Driving style, road geometry, and traffic conditions have a significant impact on vehicles' fuel economy. In general, drivers are not aware of the optimal velocity profile for a given route. Indeed, the global optimal velocity trajectory depends on many factors, and its calculation requires intensive computations. In this paper, we discuss the optimization of the speed trajectory to minimize fuel consumption and communicate it to the driver. With this information the driver can adjust his/her speed profile to reduce the overall fuel consumption. We propose to perform the computation-intensive calculations on a distinct computing platform called the "cloud." In our approach, the driver sends the information of the intended travel destination to the cloud. In the cloud, the server generates a route, collects the associated traffic and geographical information, and solves the optimization problem by a spatial domain dynamic programming (DP) algorithm that utilizes accurate vehicle and fuel consumption models to determine the optimal speed trajectory along the route. Then, the server sends the speed trajectory to the vehicle where it is communicated to the driver. We tested the approach on a prototype vehicle equipped with a visual interface mounted on the dash of a test vehicle. The test results show 5%-15% improvement in fuel economy depending on the driver and route without a significant effect on the travel time. Although this paper implements the speed advisory system in a conventional vehicle, the solution is generic, and it is applicable to any kind of powertrain structure.

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Hybrid Electric Vehicles (HEVs) with path-forecasting belong to the class of fuelefficient vehicles, which use external sensory information and powertrains with multiple operating modes in order to increase fuel economy. Their main characteristic is that the decision to charge and discharge the battery is made in part by using a prediction of future road conditions. The increasing presence of GPS navigational systems in the standard feature sets of the modern vehicles suggests that path predictive methods applied to HEVs constitute one of the most promising directions towards the solution of serious problems of our era, such as the energy problem, the increasing cost of oil, and the greenhouse gas emissions. In the current project we are given a route and an HEV simulation model, and we aim to minimize the fuel consumption of the vehicle along the route. Towards this direction, we adopt a novel way of decomposing the route into a series of route segments connected to each other and linking the origin to the destination. For each route segment, the road grade, the segment length, and the nominal speed are available. Then, the main idea of our method is to prescribe those set-points of the state of charge of the battery for each road segment, that result in the most fuel efficient travel between the origin and the destination.

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This paper presents a safety-based route planner that exploits vehicle-to-cloud-to-vehicle (V2C2V) connectivity. Time and road risk index (RRI) are considered as metrics to be balanced based on user preference. To evaluate road segment risk, a road and accident database from the highway safety information system is mined with a hybrid neural network model to predict RRI. Real-time factors such as time of day, day of the week, and weather are included as correction factors to the static RRI prediction. With real-time RRI and expected travel time, route planning is formulated as a multiobjective network flow problem and further reduced to a mixed-integer programming problem. A V2C2V implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources. A real-world case study, route planning through the city of Columbus, Ohio, is presented. Several scenarios illustrate how the "best" route can be adjusted to favor time versus safety metrics.