A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications

Genikomsakis, Konstantinos N.; Mitrentsis, Georgios

doi:10.1016/j.trd.2016.10.014

Cited by 121 publications

(88 citation statements)

References 27 publications

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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: 97%

“…The regeneration factor, k, which determines the percentage of the total braking power which can be practically recovered is speed-dependent function as it is shown in the following equation [22,24]:…”

Section: Electric Vehicle Consumption Modelmentioning

confidence: 99%

“…The two most critical parameters that affect this energy estimation are the uncertainty in the rolling friction coefficient and the uncertainty of vehicle efficiency. A combined dynamic generic model with some high-level technical specifications of the main components of the electric vehicle has been used in [22] to estimate the vehicle's energy demand. The authors proposed a variable speed-dependent regeneration factor.…”

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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Section: Electric Vehicle Consumption Modelmentioning

confidence: 97%

Section: Electric Vehicle Consumption Modelmentioning

confidence: 99%

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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“…Genikomsakis and Mitrentsis [85] presented a more realistic electric engine model: the load-efficiency curve is approximated by piecewise function and the normalization factor is added to take into account the motor size. The authors used the recuperation energy factor dependent on the vehicle speed as follows: below V there is no energy recuperation, beyond V maximum energy is recuperated, and in between linear interpolation of energy recuperation is assumed.…”

Section: Energy Consumptionmentioning

confidence: 99%

“…The faster routes increase the BEV's energy consumption while the congested and lowspeed arterial routes consume less energy. Masmoudi et al [84] compared the realistic energy consumption model of Genikomsakis and Mitrentsis [85] to the constant consumption model (237.5 W/km) on instances for the dial-aride problem with EVs. The authors concluded that using the realistic model is more efficient with 0.14% difference between realistic and constant energy consumption from the best-known solutions (BKS).…”

Section: Energy Consumptionmentioning

confidence: 99%

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Erdelić

Carić

2019

Journal of Advanced Transportation

175

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In order to ensure high-quality and on-time delivery in logistic distribution processes, it is necessary to efficiently manage the delivery fleet. Nowadays, due to the new policies and regulations related to greenhouse gas emission in the transport sector, logistic companies are paying higher penalties for each emission gram of CO2/km. With electric vehicle market penetration, many companies are evaluating the integration of electric vehicles in their fleet, as they do not have local greenhouse gas emissions, produce minimal noise, and are independent of the fluctuating oil price. The well-researched vehicle routing problem (VRP) is extended to the electric vehicle routing problem (E-VRP), which takes into account specific characteristics of electric vehicles. In this paper, a literature review on recent developments regarding the E-VRP is presented. The challenges that emerged with the integration of electric vehicles in the delivery processes are described, together with electric vehicle characteristics and recent energy consumption models. Several variants of the E-VRP and related problems are observed. To cope with the new routing challenges in E-VRP, efficient VRP heuristics and metaheuristics had to be adapted. An overview of the state-of-the-art procedures for solving the E-VRP and related problems is presented.

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A mat‐heuristics approach for electric vehicle route optimization under multiple recharging options and time‐of‐use energy prices

Aggarwal

Rani

Bedi

et al. 2023

Concurrency and Computation

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SummaryTraveling has contributed a lot to the evolution of mankind. Today, electric vehicles (EVs) are being preferred due to their greater efficiency, comfort, and environment‐friendly qualities. The EVs' contribution to future mobility is projected to rise exponentially in years to come. To make this innovative technology more successful, there is a dire need to install a sufficient number of charging stations (CSs). As the EVs are limited by their cruising range, they require multiple recharging to cover long distances (especially in the case of logistics delivery services). Thus, there is a great need to develop an efficient and cost‐effective EV route optimization approach considering multiple recharging options and time‐of‐use (ToU) energy prices. In this regard, a novel mat‐heuristic approach named (firefly with ant colony algorithm) has been proposed to solve the problem of EVRPTW (electric vehicle routing problem with time windows) incorporating detailed modeling of multiple charging flexibility (i.e., battery swapping, partial recharge, and different charging levels) and ToU energy prices. Our proposed approach aims to minimize the total cost of traveling, which is highly influenced by the cost of recharging. Ant colony algorithm (ACA) serves as the basic optimization framework in the proposed approach, while the firefly approach explores hitherto unexplored solution space and avoids local optima. The computation performance of the proposed approach is compared with existing state‐of‐the‐art similar domain approaches such as variable neighborhood search (VNS) and ant colony optimization using local search (ACO‐LS) which has average deviation of nearly 20%–25% from with optimal solution achieved by the proposed . The proposed approach yields a near‐optimal solution with a faster convergence rate (approximately 50%) compared to other existing approaches. Moreover, the multiple recharging options modeled in our proposed approach justify their significance in terms of cost‐effectiveness for most scenarios.

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A computationally efficient simulation model for estimating energy consumption of electric vehicles in the context of route planning applications

Cited by 121 publications

References 27 publications

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

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

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

A mat‐heuristics approach for electric vehicle route optimization under multiple recharging options and time‐of‐use energy prices

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