Supervisory Control Optimization for a Series Hybrid Electric Vehicle with Consideration of Battery Thermal Management and Aging

This dissertation integrates battery thermal management and aging into the supervisory control optimization for a heavy-duty series hybrid electric vehicle (HEV). The framework for multi-objective optimization relies on novel implementation of the Dynamic Programing algorithm, and predictive models of critical phenomena. Electrochemistry based battery aging model is integrated into the framework to assesses the battery aging rate by considering instantaneous lithium ion (Li+) surface concentration rather than average concentration. This creates a large state-action space. Therefore, the computational effort required to solve a Deterministic or Stochastic Dynamic Programming becomes prohibitively intense, and a neuro-dynamic programming approach is proposed to remove the ‘curse of dimensionality’ in classical dynamic programming. First, unified simulation framework is developed for in-depth studies of series HEV system. The integration of a refrigerant system model enables prediction of energy use for cooling the battery pack. Side reaction, electrolyte decomposition, is considered as the main aging mechanism of LiFePO4/Graphite battery, and an electrochemical model is integrated to predict side reaction rate and the resulting fading of capacity and power. An approximate analytical solution is used to solve the partial difference equations (PDEs) for Li+ diffusion. Comparing with finite difference method, it largely reduces the number of states with only a slight penalty on prediction accuracy. This improves computational efficiency, and enables inclusion of the electrochemistry based aging model in the power management optimization framework. Next, a stochastic dynamic programming (SDP) approach is applied to the optimization of supervisory control. Auxiliary cooling power is included in addition to vehicle propulsion. Two objectives, fuel economy and battery life, are optimized by weighted sum method. To reduce the computation load, a simplified battery aging model coupled with equivalent circuit model is used in SDP optimization; Li+ diffusion dynamics are disregarded, and surface concentration is represented by the average concentration. This reduces the system state number to four with two control inputs. A real-time implementable strategy is generated and embedded into the supervisory controller. The result shows that SDP strategy can improve fuel economy and battery life simultaneously, comparing with Thermostatic SOC strategy. Further, the tradeoff between fuel consumption and active Li+ loss is studied under different battery temperature. Finally, the accuracy of battery aging model for optimization is improved by adding Li+ diffusion dynamics. This increases the number of states and brings challenges to classical dynamic programming algorithms. Hence, a neuro-dynamic programming (NDP) approach is proposed for the problem with large state-action space. It combines the idea of functional approximation and temporal difference learning with dynamic programming; in that case the computation load increases linearly with the number of parameters in the approximate function, rather than exponentially with state space. The result shows that ability of NDP to solve the complex control optimization problem reliably and efficiently. The battery-aging conscientious strategy generated by NDP optimization framework further improves battery life by 3.8% without penalty on fuel economy, compared to SDP strategy. Improvements of battery life compared to the heuristic strategy are much larger, on the order of 65%. This leads to progressively larger fuel economy gains over time

Development and Application of Semi-automated ITK Tools Development and Application of Semi-automated ITK Tools for the Segmentation of Brain MR Images

Image segmentation is a process to identify regions of interest from digital images. Image segmentation plays an important role in medical image processing which enables a variety of clinical applications. It is also a tool to facilitate the detection of abnormalities such as cancerous lesions in the brain. Although numerous efforts in recent years have advanced this technique, no single approach solves the problem of segmentation for the large variety of image modalities existing today. Consequently, brain MRI segmentation remains a challenging task. The purpose of this thesis is to demonstrate brain MRI segmentation for delineation of tumors, ventricles and other anatomical structures using Insight Segmentation and Registration Toolkit (ITK) routines as the foundation. ITK is an open-source software system to support the Visible Human Project. Visible Human Project is the creation of complete, anatomically detailed, three-dimensional representations of the normal male and female human bodies. Currently under active development, ITK employs leading-edge segmentation and registration algorithms in two, three, and more dimensions. A goal of this thesis is to implement those algorithms to facilitate brain segmentation for a brain cancer research scientist

