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A method based on strong tracking unscented Kalman filter with multiple suboptimal fading factors (MSTUKF) was proposed to accurately estimate the state of charge (SOC) of power batteries of electric vehicles online. Taking a certain lithium-ion battery as the research object, a second-order RC equivalent circuit model of the battery was established based on its external characteristics and related mechanism. Then the recursive least squares method with forgetting factor was adopted to identify the model parameters, and the MSTUKF nonlinear state space equation of the battery was established according to the equivalent circuit model. Finally, the SOC estimation algorithm was verified by simulation experiments under ECE15 and UDDS conditions. The results show that the error of MSTUKF in SOC estimation of lithium-ion battery is kept within 1.5%, so this method can estimate battery SOC accurately. [ABSTRACT FROM AUTHOR]

The development of a secure battery management system (BMS) for electric vehicles depends heavily on the correct assessment of the online state-of-charge (SOC) of Li-ion batteries. The ternary lithium battery is used as the research object in this paper, and a second-order RC equivalent circuit model is developed to characterize the dynamic operating characteristics of the battery. In order to solve the problem that the adaptive unscented Kalman filter (AUKF) algorithm is easy to fail SOC estimation because the error covariance matrix is not positively definite due to the incomplete accuracy of the equivalent circuit model, a corresponding solution is proposed. Considering the poor real-time battery SOC estimate caused by the battery model's fixed parameters, therefore we propose the Variable Forgetting Factor Recursive Least Squares (VFFRLS) algorithm for joint estimation of Li-battery SOC and the Singular Value Decomposition-AUKF (SVD-AUKF) algorithm. The SVD-AUKF algorithm can accurately estimate the SOC of the battery when the error covariance is negative. The algorithm can be adaptively adjusted in both the parameter identification and SOC estimation stages, which can effectively solve the problem of poor estimation accuracy caused by fixed parameters. According to experiments, under two separate dynamic operating situations, the joint estimation algorithm's error is less than 2%, and its stability has also been greatly enhanced. At the same time, when the initial SOC value is set incorrectly, the convergence time of the algorithm proposed in this paper can reach within 2.1 seconds for BBDST and DST conditions, which can be well adapted to complex working conditions. [ABSTRACT FROM AUTHOR]

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The state of charge (SOC) for a lithium-ion battery is a key index closely related to battery performance and safety with respect to the power supply system of electric vehicles. The Kalman filter (KF) or extended KF (EKF) is normally employed to estimate SOC in association with the relatively simple and fast second-order resistor-capacitor (RC) equivalent circuit model for SOC estimations. To improve the stability of SOC estimation, a two-stage method is developed by combining the second-order RC equivalent circuit model and the eXogenous Kalman filter (XKF) to estimate the SOC of a lithium-ion battery. First, approximate SOC estimation values are observed with relatively poor accuracy by a stable observer without considering parameter uncertainty. Second, the poor accuracy SOC results are further fed into XKF to obtain relative stable and accurate SOC estimation values. Experiments demonstrate that the SOC estimation results of the present method are superior to those of the commonly used EKF method. It is expected that the present two-stage XKF method will be useful for the stable and accurate estimation of SOC in the power supply system of electric vehicles. [ABSTRACT FROM AUTHOR]

The validity of the equivalent circuit model (ECM), which is crucial for the development of lithium-ion batteries (LIBs) and state evaluation, is primarily dependent on the precision of the findings of parameter identification. In this study, the commonly used first-order RC (1-RC) circuit and second-order RC (2-RC) circuit models were selected for parameter identification. A time series of voltage with different sample intervals were used for function fitting based on the least square method, which were extracted from the hybrid pulse power characteristic (HPPC) test data of a commercial square punch LIB, and the sample intervals were set to be 0.1 s, 0.2 s, 0.5 s, and 1.0 s to evaluate the effect of sample interval on the parameter identification results. When the sample interval is more than 0.5 s, the results reveal that the 2-RC circuit model's goodness of fit marginally declines, and for some data scenarios, the bias between the fitted terminal voltage curve and test curve increases obviously. With all of the sample intervals under consideration, the 1-RC circuit model's imitative effect is satisfactory. This work demonstrates that the sample interval of data samples, in addition to the method itself, affects the accuracy and robustness of parameter identification, with the 1-RC circuit model showing larger advantages under low sample frequency compared to the 2-RC circuit model. [ABSTRACT FROM AUTHOR]

