The safety assurance is very important for the unmanned aerial vehicle lithium ion batteries, in which the state of charge estimation is the basis of its energy management and safety protection. A new equivalent modeling method is proposed for the mathematical expression of different structural characteristics, and an improved reduce particle‐adaptive Kalman filtering model is designed and built, in which the incorporate multiple featured information is absorbed to explore the optimal representation by abandoning the redundant and abnormal information. And then, the multiple parameter identification is investigated that has the ability of adapting the current varying conditions, according to which the hybrid pulse power characterization test is accommodated. As can be known from the experimental results, the polynomial fitting treatment is carried out by conducting the curve fitting treatment and the maximum estimation error of the closed‐circuit‐voltage is 0.48% and its state of charge estimation error is lower than 0.30% in the hybrid pulse power characterization test, which is also within 2.00% under complex current varying working conditions. The iterate calculation process is conducted for the unmanned aerial vehicle lithium ion batteries together with the compound equivalent modeling, realizing its adaptive power state estimation and safety protection effectively.

It is crucial to conduct highly accurate estimation of the state of charge (SOC) of lithium-ion batteries during the real-time monitoring and safety control. Based on residual constraint fading factor unscented Kalman filter, the paper proposes an SOC estimation method to improve the accuracy of online estimating SOC. A priori values of terminal voltage were fitted using cubic Hermite interpolation. In combination with the Thevenin equivalent circuit model, the method of adaptive forgetting factor recursive least squares is used to identify the model parameters. To address the problem that the UKF method is strongly influenced by system noise and observation noise, the paper proposes an improved method of residual constrained fading factor. Finally, the effectiveness of this method was verified by the test of Hybrid Pulse Power Characteristic and Beijing Bus Dynamic Stress Test. Results show that under HPPC conditions, compared with other methods, the algorithm in the paper estimates that the SOC error of the battery remains between -0.38% and 0.948%, reducing the absolute maximum error by 51.5% at least and the average error by 62.7% at least. Moreover, under the condition of Beijing Bus Dynamic Stress Test the algorithm estimates the SOC error of the battery stays between -0.811% and 0.526%, and the SOC estimation errors are all within 0.2% after ten seconds of operation. Compared with other methods, the absolute maximum error can be reduced by 42.7% at least and the average error is reduced by 95% at least. And the test proves that the method is of higher accuracy, better convergence and stronger robustness.INDEX TERMS lithium-ion battery, state of charge estimation, Residual constraint fading factorunscented Kalman filter, adaptive forgetting factor recursive least square, cubic Hermite interpolation.

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With the continuous improvement on mine equipment automation level and the progress of battery manufacturing technology, Lithium-ion batteries are widely used in mining transportation, monitoring communication and emergency facilities. This paper designs a kind of lithium-ion battery management system for explosion-proof mining electric vehicle according to GB3836-20210 series standard. And the management system takes STM32F103 as the main controller and LTC6811 as the core, using passive equalization strategy to realize battery voltage equalization. The test results show that the maximum error of 100 batteries after equalization is 0.32v, the average error is 0.03v and the maximum error between the monitoring value and the measured value is 0.3v, the average error is 0.0019v, which prove that the management system has high accuracy and effectiveness.