Real-time Predictive Energy Management of Hybrid Electric Heavy Vehicles by Sequential Programming

With the objective of reducing fuel consumption, this paper presents real-time predictive energy management of hybrid electric heavy vehicles. We propose an optimal control strategy that determines the power split between different vehicle power sources and brakes. Based on the model predictive control (MPC) and sequential programming, the optimal trajectories of the vehicle velocity and battery state of charge are found for upcoming horizons with a length of 5-20 km. Then, acceleration and brake pedal positions together with the battery usage are regulated to follow the requested speed and state of charge that is verified using a vehicle plant model. The main contribution of this paper is the development of a sequential linear program for predictive energy management that is faster and simpler than sequential quadratic programming in tested solvers and gives trajectories that are very close to the best trajectories found by nonlinear programming. The performance of the method is also compared to two different sequential quadratic programs

Efficient hybrid algorithms to solve mixed discrete-continuous optimization problems: A comparative study

Purpose: – In real world cases, it is common to encounter mixed discrete-continuous problems where some or all of the variables may take only discrete values. To solve these non-linear optimization problems, it is very time-consuming in use of finite element methods. The purpose of this paper is to study the efficiency of the proposed hybrid algorithms for the mixed discrete-continuous optimization, and compares it with the performance of Genetic Algorithms (GA). Design/methodology/approach: – In this paper, the enhanced multipoint approximation method (MAM) is utilized to reduce the original nonlinear optimization problem to a sequence of approximations. Then, the Sequential Quadratic Programming (SQP) technique is applied to find the continuous solution. Following that, the implementation of discrete capability into the MAM is developed to solve the mixed discrete-continuous optimization problems. Findings: – The efficiency and rate of convergence of the developed hybrid algorithms outperforming GA are examined by six detailed case studies in the ten-bar planar truss problem and the superiority of the Hooke-Jeeves assisted MAM algorithm over the other two hybrid algorithms and GAs is concluded. Originality/value: – The authors propose three efficient hybrid algorithms: the rounding-off, the coordinate search, and the Hooke-Jeeves search assisted MAMs, to solve nonlinear mixed discrete-continuous optimization problems. Implementations include the development of new procedures for sampling discrete points, the modification of the trust region adaptation strategy, and strategies for solving mix optimization problems. To improve the efficiency and effectiveness of metamodel construction, regressors φ defined in this paper can have the form in common with the empirical formulation of the problems in many engineering subjects

Implementation of Discrete Capability into the enhanced Multipoint Approximation Method for solving mixed integer-continuous optimization problems

Multipoint approximation method (MAM) focuses on the development of metamodels for the objective and constraint functions in solving a mid-range optimization problem within a trust region. To develop an optimization technique applicable to mixed integer-continuous design optimization problems in which the objective and constraint functions are computationally expensive and could be impossible to evaluate at some combinations of design variables, a simple and efficient algorithm, coordinate search, is implemented in the MAM. This discrete optimization capability is examined by the well established benchmark problem and its effectiveness is also evaluated as the discreteness interval for discrete design variables is increased from 0.2 to 1. Furthermore, an application to the optimization of a lattice composite fuselage structure where one of design variables (number of helical ribs) is integer is also presented to demonstrate the efficiency of this capability

Intermodal Transfer Coordination in Logistic Networks

Increasing awareness that globalization and information technology affect the patterns of transport and logistic activities has increased interest in the integration of intermodal transport resources. There are many significant advantages provided by integration of multiple transport schedules, such as: (1) Eliminating direct routes connecting all origin-destinations pairs and concentrating cargos on major routes; (2) improving the utilization of existing transportation infrastructure; (3) reducing the requirements for warehouses and storage areas due to poor connections, and (4) reducing other impacts including traffic congestion, fuel consumption and emissions. This dissertation examines a series of optimization problems for transfer coordination in intermodal and intra-modal logistic networks. The first optimization model is developed for coordinating vehicle schedules and cargo transfers at freight terminals, in order to improve system operational efficiency. A mixed integer nonlinear programming problem (MINLP) within the studied multi-mode, multi-hub, and multi-commodity network is formulated and solved by using sequential quadratic programming (SQP), genetic algorithms (GA) and a hybrid GA-SQP heuristic algorithm. This is done primarily by optimizing service frequencies and slack times for system coordination, while also considering loading and unloading, storage and cargo processing operations at the transfer terminals. Through a series of case studies, the model has shown its ability to optimize service frequencies (or headways) and slack times based on given input information. The second model is developed for countering schedule disruptions within intermodal freight systems operating in time-dependent, stochastic and dynamic environments. When routine disruptions occur (e.g. traffic congestion, vehicle failures or demand fluctuations) in pre-planned intermodal timed-transfer systems, the proposed dispatching control method determines through an optimization process whether each ready outbound vehicle should be dispatched immediately or held waiting for some late incoming vehicles with connecting freight. An additional sub-model is developed to deal with the freight left over due to missed transfers. During the phases of disruption responses, alleviations and management, the proposed real-time control model may also consider the propagation of delays at further downstream terminals. For attenuating delay propagations, an integrated dispatching control model and an analysis of sensitivity to slack times are presented

Design, Development, and Testing of Near-Optimal Satellite Attitude Control Strategies

Advances in space technology and interest toward remote sensing mission have grown in the recent years, requiring the attitude control subsystems of observation satellites to increase their performances in terms of pointing accuracy and on-board implementability. Moreover, an increased interest in small satellite missions and the recent technological developments related to the CubeSats standard have drastically reduced the cost of producing and flying a satellite mission. In this context, the proposed research aims to improve the state of the art for satellite attitude control methodologies by proposing a near-optimal attitude control strategy, simulated in a high-fidelity environment. Two strategies are presented, both are based on the implementation of a direct method, the Inverse Dynamics in the Virtual Domain (IDVD), and a nonlinear programming solver, the Sequential Gradient-Restoration Algorithm (SGRA). The IDVD allows the transcription of the original optimal control problem into an equivalent nonlinear programming problem. SGRA is adopted for the quick determination of near-optimal attitude trajectories. The two optimization criteria considered are the target acquisition time and the maneuver energy associated to the actuation torques. In addition, the development and initial testing of a satellite attitude simulator testbed for on-ground experimentation of attitude, determination, and control methodologies is proposed. The Suspended Satellite Three-Axis Rotation Testbed (START) is a novel low-cost satellite three-axis attitude simulator testbed, it is located at the Aerospace Robotics Testbed Laboratory (ARTLAB). START is mainly composed by a 3D printed base, a single-board computer, a set of actuators, and an electric battery. The suspension system is based on three thin high tensile strength wires allowing a three degrees-of freedom rotation range comparable to the one of air bearing-based floating testbeds, and minimal resistive torque in all the rotations axis. This set up will enable the hardware in-the-loop experimentation of attitude guidance navigation and control strategies. Finally, a set of guidelines to select a solver for the solution of nonlinear programming problems is proposed. With this in mind, a comparison of the convergence performances of commonly used solvers for both unconstrained and constrained nonlinear programming problems is presented. The terms of comparison involve accuracy, convergence rate, and convergence speed. Because of its popularity among research teams in academia and industry, MATLAB is used as common implementation platform for the solvers