Range Prediction Models for E-Vehicles in Urban Freight Logistics Based on Machine Learning

Helgert, Christian; Gebhardt, Kai; Theiß, Christoph; Schau, Volkmar

doi:10.1007/978-3-319-40973-3_17

Cited by 8 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, operating conditions can be categorized based on (i) the vehicle design; (ii) driver characteristics; (iii) travel conditions; (iv) traffic flow conditions (e.g., congestion); (v) road conditions; (vi) ambient conditions and (viii) regenerative braking (Bucher and Bradley 2018;Dias et al 2019;Nicolaides et al 2019). For instance, a number of studies (Cicconi et al 2016;Kretzschmar et al 2016;Rosero et al 2021;Xu et al 2015)use GPS measurements to model energy consumption for each vehicle as a function of operating conditions (e.g., travel conditions and driver characteristics) (Kretzschmar et al 2016;Xu et al 2015). Several operational factors or conditions can influence energy consumption prediction in actual traffic conditions.…”

Section: Predictive Applicationsmentioning

confidence: 99%

A Review of Big Data in Road Freight Transport Modeling–Gaps and Potentials

et al. 2022

View full text Add to dashboard Cite

Section: Predictive Applicationsmentioning

confidence: 99%

A Review of Big Data in Road Freight Transport Modeling–Gaps and Potentials

et al. 2022

View full text Add to dashboard Cite

“…Kretzschmar et al presented range prediction models that are based on machine learning and take into account a wide range of input parameters, such as weather, traffic level, and topographic data. The presented models have their application in the environment of urban logistics [39]. In addition to external parameters and external planning factors, it is necessary to take into consideration the specifics of the vehicles themselves.…”

Section: Literature Reviewmentioning

confidence: 99%

Research into the Impacts of Driving Cycles and Load Weight on the Operation of a Light Commercial Electric Vehicle

et al. 2021

View full text Add to dashboard Cite

The European Parliament has adopted Directive 2019/1161 on the promotion of environmentally friendly and energy-efficient road transport vehicles, which also defines the obligations and forms of support for the procurement of environmentally friendly vehicles in urban logistics. The increase in the number of shipments delivered within e-commerce, which is also the result of the COVID-19 pandemic, requires a transition to a sustainable logistics system. New research questions are being raised in the preparation of new projects for the introduction of small electric commercial vehicles in particular. One of the main research questions about deployment itself is whether light commercial electric vehicles are able to fully replace conventionally powered vehicles. What operating conditions are optimal for the operation of them? How does load weight affect the energy efficiency of operating a light commercial electric vehicle? The authors decided to carry out research into the impacts of weight and the nature of a driving cycle under laboratory conditions to eliminate all external factors that could distort individual measurements and their results. In order to simulate driving cycles, an urban driving cycle was designed on the basis of the course of speed, acceleration, deceleration and slope conditions of roads in the selected regional city of Žilina (Slovakia). In the case of the operation of an electrically powered light commercial vehicle, the impact of load weight on the range of the vehicle is low, and is below the level of the theoretical maximum range of the vehicle in urban logistics applications. The operation of electrically powered vehicles in hilly terrains with relatively longer gradients and steeper slopes increases electricity consumption and, thereby, reduces their range.

show abstract

“…Thus, several studies [96,111] build their ML models based on more than ten features as inputs, without giving importance to prioritizing their features and keeping the most essential ones. The authors of [96] propose an ML-based method for predicting ECEV in real-world driving circumstances.…”

mentioning

confidence: 99%

“…Unsupervised learning (UL): Of the 156 selected studies, only 3 studies (2%) [111,133,156] use UL algorithms. Kretzschmar et al [111] present an approach using the x-means algorithm to predict the energy loss of an EV in a safe way for urban roads. In fact, the clustering process creates a constant converging error value.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

2023

View full text Add to dashboard Cite

Electric vehicles are growing in popularity as a form of transportation, but are still underused for several reasons, such as their relatively low range and the high costs associated with manufacturing and maintaining batteries. Many studies using several approaches have been conducted on electric vehicles. Among all studied subjects, here we are interested in the use of machine learning to efficiently manage the energy consumption of electric vehicles, in order to develop intelligent electric vehicles that make quick unprogrammed decisions based on observed data allowing minimal electricity consumption. Our interest is motivated by the adequate results obtained using machine learning in many fields and the increasing but still insufficient use of machine learning to efficiently manage the energy consumption of electric vehicles. From this standpoint, we have built this comprehensive survey covering a broad variety of scientific papers in the field published over the last few years. According to the findings, we identified the current trend and revealed future perspectives.

show abstract

Range Prediction Models for E-Vehicles in Urban Freight Logistics Based on Machine Learning

Cited by 8 publications

References 9 publications

A Review of Big Data in Road Freight Transport Modeling–Gaps and Potentials

A Review of Big Data in Road Freight Transport Modeling–Gaps and Potentials

Research into the Impacts of Driving Cycles and Load Weight on the Operation of a Light Commercial Electric Vehicle

Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey

Contact Info

Product

Resources

About