1. SISN
  2. All Volumes and Issues
  3. Volume 13, 2023
  4. Intelligent system for analyzing battery charge consumption processes

Intelligent system for analyzing battery charge consumption processes

SISN.
2023;
Volume 13
: pp. 251 - 273
https://doi.org/10.23939/sisn2023.13.251
Authors:
  1. Olena Pavliuk
  2. Mykola Medykovskyy
  3. Natalya Lysa
  4. Myroslav Mishchuk
1
Lviv Politechnik National University
2
Lviv Polytechnic National University, Lviv, Ukraine
3
Lviv Polytechnic National University, Department of Automated Control Systems
4
Lviv Polytechnic National University, Ukraine

The article develops an intelligent system of analysis and neural network forecasting of battery charge consumption for automated vehicles (AGVs). For this purpose, the types of AGV and the methods of effective forecasting of their battery charge consumption were analyzed. It is established that they are based on optimal robot control processes; application of technologies to increase capacity and extend service life.

The data for the forecast was collected using the UAExpert OPC UA client, which allowed to convert the informative components of the data vector into a format suitable for further processing (csv). To eliminate outliers in the signals, a dispersion analysis of each parameter of AGV was carried out. Data for which the sigma value exceeded 1.5 were considered partialle lost and were replaced by a moving average of 12 points (the number of ANN inputs). For training, verification and testing of neural networks, parameters with high and medium positive correlation dependence were selected according to the Pearson correlation coefficient. Short-term and medium-term forecasting of battery charge consumption for AKTZ was carried out on the basis of ANN with deep learning, the model of which was tested in two modes: forecasting and prediction.

The effectiveness of the developed system was investigated by testing it on the data obtained from Formica-1 AGV. The average absolute testing error was less than 1 %. The highest value of the prediction error was less than 9 % when predicting such parameters as current position and X-coordinate, which are correlated with battery charge consumption for AGV. It has been established experimentally that the accuracy of the forecast of battery charge consumption for various types of AGV has been improved.

