State of charge estimation method based on the extended Kalman filter algorithm with consideration of time‐varying battery parameters

Abstract

In developing battery management systems, estimating state-of-charge (SOC) is important yet challenging. Compared with traditional SOC estimation methods (eg, the ampere-hour integration method), extended Kalman filter (EKF) algorithm does not depend on the initial value of SOC and has no accumulated error, which is suitable for the actual working condition of electric vehicles. EKF is a model-based algorithm; the accuracy of SOC estimated by this algorithm was greatly influenced by the accuracy of battery model and model parameters. The parameters of battery change with many factors and exhibit strong nonlinearity and time variance. Typical EKF algorithm approximates battery as a linear, timeinvariant system; however, this approach introduces estimation errors. To minimize such errors, previous studies have focused on improving the accuracy of identifying battery parameters. Although studies on battery model with timevarying parameters have been carried out, few have studied the combination of time-varying battery parameters and EKF algorithm. A SOC estimation method that combines time-varying battery parameters with EKF algorithm is proposed to improve the accuracy of SOC estimation. Battery parameter data were obtained experimentally under different temperatures, SOC levels, and discharge rates. The results of parameter identification are made into a data table, and the battery parameters in the EKF system matrix are updated by looking up the data in the table. Simulation and experimental results shown that, average error of SOC estimated by the proposed algorithm is 2.39% under 0.9 C constant current discharge and 2.4% under 1.3 C, which is 1.91% and 2.35% lower than that of EKF algorithm with fixed battery parameters. Under intermittent discharge with constant current (1.1 C) and capacity (10%), the average error of SOC estimated by the proposed algorithm is 1.4%, which is 0.3% lower than that of EKF algorithm with fixed battery parameters. The average error of SOC estimated by the proposed algorithm under the New European Driving Cycle (NEDC) is 1.6%, which is 0.2% lower than that of EKF algorithm with fixed battery parameters. Relative to the EKF algorithm with fixed battery parameters, the proposed EFK algorithm with timevarying battery parameters yields higher accuracy.

Nonlinear Dynamic Model for Parameter Estimation of Li-Ion Batteries Using Supply–Demand Algorithm

“…The estimation of the SOC is a critical requirement for achieving the appropriate supervision and control of battery charging and discharging [10]. Furthermore, a precise prediction of the battery's characteristics and SOC is critical for a variety of reasons, including increasing the battery's life, regulating the battery's state of charge, enhancing battery performance [11], optimizing energy management, and monitoring battery safety [12]. However, this estimating approach has various drawbacks, including its expense, higher computing capacity, and time consumption, due to the presence of recurrent computing activities.…”

A combined robust approach based on auto‐regressive long short‐term memory network and moving horizon estimation for state‐of‐charge estimation of lithium‐ion batteries

“…The practical experimental results of the method show superiority over the direct estimation. 33,34 Li et al, proposed a novel Gaussian process regression model and data-driven prediction technique to estimate the state of health (SOH) where they take the physicochemical characteristics of battery degradation as the key point. 35,36 Wang et al, proposed a novel migration-based framework for different types of cells with temperature and aging treatment as uncertainties.…”

The optimization of state of charge and state of health estimation for lithium‐ions battery using combined deep learning and Kalman filter methods

“…After that, different Bayesian state estimation algorithms are applied to estimate the battery SOC and capacity concurrently. The filters used include multi‐scale dual Kalman filters, 4,7 unscented Kalman filter (UKF), 8 the PI filter, 9 H infinity filters, 10 unscented particle filters (UPF), 11 and extended Kalman filter (EKF) 12…”