Engine Operation Control

Systems and methods of controlling operation of a vehicle engine are provided. For instance, one example aspect of the present disclosure is directed to determining a spark timing associated with a combustion engine. For instance, a combustion phasing target to be implemented by a combustion engine can be received. A spark timing associated with the combustion engine can be determined based at least in part on the combustion phasing target. The spark timing can be determined based at least in part on an optimization comprising one or more iterations determined during an engine cycle. The spark timing is determined based at least in part on a combustion phasing prediction model determined based at least in part on at least one of laminar flame speed, residual gas mass, or turbulent intensity

An Experimental Characterization of a High Degree of Freedom Spark-Ignition Engine to Achieve Optimized Ignition Timing Control.

Pressure to improve fuel economy and emissions allows for the introduction of more complex and expensive spark-ignition engine technologies. As engine complexity increases, traditional ignition timing control methods become restrictive, creating a need for new approaches based on analytical techniques and experimental insight. The addition of variable valve actuation and other intake charge motion altering devices provides unprecedented opportunities for improving engine attributes, but poses significant challenges for developing robust control systems. In particular, internal residual fraction and turbulence level vary over a much broader range than in a traditional engine and have a critical impact on combustion. Hence, the goals of this thesis are two-fold. First, new diagnostic procedures that experimentally characterize key combustion parameters are developed. Then, the new information is used to create a universal physics-based ignition timing prediction model valid over a wide range of residual and in-cylinder turbulence levels. Residual gas fraction is experimentally quantified using several different methods that incorporate fast response emissions analyzers, such as the Fast FID analyzer for unburned hydrocarbons, and a fast NDIR analyzer for CO2. A technique relying on simultaneous measurement of in-cylinder and exhaust CO2 concentration is demonstrated, and proves to be the most accurate and reliable. Turbulence intensity is quantified using a newly developed inverse-model of turbulent flame entrainment in conjunction with experimental combustion diagnostics. Experimental findings are subsequently used to generate semi-empirical models for residual fraction and turbulence intensity capable of running real-time within an engine controller. The newly developed experimental techniques and semi-empirical models enable the development of a physics-based ignition timing control model. The proposed algorithm is loosely based on a well-established turbulent entrainment combustion model, ensuring robust and universal application. The model is divided into two sub-sections; one to predict combustion duration and another for combustion phasing. The duration sub-model predicts the time from ignition to fifty percent mass fraction burned for each operating condition, using an estimated flame entrainment rate, with an RMSE of 2.3 CAD. The combustion phasing sub-model is then used to determine required ignition timing, based on a desired location of fifty percent mass fraction burned.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58501/1/rprucka_1.pd

A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation

One major cost of improving the automotive fuel economy while simultaneously reducing tailpipe emissions is increased powertrain complexity. This complexity has consequently increased the resources (both time and money) needed to develop such powertrains. Powertrain performance is heavily influenced by the quality of the controller/calibration. Since traditional control development processes are becoming resource-intensive, better alternate methods are worth pursuing. Recently, reinforcement learning (RL), a machine learning technique, has proven capable of creating optimal controllers for complex systems. The model-free nature of RL has the potential to streamline the control development process, possibly reducing the time and money required. This article reviews the impact of choices in two areas on the performance of RL-based powertrain controllers to provide a better awareness of their benefits and consequences. First, we examine how RL algorithm action continuities and control–actuator continuities are matched, via native operation or conversion. Secondly, we discuss the formulation of the reward function. RL is able to optimize control policies defined by a wide spectrum of reward functions, including some functions that are difficult to implement with other techniques. RL action and control–actuator continuity matching affects the ability of the RL-based controller to understand and operate the powertrain while the reward function defines optimal behavior. Finally, opportunities for future RL-based powertrain control development are identified and discussed

Editors’ perspectives: synergistic technologies for dedicated hybrid powertrains

Regulatory and market pressure to reduce greenhouse gas (GHG) emissions has directed the path of powertrain development towards expanded use of electrification. Powertrain electrification contributes to GHG reduction by lowering the demands on the engine for improved durability and efficiency and by introducing synergistic technologies such as kinetic energy recovery. This work reviews the advancement of energy-efficient hybrid powertrains with application of dual motive powers, internal combustion engine and electric motors. The focus of this review is on the industrialisation of dedicated hybrid engines (DHEs) and transmissions (DHT). Based on the DHT framework, DHEs are exemplified through those successful hybrid vehicles in the market. Technology challenges for both DHE and DHT are discussed. The key enablers of controls and model-based design are reviewed to disclose the progress of hybrid powertrain development by using both endogenous fuel and exogenous electricity if applicable. Case studies for both passenger cars and commercial vehicles are also presented