John Ottavio Michelini

Optimally controlling Hybrid Electric Vehicles using path forecasting

The paper examines path-dependent control of Hybrid Electric Vehicles (HEVs). In this approach we seek to improve HEV fuel economy by optimizing charging and discharging of the vehicle battery depending on the forecasted vehicle route. The route is decomposed into a series connection of route segments with (partially) known properties. The dynamic programming is used as a tool to quantify the benefits offered by route information availability.

Cloud aided semi-active suspension control

This paper considers the problem of vehicle suspension control from the perspective of a Vehicle-to-Cloud-to-Vehicle (V2C2V) distributed implementation. A simplified variant of the problem is examined based on the linear quarter-car model of semi-active suspension dynamics. Road disturbance is modeled as a combination of a known road profile, an unmeasured stochastic road profile and potholes. Suspension response when the vehicle hits the pothole is modeled as an impulsive change in wheel velocity with magnitude linked to physical characteristics of the pothole and of the vehicle. The problem of selecting the optimal damping mode from a finite set of damping modes is considered, based on road profile data. The information flow and V2C2V implementation are defined based on partitioning the computations and data between the vehicle and the cloud. A simulation example is presented.

Stochastic dynamic programming control policies for fuel efficient in-traffic driving

This paper demonstrates a methodology, based on stochastic dynamic programming, for developing a control policy that prescribes vehicle speed to minimize on average a weighted sum of fuel consumption and travel time, while travelling along the same route or a set of routes in a given geographic area. Given the current road grade, traffic speed and vehicle speed, the control policy prescribes an offset in vehicle speed relative to current traffic speed, which when added to the predicted value of traffic speed, gives a vehicle speed set point for an adaptive cruise control system. It is shown that transition probability matrices necessary to generate the control policy can be constructed from gathered data. A virtual testing environment based on CarSim is used for simulations that can effectively handle vehicle following and adaptive cruise control scenarios. Comparative fuel savings are shown to depend on time of travel (off-peak hours or rush hour) and traffic assumptions.

Modeling of vehicle driving conditions using transition probability models

This paper considers modeling of vehicle driving conditions using transition probability models (TPMs) for applications of dynamic optimization. The properties of transition probabilities for vehicle speed, vehicle acceleration, and road grade are discussed based on the analysis and experimental vehicle data. The KL-divergence is shown to provide an effective metric that can differentiate similar driving conditions from dissimilar ones.

Path dependent receding horizon control policies for Hybrid Electric Vehicles

Future Hybrid Electric Vehicles (HEVs) may use path-dependent operating policies to improve fuel economy. In our previous work, we developed a dynamic programming (DP) algorithm for prescribing the battery State of Charge (SoC) set-point, which in combination with a novel approach of route decomposition, has been shown to reduce fuel consumption over selected routes. In this paper, we propose and illustrate a receding horizon control (RHC) strategy for the on-board optimization of the fuel consumption. As compared to the DP approach, the computational requirements of the RHC strategy are lower. In addition, the RHC strategy is capable of correcting for differences between characteristics of a predicted route and a route actually traveled. Our numerical results indicate that the fuel economy potential of the RHC solution can approach that of the DP solution.

Road Risk Modeling and Cloud-Aided Safety-Based Route Planning

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.

Cloud aided safety-based route planning.

This paper proposes a novel multi-objective route planning approach within the framework of a Vehicle-to-Cloud-to-Vehicle (V2C2V) architecture. Time and road risk index (RRI) are both considered as metrics. To evaluate road segment risk, an accident database from the Highway Safety Information System (HSIS) is processed to build a comprehensive road risk assessment model. Route planning is formulated as a multi-objective network flow problem and further reduced to a Mixed Integer Programming (MIP) problem. A real-world case study, route planning through the city of Columbus, Ohio, is presented. The Vehicle-to-Cloud-to-Vehicle (V2C2V) based implementation of our safety-based route planning approach is proposed to facilitate access to real-time information and computing resources.

Analytical Solution to the Minimum Energy Consumption Based Velocity Profile Optimization Problem With Variable Road Grade

Abstract The importance of eco-driving in reducing cumulative fuel consumption of on road vehicles is a well known issue. However, so far a generic algorithm that can globally solve the non-linear optimization problem and still implementable in to the current state of computing on board units is not present. In this study, we examine one aspect of the problem by incorporating the effects of road grade to the optimization problem and generate an optimal velocity trajectory for a given road grade profile. We developed simple yet accurate vehicle and fuel consumption models and employed the models as the objective function and state trajectory constraint of the optimization problem. The necessary conditions derived by employing the calculus of variation theory require to separately solve the differential equations with a set of interior point constraints for each road grade interval. As the control became linear to the Hamiltonian function we defined a set of singular arcs and derived the state and optimal control input trajectories along the arcs. We have tested the analytical solution in two example problems and compared the results with a dynamic programming (DP) solution and constant speed cruise operation. The results have shown that the analytical and DP solutions generate very close velocity trajectories which are around 8 – 10% more efficient than the constant cruise speed control case for the given examples. Moreover the calculation time of the analytical solution is significantly shorter than the DP solution rendering it possible to real-time on board implementations.

Analytical and numerical solutions for energy minimization of road vehicles with the existence of multiple traffic lights

In this paper, we seek an optimal speed trajectory to minimize fuel consumption of a conventional vehicle on a route consisting of multiple traffic lights. We propose to solve the problem in two stages. In the first stage a high level control algorithm determines the traffic light arrival times. In the second stage we calculate the optimal velocity profile by utilizing the vehicle models and traffic light arrival times. We present two solution methods for the second stage. The first method proposes a closed form (analytical) solution to the optimization problem using simplified vehicle longitudinal and fuel consumption models. In the second method we utilize non-linear vehicle dynamics and solve the problem numerically. The two methods solve an example optimization problem with the existence of several traffic lights. We have compared the investigated optimal trajectories and evaluated the performance of the solutions in terms of calculation time and consumed fuel amount. The results show that the closed form solution requires significantly shorter calculation time with less than 0.1 % degradation in fuel economy compared to the numerical method.

Stochastic Fuel Efficient Optimal Control of Vehicle Speed

Stochastic dynamic programming (SDP) is applied to generate control policies that adjust vehicle speed to improve average fuel economy without degrading, significantly, the average travel speed. The SDP policies take into account statistical patterns in traffic speed and road topography. Specific problems of fuel efficient in-traffic driving and fuel efficient lead vehicle following are considered, and it is shown how these problems can be treated within an SDP framework. Simulation results are summarized to quantify fuel economy improvements, and experimental results are reported for the fuel efficient lead vehicle following case. The properties of vehicle speed trajectories induced by SDP policies are examined.