Sensitivity analysis for energy demand estimation of electric vehicles

Asamer, Johannes; Gräser, Axel; Heilmann, Bernhard; Ruthmair, Mario

doi:10.1016/j.trd.2016.03.017

Cited by 134 publications

(87 citation statements)

References 11 publications

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“…To allow the MLR to detect the individual influences on the energy consumption more easily, the trips are split into shorter segments, so more variability resides in the measured data. The sensitivity analysis of the energy demand, presented in [34], highlights the effect of a variable rolling resistance and, to a lesser extent, vehicle mass on the energy consumption of the EV. The rolling resistance coefficient can vary considerably because of many factors, such as road surface, road wetness, tires and tire pressure, with reported variations up to 65% [22,35], and therefore require extensive measurements to characterize it.…”

Section: Energy Estimation Modelmentioning

confidence: 99%

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

et al. 2017

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Limited driving range remains one of the barriers for widespread adoption of electric vehicles (EVs). To address the problem of range anxiety, this paper presents an energy consumption prediction method for EVs, designed for energy-efficient routing. This data-driven methodology combines real-world measured driving data with geographical and weather data to predict the consumption over any given road in a road network. The driving data are linked to the road network using geographic information system software that allows to separate trips into segments with similar road characteristics. The energy consumption over road segments is estimated using a multiple linear regression (MLR) model that links the energy consumption with microscopic driving parameters (such as speed and acceleration) and external parameters (such as temperature). A neural network (NN) is used to predict the unknown microscopic driving parameters over a segment prior to departure, given the road segment characteristics and weather conditions. The complete proposed model predicts the energy consumption with a mean absolute error (MAE) of 12-14% of the average trip consumption, of which 7-9% is caused by the energy consumption estimation of the MLR model. This method allows for prediction of energy consumption over any route in the road network prior to departure, and enables cost-optimization algorithms to calculate energy efficient routes. The data-driven approach has the advantage that the model can easily be updated over time with changing conditions.

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Section: Energy Estimation Modelmentioning

confidence: 99%

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

et al. 2017

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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: 96%

“…Several models of battery consumption for electric vehicles have been published in the literature, considering the longitudinal dynamic model (LDM) of the car [18][19][20][21]. A sensitivity analysis for this dynamic model is presented in [21], providing a parameter prioritization that allows quantifying their impact on the accuracy of the energy demand estimation.…”

Section: Introductionmentioning

confidence: 99%

“…A sensitivity analysis for this dynamic model is presented in [21], providing a parameter prioritization that allows quantifying their impact on the accuracy of the energy demand estimation. The two most critical parameters that affect this energy estimation are the uncertainty in the rolling friction coefficient and the uncertainty of vehicle efficiency.…”

Section: Introductionmentioning

confidence: 99%

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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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“…En el VE, el motor de corriente continua o alterna deja de consumir electricidad y comienza a generar, al poseer la alternativa de regeneración al freno, con la ayuda de interfaces para en flujo bidireccional y convertidores que aporta en el sentido de trabajo de las corrientes, como se muestra en la figura 1 (Asamer et al, 2016;Crolla, 2009;Yong et al, 2015). …”

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Evaluación del Sistema de Tracción en un Vehículo Eléctrico Biplaza de Estructura Tubular

et al. 2017

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ResumenEn este artículo se presenta una alternativa a la contaminación de los vehículos de gasolina tipo go-kart 7.5 kW de la ciudad de Baños en Ecuador, con el fin de reducir las emisiones contaminantes. La propuesta es un prototipo de vehículo eléctrico tubular de dos asientos que responden a requisitos de diseño tales como: capacidad máxima de 420 kg, con una media de velocidad 50 km/h, subir pendientes de 14%, y mantener el confort de los ocupantes. La topografía de la ciudad ha sido considerada en el análisis del modelo dinámico del vehículo para la selección de los componentes eléctricos, tales como motor de corriente continua, controlador electrónico, el sistema de frenado regenerativo y baterías. El rendimiento del vehículo eléctrico se evalúa en cuatro condiciones: (i) prueba de frenado, a una velocidad de 13.8 m/s el vehículo eléctrico se detiene en 5.1 m; (ii) prueba de potencia en línea recta y a una pendiente del 14%, se obtuvo 9 kW y 11 kW, respectivamente; (iii) prueba de frenado regenerativo se recupera en promedio 0.6 kW; y (iv) test de autonomía en la que se consiguió 30.4 km a plena carga. Se concluye que hay dos componentes principales que deben ser consideradas antes de hacer la conversión EV para lograr mayor eficiencia, la batería y los motores eléctricos. Palabras clave: controlador electrónico; dinámica del vehículo; freno regenerativo; vehículo eléctrico Evaluation traction system of electric vehicle a two-seat tubular structure AbstractThis article presents an alternative to reduce pollutant emissions from go-kart 7.5 kW gasoline vehicles in the city of Baños in Ecuador. The proposal is a tubular prototype two-seat electric vehicle that considers design requirements such as: maximum capacity 420 kg, average speed 50 km/h, climbing slopes of 14%, to maintain occupant comfort. The topography of the city has been considered in the analysis of the dynamic vehicle model for the selection of electrical components such as direct current motor, electronic controller, regenerative braking system and batteries. The performance of electric vehicle is evaluated in four conditions: (i) braking test in a speed of 13.8 m/s the electric vehicle stops at 5.1 m; (ii) power test in straight line and one slope of 14%, was obtained 9 kW and 11kW respectively; (iii) regenerative braking test it recovers in average 0.6 kW; and (iv) autonomy test in which 30.4 km was obtained at maximum load. It is concluded that there are two main components to be considered before making the EV conversion, the battery and electric motors, to achieve maximum efficiency.

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Sensitivity analysis for energy demand estimation of electric vehicles

Cited by 134 publications

References 11 publications

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions

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

Evaluación del Sistema de Tracción en un Vehículo Eléctrico Biplaza de Estructura Tubular

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