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Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

Author

Listed:
  • Giuseppe Di Luca

    (Department of Energy (DENERG), Politecnico di Torino, 10125 Torino, Italy
    Istituto di Scienze e Tecnologie per la Mobilità Sostenibili (STEMS), National Research Council, 80125 Napoli, Italy)

  • Gabriele Di Blasio

    (Istituto di Scienze e Tecnologie per la Mobilità Sostenibili (STEMS), National Research Council, 80125 Napoli, Italy)

  • Alfredo Gimelli

    (Department of Industrial Engineering (DII), Università di Napoli Federico II, 80126 Napoli, Italy)

  • Daniela Anna Misul

    (Department of Energy (DENERG), Politecnico di Torino, 10125 Torino, Italy)

Abstract

The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions.

Suggested Citation

  • Giuseppe Di Luca & Gabriele Di Blasio & Alfredo Gimelli & Daniela Anna Misul, 2023. "Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles," Energies, MDPI, vol. 17(1), pages 1-34, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:202-:d:1310351
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    References listed on IDEAS

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    1. You, Gae-won & Park, Sangdo & Oh, Dukjin, 2016. "Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach," Applied Energy, Elsevier, vol. 176(C), pages 92-103.
    2. Waag, Wladislaw & Sauer, Dirk Uwe, 2013. "Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination," Applied Energy, Elsevier, vol. 111(C), pages 416-427.
    3. Xiong, Rui & Sun, Fengchun & Gong, Xianzhi & Gao, Chenchen, 2014. "A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles," Applied Energy, Elsevier, vol. 113(C), pages 1421-1433.
    4. Bizhong Xia & Zhen Sun & Ruifeng Zhang & Zizhou Lao, 2017. "A Cubature Particle Filter Algorithm to Estimate the State of the Charge of Lithium-Ion Batteries Based on a Second-Order Equivalent Circuit Model," Energies, MDPI, vol. 10(4), pages 1-15, April.
    5. Lin, Cheng & Mu, Hao & Xiong, Rui & Shen, Weixiang, 2016. "A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm," Applied Energy, Elsevier, vol. 166(C), pages 76-83.
    6. Anselma, Pier Giuseppe & Kollmeyer, Phillip & Lempert, Jeremy & Zhao, Ziyu & Belingardi, Giovanni & Emadi, Ali, 2021. "Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation," Applied Energy, Elsevier, vol. 285(C).
    7. Ye, Min & Guo, Hui & Cao, Binggang, 2017. "A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter," Applied Energy, Elsevier, vol. 190(C), pages 740-748.
    8. Costa, M. & Di Blasio, G. & Prati, M.V. & Costagliola, M.A. & Cirillo, D. & La Villetta, M. & Caputo, C. & Martoriello, G., 2020. "Multi-objective optimization of a syngas powered reciprocating engine equipping a combined heat and power unit," Applied Energy, Elsevier, vol. 275(C).
    9. Yun Bao & Wenbin Dong & Dian Wang, 2018. "Online Internal Resistance Measurement Application in Lithium Ion Battery Capacity and State of Charge Estimation," Energies, MDPI, vol. 11(5), pages 1-11, April.
    10. Vongdala Noudeng & Nguyen Van Quan & Tran Dang Xuan, 2022. "A Future Perspective on Waste Management of Lithium-Ion Batteries for Electric Vehicles in Lao PDR: Current Status and Challenges," IJERPH, MDPI, vol. 19(23), pages 1-22, December.
    11. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    12. Hjelkrem, Odd André & Arnesen, Petter & Aarseth Bø, Torstein & Sondell, Rebecka Snefuglli, 2020. "Estimation of tank-to-wheel efficiency functions based on type approval data," Applied Energy, Elsevier, vol. 276(C).
    13. Wu, Muyao & Wang, Li & Wu, Ji, 2023. "State of health estimation of the LiFePO4 power battery based on the forgetting factor recursive Total Least Squares and the temperature correction," Energy, Elsevier, vol. 282(C).
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