Vehicle Modelling and Washout Filter Tuning for the Chalmers Vehicle Simulator

In this work three washout filters [1][3][4][5] originally developed for NASA airplane simulators are considered for the Chalmers Vehicle Simulator (CVS). The Classical and Optimal washout filters are implemented for real-time use, while the Adaptive washout filter is tested only by off-line simulation. The washout filter parameters are tuned by optimization algorithms. A Genetic Algorithm is used to find a starting point in the parameter space for the ensuing local optimization where a Riccati Algebraic Solver and the Steepest Descent Method are used. The optimization is performed on a computer simulation model of the CVS, taking standard driving manoeuvres as inputs. The obtained Classical and Optimal washout filters were tested in real time on the CVS with several “test drivers”. During all the tests, the platform never hit the physical boundaries, but moved very close to them, thus using most of the actuator’s movement. According to the test drivers’ impressions, the washout filters produced realistic driving experience

Predictive cruise control with autonomous overtaking

This paper studies the problem of optimally controlling an autonomous vehicle, to safely overtake a slow-moving leading vehicle. The problem is formulated to minimize deviation from a reference velocity and position trajectory, while keeping the vehicle on the road and avoiding collision with surrounding vehicles. We show that the optimization problem can be formulated as a convex program, by providing convex modeling steps that include change of reference frame, change of variables, sampling in relative longitudinal distance, convex relaxation and linearization. A case study is provided showing overtaking scenarios in proximity of an oncoming vehicle, and a vehicle driving on an adjacent lane and in the same direction as the leading vehicle

Novel Results on Output-Feedback LQR Design

This paper provides novel developments in output-feedback stabilization for linear time-invariant systems within the linear quadratic regulator (LQR) framework. First, we derive the necessary and sufficient conditions for output-feedback stabilizability in connection with the LQR framework. Then, we propose a novel iterative Newton\u27s method for output-feedback LQR design and a computationally efficient modified approach that requires solving only a Lyapunov equation\ua0at each iteration step. We show that the proposed modified approach guarantees convergence from a stabilizing state-feedback to a stabilizing output-feedback solution and succeeds in solving high dimensional problems where other, state-of-the-art methods, fail. Finally, numerical examples illustrate the effectiveness of the proposed methods

A computationally efficient approach for robust gain-scheduled output-feedback LQR design for large-scale systems

This paper proposes a novel and simple control design procedure for sub-optimal robust gain-scheduled (GS) output-feedback linear quadratic regulator (LQR) design for large-scale uncertain linear parameter-varying (LPV) systems. First, we introduce a simple and practical technique to convexify the controller design problem in the scheduled parameters. Then, we propose a computationally efficient iterative Newton-based approach for gain-scheduled output-feedback LQR design. Next, we propose a simple modification to the proposed algorithm to design robust GS controllers. Finally, the proposed algorithm is applied for air management and fueling strategy of diesel engines, where the designed robust GS proportional-integral-derivative (PID) controller is validated on a benchmark model using real-world road profile data

A methodology and a tool for evaluating hybrid electric powertrain configurations

This paper describes a methodology for automatic optimisation of hybrid electric powertrains. This methodology is developed and implemented in a tool, CAPSimO, and the paper is written in the form of describing the tool. Given the user inputs, which are dynamic vehicle model, driving cycle and optimisation criterion, the tool first produces a simplified powertrain model in a form of static maps, before dynamic programming is used to find an optimal power split which minimises the chosen criterion. The tool does not require that the vehicle model is transparent, which makes it possible to work on models hidden for intellectual property reasons. The paper presents two examples of powertrain evaluation, in terms of fuel consumption, for a parallel and a parallel-series powertrain

