Publicação
Speed predictions for electric trucks using telematics data and environmental features
| Resumo: | A critical challenge in the widespread adoption of battery electric trucks is accurate energy range prediction, which is heavily dependent on precise speed inputs. Current speed predictions from leading mapping data provider HERE Technologies tend to overestimate speeds, resulting in conservative range estimates for Daimler Truck’s vehicles. This study leverages telematics data – information collected in the vehicle such as speed and location – in combination with environmental features to develop more accurate speed predictions, thereby enhancing the reliability of energy range forecasts for Daimler Truck to support the transition to sustainable transportation. |
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| Autores principais: | Mahlstedt, Julia Caroline |
| Assunto: | Telematics Speed prediction Machine learning Electric trucks Range optimization Mapping Random forest regression SARIMAX |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | A critical challenge in the widespread adoption of battery electric trucks is accurate energy range prediction, which is heavily dependent on precise speed inputs. Current speed predictions from leading mapping data provider HERE Technologies tend to overestimate speeds, resulting in conservative range estimates for Daimler Truck’s vehicles. This study leverages telematics data – information collected in the vehicle such as speed and location – in combination with environmental features to develop more accurate speed predictions, thereby enhancing the reliability of energy range forecasts for Daimler Truck to support the transition to sustainable transportation. |
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