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Abstract Thermal runaway and its propagation are bottlenecks for the safe operation of lithium-ion battery systems. This study investigates the influence of characteristic thermophysical parameters during battery thermal runaway, such as the self-heating temperature (T1), triggering temperature (T2), mass loss, and critical heat transfer power (Pc), on the failure propagation behavior in a battery system. A parametric study is conducted based on a failure propagation model. This model not only captures the behavior of thermal failure, but also accounts for the changes in the thermophysical parameters before and after thermal runaway. The results of the modeling analysis demonstrate that increasing T1 and T2 can both delay the thermal runaway propagation. The delay achieved by increasing T2 is greater than that observed by increasing T1. The peak heat transfer power Pc plays a critical role in delaying the thermal runaway propagation. When the peak heat transfer power level is greater than Pc, thermal runaway propagation mainly results from heat transfer, whereas when the peak heat transfer power level is less than Pc, thermal runaway propagation mainly arises from self-heating. This study reveals the dynamic mechanism of thermal runaway propagation within a battery module, thus providing guidance for the safety design of battery systems.

This paper proposes a novel algorithm based on the identification of the most influential lines to speed up the simulation of severe cascading failures in power systems. To this end, we first extend the concept of interaction graph to take into account the impact of hidden failures. Then we devise a PageRank-based algorithm to assess the influence of individual lines, which remarkably facilitates finding such severe cascading failures. Numerical experiments carried out on the New England system show that our algorithm can enhance the efficiency of finding severe blackouts of 1∼2 orders of magnitude.

The two-stroke engine is a common power source for small and medium-sized unmanned aerial vehicles (UAV), which has wide civil and military applications. To improve the engine performance, we chose a prototype two-stroke small areoengine, and optimized the geometric parameters of the scavenging ports by performing one-dimensional (1D) and three-dimensional (3D) computational fluid dynamics (CFD) coupling simulations. The prototype engine is tested on a dynamometer to measure in-cylinder pressure curves, as a reference for subsequent simulations. A GT Power simulation model is established and validated against experimental data to provide initial conditions and boundary conditions for the subsequent AVL FIRE simulations. Four parameters are considered as optimal design factors in this research: Tilt angle of the central scavenging port, tilt angle of lateral scavenging ports, slip angle of lateral scavenging ports, and width ratio of the central scavenging port. An evaluation objective function based on the Benson/Bradham model is selected as the optimization goal. Two different operating conditions, including the take-off and cruise of the UAV are considered. The results include: (1) Orthogonal experiments are analyzed, and the significance of parameters are discussed; (2) the best factors combination is concluded, followed by simulation verification; (3) results before and after optimization are compared in details, including specific scavenging indexes (delivery ratio, trapping efficiency, scavenging efficiency, etc.), conventional performance indicators, and the sectional views of gas composition distribution inside the cylinder.

In China, the power sector is currently the largest carbon emitter and the transportation sector is the fastest-growing carbon emitter. This paper proposes a model of solar-powered charging stations for electric vehicles to mitigate problems encountered in China’s renewable energy utilization processes and to cope with the increasing power demand by electric vehicles for the near future. This study applies the proposed model to Shenzhen City to verify its technical and economic feasibility. Modeling results showed that the total net present value of a photovoltaic power charging station that meets the daily electricity demand of 4500 kWh is $3,579,236 and that the cost of energy of the combined energy system is $0.098/kWh. In addition, the photovoltaic powered electric vehicle model has pollutant reduction potentials of 99.8%, 99.7% and 100% for carbon dioxide, sulfur dioxide, and nitrogen oxides, respectively, compared with a traditional gasoline-fueled car. Sensitivity analysis results indicated that interest rate has a relatively strong influence on COE (Cost of Energy). An increase in the interest rate from 0% to 6% increases COE from $0.027/kWh to $0.097/kWh. This analysis also suggests that carbon pricing promotes renewable energy only when the price of carbon is above $20/t.

In power system operation, characterizing the stochastic nature of wind power is an important albeit challenging issue. It is well known that distributions of wind power forecast errors often exhibit significant variability with respect to different forecast values. Therefore, appropriate probabilistic models that can provide accurate information for conditional forecast error distributions are of great need. On the basis of Gaussian mixture model, this paper constructs analytical conditional distributions of forecast errors for multiple wind farms with respect to forecast values. The accuracy of the proposed probabilistic models is verified by using historical data. Thereafter, a fast sampling method is proposed to generate scenarios from the conditional distributions which are non-Gaussian and interdependent. The efficiency of the proposed sampling method is verified.