The particle filter (PF) has been widely used for state of charge (SOC) estimation. However, the particle degradation phenomenon will affect the estimation accuracy. In response to this issue, a state of charge estima...
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The particle filter (PF) has been widely used for state of charge (SOC) estimation. However, the particle degradation phenomenon will affect the estimation accuracy. In response to this issue, a state of charge estimation method for lithium-ion batteries based on adaptive central difference particle filter with weight reconstruction (ACDPF-WR) is designed in this paper. This method combines the preferred importance density function and the optimized resampling strategy. Firstly, the central difference Kalman filter (CDKF) is used to update the sampled particles to reduce the influence of particle degradation. Secondly, the Gaussian processregression (GPR) model of particles and weights is constructed by combining the offline learning experimental battery data set, and the GPR is used to generate the weight distribution of the particle filter. Then, an adaptive step size mechanism is introduced, which determines the optimal step size by calculating the mean square error of the weight distribution under different steps. Finally, the weight distribution generated by the central difference particle filter (CDPF) based on the optimal step size is combined with the typical resampling algorithm to select high-quality particles to achieve the optimal estimation. The adaptability and robustness of the algorithm are verified under Beijing Dynamic Stress Test (BJDST), US06 Highway Driving Schedule (US06), and Dynamic Stress Test (DST) conditions, and evaluated by mean absolute error (MAE) and root mean square error (RMSE) indicators. The average comparison results of the three working conditions show that the MAE of ACDPF-WR is 54.1 % higher than that of EPF and 24.3 % higher than that of CDPF, and the RMSE of ACDPF-WR is 64.6 % higher than that of EPF and 24.5 % higher than that of CDPF. The proposed algorithm achieves better performance and provides new insights and methods for the optimization and improvement of the battery management system.
Advanced propulsion and power-generation systems often operate under extreme conditions, where thermophysical properties of the working fluids undergo complex variations in a wide range of fluid states, where empirica...
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Advanced propulsion and power-generation systems often operate under extreme conditions, where thermophysical properties of the working fluids undergo complex variations in a wide range of fluid states, where empirical cubic equations of state could yield substantial errors in density prediction. The present work develops data-driven models for accurate density estimation of general fluids across all thermodynamic regimes. The model starts with the cubic equation of state, whose alpha function is calibrated in a data-driven manner with statistical correction accounting for inherent correlations among training data samples. The developed models are examined for the representative pure substances in aerospace propulsion systems, including oxygen, nitrogen, carbon dioxide, and hydrocarbon fuels. Results show that the model with pressure and temperature as input variables provides consistently superior accuracy over wide ranges of temperatures and pressures, especially in the compressed-liquid region, where the Peng-Robinson equation of state significantly underperforms. The corresponding absolute average relative deviation for the studied substances is below 0.65% at different pressures, compared to 7.16% by the Peng-Robinson equation of state. The model is also extended to examine the density calculations of the selected binary and ternary mixtures, and the consistent result is obtained. The data-driven approach can be adopted to evaluate other thermodynamic properties of fluids and fluid mixtures and characteristics of vapor-liquid equilibrium, and further incorporated into large-scale multiphysics simulations where nonideal gas behavior occurs in the future.
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