Multiscale Modelling of Tunnel Ventilation Flows and Fires

F Colella, Multiscale analysis of tunnel ventilation flows and fires, PhD Thesis, Politecnico di Torino, Dipartimento di Energetica. May 2010Tunnels represent a key part of world transportation system with a role both in people and freight transport. Past events show that fire poses a severe threat to safety in tunnels. Indeed in the past decades over four hundred people worldwide have died as a result of fires in road, rail and metro tunnels. In Europe alone, fires in tunnels have brought vital parts of the road network to a standstill and have cost the European economy billions of euros. Disasters like Mont Blanc tunnel (Italy, 1999) and the more recent three Channel Tunnel fires (2008, 2006 and 1996) show that tunnel fire emergencies must be managed by a global safety system and strategies capable of integrating detection, ventilation, evacuation and fire fighting response, keeping as low as possible damage to occupants, rescue teams and structures. Within this safety strategy, the ventilation system plays a crucial role because it takes charge of maintaining tenable conditions to allow safe evacuation and rescue procedures as well as fire fighting. The response of the ventilation system during a fire is a complex problem. The resulting air flow within a tunnel is dependent on the combination of the fire-induced flows and the active ventilation devices (jet fans, axial fans), tunnel layout, atmospheric conditions at the portals and the presence of vehicles. The calculation of tunnel ventilation flows and fires is more economical and time efficient when done using numerical models but physical accuracy is an issue. Different modelling approaches can be used depending on the accuracy required and the resources available. If details of the flow field are needed, 2D or 3D computational fluid dynamics (CFD) tools can be used providing details of the flow behaviour around walls, flames, ventilation devices and obstructions. The computational cost of CFD is very high, even for medium size tunnels (few hundreds meters). If the analysis requires only bulk flow velocities, 1D models can be adopted. Their low computational cost favours large number of parametric studies involving broad range ventilation scenarios, portal conditions and fire sizes/locations.Another class of methods, called multiscale methods, adopts different levels of complexity in the numerical representation of the system. Regions of interest are described using more detailed models (i.e. CFD models), while the rest of the system XIX can be represented using a simpler approach (i.e. 1D models). Multiscale methods are characterized by low computational complexity compared to full CFD models but provide the same accuracy. The much lower computational cost is of great engineering value, especially for parametric and sensitivity studies required in the design or assessment of ventilation and fire safety systems. Multiscale techniques are used here for the first time to model tunnel ventilation flows and fires.This thesis provides in Chapter 1 a general introduction on the fundamentals of tunnel ventilation flows and fires. Chapter 2 contains a description of 1D models, and a case study on the Frejus tunnel (IT) involving some comparisons to experimental data. Chapter 3 discusses CFD techniques with an extensive review of the literature in the last 30 years. The chapter provides also two model validations for cold ventilation flows in the Norfolk Tunnels (AU) and fire induced flows in a small scale tunnel. Chapter 4 introduces multiscale methods and addresses the typical 1D-CFD coupling strategies. Chapter 5 applies multiscale modelling for cold flow steady-state scenarios in the Dartford Tunnels (UK) where a further validation against experimental data has been introduced. Chapter 6 present the calculations from coupling fire and ventilation flows in realistic modern tunnel layout and investigates the accuracy of the multiscale predictions as compared to full CFD. Chapter 7 represents application of multiscale computing techniques to transient problems involving the dynamic response of the ventilation system. The multiscale model has been demonstrated to be a valid technique for the simulation of complex tunnel ventilation systems both in steady-state and timedependent problems. It is as accurate as full CFD models and it can be successfully adopted to conduct parametric and sensitivity studies in long tunnels, to design ventilation systems, to assess system redundancy and the performance under different hazards conditions. Time-dependent simulations allow determining the evolution of hazardous zones in the tunnel domain or to determine the correct timing for the activation of fixed fire fighting systems. Another significant advantage is that it allows for full coupling of the fire and the whole tunnel domain including the ventilation devices. This allows for an accurate assessment of the fire throttling effect that is shown here to be significant and for a prediction of the minimum number of jet fans needed to cope with a certain fire size. Furthermore, it is firmly believed that the multiscale methodology represents the only feasible tool to conduct accurate simulations in tunnels longer than few kilometres, when the limitation of the computational cost becomes too restrictive