Abstract
State-of-charge (SOC) estimations of Li-ion batteries have been the focus of many research studies in previous years. Many articles discussed the dynamic model’s parameters estimation of the Li-ion battery, where the fixed forgetting factor recursive least square estimation methodology is employed. However, the change rate of each parameter to reach the true value is not taken into consideration, which may tend to poor estimation. This article discusses this issue, and proposes two solutions to solve it. The first solution is the usage of a variable forgetting factor instead of a fixed one, while the second solution is defining a vector of forgetting factors, which means one factor for each parameter. After parameters estimation, a new idea is proposed to estimate state-of-charge (SOC) of the Li-ion battery based on Newton’s method. Also, the error percentage and computational cost are discussed and compared with that of nonlinear Kalman filters. This methodology is applied on a 36 V 30 A Li-ion pack to validate this idea.
Highlights
Batteries have become a common power source in many applications
Studied the electrochemical modeling technique (ECM), which depends on the internal structure of the battery
Others studied an empirical modeling technique (EPM) as in [5], which does not clarify the behavior of the internal states of the Li-ion battery
Summary
Batteries have become a common power source in many applications. This is especially true of. Fixed forgetting factor recursive least square estimator of Li-ion battery parameters was used to. Kalman filters (KF) to estimate the states changes of the Li-ion battery, especially SOC, except [4], filter and particle filter (PF). The change rate of parameters estimation is taken into consideration, andintwo filter (UKF) [19], and particle filter (PF) [20]. All of these KFs are affected by significant noise the solutions are proposed to solve this problem. In this paper,least the square changeestimator rate of parameters is taken intowhile consideration, two defines a vector of forgetting factors for a recursive least squares estimator.
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