The automotive industry is seen to be making a monumental paradigm shift from manual to semi-autonomous to fully Autonomous Vehicles. An Advanced Driver Assistance System (ADAS) forms a major building block for realizing these next generation of highly Autonomous-capable Vehicles. Although the general ADAS architecture is widely discussed, limited details are available about the functionality of the modules and their interactions, backed up by scientific justification. This limits the utilization of such an architecture for pragmatic implementation. A Cognitive ADAS Architecture for level 4 Autonomous-capable Electrified Vehicles (EV) is proposed in this thesis. Variations for levels 3 and 3.5 (combination of levels 3 and 4, with the primary fallback through a human driver and the secondary through an Automated Driving System) are also presented. A validated simulation framework is built for highway driving based on the proposed level 4 architecture for an enhanced Tesla Model S. It was concluded that the autonomous control provided a 28% energy economy increase, on average, compared to human driver control. Through a quantitative sensitivity analysis, the optimal Mission/Motion Planning and energy management are seen in addition to a positive impact on the EV battery, motor, and dynamics, realized from the minimized instantaneous fluctuations. These factors are considered to contribute to this significant increase in the energy economy of an autonomous-controlled EV. Furthermore, this impact was seen to be relatively higher for autonomous longitudinal vehicle control compared to lateral. This difference in the improved operation of the Autonomous-capable EV components between the Automated Driving System and the human driver control was seen to be the highest for the battery current. In overall, an increase in vehicle autonomy, resulted in an improvement in the EV performance, dynamics and operation of the battery and motor, compared to a human driver control. / Thesis / Doctor of Philosophy (PhD)

This thesis proposes a novel framework for solving the energy management problem of Hybrid Electric Vehicles (HEVs). We aim to establish a practical and effective approach targeting an optimal Energy Management Strategy (EMS). A situation-specific Equivalent Consumption Minimization Strategy (ECMS) is developed to minimize fuel consumption and improve battery charge sustainability while maintaining an acceptable drive quality. The investigated methodology will be broadly applicable to all HEV applications; however, it will be well-suited for hybrid electric delivery applications. / Thesis / Master of Applied Science (MASc)

This thesis provides an insight into battery modeling in electric vehicles which includes degradation mechanisms as in automotive operation in electric vehicles. As electric vehicles with lithium ion batteries increase in popularity there is an increased need to study and model the capacity losses in such batteries. If there is a good understanding of the phenomena involved and an ability to predict these losses there is also a foundation to take measures to minimize these losses. In this thesis a battery model for lithium ion batteries which includes heat dissipation is used as groundwork. This model is expanded with the addition of capacity losses due to usage as well as storage. By combining this with a simple vehicle model one can use these models to achieve an understanding as to how a battery or pack of several batteries would behave in a specific driving scenario. Much of the focus in the thesis is put into comparing the different factors of degradation to highlight what the major contributors are. The conclusion is drawn that heat is the main cause for degradation for batteries in electric vehicles. This applies for driving usage as well as during storage. As heat is generated when a battery is used, the level of current is also a factor, as well as in which state of charge region the battery is used.

The mass-market adoption of electrification in the transportation sector mandates stringent and aggressive requirements in terms of cost, power rating, efficiency, power density, and specific density of power electronics. Modular packaging of power electronics is advantageous and thus ubiquitously used by the automotive industry. A trend of shrinking die sizes and increased integration is evident and will inevitably continue. The thermal management system has become ever more significant as it is one of the main obstacles to higher power densities. The cooling system must be cost-effective, simple, efficient, reliable, and compatible with system requirements. Pin fins are a reliable and effective means of augmenting heat transfer. They rely on inducing turbulence, increasing the effective wetted surface, and accelerating fluid velocity. Unavoidably the pin fin array also produces an undesirable pressure drop that is commensurate to the pumping power required for the system. In this thesis, a tool is developed for the geometry optimization of pin fin arrays to dissipate the heat at a rate large enough to ensure junction temperatures do not exceed the maximum value possible at a minimal pressure drop. It is hoped that this tool would contribute to the multi-physics optimization and integration of power electronics for electrified vehicles. This optimization is confined to equalaterally spaced short pin fins, aspect ratios less than three. The tool employs empirical correlations since flow is too complex to solve analytically and numerical solutions or CFD-simulations are too time and computationally extensive. The tool development is done in a comprehensive manner. Starting from the first principles of a two-level voltage source inverter's operation. Next, the inevitable power losses from the operation are explained and a method for their calculations is presented. Correlations in the literature related to both pressure drop and heat transfer are reviewed afterward. Then the methodology of the construction of the tool is explicated in detail. Employing a commercial power module to benchmark results; three scenarios with different flow rates and inlet temperatures are optimized for. Simulations in ANSYS Fluent are run to verify the accuracy of correlations used in the tool. Comparing the optimized geometry of pin fins to the original benchmarking geometry it is evident that employing this tool on a per-application basis provides superior performance. / Thesis / Master of Applied Science (MASc)

