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Power limit estimation of a lithium-ion battery pack can be employed by a battery management system (BMS) to balance a variety of operational considerations, including optimization of pulse capability while avoiding damage and minimizing aging. Consideration of cell-to-cell performance variability of lithium-ion batteries is critical to correct estimation of the battery pack power limit as well as proper sizing of the individual cells in the battery. Further, understanding of cell variability is necessary to protect the cell and other system components (e.g., fuse and contactor, from over-current damage). In this work, we present the use of an equivalent circuit model for estimation of the power limit of lithium-ion battery packs by considering the individual cell variability under current or voltage constraints. We compare the power limit estimation by using individual cell characteristics compared to the estimate found using only max/min values of cell characteristics.

Competitive engineering of battery packs for vehicle applications requires a careful alignment of function against vehicle manufacturer requirements. Traditional battery engineering practices focus on flow down of requirements from the top-level system requirements through to low-level components, meeting or exceeding each requirement at every level. This process can easily produce an over-engineered, cost-uncompetitive product. By integrating the key limiting factors of battery performance, we can directly compare battery capability to requirements. Here, we consider a power-oriented microhybrid battery system using coupled thermal and electrochemical modeling. We demonstrate that using dynamic resistance acquired from drive cycle characteristics can reduce the total size of the pack compared to typical static, fixed-duration resistance values.

Selective Catalytic Reduction (SCR) is a leading aftertreatment technology for the removal of nitrogen oxide (NOx) from exhaust gases (DeNOx). It presents an interesting control challenge, especially at high conversion, because both reagents (NOx and ammonia) are toxic, and therefore an excess of either is highly undesirable. Numerous system layouts and control methods have been developed for SCR systems, driven by the need to meet future emission standards. This paper summarizes the current state-of-the-art control methods for the SCR aftertreatment systems, and provides a structured and comprehensive overview of the research on SCR control. The existing control techniques fall into three main categories: traditional SCR control methods, model-based SCR control methods, and advanced SCR control methods. For each category, the basic control technique is defined. Further techniques in the same category are then explained and appreciated for their relative advantages and disadvantages.