Comparative Study of Energy Consumption for Electric Vehicles with Various On-board Energy Storage Systems

Saikong, Waiard; Kulworawanichpong, Thanatchai

doi:10.1016/j.egypro.2017.10.060

Cited by 17 publications

(6 citation statements)

References 4 publications

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“…For city traffic under frequent stop-and-drive conditions, HESS is an appropriate solution. However, lithium-ion battery and HESS do not have a significant difference on the extra urban driving cycle route [14].…”

Section: Introductionmentioning

confidence: 93%

“…2018, 8, x FOR PEER REVIEW 3 of 12 solution. However, lithium-ion battery and HESS do not have a significant difference on the extra urban driving cycle route [14]. In this study, the research target is to evaluate charging characteristics and propose a new algorithm for improving determination accuracy of SOC during the regenerative braking period of TMED type parallel hybrid electric vehicles.…”

Section: Chassis Dynamometer and Experimental Vehiclementioning

confidence: 99%

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Experimental Evaluation and Prediction Algorithm Suggestion for Determining SOC of Lithium Polymer Battery in a Parallel Hybrid Electric Vehicle

et al. 2018

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The necessity of hybrid vehicles and electric vehicles is widely known for reasons such as fossil fuel depletion, climate change, emission norms mandated by regulations, and so on. Expansion of the hybrid vehicle market is a realistic way to respond to fuel efficiency regulations. Hybrid electric vehicles are continuously challenged to meet cross-attribute performance while minimizing energy usage and component cost in a highly competitive automotive market. Current optimization strategy for a parallel hybrid requires much computational time and relies heavily on the drive cycle to accurately represent driving conditions in the future. With increasing application of the lithium-ion battery technology in the automotive industry, development processes and validation methods for the battery management system (BMS) have attracted attention. The purpose of this study is to propose an algorithm to analyze charging characteristics and improve accuracy for determining state of charge (SOC), the equivalent of a fuel gauge for the battery pack, during the regenerative braking period of a TMED type parallel hybrid electric vehicle.

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Section: Introductionmentioning

confidence: 93%

Section: Chassis Dynamometer and Experimental Vehiclementioning

confidence: 99%

Experimental Evaluation and Prediction Algorithm Suggestion for Determining SOC of Lithium Polymer Battery in a Parallel Hybrid Electric Vehicle

et al. 2018

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“…1, the vehicle motion is opposed by various forces, e.g. aerodynamic drag (F ad ), gradient force (F rg ), and rolling resistance force (F rr ) [1,15]. a is an acceleration and is called rotational inertia factor [3].…”

Section: Mathematical Model and Optimization Formulationmentioning

confidence: 99%

Velocity Trajectory Optimization with Time Management in Electric Vehicles

Saikong¹,

Sumpavakup²,

Kulworawanichpong³

2019

IJIES

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An electric vehicle is one of the crucial technologies for transportation of passengers and goods to reduce the consumption of fossil fuel which is the main factor of air pollution. Currently, energy and power density of batteries are low when compared to fossil fuel, having an effect that electric vehicles have limited driving range. As a result, simulating velocity trajectory for optimal energy consumption for electric vehicle is necessary. This paper introduced the algorithm to determine optimal velocity trajectory or velocity profile to consume the least energy in the required cycle with the condition that the optimal velocity had to be similar to the profile of the reference velocity or the original velocity so that it was capable of driving in the traffic circumstance of the sample cycles. To find the optimal velocity with the least energy consumption, the simple vehicle model and mathematic approach to simulate optimal vehicle trajectory by particles swarm optimization (PSO) were used in this study. The route used in the study were divided into elementary driving cycles. The algorithm will simulate the acceleration and create the optimal velocity trajectory of each segment to find the trajectory that consumed the least energy regarding the condition of driving time determined by user. The result of the simulation found that the algorithm reduced the consumption of energy and maximum electric power with significance. That were 13.44% and 14.225% respectively when the electric vehicle was determined to arrive at the destination by 1 minute late.

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“…The midway is the use of different heterogeneous energy sources, which combines low power battery for normal power and a supercapacitor for transient power during picks and deceleration. With hybridization, two elements can be combined to form an efficient storage system, one having huge energy density, such as a super capacitor, and the other having large power density, such as a battery [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Battery/Supercapacitor Energy Management system for Electric Vehicle

Rade,

More,

Kanse

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Electric vehicles (EVs) require appropriate design and sizing of their energy sources and proper management. The solution will be an energy efficient with cost-effective, increased lifespan, and an increase in the vehicle’s range. As per theory of evolution the model and rating calculations are discussed. Also this paper addresses the distribution of power within two energy sources, adopting a converter. Only one energy element unable to meet each preferred parameters. In addition to assisting electric vehicles in acceleration, the Hybrid Energy Storage System captures regenerative braking energy. By controlling the current of battery as consistent as viable at the time of picks, power management is primarily concerned with reducing battery stress and merging supercapacitors to supply instantaneous current at the time of pick and to recover power during deceleration. According to the results of HESS and experimental setup, current of battery is controlled as consistent as at the time of acceleration when the power management algorithm is implemented. In other words, only normal current is provided by the battery at the time of picks, while transitory current is provided via the ultracapacitor during acceleration.

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Comparative Study of Energy Consumption for Electric Vehicles with Various On-board Energy Storage Systems

Cited by 17 publications

References 4 publications

Experimental Evaluation and Prediction Algorithm Suggestion for Determining SOC of Lithium Polymer Battery in a Parallel Hybrid Electric Vehicle

Experimental Evaluation and Prediction Algorithm Suggestion for Determining SOC of Lithium Polymer Battery in a Parallel Hybrid Electric Vehicle

Velocity Trajectory Optimization with Time Management in Electric Vehicles

Battery/Supercapacitor Energy Management system for Electric Vehicle

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