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Adaptive control of plug-in hybrid electric vehicles based on energy management strategy and dynamic programming algorithm

College of Mechanical and Electrical Engineering, Central South University of Forestry and Technology, Changsha, 410004, China

Received: 12 Jul 2024; Revised: 26 Sep 2024; Accepted: 7 Oct 2024; Available online: 16 Oct 2024; Published: 1 Nov 2024.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2024 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

This study mainly analyses the fuel consumption of plug-in hybrid vehicles during operation. A new control method for automobiles based on energy management strategy and dynamic programming algorithm is proposed. The new method plans and analyses the minimum electricity consumption, and then uses dynamic programming algorithms to analyse this parameter. The research results indicated that the vehicle state was constantly changing with the variation of SOC value during driving. The energy mobilization of the vehicle was more obvious after adding dynamic programming strategy. The efficiency of the vehicle was relatively high in driving state 1, with a minimum value of 70%, which was about 20% higher than in driving state 4. The average fuel consumption in driving state 2 was 1.8L higher than in other driving states. The overall efficiency of automobiles after incorporating dynamic programming was improved, with a shorter time to reach the lowest efficiency point compared with not incorporating dynamic programming algorithms. The highest efficiency value was 7.86% higher than that of not incorporating dynamic programming models. The new control method can reduce energy consumption and improve the energy management and control effect. The study provides a better research direction for energy management and control of hybrid electric vehicles in the future.

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Keywords: Electric vehicles; Consumption; DP algorithm; Energy management; Control; efficiency

