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Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications-based information, is difficult and error-prone.

Lean NOx Trap (LNT) is an aftertreatment device typically used to reduce oxides of nitrogen (NOx) emissions for a lean burn engine. NOx is stored in the LNT during the lean operation of an engine. When the air-fuel ratio becomes rich, the stored NOx is released and catalytically reduced by the reductants such as CO, H2 and HC. Tailpipe NOx emissions can be significantly reduced by properly modulating the lean (storage) and rich (purge) periods. A control-oriented lumped parameter model is presented in this paper. The model captures the key steady state and transient characteristics of an LNT and includes the effects of the important engine operating parameters. The model can be used for system performance evaluation and control strategy development.