Reducing range estimation uncertainty with a hybrid powertrain model and online parameter estimation

Sautermeister, Stefan; Ott, Florian; Vaillant, Moritz; Gauterin, Frank

doi:10.1109/itsc.2017.8317633

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…This model is used in the decisions of intelligent navigation and eco-driving assistance systems in EVs. Sautermeister et al [84] propose driving range estimation taking into account system uncertainties. They use a recursive algorithm with multi-model estimation to include driving endurance parameters in the forecast.…”

Section: Estimating Remaining Driving Ranges In Evsmentioning

confidence: 99%

Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation

et al. 2021

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The increasing use of electric vehicles in road and air transportation, especially in last-mile delivery and city mobility, raises new operational challenges due to the limited capacity of electric batteries. These limitations impose additional driving range constraints when optimizing the distribution and mobility plans. During the last years, several researchers from the Computer Science, Artificial Intelligence, and Operations Research communities have been developing optimization, simulation, and machine learning approaches that aim at generating efficient and sustainable routing plans for hybrid fleets, including both electric and internal combustion engine vehicles. After contextualizing the relevance of electric vehicles in promoting sustainable transportation practices, this paper reviews the existing work in the field of electric vehicle routing problems. In particular, we focus on articles related to the well-known vehicle routing, arc routing, and team orienteering problems. The review is followed by numerical examples that illustrate the gains that can be obtained by employing optimization methods in the aforementioned field. Finally, several research opportunities are highlighted.

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Section: Estimating Remaining Driving Ranges In Evsmentioning

confidence: 99%

Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation

et al. 2021

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“…This section introduces the model used in this work. This model has previously been used in the related works: [25], [26], [27].…”

Section: Energy Consumption Modelmentioning

confidence: 99%

An Investigation Into Key Influence Factors for the Everyday Usability of Electric Vehicles

Thorgeirsson¹,

Scheubner

Funfgeld³

et al. 2020

IEEE Open J. Veh. Technol.

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Society relies on electric mobility to decrease the problems associated with local emissions and global climate change. One key factor for the success of electric vehicles is their everyday usability. Today's users must deal with limited driving range in a sparse charging infrastructure. In this work, we investigate central features influencing the everyday usability of electric vehicles, such as battery capacity, charging infrastructure, range prediction accuracy and vehicle concepts. Since the influence of these aspects depend on each other, they cannot be examined separately. Therefore, we created a stochastic simulation framework with vehicle models and map data to calculate a large amount of different feature variations. One of our key findings is that battery capacities beyond 100 kWh are not feasible. In addition, we stated the importance of an accurate range estimation algorithm and a dense network of high-performance charging points for everyday usability. Taking these results into consideration would help policy makers and automobile manufacturers achieve worldwide acceptance of electric vehicles.

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“…The same study also indicates that it is beneficial to include up-to-date estimations of rolling resistance and mass during driving to give a more accurate prediction during the trip. The same is suggested by [7]. However, most of this research focuses on predicting certain parameters of the vehicle model, based on the observed difference between measured and modeled energy consumption, while omitting the effect of the current vehicle velocity has on the prediction.…”

Section: Introductionmentioning

confidence: 99%

On-line Test of a Real-Time Velocity Prediction for E-bus Energy Consumption Estimation

Beckers

Besselink

Nijmeijer

2021

2021 IEEE Vehicle Power and Propulsion Conference (VPPC)

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Reducing range estimation uncertainty with a hybrid powertrain model and online parameter estimation

Cited by 6 publications

References 29 publications

Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation

Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation

An Investigation Into Key Influence Factors for the Everyday Usability of Electric Vehicles

On-line Test of a Real-Time Velocity Prediction for E-bus Energy Consumption Estimation

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