A survey of the existing literature indicates that optimization on the power management logic of hybrid electric vehicle is mostly performed after the design of the powertrain architecture or the power source components are finalized. The goal of this research is to utilize Multidisciplinary Design Optimization (MDO) to automate and optimize the vehicle’s powertrain component sizes, while simultaneously determining the optimal power management logic in developing the most cost-effective system solution. A generic, modular, and flexible vehicle model utilizing a backward-looking architecture is created using scalable powertrain components. The objective of the research work is to study the energy efficiency of the vehicle system, where the dynamics of the vehicle is not of concern; a backward-looking architecture could be used to compute the power consumption and the overall efficiency accurately while minimizing the required computing resource. An optimization software platform utilizing multidisciplinary design optimization approach is implemented containing the generic vehicle model and an optimizer of the user’s choice. The software model is created in the MATLAB/Simulink environment, where the optimization code and the powertrain component properties are implemented using m-files, and the power consumption calculations of the vehicle system are performed in Simulink. Furthermore, a feature-based optimization technique is developed with the motivation of significantly reducing the simulation run-time. To demonstrate the capabilities of the developed approach and contributions of the research, two optimization case studies are undertaken: (i) series hybrid electric vehicles, and (ii) police vehicle anti-idling system. As the first case study, a plug-in battery-only series hybrid electric vehicle with similar power components as the Chevrolet Volt is created, where the battery size and the power management logic are simultaneously optimized. The objective function of the optimizer is defined from the financial cost perspective, where the objective is to minimize the initial cost of batteries, gasoline and electricity consumption over a period of five years, and the carbon tax as a penalty function for fuel emissions. The battery-only series hybrid electric vehicle is subsequently extended to include ultracapacitors, and the optimization process is expanded to the rest of the powertrain components and power management logic. A comparison between the optimization algorithms found that only genetic algorithm (GA) was capable of finding the optimal solution during a full simulation, while simulated annealing and pattern search were not able to converge to any solution after a 24-hour period. A comparison between the full genetic algorithm optimization and the feature-based (FB) method with secondary optimization found that although the final cost function of the FB methodology is higher than that of the full GA optimization, the total simulation run-time is approximately ten times less using the FB method. The behaviour of the solutions found via both methods exhibited almost identical characteristics, further confirming the validity of the feature-based methodology. Finally, a benchmarking comparison found that with more accurate manufacturers’ component data and additional appropriate performance requirements, the proposed software platform will be capable of predicting a solution that is comparable to the Chevrolet Volt. The second case study involves optimizing an anti-idling system for police vehicles using the same optimization algorithm and generic vehicle model. The goal of the optimization study is to select an additional battery and determine the power management logic to reduce the engine idling time of a police vehicle. It is found that depending on the SOC threshold, the duration of time over which the engine is activated varies in a non-linear fashion, where local minima and maxima exist. A design study confirmed that by utilizing the anti-idling system, significant cost reduction can be realized when compared to one without the anti-idling system. A comparison between the various optimization algorithms showed that the feature-based optimization can obtain a relatively accurate solution while reducing simulation time by approximately 90%. This significant reduction in simulation time warrants the feature-based optimization technique a powerful tool for vehicle design. Due to the high cost of the electrical energy storage components, it is currently still more cost-effective to use the fossil fuel as the primary energy source for transportation. However, given the rise of fuel cost and the advancement in the electrical energy storage technology, it is inevitable that the cost of the electrical and chemical energy storage method will reach a balance point. The proposed optimization platform allows the user the capability and flexibility to obtain the optimal vehicle solution with ease at any given time in the future.

Thesis (M.S.)--West Virginia University, 1999. / Title from document title page. Document formatted into pages; contains viii, 69 p. : ill. Includes abstract. Includes bibliographical references (p. 68-69).

Thesis (M.S.)--West Virginia University, 2009. / Title from document title page. Document formatted into pages; contains viii, 61 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 59-61).

