Drive cycles for battery electric vehicles and their fleet management

Milligan, Ross

doi:10.1016/b978-0-12-803021-9.00013-6

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…The widely used electrolyte solvent systems are carbonate mixed solvents with a low flash point, including vinyl carbonate (VC), diethyl carbonate (Dec), dimethyl carbonate (DMC), ethyl carbonate (EMC), etc. generally, propylene carbonate (PC), ethylene glycol dimethyl ether (DME) and other organic solvents mainly used for primary lithium (Li) battery are not used [87]. The flashpoint, melting point and boiling point of the above solvents are shown in Tab.2.…”

Section: F Sensitive Issues Such As Transportation Hazards In the Whmentioning

confidence: 99%

Big-Data-Based Power Battery Recycling for New Energy Vehicles: Information Sharing Platform and Intelligent Transportation Optimization

Dai

Tian

et al. 2020

IEEE Access

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This paper focuses on the principal problems in the actual transaction of decommissioned power batteries such as the asymmetry of information, huge risk and difficult issues such as recovery and trace. And constructed a new energy vehicle decommissioned power battery recycling platform based on the big data technology. Integrated characteristics of big data information, this paper analyzes the operating mechanism of the Big-Data-Based power battery recovery platform. The functional module on this platform is designed and investigated for the functional requirements of users and shared information based on big data. Based on the analysis of traffic big data, a traction battery dangerous goods transportation optimization system is established by using Baidu map application program interface (API). The improved ant algorithm was used to obtain the shortest path model and decrease the transportation costs and risks, besides, it boosts the value of information resources maximum and gives impetus to the power battery recycling industrial transformation and upgrading.

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Section: F Sensitive Issues Such As Transportation Hazards In the Whmentioning

confidence: 99%

Big-Data-Based Power Battery Recycling for New Energy Vehicles: Information Sharing Platform and Intelligent Transportation Optimization

Dai

Tian

et al. 2020

IEEE Access

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“…The right hand side of Equation (1a) has components of energy that include, from left to right, tyre friction, ill climbing, wind drag and change in kinetic energy [14]. Assuming travel on a level road (Equation (1b)) and making µ the subject in Equation (1b), Equation (1c) [64] was used to estimate the friction coefficient for the vehicles in this test, with a value of 0.0269; thus, the coefficient utilised for the simulation was between 0.025 and 0.03 depending on the surface material of the road and the tyre pressure [43]. Note that the authors used During summer, the autonomy of the vehicle was 143.2 km, but in winter, the available energy was only enough for 114.2 km.…”

Section: Vedec Simulation Softwarementioning

confidence: 99%

“…CAN bus information was recorded every second, while the GPS position was logged every 5 s. This research contemplated four different studies, which are detailed in the following section and summarised in Table 2. The experimental trials were carried out in summer and winter climate situations to obtain greater accuracy in the results and to allow data study on both air conditioning and heater usage and the auxiliary energy consumption used for heating the seats or defrosting before driving, among other features, that the driver would use in a normal situation [43]. (a) 80 kilometre route for 3 BEVs This study compared the energy consumption of the previously mentioned three electric vehicles covering the same distance on the same route segment.…”

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Driven Performance of Electric Vehicles in Edinburgh and Its Environs

et al. 2019

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Fuelled by energy security problems and urban air pollution challenges, several countries worldwide have set the objective to gradually eliminate petrol and diesel cars. The increasing support from government and demands for environmental friendly means of transportation are accelerating the use of battery electric vehicles. However, it is indispensable to have accurate and complete information about their behaviour in different traffic situations and road conditions. For the experimental analysis carried out in this study, three different electric vehicles from the Edinburgh College leasing program were equipped and tracked to obtain over 50 GPS and energy consumption data for short distance journeys in the Edinburgh area and long-range tests between Edinburgh and Bristol (UK). The results showed that the vehicles’ energy intensities were significantly affected by the driving cycle pattern, with a noticeable diminution due to low temperatures. It was found that the real available range of the electric vehicle in some situations could be 17% lower than the predicted mileage shown in the dashboard of the vehicle. The difference from the New European Driving Cycle (NEDC) values was even higher. The study has also provided a discussion on the effect of the electricity mix on carbon emission reduction.

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“…The driver pattern modeling could improve fleet management, increasing the efficiency and sustainability and it could be used to forecast demand, some of these cases are: vehicular driving patterns in the Edinburgh region and to offer an option of battery electric vehicles for sustainable mobility is estimated in Ref. [38]; usage patterns and the user perception are the main objectives of a longitudinal assessment of the viability of EV for daily use study in Ref. [39]; the usage of autonomous vehicles and eco-routing like in [40]; or, ridesharing of shared autonomous vehicle fleet [41].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Guerrero¹,

Personal²,

Parejo³

et al. 2019

Advanced Communication and Control Methods for Future Smartgrids

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Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid. distribution, and retailer levels. The main affected frameworks inside these levels are energy management, distribution management, and demand response. The energy management systems compound several functions [9]. One of them is the control of energy flows. The charging of EV can be made in any point of the grid that has a charging unit. If the system has information about the expected use of the charging unit, the energy flow will be easier to manage [10,11]. The distribution management is related to distribution system operators (DSO). Usually, the charging infrastructure oversees DSOs. Thus, the DSOs must manage these facilities and maintain the information about them. Finally, the demand response concerns retailers and DSOs, and the main problem is demand curve flattening and price management [12]. Nevertheless, the new paradigm proposed by standard organizations, like National Institute of Standards and Technology (NIST), International Electrotechnical Commission (IEC), etc., related to the V2G proposed that the EV could charge or discharge the batteries [13]. Thus, the EV is a power source in specific scenarios. In these cases, the distributed resource management is affected by the new V2G technologies as a distributed power resource in low voltage without total availability, like some renewable energy resources, for example, wind and solar energy [14].This paper proposed a solution for fleet charging prioritization, based on the concept of virtual power plant (VPP) and using distributed evolutionary computation algorithms to optimize the prioritization of EV fleets at different levels of SG ecosystems. A comparison of different evolutionary algorithms is performed.This paper shows the proposed solution, starting with a bibliographical review. Then the architecture over different levels of SG is described, including the information flows. The evolutionary algorithm is described at different levels of SG ecosystem. Finally, the results of test the evolutionary algorithms are shown. Bibliographic reviewThere are several research lines that are related with EVs, involving batteries (e.g., in [15], renewable energy [16], battery management systems [17, 18], energy management systems [19], charging spots [20, 21], driver assistance system [22], etc.). The EVs in the last millen...

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Drive cycles for battery electric vehicles and their fleet management

Cited by 4 publications

References 13 publications

Big-Data-Based Power Battery Recycling for New Energy Vehicles: Information Sharing Platform and Intelligent Transportation Optimization

Big-Data-Based Power Battery Recycling for New Energy Vehicles: Information Sharing Platform and Intelligent Transportation Optimization

Driven Performance of Electric Vehicles in Edinburgh and Its Environs

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

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