Look-ahead vehicle energy management with traffic predictions

This paper presents a vehicle energy management system that uses information about upcoming topography and speed limits along the planned route to schedule the speed and the gear shifts of a heavy diesel truck. The proposed control scheme divides the predictive control problem into two layers that operate with different update frequencies and prediction horizons. The focus in the paper is on the top layer that plans the vehicle speed in a convex optimization problem leaving the gear decision to be optimized in the lower layer in a dynamic program. The paper describes how predictive information of the movement pattern of surrounding vehicles can be incorporated into the convex optimization of the vehicle speed by using a moving time window constraint

Engine on/off control for dimensioning hybrid electric powertrains via convex optimization

This paper presents a novel heuristic method for optimal control of mixed-integer problems that, for given feasible values of the integer variables, are convex in the rest of the variables. The method is based on Pontryagin's maximum principle and allows the problem to be solved using convex optimization techniques. The advantage of this approach is the short computation time for obtaining a solution near the global optimum, which may otherwise need very long computation time when solved by algorithms guaranteeing global optimum, such as dynamic programming (DP). In this paper, the method is applied to the problem of battery dimensioning and power split control of a plug-in hybrid electric vehicle (PHEV), where the only integer variable is the engine on/off control, but the method can be extended to problems with more integer variables. The studied vehicle is a city bus, which is driven along a perfectly known bus line with a fixed charging infrastructure. The bus can charge either at standstill or while driving along a tramline (slide in). The problem is approached in two different scenarios: First, only the optimal power split control is obtained for several fixed battery sizes; and second, both battery size and power split control are optimized simultaneously. Optimizations are performed over four different bus lines and two different battery types, giving solutions that are very close to the global optimum obtained by DP

Interaction-Aware Trajectory Prediction and Planning in Dense Highway Traffic using Distributed Model Predictive Control

In this paper we treat optimal trajectory planning for an autonomous vehicle (AV) operating in dense traffic, where vehicles closely interact with each other. To tackle this problem, we present a novel framework that couples trajectory prediction and planning in multi-agent environments, using distributed model predictive control. A demonstration of our framework is presented in simulation, employing a trajectory planner using non-linear model predictive control. We analyze performance and convergence of our framework, subject to different prediction errors. The results indicate that the obtained locally optimal solutions are improved, compared with decoupled prediction and planning

Joint Component Sizing and Energy Management for Fuel Cell Hybrid Electric Trucks

This paper proposes a cost-effective way to design and operate fuel cell hybrid electric trucks (FCHETs) where a chance-constrained optimization is formulated. The aim of the introduced problem is to minimize a summation of component cost and operational cost with consideration of fuel cell (FC)degradation and cycle life of energy buffer. We propose to decompose the problem into two sub-problems that are solved by sequential convex programming. The delivered power satisfies a cumulative distribution function of the wheel power demand, while the truck can still traverse driving cycles with a similar speed and travel time without delivering unnecessarily high power. This allows to downsize powertrain components, includingelectric machine, FC and energy buffer. A case study considering different energy buffer technologies, including supercapacitor (SC), lithium-ion battery (LiB), and lithium-ion capacitor (LiC) is investigated in a set of trucking applications, i.e. urban delivery, regional delivery, construction, and long-haul. Results show that the power rating of the electric machine is drastically reduced when the delivered power is satisfied in a probabilistic sense. Moreover, the configuration with LiB as the energy buffer has the lowest expense but the truck with LiC can carry more payload

Convex modeling of energy buffers in power control applications

This paper describes modeling steps for presenting energy buffers as convex models in power control applications. Except obtaining the optimal control, the paper also shows how convex optimization can be used to simultaneously size the energy buffer while optimally controlling a trajectory following system. The energy buffers are capacitors and batteries with quadratic power losses, while the resulting convex problem is a semidefinite program. The convex modeling steps are described through a problem of optimal buffer sizing and control of a hybrid electric vehicle. The studied vehicle is a city bus driven along a perfectly known bus line. The paper also shows modeling steps for alternative convex models where power losses and power limits of the energy buffer are approximated. The approximated models show significant decrease in computation time without visible impact on the optimal result