THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Pattern recognition

Parallel control and management have been proposed as a new mechanism for conducting operations of complex systems, especially those that involved complexity issues of both engineering and social dimensions, such as transportation systems. This paper presents an overview of the background, concepts, basic methods, major issues, and current applications of Parallel transportation Management Systems (PtMS). In essence, parallel control and management is a data-driven approach for modeling, analysis, and decision-making that considers both the engineering and social complexity in its processes. The developments and applications described here clearly indicate that PtMS is effective for use in networked complex traffic systems and is closely related to emerging technologies in cloud computing, social computing, and cyberphysical-social systems. A description of PtMS system architectures, processes, and components, including OTSt, Dyna CAS, aDAPTS, iTOP, and TransWorld is presented and discussed. Finally, the experiments and examples of real-world applications are illustrated and analyzed.

Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications

The agent computing paradigm is rapidly emerging as one of the powerful technologies for the development of large-scale distributed systems to deal with the uncertainty in a dynamic environment. The domain of traffic and transportation systems is well suited for an agent-based approach because transportation systems are usually geographically distributed in dynamic changing environments. Our literature survey shows that the techniques and methods resulting from the field of agent and multiagent systems have been applied to many aspects of traffic and transportation systems, including modeling and simulation, dynamic routing and congestion management, and intelligent traffic control. This paper examines an agent-based approach and its applications in different modes of transportation, including roadway, railway, and air transportation. This paper also addresses some critical issues in developing agent-based traffic control and management systems, such as interoperability, flexibility, and extendibility. Finally, several future research directions toward the successful deployment of agent technology in traffic and transportation systems are discussed.

A Review of the Applications of Agent Technology in Traffic and Transportation Systems

Knocking combustion in spark-ignition engines

Generalized Nash equilibrium problem based electric vehicle charging management in distribution networks

The vehicle driving cycles affect the performance of a hybrid vehicle control strategy, as a result, the overall performance of the vehicle, such as fuel consumption and emission. By identifying the driving cycles of a vehicle, the control system is able to dynamically change the control strategy (or parameters) to the best one to adapt to the changes of vehicle driving patterns. This paper studies the supervised driving cycle recognition using pattern recognition approach. With pattern recognition method, a driving cycle is represented by feature vectors that are formed by a set of parameters to which the driving cycle is sensitive. The on-line driving pattern recognition is achieved by calculating the feature vectors and classifying these feature vectors to one of the driving patterns in the reference database. To establish reference driving cycle database, the representative feature vectors for four federal driving cycles are generated using feature extraction method. The quality of representative feature vectors with different feature extraction methods is evaluated by examining the separation of feature vectors in the feature space and the success rate of the pattern recognition. The performance of the presented adaptive control strategy based on driving pattern recognition is evaluated using a powertrain/propulsion simulation and analysis software Autonomie. ____________________________________ 169 CITATION: Feng, L., Liu, W. and Chen, B., "Driving Pattern Recognition for Adaptive Hybrid Vehicle Control," SAE Int. J. Alt. Power. 1(1):2012, doi:10.4271/2012-01-0742. THIS DOCUMENT IS PROTECTED BY U.S. AND INTERNATIONAL COPYRIGHT It may not be reproduced, stored in a retrieval system, distributed or transmitted, in whole or in part, in any form or by any means. Downloaded from SAE International by Brought to by the J. Robert Van Pelt Library / Michigan Technological Univ. , Saturday, June 16, 2012 11:41:21 PM

Driving Pattern Recognition for Adaptive Hybrid Vehicle Control

This paper studies the minimization of the fuel consumption of hybrid electric vehicles (HEV) using model predictive control (MPC) method. The presented MPC-based controller calculates an optimal sequence of control inputs to the vehicle plant over the receding prediction horizon based on the torque demand estimated from the desired vehicle speed and the desired state of charge (SOC) of the battery. The controller is implemented in Simulink using Matlab model predictive control toolbox. The effectiveness of the MPC-based controller is validated by the 2004 Toyota Prius, a power-split HEV. The simulation result presents sound transient response performance and the ability to remain the SOC within a reasonable range.

Minimizing HEV fuel consumption using model predictive control

With the advancement of vehicle-to-vehicle and vehicle-to-infrastructure technologies, more and more real-time information regarding traffic and transportation system will be available to vehicles. This paper presents the development of a novel algorithm that uses available velocity bounds and powertrain information to generate an optimal velocity trajectory over a prediction horizon. When utilised by a vehicle, this optimal velocity trajectory reduces fuel consumption. The objective of this optimisation problem is to reduce dynamic losses, required tractive force, and completing trip distance with a given travel time. Sequential quadratic programming method is employed for this nonlinearly constrained optimisation problem. When applied to a GM Volt-2, the generated velocity trajectory saves fuel compared to a real-world drive cycle. The simulation results confirm the fuel consumption reduction with the rule-based mode selection and the energy management strategy of a GM Volt 2 model in Autonomie.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-its.2018.5110

Bo Chen

Papers

Generalized Nash equilibrium problem based electric vehicle charging management in distribution networks

Driving Pattern Recognition for Adaptive Hybrid Vehicle Control

Minimizing HEV fuel consumption using model predictive control

Optimal velocity prediction for fuel economy improvement of connected vehicles

Optimal control of mobile monitoring agents in immune-inspired wireless monitoring networks