Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution

Abstract

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.

Simultaneous Optimization of Speed Profile and Allocation of Wireless Power Transfer System for Autonomous Driving Electric Vehicles

“…2. In this comparison, for conventional approach, we assume that EV speed profile with energy-saving operation is precalculated (36)- (40) , and then allocation of WPTSys is optimally designed for this energy-saving operation (8)- (13) . Figure 14 compares numerical calculations between the conventional approach and our proposed approach.…”

Look-Ahead Information Based Optimization Strategy for Hybrid Electric Vehicles

“…The question remains open on how to obtain the requisite information about the future driving conditions [1]. However, with the advancement of technology it has become possible to acquire future information of the trip through various means including cloud and GIS systems [2]. A look-ahead information strategy utilizes future information of the driving conditions.…”

“…The cloud-based approach is a proposed solution to perform global optimization for computationally intensive applications [2]. In this method, the information of the destination and the current driving conditions are sent to a server as an input for the optimization algorithm.…”

Iterative Dynamic Programming for Optimal Control Problem with Isoperimetric Constraint and Its Application to Optimal Eco-driving Control of Electric Vehicle

“…Since drawback of DP is its computation complexity, it can not be implemented on current vehicle computing units for real-time calculations. For solving this problem, the computation-intensive calculations are performed on a distinct computing platform, the so-called "cloud" (10) . Another approach for considering real-time aspects of DP is to use coarse resolutions, thus the solution is obtained with an acceptance of error (11) .…”