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Transportation Vehicles

University of California, Berkeley

Predictive Data-Driven Vehicle Dynamics and Powertrain Control: from ECU to the Cloud

The University of California at Berkeley (UC Berkeley) will lead a team that includes Sensys Networks and Hyundai America Technical Center to develop a novel control technology to reduce energy consumption of a plug-in hybrid electric vehicle by at least 20% without changing its drivability. Through connectivity with other vehicles and the roadway, the vehicle will access data such as signal phase and timing, traffic queue length and position, traffic volume and speed, and position and speed of nearby cars. The powertrain and vehicle dynamic controllers developed in this project will utilize this data to optimize how the plug-in hybrid test vehicles operate by adjusting parameters such as vehicle speed, electric motor torque, and battery charging power. The technology will be demonstrated with a fleet of vehicles in three applications: cooperative adaptive cruise control, speed harmonization with merging vehicles, and optimal approach/departure at intersections with traffic signals. The ability to work in real-time with a large number of factors and scenarios is enabled by computation conducted both onboard the vehicle and off-board using cloud-based computers. The team combines expertise in algorithm development and predictive controls from UC Berkeley with a leading technology for roadway sensing and vehicle to infrastructure (V2I) communication from Sensys Networks. Hyundai, a major global car manufacturer, will provide a state-of-the-art plug-in hybrid vehicle platform, extensive vehicle testing capability, and also a path to commercialization for the proposed controller technology into the high-volume light-duty vehicle market.

University of California, Riverside

An Innovative Vehicle-Powertrain Eco-Operation System for Efficient Plug-in Hybrid Electric Buses

The University of California, Riverside team will design, develop, and test an innovative vehicle-powertrain eco-operation system for natural-gas-fueled plug-in hybrid electric buses. This system will use emerging connected and automated vehicle applications like predictive approach and departure at traffic signals, efficient adaptive cruise, and optimized stopping and accelerating from stop signs and bus stops. Since stop-and-go operation wastes a large amount of energy, optimizing these maneuvers for an urban transit bus presents significant opportunities for improving energy efficiency. Using look-ahead information on traffic and road grade, the team will optimize the powertrain operation by managing combustion engine output, electric motor output and battery state of charge in this hybrid application.

University of Colorado, Boulder

Efficient Capacitive Wireless Power Transfer System for Electric Vehicles

The University of Colorado at Boulder proposes to develop a capacitive wireless power transfer (WPT) architecture to dynamically charge EVs. Dynamic charging poses serious technical challenges. Transmitters must be connected to the plates in the road while rectifiers and battery charging is integrated with the plates in the vehicle. While energy transfer through the air is efficient, the large distance between the embedded vehicle plates and the road results in a weaker pairing between the two. To effectively transfer kilowatts of power without exceeding safe voltages, the operating frequency of the resonant inverters has to be very high. Today's WPT systems operate with resonant magnetic fields focused with hefty ferrite cores and losses in these ferrites limit the frequency at which these systems can operate to less than 150 kHz. This project focuses on capacitive WPT with potentially higher efficiency than resonant inductive power transfer, while reducing size and cost. The team will develop a novel MHz frequency capacitive WPT system that safely operates within the industrial, scientific, and medical (ISM) radio spectrum. The team's WPT technology aims to improve EVs by reducing the need for expensive and bulky on-board batteries, enable unlimited driving range, and accelerate electric vehicle penetration. The project aims to design a 1-kW 12-cm air gap capacitive WPT, which targets >90% efficiency and 50 kW/m2 power transfer density, a power density improvement of 2 over current methods.

University of Delaware

High-Energy Permanent Magnets for Hybrid Vehicles and Alternative Energy

The University of Delaware is developing permanent magnets that contain less rare earth material and produce twice the energy of the strongest rare earth magnets currently available. The University of Delaware is creating these magnets by mixing existing permanent magnet materials with those that are more abundant, like iron. Both materials are first prepared in the form of nanoparticles via techniques ranging from wet chemistry to ball milling. After that, the nanoparticles must be assembled in a 3-D array and consolidated at low temperatures to form a magnet. With small size particles and good contact between these two materials, the best qualities of each allow for the development of exceptionally strong composite magnets.

University of Delaware

Simultaneous Optimization of Vehicle and Powertrain Operation Using Connectivity and Automation

The University of Delaware will develop and implement a control technology aimed at maximizing the energy efficiency of a 2016 Audi A3 plug-in hybrid vehicle by more than 20% without reducing the vehicle's drivability, performance, emissions, and safety. The technology will use connectivity between vehicles and infrastructure to co-optimize vehicle dynamic and powertrain controls. It will compute optimal routing for desired destinations while bypassing bottlenecks, accidents, special events, and other conditions that affect traffic flow. The vehicle will optimize acceleration and braking events in coordination with the hybrid powertrain controller such that energy efficiency is maintained, even in areas of congestion. The control technology will consist of a vehicle dynamic (VD) controller, a powertrain (PT) controller, and a supervisory controller. The supervisory controller will (1) oversee the VD and PT controllers, (2) communicate the internal and external data appropriately, (3) compute the optimal routing for any desired destination, (4) determine the regions where electric driving will have a major impact and derive a desired battery state-of-charge trajectory, and (5) create a description of the upcoming road segment from the connected data and communicate it to the VD controller. The VD controller will optimize the acceleration/deceleration and speed profile of the vehicle, and thus torque demand. The PT controller will compute the optimal nominal operation ("setpoints") for the engine, motor, battery, and transmission corresponding to the optimal solution of the VD controller. By considering the vehicle as part of a large system of many vehicles that are wirelessly connected to each other and to infrastructure, the project aims to significantly increase vehicle energy efficiency.

