The large-scale grouping of the battery system leads to the inconsistency of the battery pack. Aiming at tacking this issue, an inconsistency evaluation method is deployed for the battery pack based on an improved Gaussian mixture model (GMM) and feature fusion approach. Specifically, the proposed adaptive forgetting factor recursive least squares (AFFRLS) algorithm allows the open-circuit voltage and other parameters to be jointly identified without the open circuit voltage-state of charge (OCV-SOC) test. An online capacity estimation approach with the extended Kalman particle filter (EPF) is put forward for capacity estimation. Further, an improved GMM is proposed to visualize battery pack inconsistency, using the K-means++ algorithm to initialize category centers. The standard deviation coefficient approach quantifies the inconsistency. Finally, the real-life vehicle data are performed to validate the effectiveness of the proposed method. The experimental results show that the proposed method can evaluate the battery parameters accurately. With the increase in service time, the inconsistency of the battery pack is gradually deteriorating.

With the rapid development of modern industry, while improving people’s living standards, the over-exploitation of coal, oil and natural gas has led to a shortage of fossil energy, global warming and an increasingly serious deterioration of the ecological environment. To mitigate the greenhouse effect caused by excessive carbon emissions, the vigorous development of integrated electricity-gas system (IEGS) dominated by clean energy is the future trend of sustainable development of energy systems. In this paper, a bi-level optimal scheduling model is proposed for an IEGS considering carbon capture, utilization and storage (CCUS), and the ladder carbon trading mechanism is introduced to convert carbon emissions into economic benefits. The upper model is an optimal distribution model of natural gas network, and the lower model is a day-ahead economic dispatch model of power system. Based on the Karush-Kuhn-Tucher (KKT) condition and strong duality theory of the lower model, the bi-level model is transformed into a mixed integer linear programming (MILP), which is solved by calling CPLEX through the Yalmip toolbox of the Matlab platform. Finally, the reasonableness and validity of the model are verified by three arithmetic simulations. The results show that the proposed bi-level model for low-carbon economic dispatch of IEGS considering CCUS can effectively reduce the operating costs and carbon emissions of the system.

Battery health assessment is crucial for the safe and stable operation of electric vehicles. Accurate and efficient estimation state of health (SOH) ensures effective battery maintenance. The estimation accuracy of data-driven methods as an essential tool for battery SOH estimation depends on the quality of the data. Data missing is undoubtedly a significant challenge for data-driven methods. Based on this, this paper proposed a novel method for lithium-ion battery SOH estimation, which relied on the reconstruction of battery voltage data. The proposed method comprises two main components. Firstly, a conditional generative adversarial network (CGAN) is trained using collected historical charging voltage data and validated under various degrees of data loss. Secondly, an improved gated recurrent unit network (GRU) with sparrow search algorithm (SSA) is proposed for the estimation of lithium-ion battery health states after data reconstruction. The proposed SOH estimation method has been validated in various battery aging experiments. Compared with several other machine learning algorithms on reconstructed datasets, a significant performance improvement is observed.