Quality of Service and Associated Communication Infrastructure for Electric Vehicles

RAMRAJ, Rajeshkumar; PASHAJAVID, Ehsan; ALAHAKOON, Sanath; JAYASINGHE, Shantha

doi:10.20944/preprints202309.1204.v1

2023

DOI: 10.20944/preprints202309.1204.v1

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Quality of Service and Associated Communication Infrastructure for Electric Vehicles

Rajeshkumar RAMRAJ,

Ehsan PASHAJAVID,

Sanath ALAHAKOON

et al.

Abstract: Transportation electrification has been identified as a crucial shift to be made for achieving energy security and emission reduction targets. Electric Vehicles (EVs) play a significant role in achieving these targets; therefore, there is a growing trend to develop new EV technologies and associated infrastructure. Along with these developments, EVs' Quality of Service (QoS) aspects should be enhanced to increase the number of EVs on the road. Various QoS factors are defined to assess the QoS of EVs, … Show more

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Cited by 3 publications

References 111 publications

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Artificial Neural Network Modeling to Predict Thermal and Electrical Performances of Batteries with Direct Oil Cooling

Garud,

Han,

Hwang

et al. 2023

Batteries

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The limitations of existing commercial indirect liquid cooling have drawn attention to direct liquid cooling for battery thermal management in next-generation electric vehicles. To commercialize direct liquid cooling for battery thermal management, an extensive database reflecting performance and operating parameters needs to be established. The development of prediction models could generate this reference database to design an effective cooling system with the least experimental effort. In the present work, artificial neural network (ANN) modeling is demonstrated to predict the thermal and electrical performances of batteries with direct oil cooling based on various operating conditions. The experiments are conducted on an 18650 battery module with direct oil cooling to generate the learning data for the development of neural network models. The neural network models are developed considering oil temperature, oil flow rate, and discharge rate as the input operating conditions and maximum temperature, temperature difference, heat transfer coefficient, and voltage as the output thermal and electrical performances. The proposed neural network models comprise two algorithms, the Levenberg–Marquardt (LM) training variant with the Tangential-Sigmoidal (Tan-Sig) transfer function and that with the Logarithmic-Sigmoidal (Log-Sig) transfer function. The ANN_LM-Tan algorithm with a structure of 3-10-10-4 shows accurate prediction of thermal and electrical performances under all operating conditions compared to the ANN_LM-Log algorithm with the same structure. The maximum prediction errors for the ANN_LM-Tan and ANN_LM-Log algorithms are restricted within ±0.97% and ±4.81%, respectively, considering all input and output parameters. The ANN_LM-Tan algorithm is suggested to accurately predict the thermal and electrical performances of batteries with direct oil cooling based on a maximum determination coefficient (R2) and variance coefficient (COV) of 0.99 and 1.65, respectively.

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Artificial Neural Network Modeling to Predict Thermal and Electrical Performances of Batteries with Direct Oil Cooling

Garud,

Han,

Hwang

et al. 2023

Batteries

View full text Add to dashboard Cite

show abstract

Network Modeling and Analysis of Internet of Electric Vehicles Architecture for Monitoring Charging Station Networks—A Case Study in Chile

Ahmed,

Guerrero,

Franco

2024

Sustainability

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Nowadays, the internet of electric vehicles (IoEV) has opened many new opportunities for various applications such as charging station selection, charging/discharging management, as well as supporting various end-user services. In Chile, the current deployment of charging station networks is still at an early stage and such stations do not support the required local and global communication and monitoring capabilities that allow the integration of such services. The underlaying communication infrastructures will play an important role in supporting different applications, such as grid-to-vehicle, vehicle-to-grid, and vehicle-to-vehicle services. This work developed an IoEV architecture for real-time monitoring of charging station networks, which consists of three layers: the physical layer, the communication network layer, and the virtual layer. In order to support reliable IoEV communications, different requirements for data rate, reliability, latency, and security are needed. We developed a communication network model for charging stations based on the IEC 61850-90-8 standard. The performance of the developed architecture has been evaluated considering different real scenarios including a standalone charging station, a group of charging stations in a university campus parking lot, and charging stations in a city. The performance of the communication network has been evaluated with respect to end-to-end latency.

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Heat Transfer Characteristics of an Electric Motor with Oil-Dripping Cooling under Overload Conditions

Kang,

Garud,

Park

et al. 2024

Symmetry

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The continuously increasing energy density of electric motors to match the performance of electric vehicles with internal-combustion-engine vehicles demands an advanced cooling strategy. Direct dripping/spray cooling is one of such potential cooling strategies to replace the conventional cooling method for achieving the desired thermal management of next-generation electric motors. In the present work, the heat transfer characteristics of an electric motor with oil-dripping cooling were experimentally investigated considering overload operating conditions. An experimental set-up comprising a 15 kW electric motor and an oil-dripping cooling system was developed. The experimental data were used to evaluate the maximum temperature, heat transfer coefficient and power consumption under the influence of a dripping hole diameter (2 mm, 3 mm, 4 mm), oil flow rate (8 LPM, 12 LPM, 16 LPM) and overload operating power (8.28 kW, 12.05 kW, 14.21 kW). The symmetrical oil distribution over the electric motor and the superior heat transfer from the electric motor to the oil was achieved when the oil-dripping cooling system was designed with the combination of a 4 mm dripping hole diameter and a 12 LPM oil flow rate. The combination of the 4 mm dripping hole diameter and 12 LPM oil flow rate showed the lowest maximum temperatures of 31.9 °C and 46.3 °C for electric motor under overload operating powers of 8.28 kW and 14.21 kW, respectively. In addition, the highest heat transfer coefficient of 7528.61 W/m2-K and lowest power consumption of 18.07 W were achieved for the oil-dripping cooling system with the combination of a 4 mm dripping hole diameter and 12 LPM oil flow rate. The best combination of the operating parameters is proposed for developing the oil-dripping cooling system that enables superior heat transfer characteristics and, thus, an enhanced thermal management of electric motors under overload conditions.

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Quality of Service and Associated Communication Infrastructure for Electric Vehicles

Cited by 3 publications

References 111 publications

Artificial Neural Network Modeling to Predict Thermal and Electrical Performances of Batteries with Direct Oil Cooling

Artificial Neural Network Modeling to Predict Thermal and Electrical Performances of Batteries with Direct Oil Cooling

Network Modeling and Analysis of Internet of Electric Vehicles Architecture for Monitoring Charging Station Networks—A Case Study in Chile

Heat Transfer Characteristics of an Electric Motor with Oil-Dripping Cooling under Overload Conditions

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