An Approach to Control Electric Automotive Water Pumps Deploying Artificial Neural Networks

Authors: Gabriel S. Adesina, Ruixue Cheng, Michael Short, Geetika Aggarwal

Abstract:

With the global shift towards sustainability and technological advancements, electric hybrid vehicles (EHVs) are increasingly being seen as viable alternatives to traditional internal combustion (IC) engine vehicles, which also require efficient cooling systems. The electric automotive water pump (AWP) has been introduced as an alternative to IC engine belt-driven pump systems. However, current control methods for AWPs typically employ fixed gain settings, which are not ideal for the varying conditions of dynamic vehicle environments, potentially leading to overheating issues. To overcome the limitations of fixed gain control, this paper proposes implementing an artificial neural network (ANN) for managing the AWP in EHVs. The proposed ANN provides an intelligent, adaptive control strategy that enhances the AWP's performance, supported through MATLAB simulation work illustrated in this paper. Comparative analysis demonstrates that the ANN-based controller surpasses conventional PID and fuzzy logic controllers (FLC), exhibiting no overshoot, 0.1 secs rapid response, and 0.0696 integral absolute error (IAE) performance. Consequently, the findings suggest that ANNs can be effectively utilized in EHVs.

Keywords: Automotive water pump, cooling system, electric hybrid vehicles, artificial neural networks, PID control, fuzzy logic control, IAE, MATLAB.

References:

[1] Liu, Y., Kang, Y., Sun, W. and Xu, X., “Power Control Strategy and Performance Evaluation of a Novel Two-pump Hydraulic Control Unit for BEVs”, 2020.
[2] Joon Sung Park, B.-G. Gu, J.-H. Choi, and I.-S. Jung, “Development of BLDC Motor Drive for Automotive Water Pump Systems,” Journal of International Council on Electrical Engineering, vol. 1, no. 4, pp. 395–399, Oct. 2011.
[3] Jibril Abdullahi Bala, Ndukwe Okpo Kalu, S. U. Hussein, and Taliha Abiodun Folorunso, “A Fuzzy Logic Control Scheme for Electric Automotive Water Pumps,” 2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON), Apr. 2022.
[4] Halima Yakubu, S. Thomas, S. U. Hussein, V. C. Anye, Gokhan Koyunlu, and Omotayo Oshiga, “Azimuth Position Control for Deep Space Antenna using Fuzzy Logic Controller,” Dec. 2019.
[5] H. Yakubu, S. Hussein, G. Koyunlu, E. Ewang, and S. Abubakar, “Fuzzy-PID Controller for Azimuth Position Control of Deep Space Antenna,” Covenant Journal of Informatics & Communication Technology, vol. 8, no. 1, Jun. 2020.
[6] Dakheel, H.S., Speed control of separately exited DC motor using artificial neural network. Journal of Engineering and Sustainable Development, 16(4), pp.349-362. 2012.
[7] W. M. Elsrogy, M. A. Fkirin, and M. A. M. Hassan, “Speed control of DC motor using PID controller based on artificial intelligence techniques,” IEEE Xplore, May 01, 2013.
[8] P. Mohindru, G. Sharma, and P. Pooja, “Simulation Performance of PID and Fuzzy Logic Controller for Higher Order System,” Communications on Applied Electronics, vol. 1, no. 7, pp. 31–35, May 2015, doi: https://doi.org/10.5120/cae-1604.
[9] M. Mohamed, M. H. S. M. S. E.-D. a. M. F., Performance of Electronically Controlled Automotive Engine Cooling System Using PID and LQR Control Techniques. IOSR J. Mech. Civ. Eng., Volume 15, pp. 42-45. 2018.
[10] D. Dvorak, D. Basciotti, and I. Gellai, “Demand-Based Control Design for Efficient Heat Pump Operation of Electric Vehicles,” Energies, vol. 13, no. 20, p. 5440, Oct. 2020.
[11] Milad Akrami, Ehsan Jamshidpour, Serge Pierfederici, and V. Frick, “Comparison of Flatness-based Control and Field-Oriented Control for PMSMs in Automotive Water Pump Application,” Jun. 2023.
[12] Sofiane Ben Abdi, Abderrazak Debilou, Aicha Guergazi, Younes Samsar, and Mahdi Labed, “Speed Control of BLDC Quadrotor Motor Using an ANN Trained with PID Controller Data Optimized by PSO Algorithm,” Apr. 2024.
[13] Ph. Q. Dzung, and Le. M. Phuong, "ANN-Control system dc motor". Faculty of Electrical and Electronic Engineering HCMC University of Technology Ho Chi Minh City-Vietnam IEEE Transactions, pp.82-90. 2002.
[14] H. Sh., “Speed Control of Separately Exited DC Motor Using Artificial Neural Network,” Journal of Engineering and Development, vol. 16, no. 4, pp. 349–362, Jan. 2012.
[15] A. Sözen, T. Menlik, and S. Ünvar, “Determination of efficiency of flat-plate solar collectors using neural network approach,” Expert Systems with Applications, vol. 35, no. 4, pp. 1533–1539, Nov. 2008.
[16] M. Caner, E. Gedik, and A. Keçebaş, “Investigation on thermal performance calculation of two type solar air collectors using artificial neural network,” Expert Systems with Applications, vol. 38, no. 3, pp. 1668–1674, Mar. 2011.
[17] N. I. Santos, A. M. Said, D. E. James, and N. H. Venkatesh, “Modeling solar still production using local weather data and artificial neural networks,” Renewable Energy, vol. 40, no. 1, pp. 71–79, Apr. 2012.
[18] S. Halder, A. Khan, Uttam Narendra Thakur, and S. Saha, “Design and Analysis of Temperature Control System using Conventional PI and Advanced ANN Controllers,” 2018 International Conference on Computing, Power and Communication Technologies (GUCON), Sep. 2018.