automated guided vehicles
industry 4.0
artificial intelligence
artificial neural networks
data analysis
  1. Chol, J., & Gun, C. R. (2023). Multi-agent based scheduling method for tandem automated guided vehicle systems. Engineering Applications of Artificial Intelligence, 123. DOI: 10.1016/j.engappai.2023.106229.
  2. De Ryck, M., Versteyhe, M., & Debrouwere, F. (2020). Automated guided vehicle systems, state-of-the-art control algorithms and techniques. Journal of Manufacturing Systems, 54, 152–173. DOI: 10.1016/j.jmsy.2019.12.002.
  3. Liang, Z., Wang, Z., Zhao, J., Wong, P. K., Yang, Z., & Ding, Z. (2023). Fixed-time prescribed performance path-following control for autonomous vehicle with complete unknown parameters. IEEE Transactions on Industrial Electronics, 70(8), 8426–8436. DOI: 10.1109/TIE.2022.3210544.
  4. Li, L., Li, Y., Liu, R., Zhou, Y., & Pan, E. (2023). A two-stage stochastic programming for AGV scheduling with random tasks and battery swapping in automated container terminals. Transportation Research Part E: Logistics and Transportation Review, 174. DOI: 10.1016/j.tre.2023.103110.
  5. Oyekanlu, E. A., Smith, A. C., Thomas, W. P., Mulroy, G., Hitesh, D., Ramsey, M., . . . Sun, D. (2020). A review of recent advances in automated guided vehicle technologies: Integration challenges and research areas for 5G- based smart manufacturing applications. IEEE Access, 8, 202312–202353. DOI: 10.1109/ACCESS.2020.3035729.
  6. Theunissen, J., Xu, H., Zhong, R. Y., & Xu, X. (2019). Smart AGV system for manufacturing shopfloor in the context of industry 4.0. Paper presented at the Proceedings of the 2018 25th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2018. DOI: 10.1109/M2VIP.2018.8600887 Retrieved from www.scopus.com.
  7. Sanogo, K., Mekhalef Benhafssa, A., Sahnoun, M., Bettayeb, B., Abderrahim, M., & Bekrar, A. (2023). A multi-agent system simulation based approach for collision avoidance in integrated job-shop scheduling problem with transportation tasks. Journal of Manufacturing Systems, 68, 209–226. DOI: 10.1016/j.jmsy.2023.03.011
  8. Martínez‐gutiérrez, A., Díez‐gonzález, J., Ferrero‐guillén, R., Verde, P., Álvarez, R., & Perez, H. (2021). Digital twin for automatic transportation in industry 4.0. Sensors, 21(10) DOI: 10.3390/s21103344.
  9. Ha, V. T., Thuong, T. T., & Ha, V. T. (2023). Experiment study of an automatic guided vehicle robot. International Journal of Power Electronics and Drive Systems, 14(2), 1300–1308. DOI: 10.11591/ ijpeds.v14.i2.pp1300-1308.
  10. D'Souza, F., Costa, J., & Pires, J. N. (2020). Development of a solution for adding a collaborative robot to an industrial AGV. Industrial Robot, 47(5), 723–735. DOI: 10.1108/IR-01-2020-0004
  11. Rahman, H. F., Janardhanan, M. N., & Nielsen, P. (2020). An integrated approach for line balancing and AGV scheduling towards smart assembly systems. Assembly Automation, 40(2), 219-234. DOI: 10.1108/AA-03-2019- 0057.
  12. Steclik, T., Cupek, R., & Drewniak, M. (2022). Automatic grouping of production data in industry 4.0: The use case of internal logistics systems based on automated guided vehicles. Journal of Computational Science, 62. DOI: 10.1016/j.jocs.2022.101693.
  13. Cupek, R., Lin, J. C., & Syu, J. H. (2022). Automated guided vehicles challenges for artificial intelligence. Paper presented at the Proceedings-2022 IEEE International Conference on Big Data, Big Data 2022, 6281–6289. DOI: 10.1109/BigData55660.2022.10021117. Retrieved from www.scopus.com.
  14. Steclik, T., Cupek, R., & Drewniak, M. (2022). Stream data clustering for engineering applications a use case of autonomous guided vehicles. Paper presented at the Proceedings-2022 IEEE International Conference on Big Data, Big Data 2022, 6347–6356. DOI: 10.1109/BigData55660.2022.10020484 Retrieved from www.scopus.com.
  15. Shubyn, B., Kostrzewa, D., Grzesik, P., Benecki, P., Maksymyuk, T., Sunderam, V., . . . Mrozek, D. (2023). Federated learning for improved prediction of failures in autonomous guided vehicles. Journal of Computational Science, 68. DOI: 10.1016/j.jocs.2023.101956.
  16. Syu, J., Lin, J. C., & Mrozek, D. (2022). An efficient and secured energy management system for automated guided vehicles. Paper presented at the Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, 6357–6363. DOI: 10.1109/BigData55660.2022.10020806 Retrieved from www.scopus.com.
