Hybrid state of charge estimation for lithium‐ion batteries: design and implementation

Abstract

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.

Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation

“…The recursive least square method and its variants has been employed to identify the parameters [58,61]. The H∞F has also been merged with some other methods to improve the SOC estimation accuracy [62][63][64][65][66][67]. This has been combined with neural networks [62], genetic algorithms (GAs) [63], and unscented Kalman filters (UKFs) [64,66].…”

Design and Implementation of a Hybrid Real-Time State of Charge Estimation Scheme for Battery Energy Storage Systems

“…To overcome the disadvantages of the above methods summarized in Table 1, researchers have proposed nonlinear methods, e.g., extended Kalman filter (EKF) [13,14], unscented Kalman filter (UKF) [15,16], particle filter [17,18], Bayesian framework [19,20], sliding mode [21,22], nonlinear observer [23,24], wavelet analysis [25,26], and H-infinity [27][28][29]. These methods are applicable to any battery and can simultaneously identify the parameters of prebuilt models and thereby estimate battery SOC through their nonlinear mapping capabilities without the need of initial SOC values.…”

“…The composition of an ANFIS system includes fuzzy inference and ANN [26][27][28]. A fuzzy inference system simulates a human's knowledge and logical inference process with if-then conditionality but requires expert knowledge or empirical rules to constantly adjust the membership functions and fuzzy rules to obtain optimal parameters, so that there is no definite quantitative analysis and numerical correction processes.…”

Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares

“…In terms of SOC estimation, an algorithm such as Kalman filter (KF) [11], extended Kalman filter (EKF) [12e14], H ∞ [15,16], or leastsquares based filter [17], is employed to be applied on battery models with different complexity levels in order to connect the measurable values of the battery, i.e., voltage, current, and temperature, with the SOC [5,7]. There is a quite noticeable issue in these algorithms which is that the accuracy of SOC estimation relies significantly on the accuracy of the model yet the battery model parameters are constantly subject to change due to the working conditions and aging [5,6,18].…”