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Software usability is a quality attribute defined as ?the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specific context of use? (ISO 9241, 1998), usability is also referred to as ?quality in use? (ISO 14598, 1999). Presenter Anabell Beltran, Stoneridge Electronics North America

The compressive behavior of lithium-iron phosphate battery cells is investigated by conducting in-plane constrained compression tests and out-of-plane compression tests of representative volume element (RVE) specimens. The results for cell RVE specimens under in-plane constrained compression tests without pre-strains and with pre-strains in the out-of-plane direction indicate that the load carrying capacity is characterized by the buckling of cell specimens. As the pre-strain increases, the nominal compressive stress-strain curve becomes higher. The nominal stress-strain curves in the out-of-plane direction were also obtained and used to determine the elastic moduli for the elastic buckling analyses of the cell components in the cell RVE specimens with different pre-strains. Based on the elastic buckling analyses for a beam with different lateral constraints due to different pre-strains in the out-of-plane direction, the number of half waves and the buckling stresses were obtained.

Electrification and hybridization show great potential for improving fuel economy and reducing emission in heavy-duty vehicles. However, high battery cost is unavoidable due to the requirement for large batteries capable of providing high electric power for propulsion. The battery size and cost can be reduced with advanced battery control strategies ensuring safe and robust operation covering infrequent extreme conditions. In this paper, the impact of such a battery control strategy on battery sizing and fuel economy is investigated under various military and heavy-duty driving cycles. The control strategy uses estimated Li-ion concentration information in the electrodes to prevent battery over-charging and over-discharging under aggressive driving conditions. Excessive battery operation is moderated by adjusting allowable battery power limits through the feedback of electrode-averaged Li-ion concentration estimated by an extended Kalman filter (EKF).

This paper proposes a methodology to develop a nonlinear model predictive control (NMPC) of a dual-independent variable valve timing (di-VVT) engine using discretized nonlinear engine models. In multiple-input-multiple-output (MIMO) systems, model based control methodologies are critical for realizing the full potential of complex hardware. Fast and accurate control oriented models (COM) that capture combustion physics, actuator and system dynamics are prerequisites for developing NMPC. We propose a multi-scale simulation approach to generate the non-linear combustion model, where the high-fidelity engine cycle simulation is utilized to characterize effects of turbulence, air-to-fuel ratio, residual fraction, and nitrogen oxide (NOx) emissions. The input-to-output relations are subsequently captured with artificial neural networks (ANNs). Manifold and actuator dynamics are discretized to reduce computation efforts.

Rechargeable energy storage systems with Lithium-ion pouch cells are subject to various ambient temperature conditions and go through thousands of charge-discharge cycles during the life time of operation. The cells may change their thickness with internal heat generation, cycling and any other mechanisms. The stacked prismatic cells thus experience face pressure and this could impact the pack electrical performance. The pack consists of stiff end plates keeping the pack in tact using bolts, cooling fins to maintain cell temperature and foam padding in between cells. The pack level thermal requirements limit the amount of temperature increase during normal operating conditions. Similarly, the structural requirements state that the stresses and the deflection in the end plates should be minimal. Uncertainties in cell, foam mechanical and thermal properties might add variation to the pack performance.

Energy security and climate change challenges provide a strong impetus for investigating Electric Vehicle (EV) concepts. EVs link two major infrastructures, the transportation and the electric power grid. This provides a chance to bring other sources of energy into transportation, displace petroleum and, with the right mix of power generation sources, reduce CO₂ emissions. The main obstacles for introducing a large numbers of EVs are cost, battery weight, and vehicle range. Battery health is also a factor, both directly and indirectly, by introducing limits on depth of discharge. This paper considers a low-cost path for extending the range of a small urban EV by integrating a parallel hydraulic system for harvesting and reusing braking energy. The idea behind the concept is to avoid replacement of lead-acid or small Li-Ion batteries with a very expensive Li-Ion pack, and instead use a low-cost hydraulic system to achieve comparable range improvements.

A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space.

New vehicle control algorithms are needed to meet future emissions and fuel economy mandates that are quite likely to require a measurement of ambient specific humidity (SH). Current practice is to obtain the SH by measurement of relative humidity (RH), temperature and barometric pressure with physical sensors, and then to estimate the SH using a fit equation. In this paper a novel approach is described: a system of neural networks trained to estimate the SH using data that already exists on the vehicle bus. The neural network system, which is referred to as a virtual SH sensor, incorporates information from the global navigation satellite system such as longitude, latitude, time and date, and from the vehicle climate control system such as temperature and barometric pressure, and outputs an estimate of SH. The conclusion of this preliminary study is that neural networks have the potential of being used as a virtual sensor for estimating ambient and intake manifold's SH.

As lightweight materials and advanced combustion engines are being used in both conventional and electrified vehicles with diverse fuels, it is necessary to evaluate the individual and combined impact of these technologies to reduce energy and greenhouse gas (GHG) emissions. This work uses life cycle assessment (LCA) to evaluate the total energy and GHG emissions for baseline and lightweight internal combustion vehicles (ICVs), hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) when they are operated with baseline and advanced gasoline and ethanol engines. Lightweight vehicle models are evaluated with primary body-in-white (BIW) mass reductions using aluminum and advanced/high strength steel (A/HSS) and secondary mass reductions that include powertrain re-sizing. Advanced engine/fuel strategies are included in the vehicle models with fuel economy maps developed from single cylinder engine models.

This book provides a broad and comprehensive look at hybrid powertrain technologies for commercial vehicles. It begins with the fundamentals of hybrid powertrain systems, government regulations, and driving cycles, then provides design guidelines and key components of hybrid powertrains for commercial vehicles. It was written for vehicle and component engineers and developers, researchers, students, policymakers, and business executives in the commercial vehicle and transportation industries to help them understand the fundamentals of hybrid powertrain technologies and market requirements for commercial vehicles. It is useful for anyone who designs or is interested in hybrid powertrains and their key components. The term ‘commercial vehicle’ applies to everything from light delivery vehicles to class 8 long haul trucks, buses, and coaches. These vehicles are used for a wide range of duties, including transporting goods or people and infrastructure service.