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Safety evaluation is crucial for the mass deployment of highly automated vehicles (HAVs). This paper presents a procedure for conducting behavior competence testing for HAVs. First, we describe an efficient test case generation scheme for cut-in and unprotected left turn scenario. We then design the algorithm for synchronizing the primary other vehicle with the vehicle under test based on optimal control. Then, scoring criteria for quantifying HAV performances is presented. Finally, we implement the testing procedure in both simulations and real world. Accurate and repeatable testing is achieved, and quantitative evaluation results are acquired for a baseline HAV.

Lane keeping control needs to achieve smooth steering operation while ensuring safety-no lane departures and maintain small lateral displacement. In this paper, we solve this challenge by developing a preview lane keeping control supervised by a safeguard controller. The preview control utilizes both tracking errors and future lane curvatures to generate the optimal steering commands. The safeguard controller is then designed to guarantee bounded tracking errors. It supervises the preview control and intervenes if and only if the tracking error is approaching the safety boundary. Both algorithms have analytical control laws and thus require little on-line computations. The integrated system is compared with a model predictive control (MPC) design in terms of both tracking performance and computing efficiency. We implemented the proposed controls on a self-driving vehicle platform and tested on open roads and in the Mcity test facility.

This paper focuses on islanded microgrids and presents a method to allocate power among the distributed generation units (DGs) to minimize the distribution power loss. Motivated by the need for decentralized control strategies with minimal communication among grid components to support a robust and plug-and-play operation, a communication-free decentralized power control approach is developed for power loss minimization based on the extremum-seeking (ES) method. In this approach, the DGs implement ES simultaneously and separately to minimize their current outputs by controlling the active power. The total power loss is thus reduced without the need for any grid structure information or communication in the optimization process. This approach results in a non-cooperative game. The existence of a Nash equilibrium to this game and its stability are proved. The numerical simulations are conducted to demonstrate the performance of the proposed approach and show its ability to maintain low-power loss under different operating conditions in a plug-and-play manner.

Planetary gear sets (PGs) play a key role in the design of hybrid electric vehicles (HEVs) because they allow the realization of many unique powertrain designs using a limited number of components. By leveraging the capability of this mechanical device, an automated design process for PG-based HEV systems focusing on both fuel economy and performance is introduced in this paper. The design process consists of five major stages. In the first stage, all possible powertrain modes of an HEV design are automatically generated with a given set of powertrain components. In the second stage, all powertrain types that can be formed with a given set of components are mathematically identified, and each feasible mode is classified under one of these powertrain types. In the third stage, computationally efficient linear programming solvers suitable for vector operations are developed for each powertrain type to assess the gradeability, launch torque, overtaking torque, and acceleration time of each mode for all PG gear ratio combinations. In the fourth stage, the combination of modes that meets the performance requirements, and the number and location of clutches that make these mode transitions possible, are explored. As a result, each potent mode combination, the clutches necessary for the mode transition, and the auxiliary modes established as a result of all clutch state combinations constitute a design that meets the performance criteria. In the last stage, the fuel economy improvement potential of each competent design is evaluated. The results show that light-duty truck performance requirements can be met by many two-PG HEV designs without sacrificing fuel economy if the appropriate analysis and synthesis techniques for exploring the entire design space are developed.

Intelligent planning and accurate execution of connected automated vehicles (CAVs) enable not only improved traffic safety but also better fuel economy. This paper presents two longitudinal speed planning algorithms for fuel-saving driving on highways with varying road slopes. One is designed on the top of the model predictive control (MPC) and the other is called equivalent kinetic-energy and fuel conversion method. The MPC algorithm solves the optimal speed profile in a receding finite horizon with repeated optimization, which is numerically solved by the Legendre pseudospectral method. The latter is designed based on an instantaneous optimization, which considers vehicle kinetic energy an admissible power source, and then minimizes a weighted sum of fuel energy and kinetic energy. This strategy is capable of generating analytical rules to get the economical speed as well as the corresponding commands of the engine, transmission, and brake. The two algorithms are featured by near-global optimization and local optimization, respectively. Their performances in fuel economy and computational load are quantitatively explored and compared in order to distinguish the potential of real implementation in CAVs.

This paper presents an integrated design method for pedestrian avoidance by considering the interaction between trajectory planning and trajectory tracking. This method aims to reduce the need for control calibration by properly considering plant uncertainties and tire force limits at the design stage. Two phases of pedestrian avoidance-trajectory planning and trajectory tracking-are designed in an integrated manner. The available tire force is distributed to the feedforward part, which is used to generate the nominal trajectory in trajectory planning phase, and to the feedback part, which is used for trajectory tracking. The trajectory planning problem is solved not by searching through a continuous spectrum of steering/braking actions, but by examining a limited set of “motion primitives,” or motion templates that can be adopted in sequence to avoid the pedestrian. An emergency rapid random tree (RRT) methodology is proposed to quickly identify a feasible solution. Subsequently, in order to guarantee accuracy and provide safety margin in trajectory tracking with presence of model uncertainties and exogenous disturbance, a simplified LQR-based funnel algorithm is proposed. Simulation results provide insight into how pedestrian collisions can be avoided under given initial vehicle and pedestrian states.

