The state of charge (SOC) estimation of the battery is one of the important functions of the battery management system of the electric vehicle, and the accurate SOC estimation is of great significance to the safe operation of the electric vehicle and the service life of the battery. Among the existing SOC estimation methods, the unscented Kalman filter (UKF) algorithm is widely used for SOC estimation due to its lossless transformation and high estimation accuracy. However, the traditional UKF algorithm is greatly affected by system noise and observation noise during SOC estimation. Therefore, we took the lithium cobalt oxide battery as the analysis object, and designed an adaptive unscented Kalman filter (AUKF) algorithm based on innovation and residuals to estimate SOC. Firstly, the second-order RC equivalent circuit model was established according to the physical characteristics of the battery, and the least square method was used to identify the parameters of the model and verify the model accuracy. Then, the AUKF algorithm was used for SOC estimation; the AUKF algorithm monitors the changes of innovation and residual in the filter and updates system noise covariance and observation noise covariance in real time using innovation and residual, so as to adjust the gain of the filter and realize the optimal estimation. Finally came the error comparison analysis of the estimation results of the UKF algorithm and AUKF algorithm; the results prove that the accuracy of the AUKF algorithm is 2.6% better than that of UKF algorithm.

With the rapid development of renewable energy technologies, islanded DC microgrids have received extensive attention in the field of distributed power generation due to their plug-and-play, flexible operation modes and convenient power conversion, and are likely to be one of the mainstream structures of microgrids in the future. The islanded DC microgrid contains multiple distributed power generation units. The battery energy storage system (BESS) is the main controlled unit used to smooth power fluctuations. The main parameter of concern is the state of charge (SOC). In order to maintain the stability of the microgrid, this paper takes the islanded DC microgrid as the research object and designs a control strategy based on the SOC of the BESS. Additionally, in the control strategy, the BESS’s energy balance control strategy and the microgrid’s operation control strategy are emphatically designed. The designed BESS control strategy adjusts the droop coefficient in real time according to the SOC of the battery energy storage unit (BESU), and controls the charge and discharge power of the BESU to achieve the SOC balance among the BESUs. The microgrid operation control strategy takes the energy storage system (ESS) as the main controlled unit to suppress power fluctuations, and distributes the power of distributed power sources according to the SOC of the BESS to achieve power balance in the microgrid, and control the DC bus voltage fluctuation deviation within 4.5%.

The development of industry 4.0 has put forward higher requirements for modern milling technology. Monitoring the degree of milling tool wear during machine tool processing can improve product quality and reduce production losses. In the machining process of machine tools, many kinds of tools are usually used, and the signal characteristics of various sensors of different tools are different. Therefore, before the tool wear assessment, this paper identified the tool type according to the spindle current data. After the tool type recognition, this paper evaluates the tool wear degree according to the tool force data, vibration data, acoustic emission signal, and other multi-sensor data. Firstly, the Elman neural network and Adaboost algorithm are combined to construct the Elman_Adaboost strong predictor. Then, the variance and mean of seven sensor data were selected as the characteristic quantities to input the strong predictor. Finally, three wear quantities were obtained to measure the wear degree of the tool. The method proposed in this paper is implemented by Matlab, and the validity of this method is verified using the competition data provided by PHM (Prognostics and Health Management) Society. The results show that the average evaluation accuracy of the same tool wear is more than 92%, and that of the similar tool wear is more than 85%.

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