Aiming at the problem that it is difficult to accurately estimate the state of charge (SOC) of lithium‐ion batteries in the strongly nonlinear interval, a novel algorithm based on a fuzzy control strategy is proposed. It integrates extended Kalman filter (EKF) and ampere‐hour (Ah) integration accurately estimate the SOC of lithium‐ion batteries. First, the algorithm uses the advantage that the EKF algorithm has high estimation accuracy in the nonlinear interval and can solve the problem of the large error caused by the inaccurate initial value of the Ah integral algorithm. Then the fuzzy‐EKF‐Ah (F‐EKF‐Ah) is used to fuse the two algorithms of EKF and Ah integral. The fused algorithm can effectively solve the problems of the cumulative error caused by the sampling accuracy of the Ah integral algorithm and the large estimation error of the EKF algorithm in the strong nonlinear interval. Finally, the equivalent circuit model is used for analysis. The experimental results show that the improved algorithm can achieve high estimation accuracy under three experimental conditions.
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.
Summary
Lithium‐ion batteries are used in a wide range of applications due to their cleanliness and stability, and the health management of lithium‐ion batteries has become a necessity. The most important aspect of health management is the prediction of the remaining useful life (RUL) of the battery. Therefore, a RUL estimation model based on the aging factor of the charging process and an improved multi‐kernel relevance vector machine is proposed in order to achieve high accuracy estimation of the RUL of lithium‐ion batteries. First, eight aging features highly correlated with lithium‐ion batteries capacity degradation are extracted based on charging current, voltage, and temperature data, then, their correlation is proved using gray relation analysis. Secondly, the improved gray wolf constrained optimization algorithm is used to determine the kernel function combination coefficients of the multi‐kernel relevance vector machine, and the RUL prediction model of the improved multi‐kernel relevance vector machine is established. Finally, using the battery dataset from NASA, aging data of three datasets, 24°C, 43°C, and 4°C, with a total of 11 batteries, were selected for validation. The validation results show that the improved multi‐kernel relevance vector machine prediction model has higher prediction accuracy and more robust long‐term prediction capability, with RUL prediction error less than 10 cycles and MAE less than 0.05, both of which are better than that of the single‐kernel relevance vector machine model and other multi‐kernel relevance vector machine models.
State of Charge (SOC) estimation is the focus of battery management systems, and it is critical to accurately estimate battery SOC in complex operating environments. To weaken the impact of unreasonable forgetting factor values on parameter estimation accuracy, an artificial fish swarm (AFS) strategy is introduced to optimize the forgetting factor of forgetting factor least squares (FFRLS) and to model the lithium-ion battery using a first-order RC model. A new method AFS-FFRLS is proposed for online parameter identification of the first-order RC model. In SOC estimation, it is not reasonable to fix the process noise covariance, and the differential evolution (DE) algorithm is combined with the extended Kalman filter (EKF) algorithm to achieve dynamic adjustment of the process noise covariance. A joint algorithm named AFS-FFRLS-DEEKF is proposed to estimate the SOC. to verify the reasonableness of the proposed algorithm, experiments are conducted under HPPC, BBDST and DST conditions, and the average errors of the joint algorithm under the three conditions are 1.9%, 2.7% and 2.4%, respectively. The validation results show that the joint algorithm improves the accuracy of SOC estimation.
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