  17. Benecki, P., Kostrzewa, D., Grzesik, P., Shubyn, B., &  Mrozek, D. (2022). Forecasting of energy consumption for anomaly detection in automated guided vehicles: Models and feature selection. Paper presented at the Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2022 October, 2073–2079. DOI: 10.1109/SMC53654.2022.9945146. Retrieved from www.scopus.com.
  18. Shubyn, B., Mrozek, D., Maksymyuk, T., Sunderam, V., Kostrzewa, D., Grzesik, P., & Benecki, P. (2022). Federated learning for Anomaly detection in Industrial IoT-enabled production environment supported by Autonomous guided vehicles. DOI: 10.1007/978-3-031-08760-8_35. Retrieved from www.scopus.com.
  19. Smith, J. D., & Johnson, K. (2018). Intelligent control of AGV for automated manufacturing systems. International Journal of Advanced Manufacturing Technology, 96(9-12), 4349–4360. DOI: 10.1007/s00170-018-1752-0.
  20. Lee, S. H., Kim, J., & Park, J. (2020). An Intelligent Routing Algorithm for AGV in Manufacturing Environment. Journal of Manufacturing Systems, 56, 104–113. DOI: 10.1016/j.jmsy.2020.08.004.
  21. Wang, Y., Li, X., Li, J., & Li, Z. (2019). An Intelligent Control Method for AGV Material Handling System. International Journal of Advanced Manufacturing Technology, 105(5-6), 1945–1954. DOI: 10.1007/s00170-019- 03987-5.
  22. Medykovskvi, M., Pavliuk, O., & Sydorenko, R. (2018). Use of machine learning technologies for the electric consumption forecast. Paper presented at the International Scientific and Technical Conference on Computer Sciences and Information Technologies, 1432–1435. DOI: 10.1109/STC-CSIT.2018.8526617.
  23. Li, X., Chen, Y., & Zhang, Y. (2021). Real-time scheduling of AGV with machine learning and optimization techniques. Proceedings of the 2021 International Conference on Robotics and Automation Sciences (ICRAS 2021), 245–251. DOI: 10.1109/ICRAS51812.2021.9433069.
  24. Lee, J., Kim, H., & Park, J. (2020). Intelligent Collision Avoidance for AGV in Warehouse Environment. Journal of Intelligent & Robotic Systems, 98(3), 591–603. DOI: 10.1007/s10846-019-01080-5.
  25. Liu, Y., Sun, S., Cai, W., & Guo, B. (2021). A Real-Time Dynamic Scheduling Algorithm for AGV based on Multi-objective Optimization. IEEE Transactions on Industrial Informatics, 17(7), 4562–4571. DOI: 10.1109/TII.2020.3021667.
  26. Wang, Y., Liu, S., & Zhao, Y. (2022). Intelligent routing of AGV in a manufacturing environment: A comparative study. Journal of Manufacturing Systems, 64, 48–58. DOI: 10.1016/j.jmsy.2021.11.010.
  27. Kim, S., Park, J., & Lee, S. (2020). Smart Control of AGV in Manufacturing Industry Using Artificial Intelligence. International Conference on Control, Automation and Systems (ICCAS), 1326–1331. DOI: 10.23919/ICCAS50221.2020.9268034.
  28. Li, J., Liu, Y., & Jiang, S. (2018). A survey of intelligent vehicle routing and scheduling problem in automated manufacturing systems. International Journal of Advanced Manufacturing Technology, 96(1–4), 259–276. DOI: 10.1007/s00170-018-1928-x.
  29. Arya, S., & Chauhan, S. S. (2021). A hybrid model of Fuzzy Logic and A* Algorithm for AGV navigation in flexible manufacturing systems. International Journal of Computational Intelligence Systems, 14(1), 1215–1226. DOI: 10.2991/ijcis.d.210414.001.
  30. Bhatia, A., Singh, A., & Luthra, S. (2020). Adaptive routing and scheduling of automated guided vehicles using a simulation-based optimization approach. Robotics and Computer-Integrated Manufacturing, 63, 101911. DOI: 10.1016/j.rcim.2019.101911.
  31. Bhatia, A., Singh, A., & Luthra, S. (2020). Adaptive routing and scheduling of automated guided vehicles using a simulation-based optimization approach. Robotics and Computer-Integrated Manufacturing, 63, 101911. DOI: 10.1016/j.rcim.2019.101911.
  32. Li, C., Chen, Y., Zhang, Y., & Li, C. (2020). A dynamic path planning algorithm for automated guided vehicles in a real-time dynamic environment. Journal of Advanced Transportation, 2020, 1–11. DOI: 10.1155/2020/8874826.
  33. Pavliuk, O., Steclik, T., & Biernacki, P. (2019). The forecast of the AGV battery discharging via the machine learning methods. 2019 IEEE International Conference on Industrial Technology (ICIT), 767–772. DOI: 10.1109/ICIT.2019.8754875.