University of Michigan

Split Micro-Hybrid Boosting Enabling Highly Diluted Combustion

The University of Michigan team will develop a compact micro-hybrid configuration that pairs an Electrically Assisted Variable Speed (EAVS) supercharger with an exhaust expander Waste Energy Recovery (WER) system. Together, the EAVS and WER can nearly eliminate the slow air-path dynamics associated with turbocharge inertia and high exhaust gas recirculation (EGR). The EAVS system compresses engine intake air to increase engine power and allows the engine to have valuable "breathing time." This breathing time allows for a coordinated intake boosting and exhaust vacuum, so that the combustion timing and fueling is always optimal. Meanwhile, the WER system will capture exhaust energy, store it in a low-voltage battery together with energy from regenerative braking and later reuse it to assist the engine under transient acceleration loads, helping to further increase fuel efficiency. The team's innovation could increase fuel economy in advanced vehicles by 20%.

University of Michigan

Integrated Power and Thermal Management for Connected and Automated Vehicles (iPTM-CAV) through Real-Time Adaptation and Optimization

The University of Michigan will develop an integrated power and thermal management system for connected and automated vehicles (iPTM-CAV), with the goal of achieving a 20% improvement in energy consumption. This increase will arise from predicting the traffic environment with transportation analytics, optimizing vehicle speed and load profiles with vehicle-to-everything (V2X) communication, coordinating power and thermal control systems with intelligent algorithms, and optimizing powertrain operation in real time. The additional information made available by V2X and new sensors provides a look-ahead preview of traffic conditions unavailable in vehicles without connectivity. This information can be used to enable intelligent decision-making at multiple levels in powertrain and vehicle control. Key to this project is the team's approach for managing vehicle heat loads and thermal management. Thermal loads have to be properly managed, as they affect multiple vehicle attributes including energy consumption, emissions, safety, passenger comfort, etc. Compared to power delivery, thermal loads cannot be served instantaneously - they take more time to respond to changes, making their prediction much more important. The team's proposed technology includes four solutions: managing and optimizing propulsive power and auxiliary thermal load, predictive thermal management of connected and automated vehicles, optimizing powertrain and exhaust aftertreatment systems by anticipating future conditions, and integrating powertrain and vehicle thermal management systems. The proposed strategies will be applicable for a range of vehicles powered by internal combustion engines, hybrid-electric, plug-in hybrid-electric, and all-electric powertrains.

University of Minnesota

Cloud Connected Delivery Vehicles: Boosting Fuel Economy using Physics-Aware Spatiotemporal Data Analytics and Realtime Powertrain Control 

The University of Minnesota will lead a team to develop technology to improve the fuel efficiency of delivery vehicles through real-time vehicle dynamic and powertrain control optimization using two-way vehicle-to-cloud (V2C) connectivity. The effort will lead to greater than 20% fuel economy improvement of a baseline 2016 E-GEN series hybrid delivery vehicle operating as part of the United Parcel Service (UPS) fleet. Large delivery vehicle fleet operators such as UPS currently use analytics to assign routes in such a way to minimize fuel consumption. Algorithms mine historical data collected from vehicles to determine routes before a driver leaves a distribution center. UPS has also invested in E-GEN series electric-powertrain vehicles that allow pure electric driving for extended periods of time and use a small range-extending gasoline engine-generator to charge the battery, allowing routes longer than 550 miles. However, the current UPS routing algorithms do not interact with the vehicle directly to improve the fuel economy in real-time. The University of Minnesota's project will integrate the E-GEN vehicles with real-time powertrain optimization and two-way V2C connectivity. The vehicle's powertrain controller will be pre-programmed at the beginning of a route to optimize efficiency using historical data and known parameters like terrain, weather, and traffic. Powertrain calibration will be optimized and downloaded to the vehicle using V2C connectivity in real-time during a delivery route, compensating for parameter changes or unpredicted driver behavior. The team's technology may also be commercialized far quicker because UPS, in particular, already uses E-GEN vehicles. Large delivery fleet operators, more broadly, are also heavily invested in data collection for reducing fuel consumption and actively track their vehicles, both factors that could potentially accelerate deployment.

Vorbeck Materials Corp.

Energy-Efficient Hybrid Medium- and Heavy-Duty Vehicle Power Systems

Vorbeck is developing a low-cost, fast-charging storage battery for hybrid vehicles. The battery cells are based on lithium-sulfur (Li-S) chemistries, which have a greater energy density compared to today's Li-Ion batteries. Vorbeck's approach involves developing a Li-S battery with radically different design for both cathode and anode. The technology has the potential to capture more energy, increasing the efficiency of hybrid vehicles by up to 20% while reducing cost and greenhouse gas emissions.

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