Real-time optimization of energy consumption under adaptive cruise control for connected HEVs

This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle (HEV) that operates with adaptive cruise control (ACC). Real-time energy optimization is an essential issue such that the HEV powertrain system is as efficient as possible. With connected vehicle technique, ACC system shows considerable potential of high energy efficiency. Combining a classical ACC algorithm, a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon. The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle. The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.     

The specification and numerical solution of a benchmark swirling laminar flow problem

A primitive variable finite element method for solving swirling incompressible flow problems is presented. A flow problem of physical importance is analysed and the results are critically compared with an earlier solution. The numerical solution of a problem characterized by a particular choice of Reynolds number and swirl ratio is discussed in detail; this problem is proposed as a benchmark for general swirling flow calculations.     

Two-stage on-board optimization of merging velocity planning with energy management for HEVs

This paper proposes a two-stage hierarchy control system with model predictive control (MPC) for connected parallel HEVs with available traffic information. In the first stage, a coordination of on-ramp merging problem using MPC is presented to optimize the merging point and trajectory for cooperative merging. After formulating the merging problem into a nonlinear optimization problem, a continuous/GMRES method is used to generate the real-time vehicle acceleration for two considered HEVs running on main road and merging road, respectively. The real-time acceleration action is used to calculate the torque demand for the dynamic system of the second stage. In the second stage, an energy management strategy (EMS) for powertrain control that optimizes the torque-split and gear ratio simultaneously is composed to improve fuel efficiency. The formulated nonlinear optimization problem is solved by sequential quadratic programming (SQP) method under the same receding horizon. The simulation results demonstrate that the vehicles can merge cooperatively and smoothly with a reasonable torque distribution and gear shift schedule.     

Gaussian kernel optimization: Complex problem and a simple solution

The Gaussian kernel function implicitly defines the feature space of an algorithm and plays an essential role in the application of kernel methods. The parameter of Gaussian kernel function is a scalar that has significant influences on final results. However, until now, it is still unclear how to choose an optimal kernel parameter. In this paper, we propose a novel data-driven method to optimize the Gaussian kernel parameter, which only depends on the original dataset distribution and yields a simple solution to this complex problem. The proposed method is task irrelevant and can be used in any Gaussian kernel-based approach, including supervised and unsupervised machine learning. Simulation experiments demonstrate the efficacy of the obtained results. A user-friendly online calculator is implemented at: www.csbio.sjtu.edu.cn/bioinf/kernel/ for public use.     

Analysing singularities of a benchmark problem

The purpose of this paper is to analyze the singularities of a well known benchmark problem “Andrews’ squeezing mechanism.” We show that for physically relevant parameter values this system admits singularities, and describe explicit conditions for the parameters. The method is based on Gröbner bases computations and ideal decomposition. It is algorithmic and can thus be applied to study constraint singularities which arise in more general situations as well.     

Supervisory control of hybrid powertrains: An experimental benchmark of offline optimization and online energy management

The paper describes a chain of tools aimed at the development and validation of energy management strategies (EMS) for hybrid powertrains. These tools comprise an offline optimizer based on Pontryagin minimum principle (PMP) and the online equivalent consumption minimization strategy (ECMS), both implemented in a dynamic simulation platform and as a real-time controller in a semi-physical testing equipment. The results presented are aimed at illustrating the continuity of the various approaches by comparing the offline-generated energy management laws with their online counterparts, both in terms of trajectories over time and in terms of global results (fuel consumption, state-of-charge deviations).     

An optimization problem in queueing theory and a numerical solution method

We consider the quasigradient method for solution of a queueing problem in stochastic programming terms. A finite iteration formula is obtained for computing the stochastic quasigradient. The proposed algorithm is applicable to the solution of large-scale problems with many servers.Translated from Kibernetika, No. 3, pp. 73–75, May–June 1990.     

An economic receding horizon optimization approach for energy management in the chlor-alkali process with hybrid renewable energy generation

This paper presents a methodology for the application of receding horizon optimization techniques to the problem of optimally managing the energy flows in the chlor-alkali process using a hybrid renewable energy system (HRES). The HRES consists of solar and wind energy generation units and fuel cells to supply energy. The HRES is also connected to the grid and allows for buying or selling electricity from and to the grid. Initially, detailed models of each system component are introduced as the basis for the simulation study. Energy management strategies are then developed to realize the objectives of meeting production requirements while minimizing the overall operating and environmental costs. Sensitivity and uncertainty analyses are carried out to elucidate the key parameters that influence the energy management strategies. Finally, production demand response is integrated into the proposed methodology.     

