The Development of a High-Performance Distributed Battery Management System for Large Lithium Ion Packs

A high performance battery management system (BMS) for large capacity cells was designed, built, and tested in a cycle of three revisions. The BMS was designed for use in applications where the battery pack configuration is unknown: parallel, series, or any combination. Each of the cells is equipped with its own battery management system to allow a peer-to-peer mesh network to monitor the safety of the cell. The BMS attached to each cell also is equipped with a 25A DC/DC converter to perform active balancing between cells in a string. This converter can transfer charge to (or from) a cell of higher potential and a cell of lower potential at the same time. The balancing circuit has a peak efficiency of 85.3%. The system draws only 53mA while balancing at 25A helping to increase low current performance. The system draws just under 5mA over all while active. Each BMS is equipped with one current sensor, which can measure ±800A with a second ±120A current range. Additionally, the board is equipped with coulomb counting to provide a better understanding of each cell. While this design has many great features, lack of full software support makes many of the subsystems dependent on user interaction to use. As a result, the design is not fully complete. Additionally, last minute design changes on the final revision resulted in detrimental effects to the accuracy of many of the analog circuits including the current sensing features

System data communication structures for active-control transport aircraft, volume 2

The application of communication structures to advanced transport aircraft are addressed. First, a set of avionic functional requirements is established, and a baseline set of avionics equipment is defined that will meet the requirements. Three alternative configurations for this equipment are then identified that represent the evolution toward more dispersed systems. Candidate communication structures are proposed for each system configuration, and these are compared using trade off analyses; these analyses emphasize reliability but also address complexity. Multiplex buses are recognized as the likely near term choice with mesh networks being desirable for advanced, highly dispersed systems

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive

Cost-optimal charging of electric vehicles using real-time pricing

The large-scale adoption of EVs presents both potential benefits and difficult challenges. The already stressed electricity grids will have to manage the influx of EV charging requirements, which is especially difficult at peak times. This calls for smart solutions to optimally charge EVs in a grid-friendly way, using demand response where possible. In line with the demand, the electricity prices at peak times can be very high and it would also be advantageous for the user to avoid charging at these times. Therefore, the goal of grid friendly charging is twofold: to avoid putting additional load on the electricity grid when it is heavily loaded already, and to reduce the cost of charging to the consumer. [Continues.