Indirect methodologies to estimate energy use in vehicles: Application to battery electric vehicles

Alves, J. Maia; Baptista, Patrícia; Gonçalves, Gonçalo; Duarte, Gonçalo

doi:10.1016/j.enconman.2016.07.014

Cited by 70 publications

(40 citation statements)

References 21 publications

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“…In view of the significance of accurate energy consumption estimation, many experimental campaigns have been directed to assessing BEVs' range as climate conditions change [28][29][30][31]. Despite that these analyses have contributed the rise of a rich library of information on the effect of auxiliary loads on vehicle range, only a few studies provide analytical estimations on energy consumption related to HVAC systems and most of them imply rather complex models.…”

Section: Introductionmentioning

confidence: 99%

Effect of Ambient Temperature on Electric Vehicles’ Energy Consumption and Range: Model Definition and Sensitivity Analysis Based on Nissan Leaf Data

Iora

Tribioli

2019

WEVJ

108

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In this paper, a general quasi-steady backward-looking model for energy consumption estimation of electric vehicles is presented. The model is based on a literature review of existing approaches and was set up using publicly available data for Nissan Leaf. The model has been used to assess the effect of ambient temperature on energy consumption and range, considering various reference driving cycles. The results are supported and validated using data available from an experimental campaign where the Nissan Leaf was driven to depletion across a broad range of winter ambient temperatures. The effect of ambient temperature and the consequent accessories consumption due to cabin heating are shown to be remarkable. For instance, in case of Federal Urban Driving Schedule (FUDS), simplified FUDS (SFUDS), and New European Driving Cycle (NEDC) driving cycles, the range exceeds 150 km at 20 °C, while it reduces to about 85 km and 60 km at 0 °C and −15 °C, respectively. Finally, a sensitivity analysis is reported to assess the impact of the hypotheses in the battery model and of making different assumptions on the regenerative braking efficiency.

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Section: Introductionmentioning

confidence: 99%

Effect of Ambient Temperature on Electric Vehicles’ Energy Consumption and Range: Model Definition and Sensitivity Analysis Based on Nissan Leaf Data

Iora

Tribioli

2019

WEVJ

108

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“…In this work, it is assumed that wind speed is negligible compared to the vehicle's speed, therefore, v wind = 0 as it was also proposed in other previous works [21][22][23][24][25]. The rolling resistance forces are acting in all tires, as it is shown on the left side of Figure 2.…”

Section: Electric Vehicle Consumption Modelmentioning

confidence: 93%

“…In [25] a LDMs of three different electric quadricycles, which are calibrated using real world data (speed, acceleration and slope), sampled at 1 Hz rate is proposed. In this case, authors evaluate the vehicle specific power (an estimation of the power per mass unit), assuming constant Big Data modeling has been used to estimate the driving range of an electric vehicle (EV) more accurately, based on battery pack data and EV operating data.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones

et al. 2018

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Air pollution and climate change are some of the main problems that humankind is currently facing. The electrification of the transport sector will help to reduce these problems, but one of the major barriers for the massive adoption of electric vehicles is their limited range. The energy consumption in these vehicles is affected, among other variables, by the driving behavior, making range a value that must be personalized to each driver and each type of electric vehicle. In this paper we offer a way to estimate a personalized energy consumption model by the use of the vehicle dynamics and the driving events detected by the use of the smartphone inertial sensors, allowing an easy and non-intrusive manner to predict the correct range for each user. This paper proposes, for the classification of events, a deep neural network (Long-Short Time Memory) which has been trained with more than 22,000 car trips, and the application to improve the consumption model taking into account the driver behavior captured across different trips, allowing a personalized prediction. Results and validation in real cases show that errors in the predicted consumption values are halved when abrupt events are considered in the model.

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“…Proton exchange membrane fuel cell (PEMFC) or polymer exchange membrane fuel is one of the most upcoming technologies for the efficient and clean generation of power generation technologies of the 21st century. 79,80 The PEMFC uses a polymer electrolyte membrane and connected with low pressure and temperature state. To split hydrogen molecules into hydrogen ions, the system uses platinum as a catalyst.…”

Section: Proton Exchange Membrane Fuel Cellmentioning

confidence: 99%

Energy storage usages: Engineering reactions,economic‐technologicalvalues for electric vehicles—A technological outlook

Arfeen

Abdullah

Hassan

et al. 2020

Int Trans Electr Energ Syst

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Summary At present with the massive induction of distributed renewable energy sources (RES), energy storage systems (ESS) have the potential to curb the intermittent nature of micro sources and provide a steady supply of power to the load. It gives an optimum solution and considers as a major part of intelligent grids. For making a green environment, Electric Vehicle (EV) is the best option that emits zero exhaust gases, cleaner, less noisy and eco‐friendly compared to engine‐based vehicles. It could embark power sanctuary by allowing open access to RES. Nonetheless, EVs presently face encounters in the deployment of ESSs, inroad to their reliability, capacity, price, and online management issues. This study comprehensive review about technical advancements of ESSs, its detailed taxonomy, features, implementation, possibilities with system differences, and additional features of particularly EV applications. Hence, in this current study, technical analysis of Energy storage systems, its leading technologies, core assets, global energy stakeholders, economic merits and techniques on energy conversion is provided. Besides, the way of deploying energy storage techniques, the barriers and assessments are also presented to give a wider scope in this particular area.

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Indirect methodologies to estimate energy use in vehicles: Application to battery electric vehicles

Cited by 70 publications

References 21 publications

Effect of Ambient Temperature on Electric Vehicles’ Energy Consumption and Range: Model Definition and Sensitivity Analysis Based on Nissan Leaf Data

Effect of Ambient Temperature on Electric Vehicles’ Energy Consumption and Range: Model Definition and Sensitivity Analysis Based on Nissan Leaf Data

Modelling the Effect of Driving Events on Electrical Vehicle Energy Consumption Using Inertial Sensors in Smartphones

Energy storage usages: Engineering reactions,economic‐technologicalvalues for electric vehicles—A technological outlook

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