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  1. Abd-Elhaleem, S., Shoeib, W., Sobaih, A.A. (2023). A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm. Energy, 265(11), 126153-126154. https://doi.org/10.1016/j.energy.2022.126153
  2. Belkhier, Y., Oubelaid, A., & Shaw, R.N. (2024). Hybrid power management and control of fuel cells‐battery energy storage system in hybrid electric vehicle under three different modes. Energy Storage, 6(1), 511-512. https://doi.org/10.1002/est2.511
  3. Cao, Y., Yao, M., & Sun, X. (2023). An overview of modelling and energy management strategies for hybrid electric vehicles. Applied Sciences, 13(10), 5947-5948. https://doi.org/10.3390/app13105947
  4. Cao, Y., Yao, M., & Sun, X. (2023). An overview of modelling and energy management strategies for hybrid electric vehicles. Applied Sciences, 13(10), 5947-5948. https://doi.org/10.3390/app13105947
  5. Chen, C., Wang, X., Lei, Z., & Shangguan, C. (2024). Research on Plug-in Hybrid Electric Vehicle (PHEV) energy management strategy with dynamic planning considering engine start/stop. World Electric Vehicle Journal. 15(8), 350-351. https://doi.org/10.3390/wevj15080350
  6. Cipek, M., Pavković, D., & Kljaić, Z. (2023). Optimized energy management control of a hybrid electric locomotive. Machines, 11(6), 589-591. https://doi.org/10.3390/machines11060589
  7. Cui, W., Cui, N., Li, T., Du, Y., & Zhang, C. (2024). Multi-objective hierarchical energy management for connected plug-in hybrid electric vehicle with cyber–physical interaction. Applied Energy, 360(4), 122816-122817. https://doi.org/10.1016/j.apenergy.2024.122816
  8. Gao, H., Yin, B., Pei, Y., Gu, H, Xu, S., & Dong, F. (2024). An energy management strategy for fuel cell hybrid electric vehicle based on a real-time model predictive control and pontryagin’s maximum principle. International Journal of Green Energy. 11(4), 1-3. https://doi.org/10.1080/15435075.2024.2322973
  9. Gao, K., Luo, P., Xie, J, Chen, B., Wu, Y., & Du, R. (2023). Energy management of plug-in hybrid electric vehicles based on speed prediction fused driving intention and LIDAR. Energy, 284(10), 1285-1286. https://doi.org/10.1016/j.energy.2023.128535
  10. Gnanaprakasam, C.N., Meena, S., & Devi, M.N. (2023). Shanmugasundaram N, Sridharan S. Robust energy management technique for plug-in hybrid electric vehicle with traffic condition identification. Applied Soft Computing. 133(5), 109937-109938. https://doi.org/10.1016/j.asoc.2022.109937
  11. Hao, J., Ruan, S., & Wang, W. (2023). Model predictive control based energy management strategy of series hybrid electric vehicles considering driving pattern recognition. Electronics, 12(6), 1418-1419. https://doi.org/10.3390/electronics12061418
  12. He, H., Meng, X., Wang, Y., Khajepour, A., An, X., Wang, R., & Sun, F. (2024). Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives. Renewable and Sustainable Energy Reviews. 192(1), 114248-114249. https://doi.org/10.1016/j.rser.2023.114248
  13. He, L., Chen, F., Tian, P., & Gou, H. (2024). An improved energy management strategy for hybrid electric powered aircraft based on deep reinforcement learning. Aerospace Science and Technology, 149(6), 109137-109138. https://doi.org/10.1016/j.ast.2024.109137
  14. He, R., Yan, Y., & Hu, D. (2021). Optimised adaptive control methodology for mode transition of hybrid electric vehicle based on the dynamic characteristics analysis. Vehicle System Dynamics, 59(8), 1282-1303. https://doi.org/10.1080/00423114.2020.1752923
  15. Hua, M., Zhang, C., Zhang, F., Li, Z., Yu, X., Xu, H., & Zhou, Q. (2023). Energy management of multi-mode plug-in hybrid electric vehicle using multi-agent deep reinforcement learning. Applied Energy, 348(10), 121526-121526. https://doi.org/10.1016/j.apenergy.2023.121526
  16. Jia, C., Qiao, W., Cui, J., & Qu, L.Y. (2023). Adaptive model-predictive-control-based real-time energy management of fuel cell hybrid electric vehicles. IEEE Transactions on Power Electronics, 38(2), 2681-2694. https://doi.org/10.1109/TPEL.2022.3214782
  17. Jung, J., Kim, D., Yang, L., & Kim, N. (2024). Optimal energy management strategy for repeat path operating fuel cell hybrid tram. Energies. 17(7), 1560-1561. https://doi.org/10.3390/en17071560
  18. Kashif, M., Singh, B., & Murshid, S. (2021). Solar PV array fed self-sensing control of PMSM drive with robust adaptive hybrid SOGI based flux observer for water pumping. IEEE Transactions on Industrial Electronics. 68(8), 6962-6972. https://doi.org/10.1109/TIE.2020.3003656
  19. Ma, Z., Luan, Y., Zhang, F., & Coskun, S., (2024). A data-driven energy management strategy for plug-in hybrid electric buses considering vehicle mass uncertainty. Journal of Energy Storage, 77(6), 109963-109964. https://doi.org/10.1016/j.est.2023.109963
  20. Milbradt, D.M., de Oliveira Evald, P.J., Hollweg, G.V., & Gründling, H.A. (2023). A hybrid robust adaptive sliding mode controller for partially modelled systems: Discrete-time lyapunov stability analysis and application. Nonlinear Analysis: Hybrid Systems. 48(1), 101333-101334. https://doi.org/10.1016/j.nahs.2023.101333
  21. Millo, F., Rolando, L., Tresca, L., & Pulvirenti, L. (2023). Development of a neural network-based energy management system for a plug-in hybrid electric vehicle. Transportation Engineering, 11(1), 100156-100158. https://doi.org/10.1016/j.treng.2022.100156
