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Lithium-Ion Batteries: Prognosis Algorithms, Challenges and Future Scenario

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Power Engineering and Intelligent Systems (PEIS 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1097))

  • 197 Accesses

Abstract

This study concentrates on different types of prognosis algorithms for forecasting lithium-ion battery parameters. Various SoC estimation techniques are examined and compared based on their SoC estimation performance indexes. SoC estimation methods are broadly classified as Kalman filter, particle filter, data-driven and hybrid methods. These types of filters are compared based on the complexity of the implementation on hardware as well as their performance parameters such as statistics (errors), driving test schedules, laboratory testing data, type of battery model used and different types of prognosis methods used in the SoC estimation. It helps in the proper selection of the hardware and methods for battery model parameter forecasting which is critical for electrical vehicle (EVs) battery management system coordination and control. This study also focuses on the challenges faced by the different SoC prognosis methods and their modern trends to improve the forecasting of important parameters of the batteries.

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Authors and Affiliations

  1. Deenbandhu Chhotu Ram University Science and Technology, Murthal, Haryana, India

    Gaurav Malik & Manish Kumar Saini

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  1. Gaurav Malik
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  2. Manish Kumar Saini
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Correspondence to Manish Kumar Saini .

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Editors and Affiliations

  1. National Institute of Technology Delhi, New Delhi, Delhi, India

    Vivek Shrivastava

  2. South Asian University New Delhi, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Electrical Engineering, Indian Institute of Technology Delhi, Delhi, India

    B. K. Panigrahi

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Malik, G., Saini, M.K. (2024). Lithium-Ion Batteries: Prognosis Algorithms, Challenges and Future Scenario. In: Shrivastava, V., Bansal, J.C., Panigrahi, B.K. (eds) Power Engineering and Intelligent Systems. PEIS 2023. Lecture Notes in Electrical Engineering, vol 1097. Springer, Singapore. https://doi.org/10.1007/978-981-99-7216-6_30

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  • DOI: https://doi.org/10.1007/978-981-99-7216-6_30

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