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Optimal battery-swapping mechanism for electric vehicles using hybrid approach

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Abstract

Battery-swapping is a mechanism that involves exchanging discharged batteries for charged ones. Battery-swapping and charging stations (BSCS) enhance operational flexibility and interact with electric vehicle (EVs) batteries. An optimal battery-swapping mechanism is proposed for electric vehicles using a hybrid approach. The proposed intelligent method is a wrapper of the radial basis function neural network (RBFNN) and the war strategy optimization (WSO) algorithm. Hence, it is known as the WSO-RBFNN method. The key objectives of the proposed method are to reduce the total cost by defining an enhanced charging schedule for EV batteries, the number of batteries pulled from inventory to fulfill all incoming EV swap orders, the risk of charging damage while using high-rate chargers, and the cost of electricity at various times of the day. The proposed method minimizes the net costs based on EV energy consumption and travel time. WSO is exploited to attain the optimum control parameters of RBFNN. The performance of the proposed method is measured in MATLAB and compared with existing methods. The simulation outcome shows that the proposed method-related cost is lower than the existing methods. The proposed method provides a low cost of 32.5 $ and a high efficiency of 90% compared with existing methods like differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO).

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Abbreviations

ACB:

Awaited charged battery

GHG:

Greenhouse gas

AGE-HN:

Adaptive-genetic-algorithms combining hill-climbing and neighborhood search

MIIT:

Ministry of Industry and Information Technology

BSCS:

Battery-swapping and charging stations

NHTSA:

National Highway Traffic Safety Administration

DE:

Differential evolution

PSO:

Particle swarm optimization

DN :

Distribution network

RBFNN:

Radial basis function neural network

DOE:

Department of energy

RESs:

Renewable energy sources

EFVs:

Electric freight vehicles

SOC:

State-of-charge

EPA:

Environmental Protection Agency

SOH :

State of health

EV:

Electric vehicle

TOU:

Time-of-use

FCBs:

Fully charged batteries

VRPs:

Vehicle routing problems

FFVs:

Fossil fuel-based vehicles

WSO:

War strategy optimization

GA:

Genetic algorithm

References

  • Ayyarao TS, RamaKrishna NS, Elavarasan RM, Polumahanthi N, Rambabu M, Saini G, Khan B, Alatas B (2022) War strategy optimization algorithm: a new effective metaheuristic algorithm for global optimization. IEEE Access 10:25073–25105

    Article  Google Scholar 

  • Ban M, Zhang Z, Li C, Li Z, Liu Y (2021) Optimal scheduling for electric vehicle battery swapping-charging system based on nanogrids. Int J Electr Power Energy Syst 130:106967

    Article  Google Scholar 

  • Behnke M, Kirschstein T, Bierwirth C (2021) A column generation methodfor an emission-oriented vehicle routing problem on a multigraph. Eur J Oper Res 288(3):794–809

    Article  Google Scholar 

  • Devarul P (2019) Hybrid electric vehicle battery charging/swapping station. Swapping Station. https://doi.org/10.2139/ssrn.3402301

    Article  Google Scholar 

  • Dhas Bensam S, Maruthu Pandi P (2019) A hybrid MWOAL approach for fast and efficient maximum power point tracking in wind energy conversion systems. J Renew Sustain Energy 11(3):033302

    Article  Google Scholar 

  • Garcia-Guarin J, Infante W, Ma J, Alvarez D, Rivera S (2020) Optimal scheduling of smart microgrids considering electric vehicle battery swapping stations. Int J Electr Comput Eng 10(5):5093

    Google Scholar 

  • Hu W, Yao W, Hu Y, Li H (2019) Collaborative optimization of distributed scheduling based on blockchain consensus mechanism considering battery-swap stations of electric vehicles. IEEE Access 7:137959–137967

    Article  Google Scholar 

  • Li J, Wang F, He Y (2020) Electric vehicle routing problem with battery swapping considering energy consumption and carbon emissions. Sustainability 12(24):10537

    Article  CAS  Google Scholar 

  • Liu H, Gao B, Liu Y (2019) Battery swap station location routing problem with capacitated electric vehicles and time windows. In: 2019 IEEE 6th international conference on industrial engineering and applications (ICIEA) pp 832–836. IEEE

  • Masmoudi MA, Hosny M, Demir E, Pesch E (2020) Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem. J Heuristics 26(1):83–118

    Article  Google Scholar 

  • Namani R, Subramaniam S, Samikannu S, Gurusamy M (2021) A simple control strategy and dynamic energy management for the operation of combined grid-connected and standalone solar photovoltaic applications. Journal of King Saud University-Eng Sci. 2021 Dec 27

  • Rajesh P, Shajin F (2020) A multi-objective hybrid algorithm for planning electrical distribution system. Eur J Electr Eng 22(4–5):224–509

    Article  Google Scholar 

  • Rajesh P, Kannan R, Vishnupriyan J, Rajani B (2022) Optimally detecting and classifying the transmission line fault in power system using hybrid technique. ISA Trans 130:253–264

    Article  CAS  PubMed  Google Scholar 

  • Salkuti SR (2022) Emerging and advanced green energy technologies for sustainable and resilient future grid. Energies 15(18):6667

    Article  Google Scholar 

  • Salkuti SR (2023) Advanced technologies for energy storage and electric vehicles. Energies 16(5):2312

