Publicação
An Approach to Estimate Electric Vehicle Driving Range
| Resumo: | The use of electric vehicle (EV) has grown rapidly over the past few years. The EV is now accepted as a reliable and eco-friendly means of transportation. When choosing an EV, usually one of the key parameters of choice for the customer is its driving range (DR) capability. This is a decisive factor since it minimizes the drivers anxiety on a trip. The DR depends on many factors that must be taken into account when attempting its prediction.In this paper, we explore the use of machine learning (ML) techniques to estimate the DR prediction.We use regression techniques on models trained with publicly available datasets, evaluated with standard metrics.The prediction results are better than those provided by statistical techniques, thus being quite encouraging.As the end result, we also provide a ML benchmark written in Python, aiming to advance future research on this topic. |
|---|---|
| Autores principais: | Albuquerque, David |
| Outros Autores: | Ferreira, Artur J; Coutinho, David P |
| Assunto: | Computers; Informatics electric vehicle; driving range predic- tion; energy consumption; dataset construction; machine learning techniques; regression techniques; Python |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | unknown |
| Instituição associada: | Instituto Superior de Engenharia de Lisboa |
| Idioma: | inglês |
| Origem: | i-ETC : ISEL Academic Journal of Electronics Telecommunications and Computers |
| Resumo: | The use of electric vehicle (EV) has grown rapidly over the past few years. The EV is now accepted as a reliable and eco-friendly means of transportation. When choosing an EV, usually one of the key parameters of choice for the customer is its driving range (DR) capability. This is a decisive factor since it minimizes the drivers anxiety on a trip. The DR depends on many factors that must be taken into account when attempting its prediction.In this paper, we explore the use of machine learning (ML) techniques to estimate the DR prediction.We use regression techniques on models trained with publicly available datasets, evaluated with standard metrics.The prediction results are better than those provided by statistical techniques, thus being quite encouraging.As the end result, we also provide a ML benchmark written in Python, aiming to advance future research on this topic. |
|---|