New hybrid scheme with local battery energy storages and electric vehicles for the power frequency service

2021 ◽  
pp. 100151
Author(s):  
Qingqing Yang ◽  
Jianwei Li ◽  
Ruixin Yang ◽  
Jin Zhu ◽  
Xuechao Wang ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Hybrid Scheme ◽  
Power Frequency ◽  
Frequency Service ◽  
Battery Energy

scholarly journals A Review on Electric Vehicles: Technologies and Challenges

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 372-404
Author(s):  
Julio A. Sanguesa ◽  
Vicente Torres-Sanz ◽  
Piedad Garrido ◽  
Francisco J. Martinez ◽  
Johann M. Marquez-Barja
Keyword(s):  
Electric Vehicles ◽  
Lithium Ion ◽  
Future Prospects ◽  
Academic Communities ◽  
Market Situation ◽  
Battery Technology ◽  
Battery Energy ◽  
New Research ◽  
Near Future ◽  
Technology Trends

Electric Vehicles (EVs) are gaining momentum due to several factors, including the price reduction as well as the climate and environmental awareness. This paper reviews the advances of EVs regarding battery technology trends, charging methods, as well as new research challenges and open opportunities. More specifically, an analysis of the worldwide market situation of EVs and their future prospects is carried out. Given that one of the fundamental aspects in EVs is the battery, the paper presents a thorough review of the battery technologies—from the Lead-acid batteries to the Lithium-ion. Moreover, we review the different standards that are available for EVs charging process, as well as the power control and battery energy management proposals. Finally, we conclude our work by presenting our vision about what is expected in the near future within this field, as well as the research aspects that are still open for both industry and academic communities.

Tradeoffs between battery energy capacity and stochastic optimal power management in plug-in hybrid electric vehicles

2010 ◽  
Vol 195 (9) ◽  
pp. 2979-2988 ◽  
Author(s):  
Scott J. Moura ◽  
Duncan S. Callaway ◽  
Hosam K. Fathy ◽  
Jeffrey L. Stein
Keyword(s):  
Power Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Energy Capacity ◽  
Optimal Power ◽  
Hybrid Electric ◽  
Battery Energy

Finding an Optimal Driving Strategy for an Electric Bus Based on Operational Data

2015 ◽  
Author(s):  
Warren Vaz ◽  
Arup K. Nandi ◽  
Umit O. Koylu
Keyword(s):  
Electric Vehicles ◽  
Management Strategy ◽  
Clean Energy ◽  
Energy Management Strategy ◽  
Multi Objective ◽  
Electric Bus ◽  
Bus Service ◽  
Battery Energy ◽  
Optimal Driving ◽  
Operational Data

One of the clean energy initiatives at Missouri S&T is an electric shuttle bus service, the Ebus. It provides valuable operational data for a fleet-type electric vehicle (EV) operating over a fixed route. The primary aim of this study is to use the daily operational data obtained from the Ebus in order to formulate an optimal driving strategy. Existing research efforts to improve EVs focus on improvements to the architecture and the energy management strategy. However, they fail to provide the driver with an optimal driving strategy leading to suboptimal use of the stored battery energy. This shortcoming was addressed here by implementing a multi-objective approach to find an optimal driving strategy for an electric bus. The driving strategy was taken to comprise two parts: a constant trip speed and an acceleration value to achieve that speed. From the operational data, the efficiency and power consumption of the electric motor were computed for different speeds. By assuming the entire trip was executed at a constant speed, the range for each speed was calculated. The speeds were ranked based on their corresponding ranges. Then, to achieve the optimal speed, the acceleration duration and energy consumption for different acceleration values were computed. The values were ranked based on the trade-off between duration and energy. The choice of driving strategy (exact speed and acceleration values) is left to the driver since different strategies would be needed for different road conditions. This multi-objective approach gives flexibility to the driver and promotes optimal use of the stored battery energy, thereby enhancing the energy efficiency and range of the Ebus. It can be easily implemented in other electric vehicles as well.

scholarly journals Just-in-time Point Prediction Using a Computationally-efficient Lebesgue-sampling-based Prognostic Method

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Camilo Reyes ◽  
Francisco Jaramillo ◽  
Bin Zhang ◽  
Chetan Kulkarni ◽  
Marcos Orchard
Keyword(s):  
Electric Vehicles ◽  
Accurate Estimation ◽  
Just In Time ◽  
Computationally Efficient ◽  
Leibler Divergence ◽  
Key Factor ◽  
Discharge Model ◽  
Battery Energy ◽  
Computational Resources ◽  
Time Point

Battery energy systems are becoming increasingly popular in a variety of systems, such as electric vehicles. Accurate estimation of the total discharge of a battery is a key element for energy management. Problems such as path planning for drones or road choices in electric vehicles would benefit greatly knowing beforehand the end of discharge time. These tasks are generally performed online and require continuously quick estimations. We propose a novel prognostic method based on a combination of classic Riemann sampling (RS) and Lebesgue sampling (LS) applied to a discharge model of a battery. The method utilizes an early and inaccurate prediction using a RS-based method combined with a particle-filter based prognostic. Once a fault condition has been detected, subsequent Just-in-Time Point (JITP) estimations are updated using a novel LS-based method. The JITP prediction are triggered when the Kullback-Leibler divergence between the probability density functions (PDF) of the long-term-based prediction and the last filtered state reaches a threshold. The CPU time needed to execute a procedure is used as a measure of the computational resources. Results show that this combined approach is several orders of magnitude faster than the classical prognosis scheme. The combination of these two methods provides a robust JITP prognosis with less computational resources, a key factor to consider in real-time applications in embedded systems.

scholarly journals Analysis and Improvement Measures of Driving Range Attenuation of Electric Vehicles in Winter

2021 ◽  
Vol 12 (4) ◽  
pp. 239
Author(s):  
Shuoyuan Mao ◽  
Meilin Han ◽  
Xuebing Han ◽  
Jie Shao ◽  
Yong Lu ◽  
...  
Keyword(s):  
Energy Loss ◽  
Electric Vehicles ◽  
Flow Diagram ◽  
Coupling Mechanism ◽  
Vehicle Dynamic ◽  
Improvement Measures ◽  
Driving Range ◽  
Driving Conditions ◽  
Ev Model ◽  
Battery Energy

A great many EVs in cold areas suffer from range attenuation in winter, which causes driver anxiety concerning the driving range, representing a hot topic. Many researchers have analyzed the reasons for range attenuation but the coupling mechanism of the battery as well as the vehicle and driving conditions have not been clearly estimated. To quantitatively investigate the driving range attenuation of electric vehicles (EVs) during winter, an EV model mainly integrated with a passenger-cabin thermal model, battery model, and vehicle dynamic model was constructed and simulated based on the mass-produced Wuling HongGuang Mini EV. Real vehicle dynamic driving data was used to validate the model. Based on NEDC driving conditions, the driving range calculation formula and energy flow diagram analysis method were used. The reason for attenuation was evaluated quantitatively. Results show that battery energy loss and breaking recovery energy loss contribute nearly half of the range attenuation, which may be alleviated by battery preheating. Suggestions for extending driving range are proposed based on the research.

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