A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development

Un-Noor, Fuad; Padmanaban, Sanjeevikumar; Mihet‐Popa, Lucian; Mollah, Md. Nurunnabi; Hossain, Eklas

doi:10.20944/preprints201705.0090.v1

Cited by 146 publications

(4 citation statements)

References 103 publications

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“…To this aim, hybrid electric vehicles (HEVs) offer a good potential. HEVs feature a longer driving range than battery electric vehicles (BEVs), thanks to the presence of the thermal engine, even though the driving range of HEVs in pure electric mode is much lower than that of BEVs, due to the smaller battery packs [2]. In general, HEVs can lead to a significant improvement in fuel economy and CO 2 emissions if compared to conventional vehicles.…”

Section: State-of-the-art On the Optimal Design For Hybrid Electric Vmentioning

confidence: 99%

Optimal Design of Power-Split HEVs Based on Total Cost of Ownership and CO2 Emission Minimization

et al. 2018

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An optimal design toolbox for hybrid electric vehicles has been developed and applied to three different vehicle segments (a compact vehicle, a small SUV and a medium-size SUV) for two separate power-split hybrid layouts, both equipped with a diesel engine. One layout features a (3gTR) whereas the other lacks of the additional 3-gear transmission. The toolbox combines the optimization of the vehicle design to that of the control strategy and is based on the minimization of the total cost of ownership (TCO) over the vehicle lifetime. The tool still retains the capability of complying with different performance and emission constraints. The identified optimal designs have proved to lead to a reduction of the CO 2 emissions by 50 to 55% and to a reduction of the TCO by 9 to 10% if compared to the conventional vehicle. Such results held for all classes of vehicle. A cost-benefit analysis and a break-even analysis have also been carried out. A mileage of 20,000 km/year over an urban driving scenario has turned out to possibly allow the driver to break even in about four years for the SUVs and in about six years for the compact vehicle. Finally, a linear correlation between the TCO and the specifications of the design components has been detected with a mean percentage error of about 0.1%. Such a correlation can be very helpful for vehicle design tasks.

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Section: State-of-the-art On the Optimal Design For Hybrid Electric Vmentioning

confidence: 99%

Optimal Design of Power-Split HEVs Based on Total Cost of Ownership and CO2 Emission Minimization

et al. 2018

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“…In order to achieve the goals of low-carbon green cities, it is necessary to transform traditional fuel vehicles into new energy vehicles [2,3]. The electric vehicle plays an important role in this transition, with its advantages of no pollution or emissions.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

Zhang

Sun

et al. 2017

Energies

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Abstract:The extended range electric vehicle (EREV) can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN) is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS) algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip.

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“…V2G has received significant attention from the research community [26][27][28][29][30]. In particular, a detailed review of V2G operation is given in [29].…”

Section: Introductionmentioning

confidence: 99%

Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

et al. 2017

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Abstract:The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%.

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A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development

Cited by 146 publications

References 103 publications

Optimal Design of Power-Split HEVs Based on Total Cost of Ownership and CO2 Emission Minimization

Optimal Design of Power-Split HEVs Based on Total Cost of Ownership and CO2 Emission Minimization

Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

Bi-Directional Coordination of Plug-In Electric Vehicles with Economic Model Predictive Control

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