Estimación del estado de carga de una batería de litio con redes neuronales y validación con FPGA-en-lazo

  1. Martínez-Vera, Erik 1
  2. Rosado-Muñoz, Alfredo 2
  3. Bañuelos-Sánchez, Pedro 1
  1. 1 Universidad de las Américas Puebla
    info

    Universidad de las Américas Puebla

    San Andrés Cholula, México

    ROR https://ror.org/01s1km724

  2. 2 Universitat de València
    info

    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

Revista:
Revista iberoamericana de automática e informática industrial ( RIAI )

ISSN: 1697-7920

Año de publicación: 2024

Volumen: 21

Número: 3

Páginas: 243-251

Tipo: Artículo

DOI: 10.4995/RIAI.2024.20718 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista iberoamericana de automática e informática industrial ( RIAI )

Resumen

Los vehículos eléctricos presentan una alternativa viable para reducir las emisiones de gases tóxicos en las concentraciones urbanas y para disminuir los efectos de los gases de invernadero. La batería de los vehículos eléctricos debe ser monitoreada con precisión para asegurar su funcionamiento adecuado y seguro. Para esto, es necesario desarrollar algoritmos eficientes que permitan estimar de forma precisa el estado de carga mediante dispositivos embarcados en el vehículo. En este trabajo, se utiliza un conjunto de datos de ciclado de una batería de Litio para entrenar una red neuronal para la estimación del estado de carga. Se realiza una optimización bayesiana para establecer la mejor arquitectura de red neuronal y se valida el comportamiento frente a las mediciones reales que ofrece el conjunto de datos. Para su utilización en un dispositivo embarcado, la red neuronal se valida con un modelo de hardware-en-lazo (HIL) en un FPGA con aritmética de punto fijo. Después del entrenamiento se observa un error promedio cuadrático menor al 2% y una precisión promedio del 97.5%.