In this paper, an improved particle filter (Improved Particle Swarm Optimized Particle Filter, IPSO-PF) algorithm is proposed to estimate the state of charge (SOC) of lithium-ion batteries. It solves the problem of inaccurate posterior estimation due to particle degradation. The algorithm divides the particle population into three parts and designs different updating methods to realize self-variation and mutual learning of particles, which effectively promotes global development and avoids falling into local optimum. Firstly, a second-order RC equivalent circuit model is established. Secondly, the model parameters are identified by the particle swarm optimization algorithm. Finally, the proposed algorithm is verified under four different driving conditions. The results show that the root mean square error (RMSE) of the proposed algorithm is within 0.4% under different driving conditions, and the maximum error (ME) is less than 1%, showing good generalization. Compared with the EKF, PF, and PSO-PF algorithms, the IPSO-PF algorithm significantly improves the estimation accuracy of SOC, which verifies the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: Lithium‐ion batteries are widely used as rechargeable energy and power storage system in smart devices and electric vehicles because of their high specific energy, high power densities, etc. The state of charge (SOC) serves as a vital feature that is monitored by the battery management system to optimize the performance, safety, and lifespan of lithium‐ion batteries. In this paper, a strong tracking adaptive fading‐extended Kalman filter (STAF‐EKF) based on the second‐order resistor–capacitor equivalent circuit model (2RC‐ECM) is proposed for accurate SOC estimation of lithium‐ion batteries under different working conditions and ambient temperatures. The characteristic parameters of the established 2RC‐ECM for the lithium‐ion battery are identified offline using the least‐squares curve fitting method with an average R‐squared value of 0.99881. Experimental data from the hybrid pulse power characterization (HPPC) is used for the estimation and verification of the proposed STAF‐EKF method under the complex Beijing bus dynamic stress test (BBDST) and the dynamic stress test (DST) working conditions at varying ambient temperatures. The results show that the established 2RC‐ECM tracks the actual voltage of the battery with a maximum error of 28.44 mV under the BBDST working condition. For the SOC estimation, the results show that the proposed STAF‐EKF has a maximum mean absolute error (MAE) and root mean square error (RMSE) values of 1.7159% and 1.8507%, while the EKF has 6.7358% and 7.2564%, respectively, at an ambient temperature of −10°C under the BBDST working condition. The proposed STAF‐EKF delivers optimal performance improvement compared to the EKF under different working conditions and ambient temperatures, serving as a basis for an accurate and robust SOC estimation method with quick convergence for the real‐time applications of lithium‐ion batteries. [ABSTRACT FROM AUTHOR]

New energy automobiles possess broad application prospects, and the charging technology of vehicle power batteries is one of the key technologies in the development of new energy automobiles. Traditional lithium battery charging mostly adopts the constant current-constant voltage method, but continuous and frequent charging application conditions will cause temperature rise and accelerated capacity decay, which easily bring about safety problems. Aiming at the above-mentioned problems related to the charging process of lithium-ion batteries, this paper proposes an optimization strategy and charging method for lithium-ion batteries based on an improved electric-thermal coupling model. Through the HPPC experiment, the parameter identification of the second-order RC equivalent circuit model was completed, and the electric-thermal coupling model of the lithium battery was established. Taking into account the two factors of charging time and charging temperature rise, the multi-stage charging strategy of the lithium-ion battery is optimized by the particle swarm optimization algorithm. The experimental results show that the multi-stage constant current charging method proposed in this paper not only reduces the maximum temperature during the charging process by an average of 0.83% compared with the maximum temperature of the battery samples charged with the traditional constant current-constant voltage (CC-CV) charging method but also reduces the charging time by an average of 13.87%. Therefore, the proposed optimized charging strategy limits the charging temperature rise to a certain extent on the basis of ensuring fast charging and provides a certain theoretical basis for the thermal management of the battery system and the design and safe charging method of the battery charging system. [ABSTRACT FROM AUTHOR]