Thesis (M.S.)--Ohio State University, 2005. / Includes bibliographical references (leaves 166-170). Available online via OhioLINK's ETD Center

Dissertation (Ph.D.)--University of Toledo, 2005. / Typescript. "A dissertation [submitted] as partial fulfillment of the requirements of the Doctor of Philosophy degree in Engineering." Bibliography: leaves 61-63.

Thesis (M.S.)--Tennessee Technological University, 2009. / Title from title page screen (viewed on June 29, 2010). Bibliography: leaves 127-131.

<p>Ever-increasing need for freight transportation and mounting environmental concerns call for a cleaner and more efficient energy source. Hybrid electric vehicles have shown potential to reduce both petroleum usage as well as harmful emissions. In this thesis, a newly developed series hybrid electric powertrain by a small start-up company is studied on a route between Florence, Kentucky and Cambridge, Ohio hubs to evaluate potential fuel savings due to hybridization.</p> <p> </p> <p>An experimental testing protocol to calculate fuel economy has been developed and the real-world fuel economy of this hybrid electric powertrain is calculated. A vehicle simulation model representing the experimental powertrain is created in Autonomie and this vehicle model is simulated on three distinct drive cycles obtained from experimental testing phase. These results are compared with a conventional class 8 truck to evaluate fuel savings. The simulation analysis indicates that fuel economy of hybrid is better on only one of the three drive cycles under consideration. Further, it is determined that the existing powertrain does not meet the gradeability criterion. To remedy this, a series electric hybrid powertrain with different compo-</p> <p>nent sizes is then modeled and simulated on the same drive cycles. The modified powertrain is found to result in fuel economy improvement on all three drive cycles considered while also meeting the gradeability requirement. The effect of drive cycle on fuel economy of a hybrid powertrain is also studied in this thesis.</p><br>

Thesis (M.S.)--West Virginia University, 2007. / Title from document title page. Document formatted into pages; contains xiii, 112 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 107-112).

Thesis (M.S.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 97-99). Also available on the World Wide Web.

Power-split hybrid-electric vehicles (HEVs) provide two power paths between the internal combustion (IC) engine and the driven wheels through gearing and electric machines (EMs) composing an electrically variable transmission (EVT). EVTs allow IC engine control such that rotational speed is independent of vehicle speed at all times. By breaking the rigid mechanical connection between the IC engine and the driven wheels, EVTs allow the IC engine to operate in the most efficient region of its characteristic brake specific fuel consumption (BSFC) map. If the most efficient IC engine operating point produces more power than is requested by the driver, the excess IC engine power can be stored in the energy storage system (ESS) and used later. Conversely, if the most efficient IC engine operating point does not meet the power request of the driver, the ESS delivers the difference to the wheels through the EMs. Therefore with an intelligent supervisory control strategy, power-split architectures can advantageously combine traditional series and parallel power paths. In the first part of this work, two different power-split HEV powertrains are compared using a two-term cost function and steady-state backward-looking simulation (BLS). BLS is used to find battery power management strategies that result in minimized fuel consumption over a user-defined drive-cycle. The supervisory control strategy design approach amounts to an exhaustive search over all kinematically admissible input operating points, leading to a minimized instantaneous cost function. While the approach provides a valuable comparison of two architectures, non-ideal engine speed fluctuations result. Therefore, in the second part of the work, two approaches for designing control strategies with refined IC engine speed transitions are investigated using high-fidelity forward-looking simulation (FLS). These two approaches include: i) smoothing the two-term cost function optimization results, and ii) introducing a three-term cost function. It is found that both achieve operable engine speed transitions, and result in fuel economy (FE) estimates which compare well to previous BLS results. It is further found that the three-term cost function finds more efficient operating points than the smoothed two-term cost function approach. From the investigations carried out in parts one and two of this work, a two-phase control strategy development process is suggested where control strategies are generated using efficient steady-state BLS models, and then further tested and verified in high-fidelity FLS models. In conclusion, the FLS results justify the efficacy of the two-phased process, suggesting rapid and effective development of implementable power-split HEV supervisory control strategies.