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A plug-in hybrid electric vehicle (PHEV) design with design parameters electric motor size, engine size, battery capacity, and battery chemistry type, is optimized with minimum cost as a measure of merit. The PHEV is required to meet a fixed set of performance constraints consisting of 0-60 mph acceleration, 50-70 mph acceleration, 0-30 mph acceleration in all electric operation, top speed, grade ability, and all electric range. The optimization is carried out for values of all electric range of 10, 20, and 40 miles. The social and economic impacts of the optimum designs in terms of reduced gasoline consumption and carbon emissions reduction are calculated. Argonne National Laboratory's Powertrain Systems Analysis Toolkit is used to simulate the performance and fuel economy of the PHEV designs. The costs of different PHEV components and the present value of battery replacements over the vehicle's life are used to determine the design's drivetrain cost.

Battery modeling is of major concern for Hybrid Electric Vehicle (HEV) and Electric vehicle (EV) modeling. The major issue lies in characterizing the battery power output in relation to battery's State of Charge (SOC) in various application conditions. In particular, the challenge is associated with the difficulty that the characteristic parameters of the battery, i.e. the accurate data on the open circuit voltage and the internal resistance are hardly obtainable in practical conditions. In this paper, a battery capacity representation and a practical way of battery modeling is introduced for simulation model development based on the experimental data. A realistic way of battery SOC representation is generated from the battery output data. Empirical formulation is derived from the data to correlate the battery current, voltage output with the battery SOC.

The objective of this paper is to develop a generalized loss management model to account for the usage of thermodynamic work potential in vehicles of any type. The key to accomplishing this is creation of a differential representation for vehicle loss as a function of operating condition. This differential model is then integrated through time to obtain an analytical estimate for total usage (and loss) of work potential consumed by each loss mechanism present during vehicle operation. The end result of this analysis is a better understanding of how the work potential initially present in the fuel, batteries, etc. is partitioned amongst all losses relevant to the vehicle's operation. The loss partitioning estimated from this loss management model can be used in conjunction with cost accounting systems to gain a better understanding of underlying drivers on vehicle manufacturing and operating costs.

Using measurable physical input variables, an implementable control algorithm for parallel architecture plug-in and non-plug-in hybrid electric vehicle (PHEV and HEV) powertrains is presented. The control of the electric drive is based on an algebraic mapping of the accelerator pedal position, the battery state-of-charge (SOC), and the vehicle velocity into a motor controller input torque command. This mapping is developed using a sequential linearization control (SLC) methodology. The internal combustion engine (ICE) control uses a modified accelerator pedal to throttle plate angle using an adjustable gain parameter that, in turn, determines the sustained battery SOC. Searches over an admissible control space or the use of pre-defined look-up tables are thus avoided. Actual on-road results for a Ford Explorer with a through-the-road (TTR) hybrid powertrain using this control methodology are presented.

This paper presents a detailed design study and associated considerations supporting the development of high-performance plug-in hybrid electric vehicles (PHEVs). Due to increasingly strict governmental regulations and increased consumer demand, automotive manufacturers have been tasked with the reduction of fuel consumption and greenhouse gas (GHG) emissions. PHEV powertrains can provide a needed balance in terms of fuel economy and vehicle performance by exploiting regenerative braking, pure electric vehicle operation, engine load-point shifting, and power-enhancing hybrid traction modes. Thus, properly designed PHEV powertrains can reduce fuel consumption while increasing vehicle utility and performance.

Hybrid-electric gas turbine generators are considered a promising technology for more efficient and sustainable air transportation. The Ohio State University is leading the NASA University Leadership Initiative (ULI) Electric Propulsion: Challenges and Opportunities, focused on the design and demonstration of advanced components and systems to enable high-efficiency hybrid turboelectric powertrains in regional aircraft to be deployed in 2030. Within this large effort, the team is optimizing the design of the battery energy storage system (ESS) and, concurrently, developing a supervisory energy management strategy for the hybrid system to reduce fuel burn while mitigating the impact on the ESS life. In this paper, an energy-based model was developed to predict the performance of a battery-hybrid turboelectric distributed-propulsion (BHTeDP) regional jet.

The global electric and hybrid aircraft market utilizing lithium-ion Energy Storage Systems (ESS) as a means of propulsion, is experiencing a period of extraordinary growth. We are witnessing the development of some of the most cutting-edge technology, and with that, some of the most complex challenges that we as an industry have ever faced. The primary challenge, and the most critical cause of concern, is a phenomenon known as a “Thermal Runaway”, in which the lithium-ion cell enters an uncontrollable, self-heating state, that if not contained, can propagate into a catastrophic fire in the aircraft. A Thermal Runaway (TR) can be caused by internal defects, damage, and/or abuse caused by an exceedance of its operational specifications, and it is a chemical reaction that cannot be stopped once the cell has reached its trigger temperature.