The rising use of fossil fuels and the resulting rise in environmental harm have fueled the advancement of automobiles that are fuel-efficient. A severe existential challenge facing the planet earth has given rise to hybrid electric vehicles (HEVs), which have developed from their incipient stage and are shown promise as a solution. Additionally, when needed to produce peaking power, batteries' efficiency is reduced. Instead, supercapacitors have smaller energy storage capacity but can withstand peaking power. Designing a clever method to manage the energy balance between a supercapacitor and a battery is the main goal of this research. Different topologies are used to study the battery-supercapacitor energy storage system in great detail. Nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbons (HC), and other harmful gases are less released when a battery-supercapacitor energy storage system is integrated. Additionally, it can lower the load on the battery, extending its life and improving its performance in HEVs.

Indiana University-Purdue University Indianapolis (IUPUI) / Plug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They can store electrical energy from a domestic power supply and can drive the vehicle alone in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80 % of the American driving public on average drives under 50 miles per day. A PHEV vehicle that can drive up to 50 miles by making maximum use of cheaper electrical energy from a domestic supply can significantly reduce the conventional fuel consumption. This may also help in improving the environment as PHEVs emit less harmful gases. However, the Energy Management System (EMS) of PHEVs would have to be very different from existing EMSs of HEVs. In this thesis, three different Energy Management Systems have been designed specifically for PHEVs using simulated study. For most of the EMS development mathematical vehicle models for powersplit drivetrain configuration are built and later on the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The main objective of the study is to design EMSs to reduce fuel consumption by the vehicle. These EMSs are compared with existing EMSs which show overall improvement. x In this thesis the final EMS is designed in three intermediate steps. First, a simple rule based EMS was designed to improve the fuel economy for parametric study. Second, an optimized EMS was designed with the main objective to improve fuel economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to obtain the optimum parameter values. This EMS has provided optimum parameters which result in optimum blended mode operation of the vehicle. Finally, to obtain optimum charge depletion and charge sustaining mode operation of the vehicle an advanced PSO EMS is designed which provides optimal results for the vehicle to operate in charge depletion and charge sustaining modes. Furthermore, to implement the developed advanced PSO EMS in real-time a possible real time implementation technique is designed using neural networks. This neural network implementation provides sub-optimal results as compared to advanced PSO EMS results but it can be implemented in real time in a vehicle. These EMSs can be used to obtain optimal results for the vehicle driving conditions such that fuel economy is improved. Moreover, the optimal designed EMS can also be implemented in real-time using the neural network procedure described.

Indiana University-Purdue University Indianapolis (IUPUI) / Today transportation is one of the rapidly evolving technologies in the world. With the stringent mandatory emission regulations and high fuel prices, researchers and manufacturers are ever increasingly pushed to the frontiers of research in pursuit of alternative propulsion systems. Electrically propelled vehicles are one of the most promising solutions among all the other alternatives, as far as; reliability, availability, feasibility and safety issues are concerned. However, the shortcomings of a fully electric vehicle in fulfilling all performance requirements make the electrification of the conventional engine powered vehicles in the form of a plug-in hybrid electric vehicle (PHEV) the most feasible propulsion systems. The optimal combination of the properly sized components such as internal combustion engine, electric motor, energy storage unit are crucial for the vehicle to meet the performance requirements, improve fuel efficiency, reduce emissions, and cost effectiveness. In this thesis an application of Particle Swarm Optimization (PSO) approach to optimally size the vehicle powertrain components (e.g. engine power, electric motor power, and battery energy capacity) while meeting all the critical performance requirements, such as acceleration, grade and maximum speed is studied. Compared to conventional optimization methods, PSO handles the nonlinear constrained optimization problems more efficiently and precisely. The PHEV powertrain configuration with the determined sizes of the components has been used in a new vehicle model in PSAT (Powertrain System Analysis Toolkit) platform. The simulation results show that with the optimized component sizes of the PHEV vehicle (via PSO), the performance and the fuel efficiency of the vehicle are significantly improved. The optimal solution of the component sizes found in this research increased the performance and the fuel efficiency of the vehicle. Furthermore, after reaching the desired values of the component sizes that meet all the performance requirements, the overall emission of hazardous pollutants from the PHEV powertrain is included in the optimization problem in order to obtain updated PHEV component sizes that would also meet additional design specifications and requirements.

