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This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source.

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.

Electronic throttle control is an integral part of an engine electronic control unit (ECU) that directly affects vehicle fuel economy, drivability, and engine-out emissions by managing engine torque and air-fuel ratio through adjusting intake charge flow to the engine. The highly nonlinear dynamics of the throttle body call for nonlinear control techniques that can be implemented in real-time and are also robust to controller implementation imprecision. Discrete sliding mode control (DSMC) is a computationally efficient controller design technique which can handle systems with high degree of nonlinearity. In this paper, a generic robust discrete sliding mode controller design is proposed and experimentally verified for the throttle position tracking problem. In addition, a novel method is used to predict and incorporate the sampling and quantization imprecisions into the DSMC structure. First, a nonlinear physical model for an electromechanical throttle body is derived.

Verification and validation (V&V) are essential stages in the design cycle of industrial controllers to remove the gap between the designed and implemented controller. In this study, a model-based adaptive methodology is proposed to enable easily verifiable controller design based on the formulation of a sliding mode controller (SMC). The proposed adaptive SMC improves the controller robustness against major implementation imprecisions including sampling and quantization. The application of the proposed technique is demonstrated on the engine cold start emission control problem in a mid-size passenger car. The cold start controller is first designed in a single-input single-output (SISO) structure with three separate sliding surfaces, and then is redesigned based on a multiinput multi-output (MIMO) SMC design technique using nonlinear balanced realization.

High hydrocarbon (HC) emission during a cold start still remains one of the major emission control challenges for spark ignition (SI) engines in spite of about three decades of research in this area. This paper proposes a cold start HC emission control strategy based on a reduced order modeling technique. A novel singular perturbation approximation (SPA) technique, based on the balanced realization principle, is developed for a nonlinear experimentally validated cold start emission model. The SPA reduced model is then utilized in the design of a model-based sliding mode controller (SMC). The controller targets to reduce cumulative tailpipe HC emission using a combination of fuel injection, spark timing, and air throttle / idle speed controls. The results from the designed multi-input multi-output (MIMO) reduced order SMC are compared with those from a full order SMC. The results show the reduced SMC outperforms the full order SMC by reducing both engine-out and tailpipe HC emission.