Microgrids can deploy traditional and/or renewable power sources to support remote sites. Utilizing renewable power sources requires more complicated control strategies to achieve acceptable power quality and maintain grid stability. In this research, we assume that the grid stability problem is already solved. As a next step, we focus on how the power can be dispatched from multiple power sources for improved grid efficiency. Isolated microgrids frequently require reconfigurations because of the grid expansion or component failures. Therefore, the control strategies ideally should be implemented in a plug-and-play fashion. Moreover, these strategies ideally require no pre-knowledge of the grid structure, and as little communication with neighboring power sources as possible. The control objective is to minimize a cost function that can be adjusted to reflect the desire to minimize energy cost and/or losses. An algorithm is designed to satisfy a derived necessary condition of function optimality. Such conditions are obtained by formulating Lagrange functions. An equivalent grid model approximates the grid structure which was later confirmed to represent the grid behavior adequately. For decentralized operations, we execute the distributed control sequentially using a simple token communication protocol. The performance of the combined system identification-Lagrange function minimization algorithm is demonstrated through simulations.

This paper compares two optimal energy management methods for parallel hybrid electric vehicles using an Automatic Manual Transmission (AMT). A control-oriented model of the powertrain and vehicle dynamics is built first. The energy management is formulated as a typical optimal control problem to trade off the fuel consumption and gear shifting frequency under admissible constraints. The Dynamic Programming (DP) and Pontryagin’s Minimum Principle (PMP) are applied to obtain the optimal solutions. Tuning with the appropriate co-states, the PMP solution is found to be very close to that from DP. The solution for the gear shifting in PMP has an algebraic expression associated with the vehicular velocity and can be implemented more efficiently in the control algorithm. The computation time of PMP is significantly less than DP.

The optimal sizing and control of a hybrid tracked vehicle is presented and solved in this paper. A driving schedule obtained from field tests is used to represent typical tracked vehicle operations. Dynamics of the diesel engine-permanent magnetic AC synchronous generator set, the lithium-ion battery pack, and the power split between them are modeled and validated through experiments. Two coupled optimizations, one for the plant parameters, forming the outer optimization loop and one for the control strategy, forming the inner optimization loop, are used to achieve minimum fuel consumption under the selected driving schedule. The dynamic programming technique is applied to find the optimal controller in the inner loop while the component parameters are optimized iteratively in the outer loop. The results are analyzed, and the relationship between the key parameters is observed to keep the optimal sizing and control simultaneously.

Tracked vehicles have been widely used in construction, agriculture, and the military. Major problems facing the industry, however, are high emissions and fuel consumption. Hybrid electric tracked vehicles have thus become increasingly popular because of their improved fuel economy and reduced emissions. While the series hybrid system has drawn the most attention and has been applied in most cases, the low efficiency caused by energy conversion losses and large propulsion motors has limited its development. A novel multi-mode powertrain with two output shafts controlling each side of the track independently is first proposed. The powertrain is a three-planetary-gear power-split system with one engine, three motors, and an ultracapacitor pack. Compared with the existing technologies, the proposed powertrain can realize skid steering without an extra steering mechanism, and significantly improve the overall efficiency. To demonstrate the advantages of the novel powertrain, a topology-control-size integrated optimization problem is solved based on drivability, fuel economy, and cost. Final simulation results show that the optimized design with downsized components can produce about a 30% improvement in drivability and a 15% improvement in fuel economy compared with the commonly used series hybrid benchmark. Moreover, the optimized design is verified to be much more economical taking cumulative cost into account, which is very attractive for potential industrial applications in the future.

Hybrid powertrain technologies are successful in the passenger car market and have been actively developed in recent years. Optimal topology selection, component sizing, and controls are required for competitive hybrid vehicles, as multiple goals must be considered simultaneously: fuel efficiency, emissions, performance, and cost. Most of the previous studies explored these three design dimensions separately. In this paper, two novel frameworks combining these three design dimensions together are presented and compared. One approach is nested optimization which searches through the whole design space exhaustively. The second approach is called enhanced iterative optimization, which executes the topology optimization and component sizing alternately. A case study shows that the later method can converge to the global optimal design generated from the nested optimization, and is much more computationally efficient. In addition, we also address a known issue of optimal designs: their sensitivity to parameters, such as varying vehicle weight, which is a concern especially for the design of hybrid buses. Therefore, the iterative optimization process is applied to design a robust multi-mode hybrid electric bus under different loading scenarios as the final design challenge of this paper.