Referencias bibliográficas

  • Ali, A., Faisal, N., Zia, Z., Makda, I., & Usman, A. (2022). Rapid Prototyping of Bidirectional DC-DC Converter Control using FPGA for Electric Vehicle Charging Applications. 2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 1-6. https://doi.org/10.1109/PEDG54999.2022.9923288
  • Almaita, E., Alshkoor, S., Abdelsalam, E., & Almomani, F. (2022). State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network. Journal of Energy Storage, 52, 104761. https://doi.org/10.1016/j.est.2022.104761
  • CBCNews. (2023). Five charts to help understand Canada's record-breaking wildfire season | CBC News. https://www.cbc.ca/news/climate/wildfire-season-2023-wrap-1.6999005
  • Christophersen, J. P. (2015). Battery Test Manual For Electric Vehicles, Revision 3. https://doi.org/10.2172/1186745
  • Cui, Z., Dai, J., Sun, J., Li, D., Wang, L., & Wang, K. (2022). Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/9616124
  • Cui, Z., Hu, W., Zhang, G., Zhang, Z., & Chen, Z. (2022). An extended Kalman filter based SOC estimation method for Li-ion battery. Energy Reports, 8, 81-87. https://doi.org/10.1016/j.egyr.2022.02.116
  • EuroNews. (2023). Libya, Greece, Brazil: Climate-driven storms cause catastrophic flooding around the world | Euronews. https://www.euronews.com/green/2023/09/13/libya-greece-brazil-climate-driven-storms-cause-catastrophic-flooding-around-the-world
  • Gao, A. Y., Zhang, F. L., Fu, Z. M., Zhang, Z. C., & Li, H. Di. (2017). The SOC estimation and simulation of power battery based on self-recurrent wavelet neural network. Proceedings - 2017 Chinese Automation Congress, CAC 2017, 2017-January, 4247-4252. https://doi.org/10.1109/CAC.2017.8243525
  • Hun, R., Xu, J., Sun, M., Zhang, S., Chen, Y., & Chiang, P. Y. (2022). Hardware-Software Co-design of Efficient Light-weight Self-Attention Neural Network for Lithium-Ion Batteries State-of-Charge Estimation. 11th International Conference on Communications, Circuits and Systems, ICCCAS 2022. https://doi.org/10.1109/ICCCAS55266.2022.9825471
  • HWMO. (2023). Wildfire in Hawaii Factsheet - Hawaii Wildfire Management Organization. https://www.hawaiiwildfire.org/fire-resource-library-blog/wildfire-in-hawaii-factsheet
  • Kang, L. W., Zhao, X., & Ma, J. (2014). A new neural network model for the state-of-charge estimation in the battery degradation process. Applied Energy, 121, 20-27. https://doi.org/10.1016/j.apenergy.2014.01.066
  • Kollmeyer, P., Vidal, C., Naguib, M., & Skells, M. (2020). LG 18650HG2 Li-ion Battery Data and Example Deep Neural Network xEV SOC Estimator Script. 3. https://doi.org/10.17632/CP3473X7XV.3
  • Leach, F., Kalghatgi, G., Stone, R., & Miles, P. (2020). The scope for improving the efficiency and environmental impact of internal combustion engines. Transportation Engineering, 1, 100005. https://doi.org/10.1016/j.treng.2020.100005
  • Liu, Y., He, Y., Bian, H., Guo, W., & Zhang, X. (2022). A review of lithium-ion battery state of charge estimation based on deep learning: Directions for improvement and future trends. Journal of Energy Storage, 52, 104664. https://doi.org/10.1016/j.est.2022.104664
  • Martinez-Vera, E., Marco, J., Ramirez-Cortes, J. M., & Rangel-Magdaleno, J. (2019). Development of a Lithium-ion Battery Model and State of Charge Estimation Algorithm with Hardware-in-the-loop Validation. 2019 IEEE International Instrumentation & Measurement Technology Conference (I2MTC), 57-61. https://doi.org/10.1109/I2MTC.2019.8827050
  • MathWorksFPGA. (n.d.). Deploy Neural Network Regression Model to FPGA/ASIC Platform - MATLAB & Simulink - MathWorks España. Retrieved October 26, 2023, from https://es.mathworks.com/help/stats/deploy-neural-network-regression-model-to-fpga-platform.html
  • MathWorksNN. (2023). Train neural network regression model - MATLAB fitrnet - MathWorks España. https://es.mathworks.com/help/stats/fitrnet.html#mw_7e3de6cb-15e5-434e-82ed-8d870213d539_sep_shared-HyperparameterOptimizationOptions
  • NASA. (2023). NASA Clocks July 2023 as Hottest Month on Record Ever Since 1880 - NASA. https://www.nasa.gov/news-release/nasa-clocks-july-2023-as-hottest-month-on-record-ever-since-1880/
  • Nocedal, J., & Wright, S. J. (2006). Numerical Optimization. Springer New York. https://doi.org/10.1007/978-0-387-40065-5
  • ONU. (2023). Ciudades - Desarrollo Sostenible. https://www.un.org/sustainabledevelopment/es/cities/
  • Rosero, F., Fonseca, N., López, J. M., & Casanova, J. (2020). Real-world fuel efficiency and emissions from an urban diesel bus engine under transient operating conditions. Applied Energy, 261, 114442. https://doi.org/10.1016/j.apenergy.2019.114442
  • Shenghao, W. (2021). Improved Algorithm Combining Wavelet Transform and Kalman Filter for Estimating SOC of Lithium-ion Battery in Vehicle. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021, 175-179. https://doi.org/10.1109/ICBAIE52039.2021.9390009
  • Su, J. (2023). Quasi-Newton methods - Cornell University Computational Optimization Open Textbook - Optimization. https://optimization.cbe.cornell.edu/index.php?title=Quasi-Newton_methods
  • TheGuardian. (2023). A visual guide to Greece's deadly wildfires | Wildfires | The Guardian. https://www.theguardian.com/world/2023/sep/01/greek-wildfires-a-visual-guide
  • Tiwari, S., Kumar, B., & Tyagi, A. (2022). Artificial Neural Network-based State of Charge (SOC) Estimation of a Lithium-Ion Battery under Different Temperatures Conditions. 2022 IEEE 10th Power India International Conference, PIICON 2022. https://doi.org/10.1109/PIICON56320.2022.10045116
  • US EPA. (2023). All EPA Emission Standards | US EPA. https://www.epa.gov/emission-standards-reference-guide/all-epa-emission-standards
  • Vidal, C., Kollmeyer, P., Chemali, E., & Emadi, A. (2019). Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning. ITEC 2019 - 2019 IEEE Transportation Electrification Conference and Expo. https://doi.org/10.1109/ITEC.2019.8790543
  • Yang, B. ;, Wang, Y. ;, Zhan, Y., Yang, B., Wang, Y., & Zhan, Y. (2022). Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network. Energies 2022, Vol. 15, Page 4670, 15(13), 4670. https://doi.org/10.3390/EN15134670
  • Ządek, P., Koczor, A., Gołek, M., Matoga, Ł., & Penkala, P. (2015). Improving Efficiency of FPGA-in-the-Loop Verification Environment. IFAC-PapersOnLine, 48(4), 180-185. https://doi.org/10.1016/j.ifacol.2015.07.029
Fuente de los datos: Dialnet