This work presents novel modeling and experimental techniques that provide insight into liquid-phase mass transport and electron transfer processes in lithium-ion batteries. These included liquid-phase ionic mass transport (conduction and diffusion), lithium diffuion in the solid phase and electronic transport in the solid phase. Fundamental understanding of these processes is necessary to efficiently design and optimize lithium-ion batteries for different applications. To understand the effect of electrode structure on the electronic resistance of the cathode, we tested power performance of cathodes with combinations of three different carbon conductivity additives: vapor-grown carbon fibers (CF), carbon black (CB) and graphite (GR). With all other factors held constant, cathodes with a mixture of CF+CB were found to have the best power-performance, followed by cells containing CF only and then by CB+GR. Thus, the use of carbon fibers as conductive additive was found to improve the power performance of cells compared to the baseline (CB+GR). The enhanced electrode performance due to the fibers also allows an increase in energy density while still meeting power goals. About one-third of the available energy was lost to irreversible processes when cells were pulse-charged or discharged at the maximum rate allowed by voltage-cutoff constraints. We developed modeling and experimental techniques to quantify tortuosity in electrolyte-filled porous battery structures (separator and active-material film). Tortuosities of separators were measured by two methods, AC impedance and polarization-interrupt, which produced consistent results. The polarization-interrupt experiment was used similarly to measure effective electrolyte transport in porous films of cathode materials, particularly films containing lithium iron phosphate. An empirical relationship between porosity and the tortuosity of the porous structures was developed. Our results demonstrate that the tortuosity-dependent mass transport resistance in porous separators and electrodes is significantly higher than that predicted by the oft-used Bruggeman relationship. To understand the dominant resistances in a lithium battery, we developed and validated a model for lithium iron phosphate cathode. In doing so we considered unique physical features of this active material. Our model is unusual in terms of the range of experimental conditions for which it is validated. Various submodel and experimental techniques were used to find physically realistic parameters. The model was tested with different discharge rates and thicknesses of cathodes, in all cases showing good agreement, which suggests that the model takes into account physical realities with different thicknesses. The model was then used to find the dominant resistance for the tested cathodes. The model suggests that the inter-particle contact resistance between carbon and the active-material particles was a dominant resistance for the tested cathodes.

<p dir="ltr">Engine downsizing with the assistance of turbomachinery and/or energy storage system has been realized to be one of the most promising and cost-effective solutions in pursuit of cleaner and more efficient engine products. Fundamental challenges however, exist in terms of control and energy management of advanced powertrain featuring turbocharged engines due to their complex dynamics, inherent coupling nature, and strict emission regulations concerning environmental preservation. For the purpose of addressing those challenges, this dissertation develops an integrated framework for modeling, robust coordinated control, and power management of advanced powertrains featuring turbocharged engines.</p><p dir="ltr">This dissertation first studies an advanced turbocharged lean-burn SI natural gas engine manufactured by Caterpillar, and develops an intuitive physics-based, control-oriented model. The obtained control-oriented model is validated against a high-fidelity truth-reference model and serves as the basis on which a robust coordinated control system is developed. The dissertation then proposes a comprehensive procedure for synthesizing a robust coordinated control system applying optimization-based H_infinity control theory. Specifically, this framework outlines a methodology of modeling uncertainties to account for system robustness, and providing valuable insights into the tuning of general coordinated control system design. For performance testing, the synthesized robust coordinated control system is implemented on the high-fidelity truth-reference model. A parallel closed-loop simulation strategy is adopted so that direct comparison between the robust coordinated control system and benchmark production control system (composed of multiple fine-tuned PID controllers) developed by Caterpillar can be carried out. Simulation results manage to demonstrate the merit of utilizing the robust coordinated control system, with better performances observed in terms of steady-state tracking, transient response, and disturbance attenuation.</p><p dir="ltr">The second part of this dissertation focuses on the development of a proposed novel hybrid electric wheel loader which features a downsized engine assisted by turbocharger and an energy storage system. Research efforts documented in this dissertation involve system configuration, controller design (both component-level and supervisory-level), simulation development (both software-in-the-loop and hardware-in-the-loop) and simulated validation for the proposed novel wheel loader. Inspired by the successful simulation results, John Deere assembled a real demo vehicle with the proposed powertrain and conducted some in-field testing, from which encouraging experimental results are observed.</p>

