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Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine

Author

Listed:
  • Ethelbert Ezemobi

    (Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy)

  • Andrea Tonoli

    (Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy)

  • Mario Silvagni

    (Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy)

Abstract

The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed with a dataset of other batteries of the same type that were aged under a constant load condition. An optimum performance with low error variance was obtained from the model result. The root mean square error (RMSE) of the validated model varies from 0.064% to 0.473%, and the mean absolute error (MAE) error from 0.034% to 0.355% for the battery sets tested. On the basis of performance, the model was compared with a deterministic extreme learning machine (ELM) and an incremental capacity analysis (ICA)-based scheme from the literature. The algorithm was tested on a Texas F28379D microcontroller unit (MCU) board with an average execution speed of 93 ?s in real time, and 0.9305% CPU occupation. These results suggest that the model is suitable for online applications.

Suggested Citation

  • Ethelbert Ezemobi & Andrea Tonoli & Mario Silvagni, 2021. "Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine," Energies, MDPI, vol. 14(8), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2243-:d:537804
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    References listed on IDEAS

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    1. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    2. Sophia Gantenbein & Michael Schönleber & Michael Weiss & Ellen Ivers-Tiffée, 2019. "Capacity Fade in Lithium-Ion Batteries and Cyclic Aging over Various State-of-Charge Ranges," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    3. Wei, Zhongbao & Zhao, Jiyun & Ji, Dongxu & Tseng, King Jet, 2017. "A multi-timescale estimator for battery state of charge and capacity dual estimation based on an online identified model," Applied Energy, Elsevier, vol. 204(C), pages 1264-1274.
    4. Zheng Liu & Xuanju Dang, 2018. "A New Method for State of Charge and Capacity Estimation of Lithium-Ion Battery Based on Dual Strong Tracking Adaptive H Infinity Filter," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-18, September.
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    Cited by:

    1. Ethelbert Ezemobi & Mario Silvagni & Ahmad Mozaffari & Andrea Tonoli & Amir Khajepour, 2022. "State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles under Dynamic Load Conditions," Energies, MDPI, vol. 15(3), pages 1-20, February.
    2. Muhammed Cavus & Dilum Dissanayake & Margaret Bell, 2025. "Next Generation of Electric Vehicles: AI-Driven Approaches for Predictive Maintenance and Battery Management," Energies, MDPI, vol. 18(5), pages 1-41, February.
    3. Mei Zhang & Wanli Chen & Jun Yin & Tao Feng, 2022. "Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost," Energies, MDPI, vol. 15(16), pages 1-18, August.
    4. Edoardo Lelli & Alessia Musa & Emilio Batista & Daniela Anna Misul & Giovanni Belingardi, 2023. "On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles," Energies, MDPI, vol. 16(12), pages 1-21, June.
    5. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.

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