Electric vehicles’ energy consumption measurement and estimation

Wu, Xinkai; Freese, David; Cabrera, Alfredo Enrique Franco; Kitch, William A.

doi:10.1016/j.trd.2014.10.007

Cited by 388 publications

(208 citation statements)

References 17 publications

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“…Firstly, we must carry on the definition of scope, choose model B2B and calculate CF of li-ion-battery, at the same time, we will research the consumption of material and energy and the emissions of greenhouse gas in the processes of raw materials acquisition, processing, manufacturing, transportation and using [14]; Secondly, we should collect plenty of data, the data include primary activity level data and secondary data, it mainly is raw material consumption, energy consumption in the processes of production and transportation and carbon emissions factor, and we should also show a clear statement of account; Finally, we will calculate the CF of li-ion-battery in the life cycle, in the process of calculation, on the basis of carbon dioxide, we change the emissions of other greenhouse gas into the emissions of carbon dioxide by using GWP, and show it with CO2 eq.…”

Section: Pas 2050 Methodsmentioning

confidence: 99%

The Secondary Battery Product and Enterprise’s Carbon Footprint Under the Low Carbon Strategy

Wang¹,

Yu²,

Huang³

et al. 2017

dtetr

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Abstract. In recent years, more and more secondary battery are used in industry and daily life, nevertheless, extensive use of secondary battery will bring some impact to the environment such as greenhouse effects, soil pollution and water pollution. Only seeing the integral situation of secondary battery in China and implementing the low carbon strategy by the secondary battery enterprises as soon as possible, can we improve the secondary battery's environmental impact and better realize the sustainable development. In this paper, we research the carbon footprint of lithium ion secondary battery based on PAS2050 'Specification for the assessment of the life cycle greenhouse gas emissions of goods and services' and select a lithium ion battery project with the annual output of 30 million in company A, calculate carbon emissions of every phase, we conclude that main phase of carbon emissions is still production phase and the proportion is extremely large. In the same time, we also describe the production of the lithium ion battery of our country on a macro level and evaluate the enterprise's carbon footprint to improve the environment effectively. In the end, the paper analyzes the existing low carbon policies in our country and put forward some relevant countermeasures for energy conservation and emissions reduction.

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Section: Pas 2050 Methodsmentioning

confidence: 99%

The Secondary Battery Product and Enterprise’s Carbon Footprint Under the Low Carbon Strategy

Wang¹,

Yu²,

Huang³

et al. 2017

dtetr

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“…The product of l s * sin θ is the height difference in this second. In fact, even with a same height difference but a different road grade angle, the electricity consumptions are not identical [5,18]. In order to quantify the influence of different gradients, the gradient should be classified into several gradient intervals [18].…”

Section: Average Energy Consumption Estimation Per Unit Distancementioning

confidence: 99%

“…According to previous studies, a variety of factors affect energy consumption, including travel-related factors [4,5], environment-related factors [6][7][8][9], vehicle-related factors [10][11][12][13][14], roadway-related factors [15], traffic-related factors [16][17][18], driver-related factors [3,[19][20][21][22], the health and degradation condition of the battery [23][24][25][26], the efficiency of braking energy recovery [27][28][29][30][31], and the charge and discharge character of the battery [32]. Some previous studies collected the information through driving-cycle experiments in the lab [4,24] and Global Positioning System (GPS) observations in the real world [32][33][34], but some results showed the significant difference between experiments and real-world conditions [35,36], leading to a relative low accuracy and poor practicality of models.…”

Section: Introductionmentioning

confidence: 99%

“…For the energy consumption estimations, either the energy consumption for an entire trip [35] or the energy consumption per unit distance [5] is used as the research object in previous studies. An estimation for an entire trip gives drivers an overview of the electricity costs for future travel, while an estimation for per unit distance provides a detailed view of the electricity consumed when driving.…”

Section: Introductionmentioning

confidence: 99%

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Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations

2017

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Improving the estimation accuracy for the energy consumption of electric vehicles (EVs) would greatly contribute to alleviating the range anxiety of drivers and serve as a critical basis for the planning, operation, and management of charging infrastructures. To address the challenges in energy consumption estimation encountered due to sparse Global Positioning System (GPS) observations, an estimation model is proposed that considers both the kinetic characteristics from sparse GPS observations and the unique attributes of EVs: (1) work opposing the rolling resistance; (2) aerodynamic friction losses; (3) energy consumption/generation depending on the grade of the route; (4) auxiliary load consumption; and (5) additional energy losses arising from the unstable power output of the electric motor. Two quantities, the average energy consumption per kilometer and the energy consumption for an entire trip, were focused on and compared for model fitness, parameter, and effectiveness, and the latter showed a higher fitness. Based on sparse GPS observations of 68 EVs in Aichi Prefecture, Japan, the traditional linear regression approach and a multilevel mixed-effects linear regression approach were used for model calibration. The proposed model showed a high accuracy and demonstrated a great potential for application in using sparse GPS observations to predict the energy consumption of EVs.

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“…A model to estimate the electric vehicles' energy consumption is proposed in [20] analysing the link among vehicle power, speed, acceleration and the road slope. Similarly, [21] examines some variants of the fuel consumption in the vehicle routing problem.…”

Section: Introductionmentioning

confidence: 99%

An ex-ante evaluation of last-mile freight distribution services for city logistics

Yalaoui

Lamari

Musolino

et al. 2015

WIT Transactions on the Built Environment

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The paper deals with methodologies to support an ex-ante assessment of the impacts generated by the introduction of city logistics measures on last-mile freight distribution services. Some models and algorithms are applied and compared to solve the vehicle routing problem for freight deliveries in the town of Reggio Calabria with the introduction of an urban distribution centre and of green freight vehicles. Heuristic routing algorithms are compared in terms of sequence of clients (retailers) visited subject to some constraints given by the presence of UDC and the limited autonomy and capacity of freight vehicles.

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Electric vehicles’ energy consumption measurement and estimation

Cited by 388 publications

References 17 publications

The Secondary Battery Product and Enterprise’s Carbon Footprint Under the Low Carbon Strategy

The Secondary Battery Product and Enterprise’s Carbon Footprint Under the Low Carbon Strategy

Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations

An ex-ante evaluation of last-mile freight distribution services for city logistics

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