Accurate prediction of the state of health (SOH) of Li-ion battery has an important role in the estimation of battery state of charge (SOC), which can not only improve the efficiency of battery usage but also ensure its safety performance.The battery capacity will decrease with the increase of charge and discharge times, while the internal resistance will become larger, which will affect battery management. The capacity attenuation characteristics of Li-ion batteries are analyzed by aging experiment. Based on the equivalent circuit model and online parameter identification, a novel adaptive dual extended Kalman filter algorithm is proposed to consider the influence of the battery SOH on the estimation of the battery SOC, and the SOC and SOH of the Li-ion battery are estimated collaboratively. The feasibility and accuracy of the model and algorithm
To solve the problem of the slow convergence speed for the battery stateof-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.
Li-ion battery health assessment has been widely used in electric vehicles, unmanned aerial vehicle and other fields. In this paper, a new linear prediction method is proposed. By weakening the sensitivity of the Rainflow algorithm to the peak data, it can be applied to the field of battery, and can accurately count the number of Li-ion battery cycles, and skip the cumbersome link of parameter identification. Then, a linear criterion is proposed based on the idea of proportion, which makes the life prediction of Li-ion battery linear. Under the verification of multiple sets of data, the prediction error of this method is kept within 2.53%. This method has the advantages of high operation efficiency and simple operation, which provides a new idea for battery life prediction in the field of electric vehicles and aerospace.
Accurate state of charge (SOC) for the lithium-ion battery is not only related to user experience but also the top target to avoid overcharge and overdischarge and to use it safely. The back propagation (BP) neural network is widely used in SOC estimation, but there exist some issues, such as easily falling local extreme value, converging slowly, or even unable to converge and even overfitting. The Drosophila algorithm has a simple algorithm and strong global optimization ability, but there is also a problem of direct inheritance to reduce the optimization ability. To solve these problems, an individual migration dynamic step Drosophila (Improved Drosophila) algorithm combined with the BP neural network is proposed to estimate the SOC of lithium-ion batteries and improve estimation accuracy. In addition, the performance of the proposed method is compared with that of its traditional algorithms and other commonly used functions. The experiments are carried out to verify the ternary lithium-ion battery under DST and BBDST conditions., the mean absolute error is less than 0.8%, and the root mean square error is less than 1.4%. The SOC estimation is carried out when the current data under the DST condition are missing, which also has good estimation performance, which shows the robustness of the algorithm. Compared to other algorithms, there is good estimation accuracy.
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