Research on intelligent detection method of new energy vehicle power battery based on improved ViBe algorithm
DOI:
https://doi.org/10.4108/ew.7304Keywords:
Battery State Detection, ViBe algorithm, New Energy Vehicles, Ghosting Effects, Battery Management System, Safety MonitoringAbstract
Background: Traditional foreground detection methods for new energy vehicles using the ViBe algorithm often suffer from ghosting effects, which can obscure the accurate detection of moving targets.
Aims: This study enhances foreground detection accuracy by addressing ghosting issues in the ViBe algorithm and improving the battery pack state detection system for new energy vehicles.
Method: The method includes analyzing global light changes before foreground detection and updating the background model using the three-frame difference method. The system integrates hardware and software to process data with the ViBe algorithm, measuring voltage from twelve 18650-type lithium batteries.
Results: The battery management system prototype exhibits an absolute measurement error within -1.2 mV compared to the high-precision multimeter. The system maintains measurement accuracy across varying temperatures, demonstrating effective environmental adaptability.
Conclusion: The enhanced system successfully reduces ghosting in foreground detection and provides reliable battery state monitoring. It is robust under extreme conditions, contributing to improved diagnostic capabilities and enhanced traffic safety.
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[1] Q. Chengqun, X .Wan, N.Wang, S. Cao, X. Ji, K.Wu, Y. Hu, and M.Meng, 2023. A novel regenerative braking energy recuperation system for electric vehicles based on driving style. Energy, 283, 2023, p.129055
[2] 2.Y.Xianyi, A. Abdin, and J.Puchinger. Sequential Optimization for the Optimal Management of Coupled Shared Autonomous Electric Vehicles and Power Grids”, Available at SSRN 4608479
[3] B.R.D. Goswami, Enhancing Electric Vehicle Safety: “AI-Driven Multiphysics Approach for Predicting Thermal Failures in Li-Ion Batteries “(Doctoral dissertation, The University of Arizona), 2024
[4] P.P.Gupta, V. Kalkhambkar, K.C Sharma, P. Jain, and R. Bhakar. Optimal electric vehicles charging scheduling for energy and reserve markets considering wind uncertainty and generator contingency", International Journal of Energy Research, 46(4), 2022, pp.4516-4539.
[5] M.D.Dean, F. de Souza, K.M Gurumurthy, and K.M Kockelman. Multi-stage charging and discharging of electric vehicle fleets", Transportation Research Part D: Transport and Environment, 118, 2023, p.103691.
[6] X. Liu, Y. Li, X.Jiang, and K.Xu. Lifespan prediction of Li-ion batteries in electrical vehicles by applying coulombic efficiency: from anode material to battery cell to vehicle application", Sustainable Energy & Fuels, 8(3), 2024, pp.621-630
[7] Z.Yao, M. Gendreau, M. Li, L. Ran, and Z.Wang. Service operations of electric vehicle carsharing systems from the perspectives of supply and demand”, Transportation Research Part C: Emerging Technologies, 140, 2022, p.103702.
[8] A. Chatzisavvas, M. Dasygenis, and M.Louta, 2022. Autonomous Unmanned Ground Vehicle in Precision Agriculture–The VELOS project”, In 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1-4). September 2022, IEEE.
[9] C.H Rivera. Quantifying the flexibility potential of electric vehicles smart charging” 2024
[10] K. Degirmenci, T.M Cerbe, and W.E Pfau, eds. Electric Vehicles in Shared Fleets: Mobility Management, Business Models, and Decision Support Systems”, World Scientific.
[11] M. Mollajafari, and F. Kouhyar. Simultaneous optimization of fuel and electrical energy consumption in the forward-looking hybrid electric vehicle", Automotive Science and Engineering, 12(1), 2022, pp.3750-3761.
[12] Y. Wang, L. Yuan, W.Jiao, Y.Qiang, J. Zhao, Q.Yang, and K.Li. A fast and secured vehicle-to-vehicle energy trading based on blockchain consensus in the internet of electric vehicles” IEEE Transactions on Vehicular Technology, 72(6), 2023, pp.7827-7843.
