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Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles

Recent rapid advances in driver assistance technologies and the deployment of vehicles with increased levels of connectivity and automation have created multiple opportunities to improve the efficiency of future vehicle fleets beyond in new ways. The projects that make up ARPA-E's NEXTCAR Program, short for "NEXT-Generation Energy Technologies for Connected and Automated On-Road Vehicles," are enabling technologies that use connectivity and automation to co-optimize vehicle dynamic controls and powertrain operation, thereby reducing energy consumption of the vehicle. Vehicle dynamic and powertrain control technologies, implemented on a single vehicle basis, across a cohort of cooperating vehicles, or across the entire vehicle fleet, could significantly improve individual vehicle and, ultimately, fleet energy efficiency.
For a detailed technical overview about this program, please click here.  

General Motors

InfoRich VD&PT Controls

General Motors will lead a team to develop "InfoRich" vehicle technologies that will combine advances in vehicle dynamic and powertrain control technologies with recent vehicle connectivity and automation technologies. The result will be a light duty gasoline vehicle that demonstrates greater than 20% fuel consumption reduction over current production vehicles while meeting all safety and exhaust emissions standards. On-board sensors and connected data will provide the vehicle with additional information such as the status of a traffic signal before a vehicle reaches an intersection, as well as traffic, weather, and accident information. This preview information enables the vehicle (and the driver) not only to react to current road conditions but also to plan for expected future conditions more efficiently. A proposed supervisory vehicle dynamic and powertrain controller will incorporate all the information available through connectivity and on-board sensors into an upper-level optimizer that determines the most fuel-efficient and safest vehicle operation. The upper-level optimizer sends brake, steering, speed, and torque requests to the two lower-level controllers: the vehicle dynamics controller (i.e. steering, acceleration and braking) and powertrain (i.e. engine, transmission) controller. The lower-level controllers, in turn, optimize their individual requests and send out commands to control the vehicle and powertrain. Overall energy efficiency increases by forecasting stopping events as early as possible, smoothing and reducing heavy acceleration, harmonizing speed, and optimizing the vehicle when approaching hills. The project combines General Motors' advanced vehicle/powertrain controls with Carnegie Mellon University's expertise in autonomous vehicles. Extensive real-world driving data available from the National Renewable Energy Laboratory's Transportation Secure Data Center and on-road tests will be used to validate improvements in fuel efficiency and assess real-world impacts.

Michigan Technological University

Connected and Automated Control for Vehicle Dynamics and Powertrain Operation on a Light-Duty Multi-Mode Hybrid Electric Vehicle

Michigan Technological University (MTU), in partnership with General Motors (GM), will develop, validate, and demonstrate a fleet of connected electric vehicles and a mobile cloud-connected computing center. The project will integrate advanced controls with connected and automated vehicle functions and enable: eco-routing, efficient approach and departure from traffic signals and cooperative driving between multiple vehicles, including speed harmonization. Use of the new vehicle dynamic and powertrain controls will allow a 20% reduction in energy consumption and a 6% increase in all-electric driving range through the first-ever implementation and connection of route planning, powertrain energy management, and model-predictive control algorithms. The selected vehicle for the fleet, the 2017 Chevrolet Volt, contains a unique powertrain architecture with multiple operating modes, including all-electric (EV) and hybrid-electric (HEV) modes, allowing the team to optimize numerous powertrain components. This project will use eight Chevrolet Volts in order to demonstrate the idea of platooning in a future automated highway system. In a platoon, vehicles follow closely together at a constant speed, thus reducing drag and lowering energy consumption and emissions. The MTU Mobile Lab (ML) will serve as a control center, vehicle-to-cloud communication hub, and mobile charging station for the fleet of Volts. The ML, a specially designed 18-wheeler, can travel with the fleet and enables real-time traffic simulation and eco-routing. The MTU team includes expertise in powertrain engineering, vehicle controls, algorithm design, and traffic simulation, while the GM team includes experts in the control and engineering of advanced electric powertrains who, if the project is successful, can facilitate the integration of the new control technology into future GM vehicles.

Ohio State University

Fuel Economy Optimization with Dynamic Skip Fire in a Connected Vehicle

The Ohio State University will develop and demonstrate a transformational powertrain control technology that uses vehicle connectivity and automated driving capabilities to enhance the energy consumption of a light duty passenger vehicle up-fitted with a mild hybrid system. At the core of the proposed powertrain control technology, is the use of a novel cylinder deactivation strategy called Dynamic Skip Fire which makes instantaneous decisions about which engine cylinders are fired or skipped thus significantly improving vehicle energy efficiency. Connected and automated vehicle technologies will allow route-based optimization of driving. Route terrain information including road slope, curvature, and speed limits will be used to calculate an energy-optimal speed trajectory for the vehicle. Traffic condition information based on V2I communication (such as traffic lights) will be used to further optimize route selection and optimize the vehicle and powertrain control. The vehicle will interact with traffic lights using Dedicated Short Range Communications and will stop and start from intersections using an energy-optimal speed trajectory. The integrated radar/camera sensor and V2V connectivity will be used to determine the immediate traffic around the vehicle. Finally, machine learning algorithms will be used to make intelligent powertrain and vehicle optimization decisions in continuously changing and uncertain environments.

