Ev Battery Controller Tuning For Efficient Thermal Management Based On Grasshopper Algorithm And Particle Swarm Optimization Algorithm
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Abstract
Electric Vehicles (EVs) offer low emissions and reduced fossil fuel dependence but require efficient battery thermal management to ensure performance and safety. This research aims for tuning proportional-derivative(PD), proportional-integral(PI) and proportional-integral-derivative (PID) controller for Electrical Vehicle (EV) Thermal Management System using Particle Swarm Optimization (PSO) and Grasshopper Optimization Algorithm method (GOA) method to optimize the compressor power consumption to contribute to the development of better EV battery thermal management systems. By minimizing and maximizing the factors involved in the challenges, optimization is the process of identifying the best way to make something as useful and effective as feasible. Simulation results show that GOA outperforms PSO for all controllers. Objective function values for GOA are lower, 1.6783 (PD), 0.8517 (PI), and 0.8114 (PID), compared to PSO, 1.7578, 0.8665, and 0.8254, respectively. Improvement percentages of GOA over PSO are 4.73% (PD), 1.70% (PI), and 1.65% (PID). The PID controller achieved the best performance overall, showing 51.65% improvement over PD and 4.91% over PI. The findings confirm that GOA is more effective than PSO in optimizing controller performance, and that PID is the most suitable for stable and efficient EV battery thermal management.
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References
A. M. Andwari, A. Pesiridis, S. Rajoo, R. Martinez-Botas, and V. Esfahanian, “A review of Battery Electric Vehicle technology and readiness levels,” Renew. Sustain. Energy Rev., 2017.
R. Zhao, J. Gu, and J. Liu, “An experimental study of heat pipe thermal management system with wet cooling method for lithium ion batteries,” J. Power Sources, vol. 273, pp. 1089–1097, Jan. 2015.
A. J. Bhosale and V. N. Deshmukh, “Efficient ways of thermal management of an EV battery,” Mater. Today Proc., vol. 72, pp. 1434–1445, Jan. 2023.
A. Steiner and A. Mladek, “Reducing the energy consumption for comfort and thermal conditioning in EVs,” in 12th International Conference on Ecological Vehicles and Renewable Energies (EVER 2017), 2017.
Z. Rao and S. Wang, “A review of power battery thermal energy management,” Renew. Sustain. Energy Rev., vol. 15, no. 9, pp. 4554–4571, Dec. 2011.
I. Naik and M. Nandgaonkar, “Review of the Approaches and Modeling Methodology for Lithium-Ion Battery Thermal Management Systems in Electric Vehicles,” in Lecture Notes in Mechanical Engineering, 2021, pp. 75–109.
C. Argue, “To what degree does temperature impact EV range?” 2025.
F. Duffner, N. Kronemeyer, J. Tübke, J. Leker, M. Winter, and R. Schmuch, “Post-lithium-ion battery cell production and its compatibility with lithium-ion cell production infrastructure,” Nat. Energy, vol. 6, no. 2, pp. 123–134, Feb. 2021.
W. Liu, T. Placke, and K. T. Chau, “Overview of batteries and battery management for electric vehicles,” Energy Reports, vol. 8, pp. 4058–4084, Nov. 2022.
Q. Wang, B. Jiang, B. Li, and Y. Yan, “A Critical Review of Thermal Management Models and Solutions of Lithium-ion Batteries for the Development of Pure Electric Vehicles,” —, 2020.
N. Selvanathan and W. J. Tee, “A Genetic Algorithm Solution to Solve the Shortest Path Problem in OSPF and MPLS,” 2003.
A. Lambora, K. Gupta, and K. Chopra, “Genetic Algorithm — A Literature Review,” in International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon 2019), 2019, pp. 380–384.
C. K. Dimou and V. K. Koumousis, “Investigation of the robustness of PSO algorithms in reliability optimal design problems,” 2009.
J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, 1995, vol. 4, pp. 1942–1948.
G. Pereira, “Particle Swarm Optimization.” 2014.
S. Saremi, S. Mirjalili, and A. Lewis, “Grasshopper Optimisation Algorithm: Theory and application,” Adv. Eng. Softw., vol. 105, pp. 30–47, Mar. 2017.
S. Padhy and S. Panda, “A hybrid stochastic fractal search and pattern search technique based cascade PI-PD controller for automatic generation control of multi-source power systems in presence of plug in electric vehicles,” CAAI Trans. Intell. Technol., vol. 2, no. 1, pp. 12–25, Mar. 2017.
G. Zhao, X. Wang, M. Negnevitsky, and C. Li, “An up-to-date review on the design improvement and optimization of the liquid-cooling battery thermal management system for electric vehicles,” Appl. Therm. Eng., vol. 219, p. 119626, Jan. 2023.
S. Padhy, S. Panda, and S. Mahapatra, “A modified GWO technique based cascade PI-PD controller for AGC of power systems in presence of Plug in Electric Vehicles,” Eng. Sci. Technol. an Int. J., vol. 20, no. 2, pp. 427–442, Apr. 2017.