A novel adaptive H‐infinity filtering method for the accurate SOC estimation of lithium‐ion batteries based on optimal forgetting factor selection

Abstract

Accurate estimation of the state of charge (SOC) of lithium‐ion batteries is quite crucial to battery safety monitoring and efficient use of energy; to improve the accuracy of lithium‐ion battery SOC estimation under complicated working conditions, the research object of this study is the ternary lithium‐ion battery; the forgetting factor recursive least square (FFRLS) method optimized by particle swarm optimization (PSO) and adaptive H‐infinity filter (HIF) algorithm are adopted to estimate battery SOC. The PSO algorithm is improved with dynamic inertia weight to optimize the forgetting factor to solve the contradiction between FFRLS convergence speed and anti‐noise ability. The noise covariance matrixes of the HIF are improved to realize adaptive correction function and improve the accuracy of SOC estimation. To verify the rationality of the joint algorithm, a second‐order Thevenin model is established to estimate the SOC under three complex operating conditions. The experimental results show that the absolute value of the maximum estimation error of the improved algorithm under the three working conditions is 0.0192, 0.0131, and 0.0111, respectively, which proves that the improved algorithm has high accuracy and offers a theoretical basis for the safe and efficient operation of the battery management system.

Online Parameter Identification and State of Charge Estimation of Lithium-Ion Batteries Based on Improved Artificial Fish Swarms Forgetting Factor Least Squares and Differential Evolution Extended Kalman Filter

“…To further verify the effectiveness of the proposed algorithm, three advanced algorithms are selected for comparison with the proposed algorithm. The algorithms compared in this section are AEKF [43], MIEKF [44] and AHIF [45] algorithms. For a fairly comparison, the proposed AFSFFRLS algorithm was used for each algorithm's parameter identification method and run 20 times independently, and the comparison is From Table 5, it can be seen that the proposed algorithm DEEKF has the best performance in terms of metrics for the three operating conditions, and the obtained MAE and RMSE values are the smallest.…”

State of Charge and State of Health Coestimation for Lithium-Ion Capacitor Based on Multi-innovation Filters

“…The CKF was proposed by Arasaratnam et al in 2009, which is derivative-free and is regarded as an optimal approximation to the Bayesian filter that could be designed in a nonlinear system [29]. In this work, the MI-CKF is used to perform state estimation.…”

“…This confirms the analysis based on voltage estimation error, and the discharge OCV is more suitable for establishing LIC model and further SOC estimation. Figure 9 depicts the comparison results between the proposed MIF method and four commonly used algorithms for SOC estimation, including the EKF [30], the H infinity filter (HIF) [29], the CKF [31], and the square-root cubature Kalman filter (SRCKF) [32], where for such algorithms, the parameters are obtained with FFRLS. In addition, the statistical results of estimation error are listed in Table 4.…”

An improved adaptive FastSLAM algorithm with time‐varying noise estimator

“…here, b is a fading factor, which is set to 0.96 [31]. Thanks to fading factor, the effect of the last collected data on the noise statistics estimator is increased [32].…”

“…$$$ {e}_{z,k} $$$ is innovation value which is the difference between observation and estimation made by robot at time $$$ k $$$. $$$ {d}_k $$$ is weighting coefficient, which is calculated as $$$$ {d}_k=\frac{1-b}{1-{b}^k}; $$$$ here, $$$ b $$$ is a fading factor, which is set to 0.96 [31]. Thanks to fading factor, the effect of the last collected data on the noise statistics estimator is increased [32].…”