One of the benefits of electric vehicles (EVs) and hybrid vehicles (HVs) is their potential to recuperate braking energy. Regenerative braking (RB) will minimize duty levels on the brakes, giving advantages including extended brake rotor and friction material life and, more significantly, reduced brake mass and minimised brake pad wear. In this thesis, a mathematical analysis (MATLAB) has been used to analyse the accessibility of regenerative braking energy during a single-stop braking event. The results have indicated that a friction brake could be downsized while maintaining the same functional requirements of the vehicle braking in the standard brakes, including thermomechanical performance (heat transfer coefficient estimation, temperature distribution, cooling and stress deformation). This would allow lighter brakes to be designed and fitted with confidence in a normal passenger car alongside a hybrid electric drive. An approach has been established and a lightweight brake disc design analysed FEA and experimentally verified is presented in this research. Thermal performance was a key factor which was studied using the 3D model in FEA simulations. Ultimately, a design approach for lightweight brake discs suitable for use in any car-sized hybrid vehicle has been developed and tested. The results from experiments on a prototype lightweight brake disc were shown to illustrate the effects of RBS/friction combination in terms of weight reduction. The design requirement, including reducing the thickness, would affect the temperature distribution and increase stress at the critical area. Based on the relationship obtained between rotor weight, thickness and each performance requirement, criteria have been established for designing lightweight brake discs in a vehicle with regenerative braking. / Ministry of Higher Education of Malaysia

Energy-efficient powertrain components and advanced vehicle control strategies are two effective methods to promote the potential of hybrid electric vehicles (HEVs). Aiming at hybrid system efficiency improvement, this thesis presents a comprehensive review of energy-efficient hybrid powertrain specific engines and proposes three improved energy management strategies (EMSs), from a basic non-adaptive real-time approach to a state-of-the-art learning-based intelligent approach. To evaluate the potential of energy-efficient powertrain components in HEV efficiency improvement, a detailed discussion of hybrid powertrain specific engines is presented. Four technological solutions, i.e., over-expansion cycle, low temperature combustion mode, alternative fuels, and waste heat recovery techniques, are reviewed thoroughly and explicitly. Benefits and challenges of each application are identified, followed by specific recommendations for future work. Opportunities to simplify hybrid-optimized engines based on cost-effective trade-offs are also investigated. To improve the practicality of HEV EMS, a real-time equivalent consumption minimization strategy (ECMS)-based HEV control scheme is proposed by incorporating powertrain inertial dynamics. Compared to the baseline ECMS without such considerations, the proposed control strategy improves the vehicle drivability and provides a more accurate prediction of fuel economy. As an improvement of the baseline ECMS, the proposed dynamic ECMS offers a more convincing and better optimal solution for practical HEV control. To address the online implementation difficulty faced by ECMS due to the equivalence factor (EF) tuning, a predictive adaptive ECMS (A-ECMS) with online EF calculation and instantaneous power distribution is proposed. With a real-time self-updating EF profile, control dependency on drive cycles is reduced, and the requirement for manual tuning is also eliminated. The proposed A-ECMS exhibits great charge sustaining capabilities on all studied drive cycles with only slight increases in fuel consumption compared to the basic non-adaptive ECMS, presenting great improvement in real-time applicability and adaptability. To take advantage of machine learning techniques for HEV EMS improvement, a deep reinforcement learning (DRL)-based intelligent EMS featuring the state-of-the-art asynchronous advantage actor-critic (A3C) algorithm is proposed. After introducing the fundamentals of reinforcement learning, formulation of the A3C-based EMS is explained in detail. The proposed algorithm is trained successfully with reasonable convergence. Training results indicate the great learning ability of the proposed strategy with excellent charge sustenance and good fuel optimality. A generalization test is also conducted to test its adaptability, and results are compared with an A-ECMS. By showing better charge sustaining performance and fuel economy, the proposed A3C-based EMS proves its potential in real-time HEV control. / Thesis / Doctor of Philosophy (PhD)

