Optimization-Based Power Management of Hybrid Power Systems with Applications in Advanced Hybrid Electric Vehicles and Wind Farms with Battery Storage

Modern hybrid electric vehicles and many stationary renewable power generation systems combine multiple power generating and energy storage devices to achieve an overall system-level efficiency and flexibility which is higher than their individual components. The power or energy management control, ``brain\u27 of these ``hybrid\u27 systems, determines adaptively and based on the power demand the power split between multiple subsystems and plays a critical role in overall system-level efficiency. This dissertation proposes that a receding horizon optimal control (aka Model Predictive Control) approach can be a natural and systematic framework for formulating this type of power management controls. More importantly the dissertation develops new results based on the classical theory of optimal control that allow solving the resulting optimal control problem in real-time, in spite of the complexities that arise due to several system nonlinearities and constraints. The dissertation focus is on two classes of hybrid systems: hybrid electric vehicles in the first part and wind farms with battery storage in the second part. The first part of the dissertation proposes and fully develops a real-time optimization-based power management strategy for hybrid electric vehicles. Current industry practice uses rule-based control techniques with ``else-then-if\u27 logic and look-up maps and tables in the power management of production hybrid vehicles. These algorithms are not guaranteed to result in the best possible fuel economy and there exists a gap between their performance and a minimum possible fuel economy benchmark. Furthermore, considerable time and effort are spent calibrating the control system in the vehicle development phase, and there is little flexibility in real-time handling of constraints and re-optimization of the system operation in the event of changing operating conditions and varying parameters. In addition, a proliferation of different powertrain configurations may result in the need for repeated control system redesign. To address these shortcomings, we formulate the power management problem as a nonlinear and constrained optimal control problem. Solution of this optimal control problem in real-time on chronometric- and memory- constrained automotive microcontrollers is quite challenging; this computational complexity is due to the highly nonlinear dynamics of the powertrain subsystems, mixed-integer switching modes of their operation, and time-varying and nonlinear hard constraints that system variables should satisfy. The main contribution of the first part of the dissertation is that it establishes methods for systematic and step-by step improvements in fuel economy while maintaining the algorithmic computational requirements in a real-time implementable framework. More specifically a linear time-varying model predictive control approach is employed first which uses sequential quadratic programming to find sub-optimal solutions to the power management problem. Next the objective function is further refined and broken into a short and a long horizon segments; the latter approximated as a function of the state using the connection between the Pontryagin minimum principle and Hamilton-Jacobi-Bellman equations. The power management problem is then solved using a nonlinear MPC framework with a dynamic programming solver and the fuel economy is further improved. Typical simplifying academic assumptions are minimal throughout this work, thanks to close collaboration with research scientists at Ford research labs and their stringent requirement that the proposed solutions be tested on high-fidelity production models. Simulation results on a high-fidelity model of a hybrid electric vehicle over multiple standard driving cycles reveal the potential for substantial fuel economy gains. To address the control calibration challenges, we also present a novel and fast calibration technique utilizing parallel computing techniques. The second part of this dissertation presents an optimization-based control strategy for the power management of a wind farm with battery storage. The strategy seeks to minimize the error between the power delivered by the wind farm with battery storage and the power demand from an operator. In addition, the strategy attempts to maximize battery life. The control strategy has two main stages. The first stage produces a family of control solutions that minimize the power error subject to the battery constraints over an optimization horizon. These solutions are parameterized by a given value for the state of charge at the end of the optimization horizon. The second stage screens the family of control solutions to select one attaining an optimal balance between power error and battery life. The battery life model used in this stage is a weighted Amp-hour (Ah) throughput model. The control strategy is modular, allowing for more sophisticated optimization models in the first stage, or more elaborate battery life models in the second stage. The strategy is implemented in real-time in the framework of Model Predictive Control (MPC)

Considerate and Cooperative Model Predictive Control for Energy-Efficient Truck Platooning of Heterogeneous Fleets