  22. Mohammed, A.S., Atnaw, S.M., Salau, A.O., & Eneh, J.N. (2023). Review of optimal sizing and power management strategies for fuel cell/battery/super capacitor hybrid electric vehicles. Energy Reports, 9(9), 2213-2228. https://doi.org/10.1016/j.egyr.2023.01.042
  23. Peng, Z., Wang, J., Liu, Z., & Li, Z. J. (2020). Adaptive gate delay-time control of Si/SiC hybrid switch for efficiency improvement in inverters. IEEE Transactions on Power Electronics, 2020, 3437-3449. https://doi.org/10.1109/TPEL.2020.3015803
  24. Punyavathi, R., Pandian, A., Singh, A.R., Bajaj, M., Tuka, M.B., & Blazek, V. (2024). Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control. Scientific Reports, 14(1), 5661-5662. https://doi.org/10.1038/s41598-024-55988-5
  25. Rasool, M., Khan, M.A., & Zou, R. (2023). A comprehensive analysis of online and offline energy management approaches for optimal performance of fuel cell hybrid electric vehicles. Energies, 16(8), 3325-3326. https://doi.org/10.3390/en16083325
  26. Ruan, J., Wu, C., Liang, Z., Liu, K., Li, B., Li, W., & Li, T. (2023). The application of machine learning-based energy management strategy in a multi-mode plug-in hybrid electric vehicle, part II: Deep deterministic policy gradient algorithm design for electric mode. Energy. 269(6), 126792-126793. https://doi.org/10.1016/j.energy.2023.126792
  27. Shi, D., Li, S., Liu, K., Xu, Y., Wang, Y., & Guo, C., (2023). Adaptive energy management strategy for plug-in hybrid electric vehicles based on intelligent recognition of driving cycle. Energy Exploration & Exploitation, 41(1), 246-272. https://doi.org/10.1177/01445987221111488
  28. Sidharthan, V.P., Kashyap, Y., & Kosmopoulos, P. (2023). Adaptive-energy-sharing-based energy management strategy of hybrid sources in electric vehicles. Energies, 16(3), 1214-1215. https://doi.org/10.3390/en16031214
  29. Song, D., Bi, D., Zeng, X., & Wang, S. (2023). Energy management strategy of plug-in hybrid electric vehicles considering thermal characteristics. International Journal of Automotive Technology, 24(3), 655-668. https://doi.org/10.1007/s12239-023-0055-0
  30. Song, S., Han, C., Lee, G., McCann, R. A., & Jang, G. (2020). Voltage-sensitivity-approach-based adaptive droop control strategy of hybrid STATCOM. IEEE Transactions on Power Systems, 36(1), 389-401. https://doi.org/10.1109/TPWRS.2020.3003582
  31. Tian, S., Zheng, Q., Wang, W., & Zhang, Q. (2024). Integrated real-time optimal energy management strategy for plug-in hybrid electric vehicles based on rule-based strategy and AECMS. International Journal of Vehicle Design, 94(1-2), 150-175. https://doi.org/10.1504/IJVD.2024.136239
  32. Usman, A. M., & Abdullah, M. K. (2023). An assessment of building energy consumption characteristics using analytical energy and carbon footprint assessment model. Green and Low-Carbon Economy, 1(1), 28-40. https://doi.org/10.47852/bonviewGLCE3202545
  33. Venkitaraman, A.K., & Kosuru, V.S. (2023). Hybrid deep learning mechanism for charging control and management of Electric Vehicles. European Journal of Electrical Engineering and Computer Science. 7(1), 38-46. https://doi.org/10.24018/ejece.2023.7.1.485
  34. Vignesh, R., & Ashok, B. (2023). Intelligent energy management through neuro-fuzzy based adaptive ECMS approach for an optimal battery utilization in plugin parallel hybrid electric vehicle. Energy Conversion and Management. 280(4), 116792-116793. https://doi.org/10.1016/j.enconman.2023.116792
  35. Vignesh, R., Ashok, B., Kumar, M.S., Szpica, D., Harikrishnan, A., & Josh, H. (2023). Adaptive neuro fuzzy inference system-based energy management controller for optimal battery charge sustaining in biofuel powered non-plugin hybrid electric vehicle. Sustainable Energy Technologies and Assessments. 59(10), 103379-103380. https://doi.org/10.1016/j.seta.2023.103379
  36. Wang, Y., Wu, Y., Tang, Y., Li, Q., & He, H., (2023). Cooperative energy management and eco-driving of plug-in hybrid electric vehicle via multi-agent reinforcement learning. Applied Energy, 332(10), 120563-120564. https://doi.org/10.1016/j.apenergy.2022.120563
  37. Yang, C., Du, X., Wang, W., Yuan, L., & Yang, L. (2024). Variable optimization domain-based cooperative energy management strategy for connected plug-in hybrid electric vehicles. Energy, 290(4), 130206-130207. https://doi.org/10.1016/j.energy.2023.130206
  38. Yao, Z., Yoon, H.S., & Hong, Y.K. (2023). Control of hybrid electric vehicle powertrain using offline-online hybrid reinforcement learning. Energies, 16(2), 652-653. https://doi.org/10.3390/en16020652
  39. Younes, D., Karim, N., & Boudiaf, M. (2023). Energy management based hybrid fuel cell/battery for electric vehicle using type 2 fuzzy logic controller. International Journal of Advanced Studies in Computer Science and Engineering, 12(1), 18-33. https://doi.org/10.1109/ICEIT48248.2020.9113162
  40. Zhang, Q., Tian, S., & Lin, X. (2023). Recent advances and applications of ai-based mathematical modeling in predictive control of hybrid electric vehicle energy management in China. Electronics, 12(2), 445-446. https://doi.org/10.3390/electronics12020445

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