Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning
The estimation of energy consumption is an important prerequisite for planning the required infrastructure for charging and optimising the schedules of battery electric buses used in public urban transport. This paper proposes a model using a reduced number of readily acquired bus trip parameters: a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/9/2340 |
| _version_ | 1797568541556211712 |
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| author | Teresa Pamuła Wiesław Pamuła |
| author_facet | Teresa Pamuła Wiesław Pamuła |
| author_sort | Teresa Pamuła |
| collection | DOAJ |
| description | The estimation of energy consumption is an important prerequisite for planning the required infrastructure for charging and optimising the schedules of battery electric buses used in public urban transport. This paper proposes a model using a reduced number of readily acquired bus trip parameters: arrival times at the bus stops, map positions of the bus stops and a parameter indicating the trip conditions. A deep learning network is developed for deriving the estimates of energy consumption stop by stop of bus lines. Deep learning networks belong to the important group of methods capable of the analysis of large datasets—“big data”. This property allows for the scaling of the method and application to different sized transport networks. Validation of the network is done using real-world data provided by bus authorities of the town of Jaworzno in Poland. The estimates of energy consumption are compared with the results obtained using a regression model that is based on the collected data. Estimation errors do not exceed 7.1% for the set of several thousand bus trips. The study results indicate spots in the public transport network of potential power deficiency which can be alleviated by introducing a charging station or correcting the bus trip schedules. |
| first_indexed | 2024-03-10T19:58:25Z |
| format | Article |
| id | doaj.art-6cca450de31b4136b9986cdc5f9cba73 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T19:58:25Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6cca450de31b4136b9986cdc5f9cba732023-11-19T23:48:21ZengMDPI AGEnergies1996-10732020-05-01139234010.3390/en13092340Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep LearningTeresa Pamuła0Wiesław Pamuła1Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8, 40-019 Katowice, PolandFaculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8, 40-019 Katowice, PolandThe estimation of energy consumption is an important prerequisite for planning the required infrastructure for charging and optimising the schedules of battery electric buses used in public urban transport. This paper proposes a model using a reduced number of readily acquired bus trip parameters: arrival times at the bus stops, map positions of the bus stops and a parameter indicating the trip conditions. A deep learning network is developed for deriving the estimates of energy consumption stop by stop of bus lines. Deep learning networks belong to the important group of methods capable of the analysis of large datasets—“big data”. This property allows for the scaling of the method and application to different sized transport networks. Validation of the network is done using real-world data provided by bus authorities of the town of Jaworzno in Poland. The estimates of energy consumption are compared with the results obtained using a regression model that is based on the collected data. Estimation errors do not exceed 7.1% for the set of several thousand bus trips. The study results indicate spots in the public transport network of potential power deficiency which can be alleviated by introducing a charging station or correcting the bus trip schedules.https://www.mdpi.com/1996-1073/13/9/2340battery electric busesenergy consumptiondeep neural networkspublic transport network |
| spellingShingle | Teresa Pamuła Wiesław Pamuła Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning Energies battery electric buses energy consumption deep neural networks public transport network |
| title | Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning |
| title_full | Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning |
| title_fullStr | Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning |
| title_full_unstemmed | Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning |
| title_short | Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning |
| title_sort | estimation of the energy consumption of battery electric buses for public transport networks using real world data and deep learning |
| topic | battery electric buses energy consumption deep neural networks public transport network |
| url | https://www.mdpi.com/1996-1073/13/9/2340 |
| work_keys_str_mv | AT teresapamuła estimationoftheenergyconsumptionofbatteryelectricbusesforpublictransportnetworksusingrealworlddataanddeeplearning AT wiesławpamuła estimationoftheenergyconsumptionofbatteryelectricbusesforpublictransportnetworksusingrealworlddataanddeeplearning |