In this paper a method is introduced to find out the optimal charge pattern (OCP) of the multistage constant current (MSCC) charge method based on the equivalent circuit model of a Li-Ion battery. Unlike the conventional methods the proposed one does not require several kinds of time-consuming and labor-intensive experiments. The OCP can be simply calculated by the developed equations with the results obtained by a simple experiment. The validity of the proposed method has been verified through the experimental results and it shows that the charge time and the charge energy can be reduced by 12% and 1.8%, respectively, and the charge efficiency of a Li-Ion battery can be improved by 0.54 % as compared to the conventional CC-CV method. A lower temperature rise during the charge can be regarded as an additional advantage of the proposed method.

As the use of energy storage systems (ESSs) and electric vehicles (EVs) increases, the importance of lithium-ion (Li-ion) batteries is also growing. The accurate capacity estimation of a battery is useful for detecting the degradation and end-of-life of the battery for scheduled maintenance and replacement, thereby improving the reliability of battery-powered systems. However, many of the capacity estimation methods developed thus far are offline methods and are not suitable for online monitoring. In this study, a novel capacity estimation method for Li-ion batteries is proposed, based on the enhanced coulomb counting (ECC) method. Typically, the battery capacity calculated by ECC has a large error owing to the error in the state of charge estimation. Therefore, additional means are required to obtain an accurate value for the battery capacity. The Kalman filter is an optimal estimator of the hidden state with Gaussian noise and is applied to the capacity values calculated by the ECC method. The performance of the proposed method in estimating capacity is verified through experiments with a Li-ion battery operated using a standard vehicle driving schedule. The results show that the online capacity estimation using the proposed method is performed successfully within 10 h with a 1.7% error.

This paper proposes a fast cell-to-cell balancing circuit for lithium-ion battery strings. The proposed method uses only one push-pull converter to transfer energy between high- and low-voltage cells directly for a fast balancing speed. The switch network for selecting a certain pair of cells is implemented using relays to achieve a low cost. The control circuit is composed of a battery-monitoring IC and a digital signal processor (DSP) to monitor the cell voltage and to protect the batteries. In order to prove the validity of the proposed method, a prototype circuit is built with twelve lithium-ion batteries in a string. The experimental results show that it takes only 50 min to balance twelve lithium-ion batteries during the charge with 89.5% maximum efficiency. The outstanding performance of the proposed cell balancing circuit is verified through its comparison with other methods in terms of several factors, such as the balancing time and the implementation cost.

State of charge (SOC) and state of health (SOH) are key issues for the application of batteries, especially the absorbent glass mat valve regulated lead-acid (AGM VRLA) type batteries used in the idle stop start systems (ISSs) that are popularly integrated into conventional engine-based vehicles. This is due to the fact that SOC and SOH estimation accuracy is crucial for optimizing battery energy utilization, ensuring safety and extending battery life cycles. The dual extended Kalman filter (DEKF), which provides an elegant and powerful solution, is widely applied in SOC and SOH estimation based on a battery parameter model. However, the battery parameters are strongly dependent on operation conditions such as the SOC, current rate and temperature. In addition, battery parameters change significantly over the life cycle of a battery. As a result, many experimental pretests investigating the effects of the internal and external conditions of a battery on its parameters are required, since the accuracy of state estimation depends on the quality of the information regarding battery parameter changes. In this paper, a novel method for SOC and SOH estimation that combines a DEKF algorithm, which considers hysteresis and diffusion effects, and an auto regressive exogenous (ARX) model for online parameters estimation is proposed. The DEKF provides precise information concerning the battery open circuit voltage (OCV) to the ARX model. Meanwhile, the ARX model continues monitoring parameter variations and supplies information on them to the DEKF. In this way, the estimation accuracy can be maintained despite the changing parameters of a battery. Moreover, online parameter estimation from the ARX model can save the time and effort used for parameter pretests. The validation of the proposed algorithm is given by simulation and experimental results.

An accurate state of charge (SOC) estimation of the lithium iron phosphate battery (LiFePO₄) is one of the most important functions for the battery management system (BMS) for electric vehicles (EVs) and energy storage systems (ESSs). However, an accurate estimation of the SOC of LiFePO₄ is challenging due to the hysteresis phenomenon occurring during the charge and discharge. Therefore, an accurate modeling of the hysteresis phenomenon is essential for reliable SOC estimation. The conventional hysteresis modeling methods, such as one-state hysteresis modeling and parallelogram modeling, are not good enough to achieve high-accuracy SOC estimation due to their errors in the approximation of the hysteresis contour. This paper proposes a novel method for accurate hysteresis modeling, which can provide a significant improvement in terms of the accuracy of the SOC estimation compared with the conventional methods. The SOC estimation is performed by using an extended Kalman filter (EKF) and the parameters of the battery are estimated by using auto regressive exogenous (ARX) model and the recursive least square (RLS) filter. The experimental results with the conventional and proposed methods are compared to show the superiority of the proposed method.