Determining Model Accuracy Requirements for Automotive Engine Coldstart Hydrocarbon Emissions Control

Abstract

In this work, a systematic method is introduced to determine the required accuracy of an automotive engine model used for real-time optimal control of coldstart hydrocarbon (HC) emissions. The engine model structure and development are briefly explained and the model predictions versus experimental results are presented. The control design problem is represented with a dynamic optimization formulation on the basis of the engine model and solved using the Pontryagin’s minimum principle (PMP). To relate the level of plant/model mismatch and the control performance degradation in practice, a sensitivity analysis using a computationally efficient method is employed. In this way, the sensitivities or the effects of small parameter variations on the optimal solution, which is the minimum of cumulative tailpipe HC emissions over the coldstart period, are calculated. There is a good agreement between the sensitivity analysis results and the experimental data. The sensitivities indicate the directions of the subsequent parameter estimation and model improvement tasks to enhance the control-relevant accuracy, and thus, the control performance. Furthermore, they provide some insights to simplify the engine model, which is critical for real-time implementation of the coldstart optimal control system.

A hybrid switching predictive controller based on bi-level kernel-based ELM and online trajectory builder for automotive coldstart emissions reduction

“…In the other cases, advanced computational techniques and control theories have been taken into account to come up with more efficient automotive controllers to meet today's increasingly tight emission standards. Obviously, the second approach can be much more cost-effective as there is no need for equipping automobiles with high-tech hardware components which in turn increase the final expense of the vehicle [2]. Moreover, since the efficacy of the devised control algorithms strictly depends on the utilized design methodology, there is further room for the improvement when seeking for advanced control algorithms.…”

A robust time delay auto-regressive exogenous fuzzy inference system for real-time estimation of catalyst temperature over engines coldstart operation: a multiobjective implementation scenario

“…In fact, the above-mentioned aspects have been proven to be the most important elements with significant impacts on the functionality of ICE components over the coldstart period. Azad et al [3] have reviewed the most important recent studies on modeling and control of ICE coldstart operations. Their literature review indicated that different strategies including heat transfer analysis, semi-empirical modeling, adaptive nonlinear models, and black-boxes have been used to cope with the considered problem.…”

“…Furthermore, by explaining the findings of the conducted researches, the authors justify the high potentials of intelligent approaches for the online monitoring of ICE coldstart phenomena. The main conducted researches have focused on the monitoring and control of catalyst temperature (T cat ) [1], engine-out HCs [3], and exhaust gas temperature (T exh ) [4,5]. In fact, the above-mentioned aspects have been proven to be the most important elements with significant impacts on the functionality of ICE components over the coldstart period.…”

Biologically inspired time-delay soft sensors for online monitoring of automotive coldstart operations: a comparative analysis

“…Such stringent governmental provisions have forced automotive manufacturers to put lots of monetary and technical budget into designing more fuel-efficient and cleaner automotive technologies, such as electric and hybrid electric propulsion systems [1,2]. The main goals are to build up vehicle systems with a higher fuel economy, and in a fashion that they are as much as possible capable of decrementing harmful environmental impacts, such as air pollution and global climate change [3,4]. To achieve these objectives, automobile companies are: (1) increasing the efficiency of their vehicles to conserve nonrenewable oil resources by technology advancements for powertrain system components [4], (2) taking strides towards alternating gasoline-powered cars with electric drive vehicles [5], and (3) optimizing powertrain control algorithms for their vehicles to reduce the amount of emissions and improve fuel economy [6].…”

“…Indeed, a more realistic scenario is to oblige the engineers of systems sciences, control, and automation to combine forces and concentrate on improving the quality of engine control algorithms to upgrade the quality of ICE propulsion systems. The main stream of the efforts within the third category of developments is twofold [3]: (1) reducing the fuel consumption rate of ICEs, and (2) reducing the emission rate of ICEs. Both of the mentioned concerns are of tremendous importance to the automotive industry.…”