In this thesis, integrated active filter auxiliary power modules (AFAPMs) is presented in electrified vehicle applications. A topological evaluation is conducted particularly for the auxiliary power module (APM) applications in the electrified vehicles. Several primary and secondary base topologies are compared in terms of VA rating and performance. Multiple input/output topology configurations are compared with different connection configurations and control schemes. The MOSFET loss analysis is given. Based on the MOSFET loss analysis, the modular full bridge current doubler with input-series-output-parallel configuration presents better performance in terms of the switch efficiency and cost analysis. Bulk capacitor banks occupy large volume and impact the reliability in the traction inverter and HV battery charger in the vehicle applications. A capacitor-less design is relatively urgent for the next generation electrified vehicle. Active filter (AF) is one potential solution to reduce the corresponding dc-link capacitance. However, additional components are required which increases the system complicity and decreases its reliability. Hence, it would be great to integrate the AF into the LV battery charger for the vehicle applications. Based on the power switch requirements, the AFAPM is evaluated for traction inverter and HV battery charger, respectively. The evaluation result shows that the AFAPM for the HV battery charger system is a feasible and attractive solution. Furthermore, a simple and effective dual-mode dual-voltage charging system operating principle is proposed. The integrated AFAPM converter charges the LV battery when the vehicle is running and operates as an AF when the vehicle is connected to the grid and the HV battery is charging. Hence, the low-frequency second-order harmonic current is alleviated without a bulk capacitor bank or an extra AF circuit in the HV battery charger. For magnetic design, there is a trend toward integration and planarization. Two planar transformers are built for two different AFAPM prototypes. A minimized leakage inductance method is presented and implemented on a 20:1 center-tapped planar transformer. Three different integrated AFAPM converters are proposed. By applying these AFAPM converters, the required extra components to form the AF for the HV battery charger are reduced and thus the cost, size and weight for the dual-voltage charging system in the electrified vehicle applications can be reduced. Two prototypes are built. The experiments show promising results confirming the effectiveness of the proposed converters. / Dissertation / Doctor of Philosophy (PhD)

Electrified vehicles (EVs) with a large electric energy storage system (ESS), including Plug-in Hybrid Electric Vehicles (PHEVs) and Pure Electric Vehicles (PEVs), provide a promising solution to utilize clean grid energy that can be generated from renewable sources and to address the increasing environmental concerns. Effectively extending the operation life of the large and costly ESS, thus lowering the lifecycle cost of EVs presents a major technical challenge at present. A hybrid energy storage system (HESS) that combines batteries and ultracapacitors (UCs) presents unique energy storage capability over traditional ESS made of pure batteries or UCs. With optimal energy management system (EMS) techniques, the HESS can considerably reduce the frequent charges and discharges on the batteries, extending their life, and fully utilizing their high energy density advantage. In this work, an intelligent energy allocation (IEA) algorithm that is based on Q-learning has been introduced. The new IEA method dynamically generate sub-optimal energy allocation strategy for the HESS based on each recognized trip of the EV. In each repeated trip, the self-learning IEA algorithm generates the optimal control schemes to distribute required current between the batteries and UCs according to the learned Q values. A RBF neural networks is trained and updated to approximate the Q values during the trip. This new method provides continuously improved energy sharing solutions better suited to each trip made by the EV, outperforming the present passive HESS and fixed-cutoff-frequency method. To efficiently recognize the repeated trips, an extended Support Vector Machine (e-SVM) method has been developed to extract significant features for classification. Comparing with the standard 2-norm SVM and linear 1-norm SVM, the new e-SVM provides a better balance between quality of classification and feature numbers, and measures feature observability. The e-SVM method is thus able to replace features with bad observability with other more observable features. Moreover, a novel pattern classification algorithm, Inertial Matching Pursuit Classification (IMPC), has been introduced for recognizing vehicle driving patterns within a shorter period of time, allowing timely update of energy management strategies, leading to improved Driver Performance Record (DPR) system resolution and accuracy. Simulation results proved that the new IMPC method is able to correctly recognize driving patterns with incomplete and inaccurate vehicle signal sample data. The combination of intelligent energy allocation (IEA) with improved e-SVM feature extraction and IMPC pattern classification techniques allowed the best characteristics of batteries and UCs in the integrated HESS to be fully utilized, while overcoming their inherent drawbacks, leading to optimal EMS for EVs with improved energy efficiency, performance, battery life, and lifecycle cost. / Graduate

