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Electric Vehicle Charge scheduling approach based on Smart Decision Hunting optimization

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Abstract

Due to technological advancements, a greater emphasis on renewable energy and the possibility to reduce the impacts of transportation on climate change, air pollution, and other environmental issues are analyzed, which gives rise to artificial intelligence techniques for efficient public transportation systems. However, minimizing energy conception, charging time, and increasing the remaining energy is the main contest for the development of EVs among traditional vehicles. In this research, an optimized scheduling technique named the Smart Decision-Hunting optimization (SDHO) algorithm is proposed for determining the available charging stations. The proposed SDHO-based scheduling scheme is used to determine the optimal CS for charging EVs. The suggested SDHO-based charge scheduling scheme is authenticated by vehicular Adhoc network simulation and attains better performance by considering the maximum number of vehicles. The system paradigm for electric vehicle charge scheduling management aims to select the most relevant charging station that requires the least amount of service time for the EV driver since the search of charging station and the waiting time may cause time delay and energy drain. The SDHO algorithm is based on swarm intelligence-based optimization approaches to start a high-level solution based on the first random population of feasible solutions and their fitness function. The Cervus deer strategy was used to enhance the performance of the electric vehicle charge schedule by using the combined characteristics of the Casanova wolf and the Cervus deer. SDHO-based charging scheduling scheme uses communal grading, bordering, stalking, detecting, and ceasing phases. The fitness function is evaluated by concerning parameters like average waiting time, and minimum distance of charging at a charging station. The proposed SDHO-based Charge scheduling scheme achieves the lowest average waiting time of 0.34 min, charged vehicles of 8, a distance of 27.812 km, and the highest remaining energy of 53.934 w, which shows the highest performance than the existing models.Thus, the proposed method provided better charge scheduling for electric vehicles, which stands as an advantage for E-Vehicle charging stations.

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Abbreviations

\(x\) :

Charging vehicle

\(y\) :

Waiting vehicle

\(z\) :

Running vehicle

\({\rm T}\) :

The energy capacity of the battery

\(n,c\) :

Different nodes

\(k\) :

Time

\(\xi_{ + }^{C}\) :

Battery during charging

\(\xi_{ - }^{C}\) :

Battery during discharging

\(p\) :

Distance

\(q\) :

Average waiting time

\(g\) :

Path slope \(V_{p,q}^{C}\)

\(C^{th}\) :

EV input speed

\(\upsilon_{p} \,and\,\upsilon_{q}\) :

Elevation parameters of the edges \(w_{p} \,and\,w_{q}\)

\(G\) :

Current iteration

\(U_{p,q}\) :

Speed of the EV

\(\lambda\) :

Consumption of energy

\(trav\) :

Travel

\(Y\) :

Length of the road segment with the set of edges

\({\rm Z}\) :

Adjacent matrix.

\(\underline{{\xi^{C} }}\) :

Lower boundaries

\(\overline{{\xi^{C} }}\) :

Upper boundary

\(f\) :

Edges

\(w_{l}\) :

The capacity of the battery

\(\vec{J}\) and \(\vec{\rm I}\) :

Coefficient vectors of the Casanova

\(\vec{\rm P}\) and \(\vec{\rm P}_{W}\) :

Position vector belongs to the Casanova wolf and the prey

\(\vec{\rm O}_{1}\) and \(\vec{\rm O}_{2}\) :

Random vectors

\(\vec{R}\) :

Coefficient vector component

\(\left( {{\rm P}^{ * } ,D^{ * } } \right)\) :

Position of the Casanova wolf

\(\left( {{\rm P},D} \right)\) :

The initial position of the Casanova wolf

\(\alpha\) :

Alpha

\(\vec{\rm P}_{4}\) :

The leading hunter till now in the hunting method

\(\vec{\rm P}_{5}\) :

Successor hunter till now in the hunting method

\(S\) :

Random number

\({\rm P}^{\rm T}\) :

The current position of the hunters

\({\rm P}_{{\left( {\alpha_{1} ,\alpha_{2} ,\alpha_{3} } \right)}}\) :

Leading hunters among the \(\left( {\alpha_{1} } \right)\), \(\left( {\alpha_{2} } \right)\), and \(\left( {\alpha_{3} } \right)\)

\(s\) :

Distance

\(u\) :

The remaining energy of the battery in the EV

\(t\) :

Average waiting time of the vehicle

V2V:

Vehicle-to-Vehicle communication

Req:

Request

SoC:

State of Charge

QoS:

Quality of Service

X:

A non-empty set of nodes

Y:

Designates the length of the road segment

Z:

Adjacent matrix

K:

Travelling time

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Authors and Affiliations

  1. Central University of Jharkhand, Ranchi, India

    Shashank Kumar Jha, Chakrapani Gandikoti & Bishnu Mohan Jha

  2. Assam Science and Technology University, Guwahati, India

    Sumit Kumar Jha

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  1. Shashank Kumar Jha
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  2. Chakrapani Gandikoti
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Jha, S.K., Gandikoti, C., Jha, S.K. et al. Electric Vehicle Charge scheduling approach based on Smart Decision Hunting optimization. Int J Interact Des Manuf 18, 331–349 (2024). https://doi.org/10.1007/s12008-023-01461-y

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