Adaptive Multiresolution Energy Consumption Prediction for Electric Vehicles

Yi, Zonggen; Bauer, Péter

doi:10.1109/tvt.2017.2720587

Cited by 42 publications

(13 citation statements)

References 46 publications

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“…Future energy consumption can moreover be estimated using mechanistic models based on physical principles and equations. [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.…”

Section: Range Estimation Routing and Charge Planningmentioning

confidence: 99%

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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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Section: Range Estimation Routing and Charge Planningmentioning

confidence: 99%

“…The data origin and the computing latencies require a smart system architecture. Future energy consumption can moreover be estimated using mechanistic models based on physical principles and equations 24‐26 . In addition, a hybrid model combining a mechanistic model and ML can as well be applied 19,27 .…”

Section: Related Workmentioning

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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show abstract

“…In Lv et al (2015) the energy flow from regenerative braking is modeled in detail. Another detailed model of the vehicle's energy consumption is presented in Yi and Bauer (2017). The goal is to find a high-resolution powertrain efficiency estimation.…”

Section: State Of the Artmentioning

confidence: 99%

Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime

Perger

Auer

2020

Energy Efficiency

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In contrast to conventional routing systems, which determine the shortest distance or the fastest path to a destination, this work designs a route planning specifically for electric vehicles by finding an energy-optimal solution while simultaneously considering stress on the battery. After finding a physical model of the energy consumption of the electric vehicle including heating, air conditioning, and other additional loads, the street network is modeled as a network with nodes and weighted edges in order to apply a shortest path algorithm that finds the route with the smallest edge costs. A variation of the Bellman-Ford algorithm, the Yen algorithm, is modified such that battery constraints can be included. Thus, the modified Yen algorithm helps solving a multi-objective optimization problem with three optimization variables representing the energy consumption with (vehicle reaching the destination with the highest state of charge possible), the journey time, and the cyclic lifetime of the battery (minimizing the number of charging/discharging cycles by minimizing the amount of energy consumed or regenerated). For the optimization problem, weights are assigned to each variable in order to put emphasis on one or the other. The route planning system is tested for a suburban area in Austria and for the city of San Francisco, CA. Topography has a strong influence on energy consumption and battery operation and therefore the choice of route. The algorithm finds different results considering different preferences, putting weights on the decision variable of the multi-objective optimization. Also, the tests are conducted for different outside temperatures and weather conditions, as well as for different vehicle types.

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“…New energy vehicles are mainly divided into two types, pure electric vehicles and hybrid electric vehicles [4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fusion Control Method for Power Motor Working Condition of New Energy Vehicle

2020

IJOMAM

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Adaptive Multiresolution Energy Consumption Prediction for Electric Vehicles

Cited by 42 publications

References 46 publications

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

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

Energy efficient route planning for electric vehicles with special consideration of the topography and battery lifetime

Adaptive Fusion Control Method for Power Motor Working Condition of New Energy Vehicle

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