Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels
The maintenance costs can represent about 15%–60% of the cost of produced goods depending on the type of goods transported. To comply with stringent emissions regulations, diesel engines are incorporated with complex after-treatment systems that demand increased maintenance. The availability of alte...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-06-01
|
| Series: | Frontiers in Mechanical Engineering |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmech.2023.1201068/full |
| _version_ | 1797804539880931328 |
|---|---|
| author | Sasanka Katreddi Arvind Thiruvengadam Gregory Thompson Natalia Schmid Vishnu Padmanaban |
| author_facet | Sasanka Katreddi Arvind Thiruvengadam Gregory Thompson Natalia Schmid Vishnu Padmanaban |
| author_sort | Sasanka Katreddi |
| collection | DOAJ |
| description | The maintenance costs can represent about 15%–60% of the cost of produced goods depending on the type of goods transported. To comply with stringent emissions regulations, diesel engines are incorporated with complex after-treatment systems that demand increased maintenance. The availability of alternative fuels such as natural gas and propane has fostered the natural gas and propane powertrain systems as well as electrification options for heavy- and medium-duty vehicles. A critical barrier to adopting alternative fuel vehicles has been the lack of knowledge on comparative vehicle maintenance/repair costs with conventional diesel. Moreover, the region of operation, the type of vehicle operation, and seasonal temperature changes also affect the duty cycle which impacts the maintenance and repair costs. This study focuses on estimating the cost-per-mile for heavy-duty vehicles using machine learning models such as random forest, xgboost, neural networks, and a super-learner model. The super-learner model achieved an error as low as 0.0068 $/mile for mean absolute error and 0.0086 $/mile for root mean square error with a coefficient of determination/R-Squared of 97.28%. Specifically, the paper investigates the data collected from the maintenance and repair costs associated with delivery trucks using diesel and natural gas fuels. Since the availability of data is the major constraint, we leveraged the data collected by West Virginia University and the partnership with fleet companies. This allows for additional information related to maintenance costs and fleet-specific maintenance practices of alternative fuel vehicles. This study promotes clean fuel technologies and enables fleet management companies to adopt alternative fuel vehicles in case of similar or lower cost of maintenance compared to diesel vehicles resulting in reduced emissions and total cost of ownership. |
| first_indexed | 2024-03-13T05:37:38Z |
| format | Article |
| id | doaj.art-8b049e40916a4d74b6b7ccd96d586208 |
| institution | Directory Open Access Journal |
| issn | 2297-3079 |
| language | English |
| last_indexed | 2024-03-13T05:37:38Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Mechanical Engineering |
| spelling | doaj.art-8b049e40916a4d74b6b7ccd96d5862082023-06-14T05:24:57ZengFrontiers Media S.A.Frontiers in Mechanical Engineering2297-30792023-06-01910.3389/fmech.2023.12010681201068Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuelsSasanka Katreddi0Arvind Thiruvengadam1Gregory Thompson2Natalia Schmid3Vishnu Padmanaban4Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, United StatesCenter for Alternate Fuels, Emissions and Engines, Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, United StatesCenter for Alternate Fuels, Emissions and Engines, Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, United StatesLane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, United StatesCenter for Alternate Fuels, Emissions and Engines, Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, United StatesThe maintenance costs can represent about 15%–60% of the cost of produced goods depending on the type of goods transported. To comply with stringent emissions regulations, diesel engines are incorporated with complex after-treatment systems that demand increased maintenance. The availability of alternative fuels such as natural gas and propane has fostered the natural gas and propane powertrain systems as well as electrification options for heavy- and medium-duty vehicles. A critical barrier to adopting alternative fuel vehicles has been the lack of knowledge on comparative vehicle maintenance/repair costs with conventional diesel. Moreover, the region of operation, the type of vehicle operation, and seasonal temperature changes also affect the duty cycle which impacts the maintenance and repair costs. This study focuses on estimating the cost-per-mile for heavy-duty vehicles using machine learning models such as random forest, xgboost, neural networks, and a super-learner model. The super-learner model achieved an error as low as 0.0068 $/mile for mean absolute error and 0.0086 $/mile for root mean square error with a coefficient of determination/R-Squared of 97.28%. Specifically, the paper investigates the data collected from the maintenance and repair costs associated with delivery trucks using diesel and natural gas fuels. Since the availability of data is the major constraint, we leveraged the data collected by West Virginia University and the partnership with fleet companies. This allows for additional information related to maintenance costs and fleet-specific maintenance practices of alternative fuel vehicles. This study promotes clean fuel technologies and enables fleet management companies to adopt alternative fuel vehicles in case of similar or lower cost of maintenance compared to diesel vehicles resulting in reduced emissions and total cost of ownership.https://www.frontiersin.org/articles/10.3389/fmech.2023.1201068/fullheavy duty vehicledieseldelivery trucksmaintenance costtotal cost of ownershipalternative fuel vehicle |
| spellingShingle | Sasanka Katreddi Arvind Thiruvengadam Gregory Thompson Natalia Schmid Vishnu Padmanaban Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels Frontiers in Mechanical Engineering heavy duty vehicle diesel delivery trucks maintenance cost total cost of ownership alternative fuel vehicle |
| title | Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels |
| title_full | Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels |
| title_fullStr | Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels |
| title_full_unstemmed | Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels |
| title_short | Machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels |
| title_sort | machine learning models for maintenance cost estimation in delivery trucks using diesel and natural gas fuels |
| topic | heavy duty vehicle diesel delivery trucks maintenance cost total cost of ownership alternative fuel vehicle |
| url | https://www.frontiersin.org/articles/10.3389/fmech.2023.1201068/full |
| work_keys_str_mv | AT sasankakatreddi machinelearningmodelsformaintenancecostestimationindeliverytrucksusingdieselandnaturalgasfuels AT arvindthiruvengadam machinelearningmodelsformaintenancecostestimationindeliverytrucksusingdieselandnaturalgasfuels AT gregorythompson machinelearningmodelsformaintenancecostestimationindeliverytrucksusingdieselandnaturalgasfuels AT nataliaschmid machinelearningmodelsformaintenancecostestimationindeliverytrucksusingdieselandnaturalgasfuels AT vishnupadmanaban machinelearningmodelsformaintenancecostestimationindeliverytrucksusingdieselandnaturalgasfuels |