Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle
In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements suc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/10/3631 |
| _version_ | 1797500194703540224 |
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| author | Zoltán Pusztai Péter Kőrös Ferenc Szauter Ferenc Friedler |
| author_facet | Zoltán Pusztai Péter Kőrös Ferenc Szauter Ferenc Friedler |
| author_sort | Zoltán Pusztai |
| collection | DOAJ |
| description | In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle’s cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory. |
| first_indexed | 2024-03-10T03:58:23Z |
| format | Article |
| id | doaj.art-c492626b0b064dfd9e4e53d0657be18a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T03:58:23Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c492626b0b064dfd9e4e53d0657be18a2023-11-23T10:50:54ZengMDPI AGEnergies1996-10732022-05-011510363110.3390/en15103631Vehicle Model-Based Driving Strategy Optimization for Lightweight VehicleZoltán Pusztai0Péter Kőrös1Ferenc Szauter2Ferenc Friedler3Vehicle Industry Research Center, Széchenyi István University, Egyetem tér 1, H-9026 Győr, HungaryVehicle Industry Research Center, Széchenyi István University, Egyetem tér 1, H-9026 Győr, HungaryVehicle Industry Research Center, Széchenyi István University, Egyetem tér 1, H-9026 Győr, HungaryVehicle Industry Research Center, Széchenyi István University, Egyetem tér 1, H-9026 Győr, HungaryIn this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle’s cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory.https://www.mdpi.com/1996-1073/15/10/3631energy efficiencyoptimizationdriving strategypowertrainShell Eco-marathonelectric vehicles |
| spellingShingle | Zoltán Pusztai Péter Kőrös Ferenc Szauter Ferenc Friedler Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle Energies energy efficiency optimization driving strategy powertrain Shell Eco-marathon electric vehicles |
| title | Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle |
| title_full | Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle |
| title_fullStr | Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle |
| title_full_unstemmed | Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle |
| title_short | Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle |
| title_sort | vehicle model based driving strategy optimization for lightweight vehicle |
| topic | energy efficiency optimization driving strategy powertrain Shell Eco-marathon electric vehicles |
| url | https://www.mdpi.com/1996-1073/15/10/3631 |
| work_keys_str_mv | AT zoltanpusztai vehiclemodelbaseddrivingstrategyoptimizationforlightweightvehicle AT peterkoros vehiclemodelbaseddrivingstrategyoptimizationforlightweightvehicle AT ferencszauter vehiclemodelbaseddrivingstrategyoptimizationforlightweightvehicle AT ferencfriedler vehiclemodelbaseddrivingstrategyoptimizationforlightweightvehicle |