In this thesis, a physics-based single particle modeling is presented to analyze a proposed direct coupled hybrid energy storage modules using lithium-ion battery and ultracapacitor. Firstly, a state of the art for the energy storage system in the electrified vehicles are summarized. Several energy storage elements including lead-acid battery, nickel-metal hydride battery, lithium-ion battery, ultracapacitor, and lithium-ion capacitor are reviewed. Requirements of the energy storage systems in electric, hybrid electric, and plug-in hybrid electric vehicles are generalized. Typical hybrid energy storage system topologies are also reviewed. Moreover, these energy storage elements and hybrid energy storage system topologies are compared to the requirements of the energy storage systems in terms of specific power and specific energy. Secondly, the performance of different battery balancing topologies, including line shunting, ring shunting, synchronous flyback, multi-winding, and dissipative shunting are analyzed based on a linear programming methodology. As a traction battery in an electric or plug-in electric vehicle, high voltage lithium-ion packs are typically configured in a modular fashion, therefore, the analysis considers the balancing topologies at module level and cell level and focuses on minimum balancing time, minimum plug-in charge time, minimum energy loss, and component counts of every balancing topology for the entire battery pack. Thirdly, different modeling techniques for the lithium-ion battery and ultracapacitor are presented. One of the main contributions of this thesis is the development of a physics-based single particle modeling embedded with a solid-electrolyte interface growth model for a lithium-ion battery in battery management system. This development considers the numerical solution of diffusion equation, cell level quantities, parametrization method, effects of number of shells in a spherical particle, SOC-SOH estimation algorithms, and aging effects. The accuracy of the modeling is validated by experimental results of a Panasonic NCR18650A lithium-ion battery cell. Fourthly, the physics-based modeling is applied to analyze the performance of a proposed direct coupled hybrid energy storage module topology based on the Panasonic NCR18650A lithium-ion battery and Maxwell BCAP0350 ultracapacitor. There are many ways to directly connect battery cells and ultracapacitor cells in a module which would influence the performance of the module. The results show that a module has 9 cells in a battery string and 14 cells in an ultracapacitor string can obtain the highest power capability and utilize the most of the energy in an ultracapacitor. More ultracapacitor strings connected in parallel would increase the power density but reduce the energy density. Moreover, the simulation and experimental results indicate that the direct coupled hybrid modules can extend the operating range and slow the capacity fade of lithium-ion battery. An SOC-SOH estimation algorithm for the hybrid module is also developed based on the physics-based modeling. Finally, a pack design methodology is proposed to meet U.S. Advanced Battery Consortium LLC PHEV-40, power-assist, and 48V HEV performance targets for the battery packs or the proposed direct coupled topologies. In order to explore replacement tradeoffs between the battery and ultracapacitor, a case study of the direct coupled topologies is presented. From the case study, ultracapacitors enhance the power capability for short term pulse power and marginally reduce the cost of an entire energy storage system. Moreover, the hybrid module topologies can keep a relatively long all-electric range when the batteries degrade. / Dissertation / Doctor of Philosophy (PhD)

No description available.

Aggressive demands for high power density and low-cost power modules in the automotive sector pose significant challenges to the thermal management systems. These challenges necessitate adopting highly effective cooling technologies in power modules to remain competitive in the semiconductor industry. Furthermore, the thermal management strategy must be simple, easy to integrate, compact, effective, efficient, reliable, and economical. This thesis is an effort to investigate the impact of fin geometry on the overall performance of finned-type liquid-cooled power electronic modules in electrified transportation. The cooling system's performance metrics, including thermal resistance, pressure drop, pumping power, and mass, are discussed in depth. Various cooling technologies are benchmarked. The finned-type cooling technique is chosen over other methods due to simplicity and low pressure drop. Integrated cooling or direct cooling of the module’s baseplate is selected due to considerable thermal resistance reduction because of thermal grease elimination. Potential fabrication techniques are thoroughly explored and compared in terms of mass production and prototyping suitability. Four different fin shapes, including circular (baseline), drop-shaped, symmetric convex lens, and offset strip in the staggered arrangement, are studied herein. The cooling agent is Water and Ethylene Glycol 50% volumetric mixture (WEG 50%). Typical operating conditions in electrified vehicles (EVs) such as flow rate and inlet temperature are assumed for the numerical analysis. A grid convergence study is carried out to ensure numerical solutions are within an acceptable error band. The thermal performance evaluation results showed that, on average, offset strip, drop-shaped, and the convex lens performed 39%, 20%, and 6% better than the baseline design, respectively. Additionally, the design candidates are compared in terms of mass and estimated machining cost. The results of the baseline case are verified against empirical correlations from the literature. The maximum deviation is less than 1% and 1.2% for finned-surface temperature and pressure drop, respectively. The difference is attributed to the end-wall effects. / Thesis / Master of Applied Science (MASc)

No description available.