Michael Donovan Todd

Simulation model performance analysis of a multiple station shared vehicle system

As an alternative transportation paradigm, shared vehicle systems have become increasingly popular in recent years. Shared vehicle systems typically consist of a fleet of vehicles that are used several times each day by diAerent users. One of the main advantages of shared vehicle systems is that they reduce the number of vehicles required to meet total travel demand. An added energy/emissions benefit comes when lowpolluting (e.g., electric) vehicles are used in the system. In order to evaluate operational issues such as vehicle availability, vehicle distribution, and energy management, a unique shared vehicle system computer simulation model has been developed. As an initial case study, the model was applied to a resort community in Southern California. The simulation model has a number of input parameters that allow for the evaluation of numerous scenarios. Several measures of eAectiveness have been determined and are calculated to characterize the overall system performance. For the case study, it was found that the most eAective number of vehicles (in terms of satisfying customer wait time) is in the range of 3‐6 vehicles per 100 trips in a 24 h day. On the other hand, if the number of relocations also is to be minimized, there should be approximately 18‐24 vehicles per 100 trips. Various inputs to the model were varied to see the overall system response. The model shows that the shared vehicle system is most sensitive to the vehicle-to-trip ratio, the relocation algorithm used, and the charging scheme employed when electric vehicles are used. A preliminary cost analysis was also performed, showing that such a system can be very competitive with present transportation systems (e.g., rental cars, taxies, etc.). ” 1999 Elsevier Science Ltd. All rights reserved.

UCR INTELLISHARE: An Intelligent Shared Electric Vehicle Testbed at the University of California, Riverside

In 1992, the University of California, Riversides College of Engineering established a Center for Environmental Research and Technology (CE-CERT). The research centers mission is to be a recognized leader in environmental education, collaborate with industry and government, create new technology, and be a major contributor in improving our understanding of the environment. There are three primary laboratories at CE-CERT dealing with: 1) emissions and fuels research; 2) environmental policy, atmospheric processes, and air quality modeling; and 3) transportation systems and vehicle technology research. This paper provides a brief background on these research laboratories. In addition, focus is placed on a particular transportation systems research program at CE-CERT: the development and operation of an intelligent shared electric vehicle testbed that operates on and around the UCR campus, called UCR IntelliShare. This program has been operational for nearly three years and has provided a wealth of data on various aspects of shared vehicle systems such as operational strategies, carsharing technology, user behavior, and how these type of systems can impact society as a whole. In this paper, the operation of the system is described in detail, along with a description of the latest results.

A comparison between two MPC algorithms for demand charge reduction in a real-world microgrid system

This paper describes an evaluation between two model predictive control (MPC) algorithms for microgrid energy management combined with solar production and battery energy storage for demand charge reduction in a real-world microgrid system. The first control algorithm is a constant threshold MPC (CT-MPC) that works well on a system with relatively stable solar generation and a well-known building load profile. CT-MPC can maintain the on-peak demand under a certain value during the entire on-peak rate period. The second control algorithm is an adjusting demand threshold MPC (ADT-MPC). ADT-MPC can better deal with unpredictable solar generation and/or changing building loads. The on-peak threshold under this algorithm is adjusted to the optimal value during the on-peak rate period. As expected, The CT-MPC algorithm performs well when coupled with accurate forecast models while the ADT-MPC algorithm excels when forecasting is more unpredictable.