Component sizing of a series hybrid electric vehicle through artificial neural network

Khamesipour, M.; Chitsaz, Iman; Salehi, Mohsen Amini; Alizadenia, S.

doi:10.1016/j.enconman.2022.115300

Cited by 18 publications

(7 citation statements)

References 34 publications

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“…Multi-layer perception (MLP) is implemented in the present study to increase the robustness and repeatability of ANN. 32 Three layers, including the input, output, and hidden layers, are used in the presented ANN. The hidden layer can consist of one or multiple different layers.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, total energy consumption is optimized in this configuration because AC/DC conversion efficiency would not waste energy. 32…”

Section: Methodsmentioning

confidence: 99%

“…30 Mohammad et al 31 utilized the support vector machine (SVM) and the feedforward Neural Network to model the NOx and other emissions of a diesel Compression-Ignition (CI) engine. Khamesipour et al 32 predicted the components sizes of a series HEV based on 3000 experimental data and utilizing the ANN; their results revealed that 1.44 kW/h for battery size and 80 kW for electric motor power were optimal for the investigated cycles. However, vehicle parameters variations were not involved within their job.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Therefore, total energy consumption is optimized in this configuration because AC/DC conversion efficiency would not waste energy. 32 The combustion engine size is calculated by averaging the instantaneous requested power in the driving cycles as equation (4). P(t) is the instantaneous required power of the vehicle integrated from time 0 to T in this equation.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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A component sizing prediction study for a series hybrid electric vehicle based on artificial neural network

Faghih,

Chitsaz,

Ghasemi

2023

International Journal of Engine Research

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In the present study, the predictive tool based on an artificial neural network is developed by means of the experimental data of two series hybrid electric vehicles. The experiments have been conducted on different driving conditions, including highways, traffic, and combined driving conditions. Then, the artificial neural network is developed to predict an arbitrary series hybrid electric vehicle’s required power. The instantaneous required power is divided into dynamic and steady power to size the combustion engine, electric motor, and high voltage battery of the series hybrid electric vehicle. The effects of different ambient conditions (including ambient temperature and altitude), the inverter and high voltage battery efficiencies, and the coast-down coefficients on the components sizing of the series hybrid electric vehicle are then investigated in different driving conditions. The results revealed that the maximum instantaneous power of the electric motor is associated with rapid acceleration in low-speed conditions, and the suburban driving route determines the combustion engine’s maximum power. Notably, the Worldwide Harmonized Light-duty vehicles Test Cycle is the most comprehensive among the available driving cycles, and most of the components’ sizes are determined by this cycle except the combustion engine’s maximum power. It is also realized that the cycle-wise investigation can be summarized into the Isfahan-Tehran route and Worldwide harmonized Light-duty vehicles Test Cycle calculations.

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Section: Methodsmentioning

confidence: 99%

“…Therefore, total energy consumption is optimized in this configuration because AC/DC conversion efficiency would not waste energy. 32…”

Section: Methodsmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

See 2 more Smart Citations

A component sizing prediction study for a series hybrid electric vehicle based on artificial neural network

Faghih,

Chitsaz,

Ghasemi

2023

International Journal of Engine Research

View full text Add to dashboard Cite

show abstract

“…In ref. [15], a method based on a genetic algorithm has been proposed to find the best location for finding EV charging stations. In the proposed method, the open data sources are analyzed during the optimization.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Scheduling of Electric Vehicles and Electrical Storage Systems in Smart Homes Using a Novel Decision Vector and AHP Method

et al. 2022

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The two-way communication of electricity and information in smart homes facilitates the optimal management of devices with the ability to charge and discharge, such as electric vehicles and electrical storage systems. These devices can be scheduled considering domestic renewable energy units, the energy consumption of householders, the electricity tariff of the grid, and other predetermined parameters in order to improve their efficiency and also the technical and economic indices of the smart home. In this paper, a novel framework based on decision vectors and the analytical hierarchy process method is investigated to find the optimal operation schedule of these devices for the day-ahead performance of smart homes. The initial data of the electric vehicle and the electrical storage system are modeled stochastically. The aim of this work is to minimize the electricity cost and the peak demand of the smart home by optimal operation of the electric vehicle and the electrical storage system. Firstly, the different decision vectors for charging and discharging these devices are introduced based on the market price, the produce power of the domestic photovoltaic panel, and the electricity demand of the smart home. Secondly, the analytical hierarchy process method is utilized to implement the various priorities of decision criteria and calculate the ultimate decision vectors. Finally, the operation schedule of the electric vehicle and the electrical storage system is selected based on the ultimate decision vectors considering the operational constraints of these devices and the constraints of charging and discharging priorities. The proposed method is applied to a sample smart home considering different priorities of decision criteria. Numerical results present that although the combination of decision criteria with a high rank of electricity demand has the highest improvement of technical and economic indices of the smart home by about 12 and 26%, the proposed method has appropriate performance in all scenarios for selecting the optimal operation schedule of the electric vehicles and the electrical storage system.

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Thermal analysis of thermo-electric generator systems in hybrid electric vehicles under different operating conditions

Khoshnevisan

Changizian

Raeesi

et al. 2023

J Therm Anal Calorim

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Component sizing of a series hybrid electric vehicle through artificial neural network

Cited by 18 publications

References 34 publications

A component sizing prediction study for a series hybrid electric vehicle based on artificial neural network

A component sizing prediction study for a series hybrid electric vehicle based on artificial neural network

Day-Ahead Scheduling of Electric Vehicles and Electrical Storage Systems in Smart Homes Using a Novel Decision Vector and AHP Method

Thermal analysis of thermo-electric generator systems in hybrid electric vehicles under different operating conditions

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