State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation

Abstract

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.

Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use

“…Hybrid models: Combining models Kalman filter [6,9] and data to achieve High cost for model development Particle filter (PF) [7,8] a better performance Data-driven approach Neural networks [11,12,18] Simple Large amount of data are required Gaussian process regression (GPR) [13] Relevance vector machine (RVM) [14] Practical Only for short-term prediction SVR and PF [15] High accuracy Hybrid approach GPR and PF [16] Combines advantages Complex RVM and PF [17] of different approaches…”

“…See Table 1 for a brief summary of SOH estimation approaches for lithium-ion batteries. The model-based approach [3][4][5][6][7][8][9][10] relies on the degradation model that describes the physical nature of the battery's degradation. In the model, the battery's SOH is linked to the battery's electrochemical parameters.…”

State of Charge Estimation for Lithium‐Ion Battery by Using Dual Square Root Cubature Kalman Filter

“…In order to solve the problem, some researchers consider two Kalman filters structure to estimate SOC [12][13][14][15]. The dual EKF (DEKF) method is a combination of two EKFs, in which the SOC is estimated by the first EKF, and the parameter is estimated by the second EKF [12][13][14]. Compared with the single filter structure, the SOC accuracy of two filters is improved greatly.…”

“…In the process of charging and discharging, the battery model parameters are not constant, which will lead to the drifting of the SOC estimation result [11]. In order to solve the problem, some researchers consider two Kalman filters structure to estimate SOC [12][13][14][15]. The dual EKF (DEKF) method is a combination of two EKFs, in which the SOC is estimated by the first EKF, and the parameter is estimated by the second EKF [12][13][14].…”

“…Several methods of estimating the battery SOC have been proposed including ampere-hour counting (Ah), open circuit voltage (OCV), artificial neural networks (ANN), fuzzy logic (FL), particle filter (PF), and improved Kalman filters [2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Among them, Ah method is one of the most commonly used methods.…”