT buses with more efficient ones, equipped with regenerative braking systems that allow the recovery of part of the energy used in travel. However, sometimes the total replacement of the fleet of UT buses is not feasible and only a partial replacement of the fleet is possible. The present study proposes a mathematical model of easy application to compare different UT routes and to locate the greatest improvement niches. The contributions of the proposed model focus on several aspects: (i) optimizing economic resources; (ii) allocating the most efficient equipment where energy consumption can be most optimized; and (iii) simplifying the task of optimizing passenger transport routes. Thanks to the proposed model, the 6 UT lines of the city of Ávila can be classified in order to maximize efficiency in a possible partial renewal of the fleet.

show abstract

Optimization based method to develop representative driving cycle for real-world fuel consumption estimation

Cited by 19 publications

References 34 publications

A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

Evaluation of the Minimum Number of Local Driving Cycles Required to Represent the Traffic of Distinct Cities: A Case Study of Two Brazilian Metropolises

Energy Consumption of the Urban Transport Fleet in UNESCO World Heritage Sites: A Case Study of Ávila (Spain)

Contact Info

Product

Resources

About