Big-data framework for electric vehicle range estimation

Rahimi-Eichi, Habiballah; Chow, Mo-Yuen

doi:10.1109/iecon.2014.7049362

Cited by 48 publications

(26 citation statements)

References 12 publications

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“…An accurate model-based EV driving range estimation mandates both an accurate estimation of future driving profile (route and velocity profiles over time) and an accurate EV power model [10], [11], [12]. Most of the previous works on the model-based estimation rather focus on the predicting the future velocity profile of the EV [12], [13].…”

Section: Hybrid Model-based Remaining Range Estimationmentioning

confidence: 99%

“…A discrete-time Markov chain helps increase the future driving profile estimation accuracy [12]. Taking into account additional environmental information such as a weather condition can further increase the estimation accuracy [10].…”

Section: Hybrid Model-based Remaining Range Estimationmentioning

confidence: 99%

“…The model coefficients α, β, γ, and A represent rolling resistance, gradient resistance, inertia resistance, and aerodynamic resistance, respectively. The vehicle dynamics model coefficients can be found from the specifications of the production vehicles [6], [10]. The custom EV power model does not require an aerodynamic resistance term due to the operational speed of the custom EV, which is lower than 35 km/h.…”

Section: A Conventional Physics Equation Based Modelmentioning

confidence: 99%

“…There were several attempts to take motor efficiency into account in the EV power consumption model. Some works assume the motor efficiency as a constant number and the others tried to get the motor efficiency map from the manufacturer and integrate with their EV power consumption model [8], [10], [14]. However, those kinds of approaches have limitations in the accuracy that they consider the EV and motor separately.…”

Section: B Advanced Dynamics Modelmentioning

confidence: 99%

“…First, the future EV velocity profile is forecasted with the speed limitation, driving pattern, traffic information, and so on. Second, the power consumption can be derived from the predicted velocity, predicted acceleration, route information, road slope, and EV specifications [10], [11].…”

Section: Hybrid Model-based Remaining Range Estimationmentioning

confidence: 99%

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Accurate remaining range estimation for Electric vehicles

Hong

Park

Chang

2016

2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC)

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EVs (Electric vehicle) generally have only around 22% driving ranges compared with ICEVs (Internal combustion engine vehicle) with a similar price range. Running out of the EV battery SoC (State of charge) while driving gives the same inconvenience as a vehicle breakdown. In this paper, we emphasize that an accurate remaining range estimation can efficiently mitigate the range anxiety of EV drivers. Most EV drivers reserve 30% of the on-dash estimated remaining range gauge of their EV because they do not trust the current remaining range estimation accuracy of production EVs. In other words, an accurate remaining range estimation is equivalent to increasing the EV battery capacity up to 30%.Just like the analogous concepts used in the power estimation of digital circuits, a model-based remaining range estimation consists of the two consecutive steps, a driving profile estimation and a power consumption estimation using the power model. In this paper, we focus on increasing the accuracy of the power model. We come up with a hybrid modeling methodology combining a physics equation based model with empirical data. We validate the accuracy of the hybrid model in the remaining range estimation with the target EV. We collect the power consumption, velocity, road inclination, etc. of the EV in every half second with an onboard monitoring system, a perform multivariable linear regression and create an accurate EV power model. The proposed remaining range estimation yields only 2.52% error while the state-of-the-art model-based EV remaining range estimation shows 9.33% error when the same future route and speed estimation are given.

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Section: Hybrid Model-based Remaining Range Estimationmentioning

confidence: 99%

Section: Hybrid Model-based Remaining Range Estimationmentioning

confidence: 99%

Section: A Conventional Physics Equation Based Modelmentioning

confidence: 99%

Section: B Advanced Dynamics Modelmentioning

confidence: 99%

Section: Hybrid Model-based Remaining Range Estimationmentioning

confidence: 99%

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Accurate remaining range estimation for Electric vehicles

Hong

Park

Chang

2016

2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC)

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Issues in Implementing a Data Integration Platform for Electric Vehicles Using the Internet of Things

Smuts

Scholtz

Wesson

2019

IFIP Advances in Information and Communication Technology

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The emergence of the Internet of Things (IoT) has brought new improvement and development opportunities to the automotive industry, such as electric vehicles (EVs). EVs are well-known for their short ranges and many studies have reported on the challenges of trip planning and accurate remaining driving range (RDR) estimation. While the demand for connected vehicle applications and its enabling technology has progressed significantly in recent years, there are several constraints for connected and collaborative vehicle application deployments. Data integration issues are currently hindering the development of effective trip planning and RDR estimation solutions for drivers of EVs. Additional constraints have been identified in developing countries, including lack of charging station networks, EV data sources, and software applications. The purpose of this paper is to report on some of the main issues hindering EV data integration, as well as to report on an implementation in South Africa of a Data Integration Platform for EV data using the IoT. The findings show that data integration issues primarily relate to data availability, data quality, and interoperability between devices, IoT platforms, and EV service providers. The paper also identifies enabling technologies, drivers, and future directions for researchers in the IoT and EV domains. Keywords: Internet of Things Á Internet of Vehicles Á Connected vehicles Á Electric Vehicles Á Trip planning Á Telematics Á Vehicular networks

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Energy Consumption Estimation for Electric Vehicles Using Routing API Data

Alateef

Thomas

2023

Computer Performance Engineering

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Electric vehicle (EV) range anxiety is an influential factor in electric vehicle's low penetration into the transportation system. There have been several developments on range estimation for electric vehicles, however, the studies which focus on determining the remaining range based on the real-time publicly available data remain low. The majority of the current methods being employed consider limited data collection and do not consider the most substantial factors that directly impact energy consumption. This paper introduces a velocity model based on route information for the range estimation of electric vehicles. It uses publicly available data sets obtained from several map services APIs and incorporates this data in the range estimation algorithm. Three map services APIs were used to collect the data for an extended period, and then we analysed this data to extract the most representative data to generate the velocity profiles. The paper uses MATLAB code and python libraries to process the representative data and apply the velocity model. Moreover, we have integrated it into an electric vehicle model, including the battery, to estimate the power demand for each trip and the remaining driving range. We observed that producing realistic driving cycles using public data is possible; furthermore, it simulates the driving patterns and satisfies the constraints of the vehicle.

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Big-data framework for electric vehicle range estimation

Cited by 48 publications

References 12 publications

Accurate remaining range estimation for Electric vehicles

Accurate remaining range estimation for Electric vehicles

Issues in Implementing a Data Integration Platform for Electric Vehicles Using the Internet of Things

Energy Consumption Estimation for Electric Vehicles Using Routing API Data

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