    Article  Google Scholar 

  • Sangeetha S, Balamurugan K, Suresh G (2023) Performance analysis of buck converter with fractional PID controller using hybrid technique. Robot Auton Syst 169:104515

    Article  Google Scholar 

  • Shajin FH, Rajesh P, Raja MR (2022) efficient VLSI architecture for fast motion estimation exploiting zero motion prejudgment technique and a new quadrant-based search algorithm in HEVC. Circ Syst Signal Process 130:1–24

    Google Scholar 

  • Shalaby AA, Shaaban MF, Mokhtar M, Zeineldin HH, El-Saadany EF (2022) A dynamic optimal battery swapping mechanism for electric vehicles using an LSTM-based rolling horizon approach. IEEE Trans Intell Trans Syst 23(9):15218–15232

    Article  Google Scholar 

  • Shao S, Guo S, Qiu X (2017) A mobile battery swapping service for electric vehicles based on a battery swapping van. Energies 10(10):1667

    Article  Google Scholar 

  • Tiwari V, Dubey HM, Pandit M, Salkuti SR (2022) CHP-based economic emission dispatch of microgrid using Harris Hawks optimization. Fluids 7(7):248

    Article  Google Scholar 

  • Varatharajalu K, Manoharan M, Palanichamy TS, Subramani S (2023) Electric vehicle parameter identification and state of charge estimation of Li-ion batteries: Hybrid WSO-HDLNN method. ISA Trans. https://doi.org/10.1016/j.isatra.2023.07.029

    Article  PubMed  Google Scholar 

  • Wang S, Yu L, Wu L, Dong Y, Wang H (2019a) An improved differential evolution algorithm for optimal location of battery swapping stations considering multi-type electric vehicle scale evolution. IEEE Access 7:73020–73035

    Article  Google Scholar 

  • Wang Y, Lai K, Chen F, Li Z, Hu C (2019b) Shadow price based co-ordination methods of microgrids and battery swapping stations. Appl Energy 253:113510

    Article  Google Scholar 

  • Wang L, Wang X, Yang W (2020) Optimal design of electric vehicle battery recycling network–From the perspective of electric vehicle manufacturers. Appl Energy 275:115328

    Article  Google Scholar 

  • Wang H, Ma H, Liu C, Wang W (2021) Optimal scheduling of electric vehicles charging in battery swapping station considering wind-photovoltaic accommodation. Electric Power Syst Res 199:107451

    Article  Google Scholar 

  • Wu H, Pang GK, Choy KL, Lam HY (2017a) A charging-scheme decision model for electric vehicle battery swapping station using varied population evolutionary algorithms. Appl Soft Comput 61:905–920

    Article  Google Scholar 

  • Wu H, Pang GK, Choy KL, Lam HY (2017b) An optimization model for electric vehicle battery charging at a battery swapping station. IEEE Trans Veh Technol 67(2):881–895

    Article  Google Scholar 

  • Yang X, Shao C, Zhuge C, Sun M, Wang P, Wang S (2021) Deploying battery swap stations for shared electric vehicles using trajectory data. Transp Res Part d Transp Environ 97:102943

    Article  Google Scholar 

  • You P, Pang JZ, Low SH (2020) Online station assignment for electric vehicle battery swapping. IEEE Trans Intell Transp Syst 23:3256–3267

    Article  Google Scholar 

  • Zhan W, Wang Z, Zhang L, Liu P, Cui D, Dorrell DG (2022) A review of siting, sizing, optimal scheduling, and cost-benefit analysis for battery swapping stations. Energy 258:124723

    Article  Google Scholar 

  • Zhang M, Li W, Yu SS, Wen K, Zhou C, Shi P (2021) A unified configurational optimization framework for battery swapping and charging stations considering electric vehicle uncertainty. Energy 218:119536

    Article  Google Scholar 

  • Zhou B, Zhao Z (2022) Multi-objective optimization of electric vehicle routing problem with battery swap and mixed time windows. Neural Comput Appl 34:1–24

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Mailam Engineering College, Mailam, Tamil Nadu, India

    N. Madhanakkumar

  2. Department of Electrical and Electronics Engineering, Mailam Engineering College, Mailam, Tamil Nadu, India

    M. Vijayaragavan

  3. Department of Electrical and Electronics Engineering, St. Joseph’s College of Engineering, Chennai, 600119, India

    V. Krishnakumar

  4. Department of Electrical and Electronics Engineering, Vivekanandha College of Engineering for Women, Tiruchengode, Namakkal, Tamil Nadu, India

    Kannan Palanisamy

Authors
  1. N. Madhanakkumar
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  2. M. Vijayaragavan
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  3. V. Krishnakumar
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  4. Kannan Palanisamy
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Contributions

N. Madhanakkumar was involved in conceptualization methodology, and original draft preparation. M. Vijayaragavan, V. Krishnakumar and Kannan Palanisamy contributed to supervision.

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Correspondence to N. Madhanakkumar.

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Madhanakkumar, N., Vijayaragavan, M., Krishnakumar, V. et al. Optimal battery-swapping mechanism for electric vehicles using hybrid approach. Clean Techn Environ Policy 26, 351–365 (2024). https://doi.org/10.1007/s10098-023-02632-x

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  • DOI: https://doi.org/10.1007/s10098-023-02632-x

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