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Over the last decade, a number of hybrid architectures have been proposed with the main goal of minimizing energy consumption of off-highway vehicles. One of the architecture subsets which has progressively gained attention is hydraulic hybrids for earth-moving equipment. Among these architectures, hydraulic hybrids with secondary-controlled drives have proven to be a reliable, implementable, and highly efficient alternative with the potential for up to 50% engine downsizing when applied to excavator truck-loading cycles. Multi-input multi-output (MIMO) robust linear control strategies have been developed by the authors' group with notable improvements on the control of the state of charge of the high pressure accumulator. Nonetheless, the challenge remains to improve the actuator position and velocity tracking.

Audio playbacks are mechanisms which read data from a storage medium and produce commands and signals which an audio system turns into music. Playbacks are constantly changed to meet market demands, requiring that the control software be updated quickly and efficiently. This paper reviews a 12 month project using the MATLAB/Simulink/Stateflow environment for model-based development, system simulation, autocode generation, and hardware-in-the-loop (HIL) verification for playbacks which read music CDs or MP3 disks. Our team began with a “clean slate” approach to playback architecture, and demonstrated working units running production-ready code. This modular, layered architecture enables rapid development and verification of new playback mechanisms, thereby reducing the time needed to evaluate playback mechanisms and integrate into a complete infotainment system.

The need for greater capacity in automotive transportation (in the midst of constrained resources) and the convergence of key technologies from multiple domains may eventually produce the emergence of a “swarm” concept of operations. The swarm, a collection of vehicles traveling at high speeds and in close proximity, will require management techniques to ensure safe, efficient, and reliable vehicle interactions. We propose a shared-autonomy approach in which the strengths of both human drivers and machines are employed in concert for this management. A fuzzy logic-based control implementation is combined with a genetic algorithm to select the shared-autonomy architecture and sensor capabilities that optimize swarm operations.

Electric machinery is typically based upon the interaction of magnetic fields and current to produce electromagnetic force or torque. However, force and torque can also be produced through the use of electric fields. The purpose of this investigation is to briefly analyze the use of a switched capacitance electric field based machine to see if it may have aerospace applications for use as either propulsion motor for unmanned aerospace vehicle (UAV) or lightweight flywheel applications for aerospace applications. It is shown that although its use as a hub propulsion motor is not feasible, it may be a candidate for use in a power flywheel energy storage system.

With the need for improvement in the fuel economy along with reduction in emissions due to stringent regulations, powertrain hybridization has become the focal point of research for the automotive sector. Hydraulic hybrids have progressively gained acceptance due to their high power density and low component costs relative to their electric counterpart and many different architectures have been proposed and implemented on both on and off-highway applications. The most commonly used architecture is the series hybrid which offers great flexibility for implementation of power management strategies. But the direct connection of the high pressure accumulator to the system often results in operation of the hydraulic units in high pressure and low displacement mode. However, in this operating mode the hydraulic units are highly inefficient. Also, the accumulator renders the system highly compliant and makes the response of the transmission sluggish.

In order to improve efficiency and increase the operation of electric vehicles, assistive energy regeneration systems can be used. A hydraulic energy recovery system is modeled to be used as a regenerative system for supplementing energy storage for a pure electric articulated passenger bus. In this study a pump/motor machine is modeled to transform kinetic energy into hydraulic energy during braking, to move the hydraulic fluid from the low pressure reservoir to the hydraulic accumulator. The simulation of the proposed system was used to estimate battery savings. It was found that on average, approximately 39% of the battery charge can be saved when using a real bus driving cycle.

An increase in the number of vehicles per capita coupled with stricter emission regulations have made the development of newer and better hybrid vehicle architectures indispensable. Although electric hybrids have more visibility and are now commercially available, hydraulic hybrids, with their higher power densities and cheaper components, have been rigorously explored as the alternative. Several architectures have been proposed and implemented for both on and off highway applications. The most commonly used architecture is the series hybrid, which requires an energy conversion from the primary source (engine) to the secondary domain. From he re, the power flows either into the secondary source (high-pressure accumulator) or to the wheels depending upon the state of charge of the accumulator. A mode-switching hydraulic hybrid, which is a combination of a hydrostatic transmission and a series hybrid, was recently developed in the author’s research group.

The Purdue University EcoMakers team has completed its first year of the EcoCAR 2 Competition, in which the team has designed a Parallel-Through-the-Road Plug-in Hybrid Electric Vehicle that meets the performance requirements of a mid-size sedan for the US market, maintaining capability, utility and consumer satisfaction while minimizing emissions, energy consumption and petroleum use. The team is utilizing a 1.7L 14 CI engine utilizing B20 (20% biodiesel, 80% diesel), a 16.2 kW-hr A123 battery pack, and a Magna E-Drive motor to power the front and rear wheels. This will allow the vehicle to have a charge-depleting range of 75 miles. The first year was focused on the simulation of the vehicle, in which the team completed the controls, packaging and integration, and electrical plans for the vehicle to be used and implemented in years two and three of the competition.

A parallel-through-the-road (PTTR) plug-in hybrid electric vehicle is being created by modifying a 2013 Chevrolet Malibu. This is being accomplished by replacing the stock 2.4L gasoline engine which powers the front wheels of the vehicle with a 1.7L diesel engine and by placing a high voltage electric motor in the rear of the vehicle to power the rear wheels. In order to meet the high voltage needs of the vehicle created by the PTTR hybrid architecture, an energy storage system (ESS) will need to be created. This paper explains considerations, such as location, structure integrity, and cooling, which are needed in order to properly design an ESS.

EcoCAR 2: Plugging In to the Future (EcoCAR) is North America's premier collegiate automotive engineering competition, challenging students with systems-level advanced powertrain design and integration. The three-year Advanced Vehicle Technology Competition (AVTC) series is organized by Argonne National Laboratory, headline sponsored by the U. S. Department of Energy (DOE) and General Motors (GM), and sponsored by more than 28 industry and government leaders. Fifteen university teams from across North America are challenged to reduce the environmental impact of a 2013 Chevrolet Malibu by redesigning the vehicle powertrain without compromising performance, safety, or consumer acceptability. During the three-year program, EcoCAR teams follow a real-world Vehicle Development Process (VDP) modeled after GM's own VDP. The VDP serves as a roadmap for the engineering process of designing, building and refining advanced technology vehicles.

In the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. A LIB is composed of several unit cells. Therefore, one of the most important factors that determine the performance of a LIB are the characteristics of the unit cell. The design of LIB cells is a challenging problem since it involves the evaluation of expensive black-box functions. These functions lack a closed-form expression and require long-running time simulations or expensive physical experiments for their evaluation. Recently, Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. Bayesian optimization has two main components: a probabilistic surrogate model of the black-box function and an acquisition function that guides the optimization.

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source.

The design of better active materials for lithium-ion batteries (LIBs) is crucial to satisfy the increasing demand of high performance batteries for portable electronics and electric vehicles. Currently, the development of new active materials is driven by physical experimentation and the designer’s intuition and expertise. During the development process, the designer interprets the experimental data to decide the next composition of the active material to be tested. After several trial-and-error iterations of data analysis and testing, promising active materials are discovered but after long development times (months or even years) and the evaluation of a large number of experiments. Bayesian global optimization (BGO) is an appealing alternative for the design of active materials for LIBs. BGO is a gradient-free optimization methodology to solve design problems that involve expensive black-box functions. An example of a black-box function is the prediction of the cycle life of LIBs.