Analysis of the main factors influencing the energy consumption of electric vehicles

Younes, Zoulficar; Boudet, Laurence; Suard, Frédéric; Gérard, Mathias; Rioux, Roland

doi:10.1109/iemdc.2013.6556260

Cited by 38 publications

(28 citation statements)

References 7 publications

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“…The road grade and the velocity profile are crucial factors affecting the energy consumption of the HEV, which was validated in a previous research [21]. To determine the target SOC for the uphill terrain, the road grade and the driving cycle need to be predicted.…”

Section: The Prediction Of the Electricity Consumption For The Uphillmentioning

confidence: 99%

“…Furthermore, for the real-time control strategies of PHEVs, a good vehicle performance, in terms of fuel economy, power, and battery life, cannot be ensured, if the driving conditions are not considered. The slope is proven to be an important factor that influences the fuel consumption of the HEV [21,22]. PHEVs usually operate in the hybrid-driving mode while driving uphill, and the engine and motor coordinately work together to ensure that the engine operates in the high-efficiency region.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information

Liu

et al. 2017

Energies

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Abstract:Energy management strategies (EMSs) are critical for the improvement of fuel economy of plug-in hybrid electric vehicles (PHEVs). However, conventional EMSs hardly consider the influence of uphill terrain on the fuel economy and battery life, leaving vehicles with insufficient battery power for continuous uphill terrains. Hence, in this study, an optimal control strategy for a PHEV based on the road grade information is proposed. The target state of charge (SOC) is estimated based on the road grade information as well as the predicted driving cycle on uphill road obtained from the GPS/GIS system. Furthermore, the trajectory of the SOC is preplanned to ensure sufficient electricity for the uphill terrain in the charge depleting (CD) and charge sustaining (CS) modes. The genetic algorithm is applied to optimize the parameters of the control strategy to maintain the SOC of battery in the CD mode. The pre-charge mode is designed to charge the battery in the CS mode from a reasonable distance before the uphill terrain. Finally, the simulation model of the powertrain system for the PHEV is established using MATLAB/Simulink platform. The results show that the proposed control strategy based on road-grade information helps successfully achieve better fuel economy and longer battery life.

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Section: The Prediction Of the Electricity Consumption For The Uphillmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information

Liu

et al. 2017

Energies

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“…These are major factors preventing the charging-type electric car market from vitalizing. 4 These factors result from the energy density of a battery: it stores electric energy, but its density is remarkably low compared to that of gasoline.…”

Section: Electric Propulsion System For General Vehiclesmentioning

confidence: 99%

“…Finally, in the object function, these two terms are added by using weights to consider both differences in center of gravity and aspect ratio. This is summarized as an object function of a multi-objective optimization problem, expressed as formula (4). The detailed calculations for object function are shown in Fig.…”

Section: Object Functionmentioning

confidence: 99%

Optimized design of electric propulsion system for small crafts using the differential evolution algorithm

Lee

Jeong

Shin

et al. 2014

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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Given the stricter environmental regulations and rising oil prices, means of transportation based on eco-friendly fuel are drawing increased attention. Electric propulsion systems (EPSs) have been applied to various means of transportation, including cars, and many studies have examined ships equipped with EPSs. Generally, because of the low energy density of the battery, EPS-equipped vehicles experience the disadvantages of limited operating time and distance compared to fossil fuel-based vehicles. In this study, we developed an algorithm that determines the optimal electric motor and battery specifications for the basic requirements of a small craft equipped with an EPS. Moreover, to control the stability of the craft, we developed an algorithm that optimally arranges EPS components, wherein the center of gravity is used as an object function. The differential evolution algorithm was used for optimization, and the effectiveness was verified by applying this algorithm to the actual design of a small craft. The proposed algorithm represents the research results for determining the basic EPS specifications for a small craft and deriving an optimal arrangement for these specifications. This algorithm is expected to be effectively applied to design a new electric propulsion ship or to convert an existing ship to an electric propulsion ship.

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“…In previous work, several factors have been found influencing EV energy consumption, such as ambient temperature, route type, driving habits, traffic conditions, vehicle accessory utilization, and so on [1]. C. Bingham et al focused on the impact of driving behaviors.…”

Section: Introductionmentioning

confidence: 99%

Analysis on the Influencing Factors of Driving Mileage of Electric Vehicles: A Case Study of Taxis in Beijing

Chen¹,

Zhang²,

Liu³

et al. 2017

dtetr

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Abstract. With the seriousness of energy and environment problem, the electric vehicle (EV) has been widely used. However, the low endurance mileage limits the popularization of electric vehicles (EVs). The aim of this work is to study the influencing factors of driving mileage and then take appropriate measures to extend it as possible. In this paper, the influencing factors of driving mileage are discussed based on the operation data of electric taxis monitored by the Electric Vehicles Monitoring and Service Center of Beijing (EVMSC). The results illustrate that the driving mileage is maximum when the ambient temperature is around 20°C, which will decrease with temperature rising or dropping. Furthermore, the aggressive driving behaviors will increase the consumption of energy and decrease the driving mileage. Lastly, the driving mileage increases with the increase of charging frequency and charging capacity. The conclusions of this work can give EV drivers of EVMSC driving instructions to help them reach the next charging station or the destination in safety and in time.

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Analysis of the main factors influencing the energy consumption of electric vehicles

Cited by 38 publications

References 7 publications

Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information

Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information

Optimized design of electric propulsion system for small crafts using the differential evolution algorithm

Analysis on the Influencing Factors of Driving Mileage of Electric Vehicles: A Case Study of Taxis in Beijing

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