Energy Consumption Prediction for Electric Vehicles Based on Real-World Data

Cauwer, Cedric De; Mierlo, Joeri Van; Coosemans, Thierry

doi:10.3390/en8088573

Cited by 224 publications

(143 citation statements)

References 15 publications

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“…One of the indispensable systems that contribute to enhanced comfort in a cabin, as well as to considerable energy consumption, is the system of HVAC (Heating Ventilation and Air-conditioning). HVAC systems drain at least 7 % of total energy consumption of a vehicle, whereas, in peak loads this could be quadruped [1,2]. Particularly a driving range of hybrid or electric vehicles is sensitive to climate, as it is demonstrated in the work of Kambly and Bradley [1].…”

Section: Introductionmentioning

confidence: 93%

Impact of measurable physical phenomena on contact thermal comfort

et al. 2017

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Abstract. Cabin HVAC (Heating Ventilation and Air-conditioning) systems have become an essential part of personal vehicles as demands for comfortable transport are still rising. In fact, 85 % of the car trips in Europe are shorter than 18 km and last only up to 30 minutes. Under such conditions, the HVAC unit cannot often ensure desired cabin environment and passengers are prone to experience thermal stress. For this reason, additional comfort systems, such as heated or ventilated seats, are available on the market. However, there is no straightforward method to evaluate thermal comfort at the contact surfaces nowadays. The aim of this work is to summarise information about heated and ventilated seats. These technologies use electrical heating and fan driven air to contact area in order to achieve enhanced comfort. It is also expected, that such measures may contribute to lower energy consumption. Yet, in real conditions it is almost impossible to measure the airflow through the ventilated seat directly. Therefore, there is a need for an approach that would correlate measurable physical phenomena with thermal comfort. For this reason, a method that exploits a measurement of temperatures and humidity at the contact area is proposed. Preliminary results that correlate comfort with measurable physical phenomena are demonstrated.

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Section: Introductionmentioning

confidence: 93%

Impact of measurable physical phenomena on contact thermal comfort

et al. 2017

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“…x i y i (9) in which, x i is a cloud drop in the domain, and y i is the degree of certainty of x i . Given the two clouds A and B, ifŝ (A) ěŝ (B), then A ě B.…”

Section: Methodsmentioning

confidence: 99%

“…Accordingly, the site selection for EVCS is a multi-objective decision making problem. Nevertheless, the current studies on the EV predominantly focus on the following fields: battery management [1][2][3][4], charging scheduling [5][6][7][8], energy consumption [9][10][11][12] and the impacts on the power system [13,14] and so on. Up till now, only a few scholars have studied the topic of electric vehicle charging site selection, for instance, You and Hsieh [15] proposed a hybrid heuristic approach to address the selection of EVCS locations, Chung and Kwon [16] formulated a multi-period optimization model based on a flow-refueling location model for strategic charging station location planning, and Guo and Zhao [17] applied a fuzzy TOPSIS method to select the optimal EVCS site.…”

Section: Introductionmentioning

confidence: 99%

Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method

et al. 2016

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Abstract:The task of site selection for electric vehicle charging stations (EVCS) is hugely important from the perspective of harmonious and sustainable development. However, flaws and inadequacies in the currently used multi-criteria decision making methods could result in inaccurate and irrational decision results. First of all, the uncertainty of the information cannot be described integrally in the evaluation of the EVCS site selection. Secondly, rigorous consideration of the mutual influence between the various criteria is lacking, which is mainly evidenced in two aspects: one is ignoring the correlation, and the other is the unconscionable measurements. Last but not least, the ranking method adopted in previous studies is not very appropriate for evaluating the EVCS site selection problem. As a result of the above analysis, a Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) method-based decision system combined with the cloud model is proposed in this paper for EVCS site selection. Firstly, the use of the PROMETHEE method can bolster the confidence and visibility for decision makers. Secondly, the cloud model is recommended to describe the fuzziness and randomness of linguistic terms integrally and accurately. Finally, the Analytical Network Process (ANP) method is adopted to measure the correlation of the indicators with a greatly simplified calculation of the parameters and the steps required.

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“…As a result, it is not a cost-efficient method. Cauwer et al [8] developed a model to estimate an electric vehicle's power consumption using the kinematic parameters of the electric vehicle (EV) or the trip data as inputs. We cannot know the amount of power consumption of charging stations in a certain area.…”

Section: Introductionmentioning

confidence: 99%

An Electric Power Consumption Analysis System for the Installation of Electric Vehicle Charging Stations

Cheon

Kang

2017

Energies

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With the rising demand for electric vehicles, the number of electric vehicle charging stations is increasing. Therefore, real-time monitoring of how the power consumption by charging stations affects the load on the peripheral power grid is important. However, related organizations generally do not provide actual power consumption data in real time, and only limited information, such as the charging time, is provided. Therefore, it is difficult to calculate and predict the power load in real time. In this paper, we propose a new model for estimating the electric power consumption from the supplied information, i.e., the charging time and the number of charging involved. The experimental results show that by displaying this information on a map, it is possible to visually monitor the electric power consumption of the charging stations with an accuracy rate of about 86%. Finally, the proposed system can be used to relocate and select the location of vehicle charging stations.

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Energy Consumption Prediction for Electric Vehicles Based on Real-World Data

Cited by 224 publications

References 15 publications

Impact of measurable physical phenomena on contact thermal comfort

Impact of measurable physical phenomena on contact thermal comfort

Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method

An Electric Power Consumption Analysis System for the Installation of Electric Vehicle Charging Stations

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