The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are two important factors which are normally predicted using the battery capacity. However, it is difficult to directly measure the capacity of lithium-ion batteries for online applications. In this paper, indirect health indicators (IHIs) are extracted from the curves of voltage, current, and temperature in the process of charging and discharging lithium-ion batteries, which respond to the battery capacity degradation process. A few reasonable indicators are selected as the inputs of SOH prediction by the grey relation analysis method. The short-term SOH prediction is carried out by combining the Gaussian process regression (GPR) method with probability predictions. Then, considering that there is a certain mapping relationship between SOH and RUL, three IHIs and the present SOH value are utilized to predict RUL of lithium-ion batteries through the GPR model. The results show that the proposed method has high prediction accuracy.

Prediction of Remaining Useful Life (RUL) of lithium-ion batteries plays a significant role in battery health management. Battery capacity is often chosen as the Health Indicator (HI) in research on lithium-ion battery RUL prediction. In the rest time of batteries, capacity will produce a certain degree of regeneration phenomenon, which exists in the use of each battery. Therefore, considering the capacity regeneration phenomenon in RUL prediction of lithium-ion batteries is helpful to improve the prediction performance of the model. In this paper, a novel method fusing the wavelet decomposition technology (WDT) and the Nonlinear Auto Regressive neural network (NARNN) model for predicting the RUL of a lithium-ion battery is proposed. Firstly, the multi-scale WDT is used to separate the global degradation and local regeneration of a battery capacity series. Then, the RUL prediction framework based on the NARNN model is constructed for the extracted global degradation and local regeneration. Finally, the two parts of the prediction results are combined to obtain the final RUL prediction result. Experiments show that the proposed method can not only effectively capture the capacity regeneration phenomenon, but also has high prediction accuracy and is less affected by different prediction starting points.

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Because of the present ineffective method of soot blowing on a boiler’s heating surface in a coal-fired power plant, and to improve the economic benefit of the boiler in the power plant, weigh the improvement of boiler efficiency and steam loss brought by soot blowing, and ensure the safe operation of the unit, an optimization model of soot blowing on the boiler’s heating surface is established. Taking the economizer of the 300 MW coal-fired power plant unit as the research object, the measurement data and basic thermodynamic calculation data of the Distributed Control System (DCS) of the thermal power plant are used to calculate the fouling rate of the heated surface in real time. By analyzing the multi-group fouling rate under the same working conditions, the incremental distribution of the same measuring point at different times is obtained, and the expectation is obtained according to the distribution curve. The state of heating of the heated surface at a time in the future is predicted by the known initial cleaning state. By analyzing the trend of the fouling rate and combining the soot blowing optimization model, a set of soot blowing optimization strategies are proposed. The method proposed in this manuscript can be applied to the guidance of boiler soot blowing operation.