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This paper proposes an effective method to estimate the state of health (SOH) of a lithium-ion battery based on the ohm internal resistance R0. Unlike other estimation methods, this work considers the variation of R0 with the state of charge (SOC). The improved unscented particle filter (IUPF) is presented to track and predict R0. That is, an unscented Kalman filter (UKF) is used to generate an importance probability density function in the particle filter, and a method to select the fittest particle in the resampling stage is proposed. Based on the experimental data, a second-order resistance-capacitance equivalent circuit model is set up and the parameters are identified. To verify the accuracy of the proposed method, UKF and IUPF are compared in the prediction of R0 at different SOC points under the same cycle and at the same SOC point of different cycles. The results show that IUPF has certain advantages, and the SOH estimation error is always less than 3% during the charge-discharge stage. [ABSTRACT FROM AUTHOR]

Short-term load forecasting is an important part in the energy management of micro-grids, and the forecasting error directly affects the economic efficiency of operation. Considering the strong randomness and high volatility of loads on the user side of micro-grids, a short-term load forecasting method based on Empirical Mode Decomposition (EMD), Extreme Learning Machine with different Kernels, and Switching Delayed Particle Swarm Optimization (SDPSO) is proposed. First, the history load dataset is decomposed into several independent Intrinsic Mode Functions (IMFs) by EMD, and the sample entropy values of the IMFs are calculated. According to the approximation degree of sample entropy values, the IMFs are divided into three categories. Then, ELM with different kernels is adopted to forecast the three categories. Finally, the prediction results are summed to obtain the final prediction result. SDPSO is used to optimize the relevant parameters in the forecasting model. Three micro-grids with different capacities are used to verify the model, and the experimental results demonstrate that the proposed forecasting model can provide better accuracy than the other two methods. The proposed model can provide practical reference for the efficient operation of micro-grids. [ABSTRACT FROM AUTHOR]