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The accuracy of lithium‐ion battery state estimation is critical to the safety of unmanned aerial vehicles (UAVs). In this paper, aiming at the high‐fidelity modeling of the UAV lithium‐ion battery, a splice‐electrochemical polarization model (S‐EPM) for UAV lithium‐ion battery is constructed by combining the traditional electrochemical model with the equivalent circuit model, which greatly improved the accuracy of the battery modeling. In addition, a novel prior generalized inverse least‐squares algorithm is proposed. Also, based on this algorithm, the full‐parameter identification and multicondition error analysis of the S‐EPM are realized based on this algorithm. Finally, a targeted complex discharge rate test and a full‐function charge–discharge test were designed to further verify the applicability of the S‐EPM to complex conditions. The experimental results show that the voltage error of the model under each working condition is 5.50 and 3.0 mV, and the maximum percentage error ratio is 0.20% and 0.07%. This experiment can provide a theoretical basis for the combination of the electrochemical model and equivalent circuit model and the accurate estimation of internal state variables of lithium‐ion batteries. [ABSTRACT FROM AUTHOR]

• A lumped thermoelectric coupling model is established at multi-time scale. • A multi-feature separation modeling idea suitable for batteries is proposed. • A strong coupling adaptive asynchronous identification strategy is designed. • Three coupled filters on different time scales are used for model identification. • Model performance evaluation is implemented using the concept of dispersion. The rapid development of new energy fields such as electric vehicles and smart grids has put forward higher requirements for the power management of battery integrated systems. Considering that internal temperature and parameter consistency are important factors affecting battery safety and state estimation accuracy, a lumped thermoelectric coupling model based on the multi-time scale effects of battery dynamics parameter is established in this paper. On this basis, a new multi-feature separation modeling idea is proposed and adopted to complete the development of the strong coupling adaptive asynchronous identification strategy to realize the solution of the model. Specifically, the high-frequency and low-frequency characteristics of the resistor–capacitor link under different time constants are distinguished on different time scales. Three sub-filters based on forgetting factor recursive least squares, extended Kalman filtering and joint Kalman filtering are used to realize the adaptive asynchronous synergistic estimation of battery high-frequency dynamics parameter, low-frequency dynamics parameter and internal temperature. In addition, the filters at different time scales are strongly coupled through the voltage response on the diffusion impedance, and the time scale drive under slow dynamics depends on the current distribution of the test conditions. The experimental results of two long-term cycles show that the proposed strategy exhibits excellent terminal voltage tracking effect and internal temperature estimation accuracy. Finally, the concept of parameter dispersion is proposed and discussed. Compared with the results under the traditional identification method, the proposed strategy reduces the maximum parameter dispersion by 51.9%. [ABSTRACT FROM AUTHOR]

The accurate modeling of lithium-ion batteries is extremely important to improve the reliability of battery management systems, and solving the problem of multi-time scales is extremely beneficial for high-accuracy battery modeling and adaptive asynchronous parameter identification. This paper distinguishes the fast and slow change characteristics of the model resistor-capacitor link parameters, a strong applicability model for the aggregate electrical characteristics of vehicle-mounted lithium-ion batteries based on multi-time scales is established. By combining the advantages of different identification algorithms, an adaptive asynchronous parameter identification strategy is proposed, which solves the problem of data saturation caused by the time scale identification strategy. Then, the complex charge-discharge pulse and the mixed discharge pulse tests are designed explicitly, and the parameter results and terminal voltage tracking effects under different identification strategies are compared. Moreover, the consistency results of the parameter identification test under single-time scale forgetting factor recursive least squares and multi-time scale adaptive asynchronous parameter identification strategy are analyzed. The results show that under different working conditions, the identification precision of the terminal voltage based on the adaptive asynchronous parameter identification strategy is increased by 0.420% and 1.114% respectively, and the maximum error of parameter consistency is reduced by 158.300%. • An adaptive online identification strategy for asynchronous parameters is proposed. • Different dynamic characteristics are characterized by different time scales. • Realized the hysteresis effect modeling of the battery open-circuit voltage. • The hysteresis effect equation is coupled iteratively with the state-space equation. • Combine FFRLS and EKF to complete the asynchronous identification of parameters. [ABSTRACT FROM AUTHOR]