[13] X. Fu, X. Wu, C. Zhang, S. Fan, and N. Liu. Planning of distributed renewable energy systems under uncertainty based on statistical machine learning” Protection and Control of Modern Power Systems, 7(4), 2022, pp.1-27
[14] X. Yang, A. Abdin, and J. Puchinger. Optimal management of coupled shared autonomous electric vehicles and power grids: Potential of renewable energy integration”Transportation Research Part C: Emerging Technologies, 165, 2024, p.104726
[15] Y.Gao, Q. Yang, S. Zhang, and D.Gao. Real‐Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring. Journal of Electrical and Computer Engineering”, 2023(1), p.9215528
[16] X. Li, C. Li, and C. Jia. Electric Vehicle and Photovoltaic Power Scenario Generation under Extreme High-Temperature Weather”, World Electric Vehicle Journal, 15(1), 2024, p.1
[17] Y. Yue, D. Xu, Z. Qian, H. Shi, & H. Zhang, Ant_vibe” improved vibe algorithm based on ant colony clustering under dynamic background”, Mathematical Problems in Engineering, 2020, 2020, 1-13.
[18] K.H Yano, S. Thomas, &Swenson, et al.. Tem in situ cube-corner indentation analysis using vibe motion detection algorithm. Journal of Nuclear Materials”, Materials Aspects of Fission and Fusion, 502, 2018, 201-212.
[19] F.H. Jufri, S. Oh, and J. Jung, „Development of photovoltaic abnormal condition detection system using combined regression and support vector machine” Energy, 176 JUN.1, 2019 457-467.
[20] A. Sabir, D. Michaelson, & J. Jiang. Load-frequency control with multi-module small modular reactor configuration: modeling and dynamic analysis” IEEE Transactions on Nuclear Science, 2021, PP(99), 1-1.
[21] He, B., & Bhatti, U. A. (2024). Smart cities and smart networks: AI applications in urban geography and industrial communication. International Journal of High Speed Electronics and Systems.
[22] Cheng, M., Tang, H., Bhatti, U. A., & Li, D. (2024). Optimized sustainable manufacturing through fuzzy control in image-based visual servoing with velocity and field-of-view constraints. IEEE Transactions on Fuzzy Systems.
[23] Xu, B., Luo, C., Tang, H., Bhatti, U. A., Wang, X., & Jiang, W. (2024). Advancing transparency in AI-based automatic modulation classification. In 2024 IEEE/CIC International Conference on Communications in China (ICCC).
[24] Tang, H., Zhang, Z., Zhang, Y., Xu, B., & Bhatti, U. A. (2024). Plug-and-work edge collaborator design for customized manufacturing. In 2024 39th Youth Academic Annual Conference of the Chinese Association of Automation.
[25] Alhatami, E., Bhatti, U. A., MengXing, H., & Feng, S. (2024). Advanced fuzzy denoising technique for agricultural remote sensing: Modified partition filter for suppressing impulsive. IEEE Access.
[26] Tang, H., Chen, D., Zhang, Y., Xu, B., & Bhatti, U. A. (2024). Bidirectional interaction techniques based on device digital twin model. In 2024 39th Youth Academic Annual Conference of the Chinese Association of Automation.
[27] Bhatti, U. A., Tang, H., Khan, A., Ghadi, Y. Y., Bhatti, M. A., & Khan, K. A. (2024). Investigating the nexus between energy, socio-economic factors and environmental pollution: A geo-spatial multi regression approach. Gondwana Research, 130, 308-325.
[28] Asif, M., Li, J. Q., Zia, M. A., Hashim, M., Bhatti, U. A., Bhatti, M. A., & Hasnain, A. (2024). Environmental Sustainability in BRICS Economies: The Nexus of Technology Innovation, Economic Growth, Financial Development, and Renewable Energy Consumption. Sustainability, 16(16), 6934.
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