Open AccessHybrid state of charge estimation for lithium‐ion batteries: design and implementation
Author(s) -
Alfi Alireza,
Charkhgard Mohammad,
Haddad Zarif Mohammad
Publication year - 2014
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2013.0746
Subject(s) - lithium (medication) , state of charge , ion , estimation , state (computer science) , charge (physics) , computer science , chemistry , engineering , battery (electricity) , algorithm , physics , systems engineering , thermodynamics , power (physics) , quantum mechanics , medicine , organic chemistry , endocrinology
This study introduces a novel hybrid method for state of charge (SOC) estimation of lithium‐ion battery types using extended H ∞ filter and radial basis function (RBF) networks. The RBF network's parameters are adjusted off‐line by acquired data from the battery in charging step. This kind of neural network approximates the non‐linear function utilised in the state‐space equations of the extended H ∞ filter. The advantages of the proposed method are 3‐fold: (i) it is not necessary to require the measurement and process noise covariance matrices as Kalman filter, (ii) the SOC is directly estimated and (3) it is a robust estimator in the sense of H ∞ criteria. The state variables are composed of the SOC and the battery terminal voltage. The experimental results illustrate the feasibility of the proposed method in terms of robustness, accuracy and convergence speed.