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ARPA-E issues RFI on energy efficiency optimization for connected and automated vehicles; vehicle dynamic and powertrain control

The Advanced Research Projects Agency - Energy (ARPA-E) has issued a request for information (DE-FOA-0001473) seeking input from researchers and developers in a broad range of disciplines including automotive vehicle control, powertrain control and transportation analytics regarding the development of advanced energy efficiency optimization technologies for future connected and automated vehicles (CAVs).

ARPA-E is interested in new and emerging full vehicle and powertrain control technologies that can reduce the energy use associated with automotive transportation, beyond those technologies currently expected to be deployed in future vehicles. The focus of the RFI is on the potential improvement in the energy efficiency of each individual vehicle in the automotive fleet through the improvement of powertrain control and vehicle dynamic control, by utilizing emerging technologies and strategies in sensing, communications, information, control and automation.

Such technologies may include, but are not limited to, advanced technologies and concepts relating to future full vehicle and powertrain control; individual vehicle and powertrain operation; control and optimization facilitated by connectivity; and the reduction of the fuel and/or energy consumed by future individual vehicles undergoing either human operation or automated operation.

Full vehicle control (or vehicle dynamic control) refers to the control of vehicle longitudinal and lateral dynamics through the operation of safety critical inputs such as accelerator (throttle), brake, transmission control and steering.

Arpae1
Arpae1
Logic flow diagrams for current vehicles (top) and L3/L4 automated vehicles (bottom). The scope of the ARPA-E is outlined by the dashed line in the bottom diagram. Source: ARPA-E. Click to enlarge.

ARPA-E envisions that the future total reduction in energy consumption of an individual vehicle will result from some combination of improved on-board powertrain controls (with improved real or virtual sensing and/or the use of V2X connectivity and real-time optimization); improved vehicle controls (using real or virtual sensing and/or the use of V2X); new inputs from external or fleet-level optimization; and ultimately the ability to operate in a driverless fashion in the case of automated vehicles (thereby removing the effect of the human driver from the vehicle and powertrain control systems).

Technologies of interest need to be able to meet the prevailing regulated vehicle emissions levels at the expected time of commercial deployment, and must ultimately result in equivalent (or acceptable) vehicle performance, utility, cost of ownership and operation, functionality, drivability, power and energy storage density, reliability and maintainability, without compromise.

From a control point of view, currently vehicles operate in isolation as a collection of single selfish entities, even in dense traffic. Developments in connectivity and automation will allow vehicles in the future to operate in a cooperative fashion with other surrounding vehicles. The effects of individual vehicle or powertrain control on the cumulative energy efficiency of a cohort of vehicles undertaking cooperative vehicle behavior have not yet been fully explored.

Technologies of interest to ARPA-E would ultimately require a demonstrable pathway through commercialization and widespread deployment to reduce the fuel and energy consumed in the current and/or future vehicle transportation fleet.

ARPA-E is emphatically not looking for more information on well-established methods of reducing individual vehicle fuel or energy consumption, such as hybridization; electrification; fuel shifting or alternative fuel substitution; weight reduction; aerodynamic drag reduction; waste energy recovery; and parasitic load reduction.

The emphasis of this RFI is on reducing the energy consumption of individual vehicles, and not on transportation system technologies such as transportation network optimization, ridesharing, or transportation mode shifting.

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