Internet of Everything (IoE) - Input Based Research Framework: Machine Learning Model for Battery Module Longevity Optimization and Failure Prediction

  • Mohd Azril Amil Faculty of Engineering and Life Sciences, Universiti Selangor
  • Mohd Darnalis A.Rahman Faculty of Engineering and Life Sciences, Universiti Selangor
  • Zahirrudin Idris Centre of Foundation and General Studies, Universiti Selangor
  • Sahrul Izwan Sarwadi Centre of Foundation and General Studies, Universiti Selangor
Keywords: Battery Optimization, Failure Prediction, Machine Learning

Abstract

 

Battery storage plays a vital role in smoothing out the fluctuations in energy demand and generation from distributed renewable sources. Batteries are susceptible to various factors such as a change in temperature, charging cycles, etc. Power storage batteries are expensive, therefore various measures should be taken to make sure it is working in the optimal conditions, these batteries also fail during operations thus affecting the power demands. The research will investigate various machine learning approaches to optimize battery operations in domestic applications (Modules Longevity and failure predictions). Machine learning can help predict battery failure and optimize battery life by using reinforced learning, it is possible to make optimal battery charging and discharging decisions. It involves two methods: supervised learning, which trains a model with previously known input and output data so it can predict future outputs, while unsupervised learning, which finds hidden patterns and intrinsic structures within the input and output data (Temperature, Charging cycles, Voltage and Current). The research will involve extensive simulations and building machine learning models. The machine learning models are trained to capture a battery’s state of health and to predict its remaining lifetime. These two concepts help to know when to recharge its battery as well as when to schedule battery module replacements. The outcome of the research will be reflected in the application of a smart power storage system for ideal self battery monitoring and optimization.

References

Ahmad, T., Chen, H., Huang, R., Yabin, G., Wang, J., Shair, J., Azeem Akram, H. M., Hassnain Mohsan, S. A., & Kazim, M. (2018). Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment. Energy. https://doi.org/10.1016/j.energy.2018.05.169
Amari, S. ichi. (2016). Machine Learning. In Applied Mathematical Sciences (Switzerland). https://doi.org/10.1007/978-4-431-55978-8_11
Buennemeyer, T. K., Nelson, T. M., Clagett, L. M., Dunning, J. P., Marchany, R. C., & Tront, J. G. (2008). Mobile device profiling and intrusion detection using smart batteries. Proceedings of the Annual Hawaii International Conference on System Sciences. https://doi.org/10.1109/HICSS.2008.319
Chon, Y., Talipov, E., Shin, H., & Cha, H. (2011). Mobility prediction-based smartphone energy optimization for everyday location monitoring. SenSys 2011 - Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems. https://doi.org/10.1145/2070942.2070952
Dong, G., Zhang, X., Zhang, C., & Chen, Z. (2015). A method for state of energy estimation of lithium-ion batteries based on neural network model. Energy. https://doi.org/10.1016/j.energy.2015.07.120
Garcia-Valle, R., & Lopes, J. A. P. (2013). Electric vehicle integration into modern power networks. In Electric Vehicle Integration into Modern Power Networks. https://doi.org/10.1007/978-1-4614-0134-6
IRENA. (2017). Electricity storage and renewables: Costs and markets to 2030. In Electricity-storage-and-renewables-costs-and-markets. https://doi.org/ISBN 978-92-9260-038-9 (PDF)
Keiner, D., Ram, M., Barbosa, L. D. S. N. S., Bogdanov, D., & Breyer, C. (2019). Cost optimal self-consumption of PV prosumers with stationary batteries, heat pumps, thermal energy storage and electric vehicles across the world up to 2050. Solar Energy. https://doi.org/10.1016/j.solener.2019.04.081
Kim, B. G., Ren, S., Van Der Schaar, M., & Lee, J. W. (2013a). Bidirectional energy trading and residential load scheduling with electric vehicles in the smart grid. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/JSAC.2013.130706
Kim, B. G., Ren, S., Van Der Schaar, M., & Lee, J. W. (2013b). Bidirectional energy trading for residential load scheduling and electric vehicles. Proceedings - IEEE INFOCOM. https://doi.org/10.1109/INFCOM.2013.6566842
Kizilel, R., Sabbah, R., Selman, J. R., & Al-Hallaj, S. (2009). An alternative cooling system to enhance the safety of Li-ion battery packs. Journal of Power Sources. https://doi.org/10.1016/j.jpowsour.2009.06.074
Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. In Informatica (Ljubljana). https://doi.org/10.31449/inf.v31i3.148
Lin, H. T., Liang, T. J., & Chen, S. M. (2013). Estimation of battery state of health using probabilistic neural network. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2012.2222650
Marom, R., Amalraj, S. F., Leifer, N., Jacob, D., & Aurbach, D. (2011). A review of advanced and practical lithium battery materials. Journal of Materials Chemistry. https://doi.org/10.1039/c0jm04225k
Mulder, G., Ridder, F. De, & Six, D. (2010). Electricity storage for grid-connected household dwellings with PV panels. Solar Energy. https://doi.org/10.1016/j.solener.2010.04.005
Ocran, T. A., Cao, J., Cao, B., & Sun, X. (2005). Artificial neural network maximum power point tracker for solar electric vehicle. Tsinghua Science and Technology. https://doi.org/10.1016/S1007-0214(05)70055-9
Silva, T. C., & Zhao, L. (2016). Machine learning in complex networks. In Machine Learning in Complex Networks. https://doi.org/10.1007/978-3-319-17290-3
Tsai, C. T., Muna, Y. B., Lin, H. Y., Kuo, C. C., & Hsiung, R. (2018). Optimal design and performance analysis of solar power microsystem for mini-grid application. Microsystem Technologies. https://doi.org/10.1007/s00542-018-4213-7
Published
2021-03-17
How to Cite
Amil, M. A., A.Rahman, M. D., Idris, Z., & Sarwadi, S. I. (2021). Internet of Everything (IoE) - Input Based Research Framework: Machine Learning Model for Battery Module Longevity Optimization and Failure Prediction. Selangor Science & Technology Review (SeSTeR), 5(2), 58-64. Retrieved from http://sester.journals.unisel.edu.my/ojs/index.php/sester/article/view/218