Andreas A. Malikopoulos

Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey

The growing necessity for environmentally benign hybrid propulsion systems has led to the development of advanced power management control algorithms to maximize fuel economy and minimize pollutant emissions. This paper surveys the control algorithms for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEVs) that have been reported in the literature to date. The exposition ranges from parallel, series, and power split HEVs and PHEVs and includes a classification of the algorithms in terms of their implementation and the chronological order of their appearance. Remaining challenges and potential future research directions are also discussed.

A Survey on the Coordination of Connected and Automated Vehicles at Intersections and Merging at Highway On-Ramps

Connected and automated vehicles (CAVs) have the potential to improve safety by reducing and mitigating traffic accidents. They can also provide opportunities to reduce transportation energy consumption and emissions by improving traffic flow. Vehicle communication with traffic structures and traffic lights can allow individual vehicles to optimize their operation and account for unpredictable changes. This paper summarizes the developments and the research trends in coordination with the CAVs that have been reported in the literature to date. Remaining challenges and potential future research directions are also discussed.

Real-time self-learning optimization of diesel engine calibration

Compression ignition engine technologies have been advanced in the past decade to provide superior fuel economy and high performance. These technologies offer increased opportunities for optimizing engine calibration. Current engine calibration methods rely on deriving static tabular relationships between a set of steady-state operating points and the corresponding values of the controllable variables. While the engine is running, these values are being interpolated for each engine operating point to coordinate optimal performance criteria, e.g., fuel economy, emissions, and acceleration. These methods, however, are not efficient in capturing transient engine operation designated by common driving habits, e.g., stop-and-go driving, rapid acceleration, and braking. An alternative approach was developed recently, which makes the engine an autonomous intelligent system, namely, one capable of learning its optimal calibration for both steady-state and transient operating points in real time. Through this approach, while the engine is running the vehicle, it progressively perceives the drivers driving style and eventually learns to operate in a manner that optimizes specified performance criteria. The major challenge to this approach is problem dimensionality when more than one controllable variable is considered. In this paper, we address this problem by proposing a decentralized learning control scheme. The scheme is evaluated through simulation of a diesel engine model, which learns the values of injection timing and variable geometry turbocharging vane position that optimize fuel economy and pollutant emissions over a segment of the FTP-75 driving cycle.

Automated and Cooperative Vehicle Merging at Highway On-Ramps

Recognition of necessities of connected and automated vehicles (CAVs) is gaining momentum. CAVs can improve both transportation network efficiency and safety through control algorithms that can harmonically use all existing information to coordinate the vehicles. This paper addresses the problem of optimally coordinating CAVs at merging roadways to achieve smooth traffic flow without stop-and-go driving. We present an optimization framework and an analytical closed-form solution that allows online coordination of vehicles at merging zones. The effectiveness of the efficiency of the proposed solution is validated through a simulation, and it is shown that coordination of vehicles can significantly reduce both fuel consumption and travel time.

An Optimization Framework for Driver Feedback Systems

Modern vehicles have sophisticated electronic control units that can control engine operation with discretion to balance fuel economy, emissions, and power. These control units are designed for specific driving conditions (e.g., different speed profiles for highway and city driving). However, individual driving styles are different and rarely match the specific driving conditions for which the units were designed. In the research reported here, we investigate driving-style factors that have a major impact on fuel economy and construct an optimization framework to optimize individual driving styles with respect to these driving factors. In this context, we construct a set of polynomial metamodels to reflect the responses produced in fuel economy by changing the driving factors. Then, we compare the optimized driving styles to the original driving styles and evaluate the effectiveness of the optimization framework. Finally, we use this proposed framework to develop a real-time feedback system, including visual instructions, to enable drivers to alter their driving styles in response to actual driving conditions to improve fuel efficiency.

A Learning Algorithm for Optimal Internal Combustion Engine Calibration in Real Time

Advanced internal combustion engine technologies have increased the number of accessible variables of an engine and our ability to control them. The optimal values of these variables are designated during engine calibration by means of a static correlation between the controllable variables and the corresponding steady-state engine operating points. While the engine is running, these correlations are being interpolated to provide values of the controllable variables for each operating point. These values are controlled by the electronic control unit to achieve desirable engine performance, for example in fuel economy, pollutant emissions, and engine acceleration. The state-of-the-art engine calibration cannot guarantee continuously optimal engine operation for the entire operating domain, especially in transient cases encountered in driving styles of different drivers. This paper presents the theoretical basis and algorithmic implementation for allowing the engine to learn the optimal set values of accessible variables in real time while running a vehicle. Through this new approach, the engine progressively perceives the driver’s driving style and eventually learns to operate in a manner that optimizes specified performance indices. The effectiveness of the approach is demonstrated through simulation of a spark ignition engine, which learns to optimize fuel economy with respect to spark ignition timing, while it is running a vehicle.Copyright

Online Optimal Control of Connected Vehicles for Efficient Traffic Flow at Merging Roads

This paper addresses the problem of coordinating online connected vehicles at merging roads to achieve a smooth traffic flow without stop-and-go driving. We present a framework and a closed-form solution that optimize the acceleration profile of each vehicle in terms of fuel economy while avoiding collision with other vehicles at the merging zone. The proposed solution is validated through simulation and it is shown that coordination of connected vehicles can reduce significantly fuel consumption and travel time at merging roads.

Optimization of driving styles for fuel economy improvement

Modern vehicles have sophisticated electronic control units, particularly to control engine operation with respect to a balance between fuel economy, emissions, and power. These control units are designed for specific driving conditions and testing. However, each individual driving style is different and rarely meets those driving conditions. In the research reported here we investigate those driving style factors that have a major impact on fuel economy. An optimization framework is proposed with the aim of optimizing driving styles with respect to these driving factors. A set of polynomial metamodels are constructed to reflect the responses produced by changes of the driving factors. Then we compare the optimized driving styles to the original ones and evaluate the efficiency and effectiveness of the optimization formulation.

Online Identification and Stochastic Control for Autonomous Internal Combustion Engines

Advanced internal combustion engine technologies have afforded an increase in the number of controllable variables and the ability to optimize engine operation. Values for these variables are determined during engine calibration by means of a tabular static correlation between the controllable variables and the corresponding steady-state engine operating points to achieve desirable engine performance, for example, in fuel economy, pollutant emissions, and engine acceleration. In engine use, table values are interpolated to match actual operating points. State-of-the-art calibration methods cannot guarantee continuously the optimal engine operation for the entire operating domain, especially in transient cases encountered in the driving styles of different drivers. This article presents brief theory and algorithmic implementation that make the engine an autonomous intelligent system capable of learning the required values of controllable variables in real time while operating a vehicle. The engine controller progressively perceives the driver’s driving style and eventually learns to operate in a manner that optimizes specified performance criteria. A gasoline engine model, which learns to optimize fuel economy with respect to spark ignition timing, demonstrates the approach. DOI: 10.1115/1.4000819

Optimal control and coordination of connected and automated vehicles at urban traffic intersections

We address the problem of coordinating online a continuous flow of connected and automated vehicles (CAVs) crossing two adjacent intersections in an urban area. We present a decentralized optimal control framework whose solution yields for each vehicle the optimal acceleration/deceleration at any time in the sense of minimizing fuel consumption. The solution, when it exists, allows the vehicles to cross the intersections without the use of traffic lights, without creating congestion on the connecting road, and under the hard safety constraint of collision avoidance. The effectiveness of the proposed solution is validated through simulation considering two intersections located in downtown Boston, and it is shown that coordination of CAVs can reduce significantly both fuel consumption and travel time.