State of Charge (SOC) is essential for a smart Battery Management System (BMS). Traditional SOC estimation methods of lithium-ion batteries are usually conducted using battery equivalent circuit models (ECMs) and the impact of current sensor bias on SOC estimation is rarely considered. For this reason, this paper proposes an online SOC estimation based on a simplified electrochemical model (EM) for lithium-ion batteries considering sensor bias. In EM-based SOC estimation structure, the errors from the current sensor bias are addressed by proportional–integral observer. Then, the accuracy of the proposed EM-based SOC estimation is validated under different operating conditions. The results indicate that the proposed method has good performance and high accuracy in SOC estimation for lithium-ion batteries, which facilitates the on-board application in advanced BMS.

Selective catalytic reduction (SCR) is currently one of the most efficient denitration technologies to reduce nitrogen oxide (NO_x) emissions of diesel engines. AdBlue (urea water solution, UWS) is the carrier of the reducing agent of SCR, and the spray process of UWS is one of the critical factors affecting denitration efficiency. In this paper, a non-air-assisted pressure-driven full process spray (NPFPS) model is proposed to illustrate the breakup mechanism and the spray distribution properties of UWS through computational fluid dynamics (CFD). In the NPFPS model, the mechanism of the primary breakup is described by the volume of fluid (VOF) approach, which realizes the quantitative study of the critical parameters determining spray characteristics such as the breakup length, inclination angle, droplet size of the primary breakup, and primary velocity. The distribution of the spray after the primary breakup is depicted by the discrete phase model (DPM) coupled with the Taylor analogy breakup (TAB) model, through which the degree of secondary breakup can be obtained including quantitative studies of the droplet size distribution and velocity distribution in the different cross-sections. To verify the accuracy and feasibility of the NPFPS model, the experimental data are employed to compare with the simulation data. The results are in good agreement, which indicate the practical value of the model.

This work established a numerical model to investigate the heat transfer characteristics and wall film formation of spray impinging on the wall in the selective catalytic reduction (SCR) system. The model is developed by the Eulerian–Lagrangian approach, where the Lagrangian approach is used to represent the spray generated by a commercial non-air-assisted pressure-driven injector and the Eulerian approach is adopted to represent exhaust gas. The Stochastic Kuhnke Model is applied to spray/wall interaction. The model considers relevant processes, which include mass transfer, momentum transfer, heat transfer, droplet phase change, spray/wall interaction, and wall film formation. The numerical results compared with that of the experiment indicate that the model can accurately estimate the heat transfer characteristics of the wall surface during the spray impingement. Based on the numerical results, the causes of the spray local cooling effect and the rapid cooling effect are analyzed. The correlation between the critical transition temperature and the critical heat flux temperature for wall film formation is derived from the trends of wall temperature and heat flux. In this work, the Stochastic Kuhnke Model is applied and compared with the Kuhnke Model, which proves that it can improve the disadvantage of sudden change during the wall film formation. When the wall temperature is below the critical transition temperature, the wall film mass is sensitive to the wall temperature and increases as the wall temperature decreases.