Pennsylvania State University

Maximizing Vehicle Fuel Economy through the Real-Time, Collaborative, and Predictive Co-Optimization of Routing, Speed, and Powertrain Control

Pennsylvania State University (Penn State) will develop a predictive control system that will use vehicle connectivity to reduce fuel consumption for a heavy duty diesel vehicle by at least 20% without compromising emissions, drivability, mobility, or safety. The technology will work to achieve four individual and complementary goals that co-optimize vehicle dynamic and powertrain control. First, it will exploit connected communication to anticipate traffic/congestion patterns on different roads, traffic light timing, and the speed trajectories of surrounding vehicles. Second, the system will coordinate with surrounding vehicles to achieve platooning on the highway, coordinated departures/arrivals at intersections, and consistency in the speed trajectories both within vehicle platoons and among neighboring vehicles that are not in a platoon. Platooning will allow vehicles to collectively reduce their aerodynamic losses, thereby reducing their fuel consumption. Coordinating vehicle departures and arrivals at intersections will minimize energy loss due to braking, idling, and inefficient departures. As its third goal, the technology will optimize vehicle dynamic control decisions such as the choice of route, the trajectory of vehicle speed versus time in a given road segment, and the choice of whether the vehicle is in an acceleration, deceleration, or coasting state at different points in time. Optimal routing will reduce fuel consumption by avoiding the fuel penalties associated with congestion and/or hilly terrains as much as possible. Finally, the technology will also optimize powertrain control decisions to eliminate unnecessary engine idling. Software for each of the goals will constitute a standalone product that can be commercialized independently of the others, but together, they will operate in an integrated manner to achieve co-optimized and coordinated vehicle control. If successful, this will result in vehicles that operate in a predictive manner, taking into account all the available data and information to produce the best outcome for vehicle fuel consumption, drivability, mobility, emissions, and safety.

Purdue University

High-Efficiency Control System for Connected and Automated Class 8 Trucks 

Purdue University will develop an integrated, connected vehicle control system for diesel-powered Class 8 trucks. Improvements from this system are expected to achieve 20% fuel consumption reduction relative to a 2016 baseline Peterbilt Class 8 truck. Class 8 trucks are large (over 33,000 lbs) vehicles such as trucks and tractor-trailer combinations like 18-wheelers. While these large trucks represent only 4% of all on-road vehicles in the U.S., they are responsible for almost 22% of global on-road fuel consumption. The Purdue team's work is based on a system-of-systems approach that integrates hardware and software components of the powertrain, vehicle dynamic control systems, and vehicle-to-everything (V2X) communication, supported by cloud computing. Communication between vehicles relies on short range radio, while cloud communications will operate over the LTE cellular network. This approach will provide the data needed to optimize single vehicle or two vehicles closely following each other in a platooning formation - reducing the platoon's overall energy consumption using technologies such as predictive cruise control and coordinated gear shifting. The proposed technology can also be applied to lighter class of trucks as the same performance shortcomings for Class 8 truck engines and transmissions also exist in lighter vehicle classes.

Southwest Research Institute

Model Predictive Control for Energy-Efficient Maneuvering of Connected Autonomous Vehicles

Southwest Research Institute (SwRI) will develop control strategies and technology to improve the energy efficiency of a 2017 Toyota Prius Prime plug-in hybrid electric vehicle through energy-conscious path planning and powertrain control. The team will modify the vehicle to take advantage of connected, autonomous vehicle information streams and develop systems that co-optimize the control of vehicle speed and engine power to minimize energy consumption, maintain safety, and deliver expected performance. Modern automobiles are designed to provide the maximum possible performance to the driver in terms of response time and acceleration. Because of this, manufacturers design engines for all possible scenarios a driver may encounter - often conflicting with efficiency needs. The SwRI team will approach this problem by augmenting the vehicle with the necessary hardware for V2V and V2I connectivity along-with leveraging the production Dynamic Radar Cruise Control (DRCC) feature of the vehicle. GPS will work with cellular data to optimize planned driving routes. Eco-approach and departure will work with traffic signals at intersections to optimize vehicle braking and acceleration for improving energy efficiency. Plug-in hybrid electric vehicles are capable of charge-depleting, and charge-sustaining modes, or combination of these two modes depending on how much the vehicle uses the electric battery or the internal combustion engine. The team will develop control algorithms that will use the new information streams to optimize the battery state of charge for both overall trip efficiency and for driving power. Vehicle testing will occur in two phases. First, it will provide the driver with information about the next plug-in opportunity and suggested route and speed profiles. Next, the project will take advantage of DRCC to fully automate longitudinal control including regulating speed and ensuring safe operation by maintaining adequate spacing between vehicles.

University of California, Berkeley

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

The University of California, 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 (UC 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 Delaware

Simultaneous Optimization of Vehicle and Powertrain Operation Using Connectivity and Automation

The University of Delaware (UD) 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

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 (UMN) 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 UMN 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.
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