Combined RLS-ΕKF Method for SOC and Parameter Estimation of Lithium-Ion Batteries

Due to their high energy density, lithium-ion rechargeable batteries have been increasingly used in various electronic devices as useful and efficient electric energy storages. On the other hand, it is well known that some critical states such as over-charge or over-discharge of a lithium-ion battery may lead to dangerous situations and may accelerate the aging of its capacity. it is thus necessary to accurately estimate the State-of-Charge (SOC) of the the batteries for safe and long lasting operations[1]. Recently, some SOC estimation methods employing the extended Kalman filtering (EKF) technique have been reported based on the state-space representation of an equivalent circuit model (Figure 1) [2]-[5]. One of the main difficulties in these model-based methods is that the physical parameters of the equivalent circuit change in time depending on the SOC and temperature of the battery. A typical approach to cope with this is to model a priori the SOC- and temperature-dependences of these parameters from the measurements obtained from AC impedance experiments [4],[5]. This approach requires a lot of time to acquire the experimental data. In this work, we take a different approach from the above previous works. We present a method for simultaneous estimation of the SOC and the equivalent circuit parameters by combining the EKF and the recursive least-squares (RLS) identification algorithm. The schematic diagram of the proposed method is illustrated in Figure 2. The advantages of the proposed method are as follows. The equivalent circuit parameters can be accurately estimated on-line by the RLS in various operating conditions without prior information nor preliminary experiments. The parameter estimation part of the proposed method is indenpendent from the nonlinear characteristics of the SOC dependence of the open circuit voltage (OCV). The effectiveness of the proposed method is verified through a discharge experiment of a LiFeO 4 battery with the nominal capacity 48.5Ah and the maximum voltage 3.5V. The SOC estimation of a LiFeO 4 battery is difficult because its SOC-OCV characteristic curve is almost flat in the middle range of the SOC. The terminal voltage and current are measured at the sampling period 1sec. The input current is a quasi-random sequence whose power is concentrated in the frequency bands around 0.07Hz and 0.03Hz (Figure 3). The RMS values of the SOC estimation errors are shown in Table1. It is seen from the table that the proposed met