The automotive industry is inevitably experiencing a paradigm shift from fossil fuels to electric powertrain with significant technological breakthroughs in vehicle electrification. Emerging hybrid electric vehicles were one of the first steps towards cleaner and greener vehicles with a higher fuel economy and lower emission levels. The energy management strategy in hybrid electric vehicles determines the power flow pattern and significantly affects vehicle performance. Therefore, in this thesis, a learning-based strategy is proposed to address the energy management problem of a hybrid electric vehicle in various driving conditions. The idea of a deep recurrent neural network-based energy management strategy is proposed, developed, and evaluated. Initially, a hybrid electric vehicle model with a rule-based supervisory controller is constructed for this case study to obtain training data for the deep recurrent neural network and to evaluate the performance of the proposed energy management strategy. Secondly, due to its capabilities to remember historical data, a long short-term memory recurrent neural network is designed and trained to estimate the powertrain control variables from vehicle parameters. Extensive simulations are conducted to improve the model accuracy and ensure its generalization capability. Also, several hyper-parameters and structures are specifically tuned and debugged for this purpose. The novel proposed energy management strategy takes sequential data as input to capture the characteristics of both driver and controller behaviors and improve the estimation/prediction accuracy. The energy management controller is defined as a time-series problem, and a network predictor module is implemented in the system-level controller of the hybrid electric vehicle model. According to the simulation results, the proposed strategy and prediction model demonstrated lower fuel consumption and higher accuracy compared to other learning-based energy management strategies. / Thesis / Master of Applied Science (MASc)

The Hybrid Electric Vehicle (HEV) is a complex electromechanical system with complex interactions among various components. Due to the large number of design variables involved, the design flexibility in the HEV makes performance studies difficult. As the system complexity and sophistication increases, it becomes much more difficult to predict these interactions and design the system accordingly. Also, different variations in the design and manufacture of various components and systems involve a large amount of work and cost to keep updated of all these variations. While the above issues ask for a flexible design environment suitable for vehicle design, most of the existing powertrain design tools are based on experiential models, such as look-up tables, which use idealized assumptions and limited experimental data. The accuracy of the results produced by these tools is not good enough for designing these new generation vehicles. Also, sometimes the designs may lead to components or systems beyond physical limitations. To make the powertrain design more efficient, the models developed must be closely related to the underlying physics of the components. Only such physics-based models can facilitate high fidelity simulations for dynamics at different time scales. This results in the quest for a design tool that manages the vehicle?s development process while maintaining tight integration between the software and physical artifacts. The thesis addresses the above issues and focuses on the modeling of HEV using model integrated computing and employing physics-based resistive companion form modeling method. For this purpose, Generic Modeling Environment (GME), software developed by Institute of Software and Integrated Systems (ISIS), Vanderbilt University is used as the platform for developing the models. A modeling environment for hybrid vehicle design is prepared and a Battery Electric Vehicle (BEV) is developed as an application of the developed environment. Resistive companion form models of various BEV components are prepared and a model interpreter is prepared for integrating the developed component models and simulating the design.

The rise in overall powertrain complexity and the stringent performance requirements of a hybrid electric vehicle (HEV) have elevated the role of its powertrain control strategy to considerable importance. Iterative modeling and simulation form an integral part of the control strategy design process and industry engineers rely on proprietary ?legacy? models to rapidly develop and implement control strategies. However, others must initiate new algorithms and models in order to develop production-capable control systems. This thesis demonstrates the development and validation of a charge-sustaining control algorithm for a through-the-road (TTR) parallel hybrid (diesel-electric) powertrain. Some unique approaches used in powertrain-level control of other commercial and prototype vehicles have been adopted to incrementally develop this control strategy. The real-time performance of the control strategy has been analyzed through on-road and chassis dynamometer tests over several standard drive cycles. Substantial quantitative improvements in the overall HEV performance over the stock configuration, including better acceleration and fuel-economy have been achieved.