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Battery SOC and SOH Estimation

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Battery Management Algorithm for Electric Vehicles

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Abstract

Battery SOC and SOH estimation are core functions performed by the BMS. Accurate SOC and SOH estimation can ensure the safe and reliable operation of the battery system, and provide the basis for energy management and safety management of EVs. However, batteries exhibit the characteristics of limited measurable parameters, coupling feature, degradation with time, strong time-varying, and nonlinearity. The vehicle applications are also encountering the requirements of series-parallel group of inconsistent complex system, various operation conditions (wide rate charge and discharge), and all-climate (–30 to 55 °C temperature range). Battery SOC and SOH estimation with high precision and strong robustness are extremely challenging, and they have been the industry’s technical difficulties and hotspots in the international academic research. This chapter will systematically describe the basic theory and application of battery SOC and SOH estimation, discuss the performance of online SOC estimation with the known static capacity and dynamic capacity as well as the necessity of SOH and SOC collaborative estimation. A detailed algorithm flow for the practical application of BMS will also be provided.

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Authors and Affiliations

  1. National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China

    Rui Xiong

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  1. Rui Xiong
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Correspondence to Rui Xiong .

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© 2020 China Machine Press

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Xiong, R. (2020). Battery SOC and SOH Estimation. In: Battery Management Algorithm for Electric Vehicles . Springer, Singapore. https://doi.org/10.1007/978-981-15-0248-4_4

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  • DOI: https://doi.org/10.1007/978-981-15-0248-4_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0247-7

  • Online ISBN: 978-981-15-0248-4

  • eBook Packages: EnergyEnergy (R0)

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