This study suggests a new method for modelling lithium-ion battery types and state-of-charge (SOC) estimation using adaptive H ∞ filter (AHF). First, a universal linear model with some free parameters is considered for dynamical behaviour of the battery. The battery voltage and SOC are used as states of the model. Then for every period in the charge/discharge process the free parameters of the model are identified. Each period of process is associated with a specific SOC value, hence the parameters can be regarded as functions of SOC in the entire process. The functions are determined based on polynomial approximation and least squares method. The proposed SOC-varying model is incorporated in AHF for SOC estimation. Moreover, a new method for adjusting the tuning parameters of the filter is suggested. The proposed method is verified by experimental tests on a lithium-ion battery and is compared with adaptive extended Kalman filter and square-root unscented Kalman filter

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 statespace 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.

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