Connectivity-enabled automation of distributed control systems allow for better anticipation of system disturbances and better prediction of the effects of actuator limitations on individual agents when incorporating a model. Automated convoy of heavy-duty trucks in the form of platooning is one such application designed to maintain close gaps between trucks to exploit drafting benefits and improve fuel economy, and has traditionally been handled with classically-designed connected and adaptive cruise control (CACC). This paper is motivated by demonstrated limitations of such a control strategy, in which a classical CACC was unable to efficiently handle real-world road grade and velocity transient disturbances without the assistance of fleet operator intervention, and is non-adaptive to varied hardware and loading conditions of the operating truck. This automation strategy is addressed by forming a cooperative model predictive control (MPC) for eco-platooning that considers interactions with trailing trucks to incentivize platoon harmonization under road disturbances, velocity transients, and engine limitations, and further improves energy economy by reducing unnecessary engine effort. This is accomplished for each truck by sharing load, maximum engine power, transmission ratios, control states, and intended trajectories with its nearest neighbors. The performance of the considerate and cooperative strategy was demonstrated on a real-world driving scenario against a similar non-considerate control strategy, and overall it was found that the considerate strategy significantly improved harmonization between the platooned trucks in a real-time implementable manner.Comment: Appears in IEEE ACC 2022. 6 pages, 6 figure

Machine Learning-based Classification of Combustion Events in an RCCI Engine Using Heat Release Rate Shapes

Reactivity controlled compression ignition (RCCI) mode offers high thermal efficiency and low nitrogen oxides (NOx) and soot emissions. However, high cyclic variability at low engine load and high pressure rise rates at high loads limit RCCI operation. Therefore, it is important to control the combustion event in an RCCI engines to prevent abnormal engine combustion. To this end, combustion in RCCI mode was studied by analyzing the heat release rates calculated from the in-cylinder pressure data at 798 different operating conditions. Five distinct heat release shapes are identified. These different heat release traces were characterized based on start of combustion, burn duration, combustion phasing, maximum pressure rise rate, maximum amount of heat release, maximum in-cylinder gas temperature and pressure. Both supervised and unsupervised machine learning approaches are used to classify different types of heat release rates. K-means clustering, an unsupervised algorithm, could not cluster the heat release traces distinctly. Convolution neural network (CNN) and decision trees, supervised classification algorithms, were designed to classify the heat release rates. The CNN algorithm showed 70% accuracy in predicting the shapes of heat release rates while decision tree resulted in 74.5% accuracy in predicting different heat release rate traces

Control-Oriented Data-Driven and Physics-Based Modeling of Maximum Pressure Rise Rate in Reactivity Controlled Compression Ignition Engines

Reactivity controlled compression ignition (RCCI) is a viable low-temperature combustion (LTC) regime that can provide high indicated thermal efficiency and very low nitrogen oxides (NOx) and particulate matter (PM) emissions compared to the traditional diesel compression ignition (CI) mode [1]. The burn duration in RCCI engines is generally shorter compared to the burn duration for CI and spark-ignition (SI) combustion modes [2, 3]. This leads to a high pressure rise rate (PRR) and limits their operational range. It is important to predict the maximum pressure rise rate (MPRR) in RCCI engines and avoid excessive MPRRs to enable safe RCCI operation over a wide range of engine conditions. In this article, two control-oriented models are presented to predict the MPRR in an RCCI engine. The first approach includes a combined physical and empirical model that uses the first principle of thermodynamics to estimate the PRR inside the cylinder, and the second approach estimates MPRR through a machine learning method based on kernelized canonical correlation analysis (KCCA) and linear parameter-varying (LPV) methods. The KCCA-LPV approach proved to have higher prediction accuracy compared to physics-based modeling while requiring less amount of calibration. The KCCA-LPV approach could estimate MPRR with an average error of 47 kPa/CAD while the physics-based approach’s average estimation error was 87 kPa/CAD

Data-driven modeling and predictive control of maximum pressure rise rate in RCCI engines

Reactivity controlled compression ignition (RCCI) is a promising low temperature combustion (LTC) regime that offers lower nitrogen oxides (NOx), soot and particulate matter (PM) emissions along with higher combustion efficiency compared to conventional diesel engines. It is critical to control maximum pressure rise rate (MPRR) in RCCI engines in order to safely and efficiently operate at varying engine loads. In this paper, a data-driven modeling (DDM) approach using support vector machines (SVM) is adapted to develop a linear parameter-varying (LPV) representation of MPRR for RCCI combustion. This LPV representation is then used in the design of a model predictive controller (MPC) to control crank angle of 50% of fuel mass fraction burn (CA50) and indicated mean effective pressure (IMEP) while limiting the MPRR. The results show that the LPV-MPC control strategy can track CA50 and IMEP with mean tracking errors of 0.9 CAD and 4.7 kPa, respectively, while limiting the MPRR to the maximum allowable value of 5.8 bar/CAD