State Estimation Technique for VRLA Batteries for Automotive Applications

Abstract

The state-of-charge (SOC) and state-of-health (SOH) estimation of batteries play important roles in managing batteries for automotive applications. However, an accurate state estimation of a battery is difficult to achieve because of certain factors, such as measurement noise, highly nonlinear characteristics, strong hysteresis phenomenon, and diffusion effect of batteries. In certain vehicular applications, such as idle stop-start systems (ISSs), significant errors in SOC/SOH estimation may lead to a failure in restarting a combustion engine after the shut-off period of the engine when the vehicle is at rest, such as at a traffic light. In this paper, a dual extended Kalman filter algorithm with a dynamic equivalent circuit model of a lead-acid battery is proposed to deal with this problem. The proposed algorithm adopts a battery model by taking into account the hysteresis phenomenon, diffusion effect, and parameter variations for accurate state estimations of the battery. The validity of the proposed algorithm is verified through experiments by using an absorbed glass mat valve-regulated lead-acid battery and a battery sensor cable for commercial ISS vehicles.

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

“…The battery electric equivalent circuit consists of an open circuit voltage (OCV) connected in series with an internal resistance R i and R-C parallel branch of the charge transfer resistance R tc along with a double layer capacitance C dl to obtain the voltage response from the battery current [2,3]. This battery model is simple and effective.…”

“…Since lead-acid batteries have nonlinear characteristics such as the hysteresis effect, their SOC and SOH estimation is more challenging than for other kinds of batteries [2,3]. In [4], the relationship between the discharge voltage and the SOC was determined for a wide range of discharge rates at different ambient temperatures for telecommunication backup power supply applications.…”

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.…”