4.7 Article

Lithium-Ion Battery Parameters and State-of-Charge Joint Estimation Based on H-Infinity and Unscented Kalman Filters

Journal

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 66, Issue 10, Pages 8693-8701

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2017.2709326

Keywords

H infinity filter; joint estimation; lithium-ion battery; state-of-charge; unscented kalman filter

Funding

  1. National Natural Science Foundation of China [51507012]
  2. Beijing Nova Program [Z171100001117063]
  3. Australian Research Council [LP110200302]
  4. Sino-polish Collaborative research in e-mobility public transportation [2015DFG81930]
  5. Australian Research Council [LP110200302] Funding Source: Australian Research Council

Ask authors/readers for more resources

Accurate estimation of state-of-charge (SoC) is vital to safe operation and efficient management of lithium-ion batteries. Currently, the existing SoC estimation methods can accurately estimate the SoC in a certain operation condition, but in uncertain operating environments, such as unforeseen road conditions and aging related effects, they may be unreliable or even divergent. This is due to the fact that the characteristics of lithium-ion batteries vary under different operation conditions and the adoption of constant parameters in battery model, which are identified offline, will affect the SoC estimation accuracy. In this paper, the joint SoC estimation method is proposed, where battery model parameters are estimated online using the H-infinity filter, while the SoC are estimated using the unscented Kalman filter. Then, the proposed method is compared with the SoC estimation methods with constant battery model parameters under different dynamic load profiles and operation temperatures. It shows that the proposed joint SoC estimation method possesses high accuracy, fast convergence, excellent robustness and adaptability.

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