A Stochastic Range Estimation Algorithm for Electric Vehicles Using Traffic Phase Classification

Scheubner, Stefan; Thorgeirsson, Adam Thor; Vaillant, Moritz; Gauterin, Frank

doi:10.1109/tvt.2019.2918544

Cited by 31 publications

(27 citation statements)

References 38 publications

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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%

“…We use vehicle parameters from test bench measurements with an electric PORSCHE BOXSTER. This model was already validated using real test drives in [27] and is applied without changes.…”

Section: Energy Consumption Modelmentioning

confidence: 99%

“…A possible approach would be to assume constant acceleration between map segments k and then calculateã from the speed difference betweenū k andū k+1 . However, in this work we use the method of [27] and calculate the change in kinetic energy ∆E kin between the segments. As a result, the predicted energy on a segmentẼ c,k is the sum of the energy prediction, not including accelerationẼ c|a=0 and the kinetic energy changeẼ c,k :…”

Section: B Tractive Energy Demand Predictionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…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%

“…We use vehicle parameters from test bench measurements with an electric PORSCHE BOXSTER. This model was already validated using real test drives in [27] and is applied without changes.…”

Section: Energy Consumption Modelmentioning

confidence: 99%

Section: B Tractive Energy Demand Predictionmentioning

confidence: 99%

See 1 more Smart Citation

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

Thorgeirsson¹,

Scheubner

Funfgeld³

et al. 2020

IEEE Open J. Veh. Technol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…[24][25][26] In addition, a hybrid model combining a mechanistic model and ML can as well be applied. 19,27 Traditionally, automotive software is implemented on electronic control units within the vehicle and uses mostly data from the vehicle itself. This is also true for range estimation and charge planning.…”

Section: Range Estimation Routing and Charge Planningmentioning

confidence: 99%

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

Thorgeirsson

Vaillant²,

Scheubner

et al. 2020

Softw Pract Exp

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Due to sparse charging infrastructure and short driving ranges, drivers of battery electric vehicles (BEVs) can experience range anxiety, which is the fear of stranding with an empty battery. To help eliminate range anxiety and make BEVs more attractive for customers, accurate range estimation methods need to be developed. In recent years, many publications have suggested machine learning algorithms as a fitting method to achieve accurate range estimations. However, these algorithms use a large amount of data and have high computational requirements. A traditional placement of the software within a vehicle's electronic control unit could lead to high latencies and thus detrimental to user experience. But since modern vehicles are connected to a backend, where software modules can be implemented, high latencies can be prevented with intelligent distribution of the algorithm parts. On the other hand, communication between vehicle and backend can be slow or expensive. In this article, an intelligent deployment of a range estimation software based on ML is analyzed. We model hardware and software to enable performance evaluation in early stages of the development process. Based on simulations, different system architectures and module placements are then analyzed in terms of latency, network usage, energy usage, and cost. We show that a distributed system with cloud-based module placement reduces the end-to-end latency significantly, when compared with a traditional vehicle-based placement. Furthermore, we show that network usage is significantly reduced. This intelligent system enables the application of complex, but accurate range estimation with low latencies, resulting in an improved user experience, which enhances the practicality and acceptance of BEVs.

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“…Often the models for determining the states of charge of vehicles are obtained in the laboratory and do not take into account the variability of driving styles; the use of auxiliaries, such as air conditioning; and the environmental conditions in which vehicles can be found. This leads to incorrect estimates of the states of charge (SOCs) of the batteries in the vehicle and failure of perception by the drivers, which is defined in literature as "range anxiety" [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

A Virtual Sensor for Electric Vehicles’ State of Charge Estimation

et al. 2020

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The estimation of the state of charge is a critical function in the operation of electric vehicles. The battery management system must provide accurate information about the battery state, even in the presence of failures in the vehicle sensors. This article presents a new methodology for the state of charge estimation (SOC) in electric vehicles without the use of a battery current sensor, relying on a virtual sensor, based on other available vehicle measurements, such as speed, battery voltage and acceleration pedal position. The estimator was derived from experimental data, employing support vector regression (SVR), principal component analysis (PCA) and a dual polarization (DP) battery model (BM). It is shown that the obtained model is able to predict the state of charge of the battery with acceptable precision in the case of a failure of the current sensor.

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A Stochastic Range Estimation Algorithm for Electric Vehicles Using Traffic Phase Classification

Cited by 31 publications

References 38 publications

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

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

Evaluating system architectures for driving range estimation and charge planning for electric vehicles

A Virtual Sensor for Electric Vehicles’ State of Charge Estimation

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