Summary: Accurate estimation of the charged state of lithium‐ion batteries is very important for the application and development of lithium‐ion batteries, it is directly related to whether or not the lithium‐ion battery is safe and reliable during use. Based on the traditional unscented Kalman filter algorithm (UKF), the square root of the state variable error covariance matrix is used for iterative calculation variables in this paper, and the method solves the problem that the traditional (UKF) algorithm may make the covariance matrix negative when measuring SOC; two spherical Unscented transformations (UT) are selected to replace the one traditional Unscented transformation, it avoids the complexity of the process of adjusting parameters, and it also changes shortcomings of filtering divergence caused by the uncertainty of system noise and observation noise; the adaptive covariance matching of noise is introduced, which can automatically update and transfer the noise covariance matrix, and it also makes the noise closer to the real situation. The improved algorithm is called the adaptive spherical square‐root double unscented Kalman filter algorithm (ASSR‐DUKF). The experimental results show that the improved ASSR‐DUKF algorithm can better estimate the SOC of the lithium‐ion battery through the second‐order RC equivalent circuit. The mean absolute errors of HPPC, BBDST, and DST are 0.53%, 0.39%, and 1.08%; the root mean square errors under three working conditions are 0.70%, 0.62%, and 1.22%; the convergence times under the three working conditions are 60, 110, and 74 s. It can be seen that the ASSR‐DUKF algorithm has higher Estimate accuracy, better convergence, and better robustness. [ABSTRACT FROM AUTHOR]

In this article, the estimation problem of the state of charge (SOC) of Lithium-ion batteries is investigated. In order to truly reflect the unreliability of the sensor measured data, the data missing phenomenon with respect to the sensor measurement (e.g., the terminal voltage) is taken into account for the addressed estimation issue. By introducing a stochastic variable obeying the Bernoulli distribution with a known probability, the random occurrence of the sensor measurement unavailability is well characterized. The second-order resistor–capacitor equivalent circuit model, where the model parameters are identified by the recursive least-squares method, is developed to govern the dynamical behaviors of a Lithium-ion battery. A data-unavailability-resistant nonlinear recursive filtering algorithm is proposed to estimate the real SOC in an unreliable industrial environment. An upper bound of the filtering error covariance is obtained, which is further minimized at each sampling instant. In addition, the filter gain is recursively parameterized by solving an optimization problem with respect to two coupled recursive Riccati-like equations, thereby being suitable for the online implementation. Finally, extensive experiments are conducted to demonstrate the validity of the proposed filtering approach. [ABSTRACT FROM AUTHOR]

Summary: Accurate state‐of‐charge estimation plays an extremely crucial role in battery management systems. To realize the real‐time and precise state‐of‐charge estimation, an intelligent weight decreasing firefly–particle filtering algorithm is proposed. In this research, the second‐order RC equivalent circuit model is established, and the parameters are identified online, and state‐of‐charge particles simulate the attraction behavior of fireflies in nature and approach the global optimal value to complete the particle optimization process. The linear weight decreasing strategy is introduced to avoid the algorithm falling into local optimization. The data of different complex conditions are used to verify the feasibility of the proposed algorithm; the results show that the root‐mean‐square error of intelligent weight decreasing firefly–particle filtering method when the initial SOC value is set to 1 under Hybrid Pulse Power Characterization and Beijing Bus Dynamic Stress Test condition can be controlled within 0.60% and 1.12%, respectively, which verifies that the proposed algorithm has high accuracy in state‐of‐charge estimation of lithium‐ion batteries. The algorithm proposed in this article provides a theoretical basis for real‐time state monitoring and security of battery management systems. [ABSTRACT FROM AUTHOR]

To solve the problem of the slow convergence speed for the battery state‐of‐charge estimation of cubature Kalman filter algorithm, the ternary lithium‐ion battery is taken as the research object, and an algorithm combining the fuzzy self‐adaptation and singular value decomposition cubature Kalman filtering is proposed. The algorithm takes the system innovation and its change rate as the fuzzy input and the output as the adjustment factor, which is used to adjust the process noise covariance matrix R. The Kalman gain is adjusted through the fuzzy control of R. To ensure the stability of the algorithm in the calculation process, the singular value decomposition is applied to cubature Kalman algorithm. Then, a second‐order RC equivalent circuit model with double internal resistance is built and tested under different conditions to verify the rationality of the improved algorithm. The verification results show that under the simple condition, the convergence speed of the proposed algorithm in the different initial state‐of‐charge values increased by 40.00% and 25.00%, the maximum estimation error of the state‐of‐charge is 2.52% and 2.51%, the Mean Absolute Error is 0.816% and 0.880%, and the Root Mean Square Error is 1.276% and 1.380%. When the initial state‐of‐charge value is 0.8, the convergence speed in the complex condition is increased by about 30.00%; the maximum estimation result error, Mean Absolute Error, and Root Mean Square Error are 2.21%, 0.222%, and 1.327%, respectively. When the initial state‐of‐charge value is 0.6, the convergence speed in the complex condition is increased by about 10.00%; the maximum estimation result error, Mean Absolute Error, and Root Mean Square Error are 2.72%, 0.941%, and 1.327%, respectively. Without reducing the estimation accuracy, the improved algorithm can significantly increase the convergence speed of predictive value tracking, which provides a theoretical basis for the wide application of lithium‐ion batteries. [ABSTRACT FROM AUTHOR]

Copyright of China Sciencepaper is the property of China Sciencepaper and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on Thevenin equivalent model, the second-order RC equivalent circuit model was established. The pulse discharge data of battery were obtained by the tests of hybrid pulse power characteristics, and the parameters of battery equivalent circuit were identified. In order to make up the iitiing errors of battery model parameter identification when the SOC of lithium-ion batteries was in the range of 90% to 100%, ampere hour integral and UKF were used comprehensively to estimate battery's SOC. Battery management system was designed by using HIL test platform and environmental simulation test platform. The results of SOC estimation of batteries under different operating conditions show that the estimation errors of SOC are from -1.5% to 1.0%. This method has relatively high accuracy and good effectiveness. [ABSTRACT FROM AUTHOR]

This work is focused on the state of charge (SOC) estimation of a lithium-ion battery based on a nonlinear observer. First, the second-order resistor-capacitor (RC) model of the battery pack is introduced by utilizing the physical behavior of the battery. Then, for the nonlinear function of the RC model, a one-sided Lipschitz condition is proposed to ensure that the nonlinear function can play a positive role in the observer design. After that, a nonlinear observer design criterion is presented based on the H∞ method, which is formulated as linear matrix inequalities (LMIs). Compared with existing nonlinear observer-based SOC estimation methods, the proposed observer design criterion does not depend on any estimates of the unknown variables. Consequently, the convergence of the proposed nonlinear observer is guaranteed for any operating conditions. Finally, both the static and dynamic experimental cases are given to show the efficiency of the proposed nonlinear observer by comparing with the classic extended Kalman filter (EKF). [ABSTRACT FROM AUTHOR]

The deterioration of lithium-ion batteries has been detected by an increase in the battery impedance by means of an alternating current method or a battery capacity test. These methods require an interruption of the operation because the battery has to be removed. A method estimating equivalent circuit parameters of a lithium-ion battery during operation is proposed in this article. The increase in the internal impedance has a close relation to the deterioration of the battery. The circuit is expressed by a resister and an RC parallel circuit connected in series. The parameters can be estimated by applying a convolution technique to the voltage and current fluctuation of the battery during operation. A diagnosis circuit using a microcomputer is developed using a simple algorithm employing z-transformation in the parameter estimation. The estimated parameters depend on its state of charge (SOC) and ambient temperature. The SOC dependency is solved by estimating timing, and the temperature dependency is corrected by a function derived in this article. The deterioration diagnosis of the battery can be applied to a solar power generation system, and the feasibility is discussed in this article. [ABSTRACT FROM AUTHOR]

Accurate estimation of battery state of charge (SOC) is of great significance for extending battery life, improving battery utilization, and ensuring battery safety. Aiming to improve the accuracy of SOC estimation, in this paper, a temperature-dependent second-order RC equivalent circuit model is established for lithium-ion batteries, based on the battery electrical characteristics at different ambient temperatures. Then, a dual Kalman filter algorithm is proposed to estimate the battery SOC, using the proposed equivalent circuit model. The SOC estimation results are compared with the SOC value obtained from experiments, and the estimation errors under different temperature conditions are found to be within ±0.4%. These results prove that the proposed SOC estimation algorithm, based on a temperature-dependent second-order RC equivalent circuit model, provides accurate SOC estimation performance with high temperature adaptability and robustness. [ABSTRACT FROM AUTHOR]