A pulp mill benchmark problem for control: problem description

This work introduces a benchmark problem of a pulping process, including both the fiber line and the chemical recovery area. The complete details of the pulp mill process are presented, including the control objectives, modes of operation, process constraints, measurements and costs. The dynamic model, including the source/binary code of all the unit operations is made available to the academic community as a benchmark for use in process system engineering studies.     

Windowing the solution space of an optimization problem

This paper introduces a graphical approach to solving optimal design problems. The technique generates graphical representations of a multi-dimensional solution space, so that the designer can identify the most appropriate solution to the problem by visual inspection of these representations. This is done via interactive computer graphics.An example is presented to show the effectiveness of the technique. Implementation requirements are outlined and discussed. In addition, the suitability of the technique to the design environment is discussed. Advantages and disadvantages are explained.     

微电网孤网实时能量优化管理

提出了双层协调调度的方法——计划调度层和实时调度层来解决微电网孤网实时能量优化管理的问题。计划层是基于不可控微电源功率预测的主要考虑微电网经济性的调度方法;实时层是在计划层的基础上对不可控微电源实际功率与预测功率之间的误差进行调度,再将调度结果叠加到计划层各个可控微电源的功率上。在实时层,可控微电源的功率范围与计划层是不同的,下限为其在计划层出力的相反数,上限为其最大出力与计划层出力差值。最后通过一个典型的微电网实例验证了该方法的正确性和优越性。     

Distributed optimal energy consumption control of HEVs under MFG-based speed consensus

This paper investigates a distributed optimal energy consumption control strategy under mean-field game based speed consensus. Large scale vehicles in a traffic flow is targeted instead of individual vehicles, and it is assumed that the propulsion power of vehicles is hybrid electric powertrain. The control scheme is designed in the following two stages. In the first stage, in order to achieve speed consensus, the acceleration control law is designed by applying the MFG (mean-field game) theory. In the second stage, optimal powertrain control for minimizing energy consumption is obtained through coordinate the engine and the motor under the acceleration constraint. The simulation is conducted to demonstrate the effectiveness of the proposed control strategy.     

Modified collaborative optimization for feasibility problem of final solution

This paper focuses on the feasibility problem of the final solution obtained by the relaxation collaborative optimization method. First, a mixed relaxation (MR) method is developed to enhance the possible premature convergence and non-solution problems of the variable relaxation method. Then the feasibility problem of the final solution is analyzed, that is the optimal solution obtained from system level may not satisfy the constraints of original optimization problem due to the inconsistency between system level and subsystem level for shared variables. A method of modified collaborative optimization (MCO) is put forward to solve this problem. In this method, the impact of the inconsistency is considered and supplemented in the subsystem-level constraints in order to approximate the original constraints as much as possible. At last, based on the MR method, the feasibility problem of the final solution is illustrated by three examples, and the effectiveness and accuracy of MCO are demonstrated by the optimization results.     

Stochastic robustness synthesis applied to a benchmark control problem

Stochastic robustness synthesis is used to find compensators that solve a benchmark problem. The synthesis minimizes a robustness cost function that is the weighted quadratic sum of stochastic robustness metrics. These metrics — probability of instability, probability of actuator saturation, and probability of settling time violation — are estimated using Monte Carlo analysis. A simple search method minimizes the robustness cost by selecting values for the design parameters of a linear quadratic Gaussian regulator. The resulting compensators are robust, require low actuator authority, and compare well with previous designs.     

Discrete time linear optimal multi-periodic repetitive control: a benchmark tracking solution

The use of optimal control tracking methodologies for the development of feedback control schemes for arbitrary discrete-time linear systems is considered for the special case when the demand signal is multi-periodic, i.e. can be expressed as a finite sum of periodic signals of different but known frequencies. By embedding the reference signal into plant dynamics, the problem is reduced to a classical linear quadratic control problem which yields an explicit formula for a globally stabilizing tracking controller. Asymptotic perfect tracking is guaranteed by the presence of the familiar internal model of the demand signal dynamics within the control structure. Transfer function analysis of the controller indicates that it consists of an inner state feedback loop plus an error actuated forward path control element containing the internal model.     

Evolutionary robustness analysis for multi-objective optimization: benchmark problems

This paper presents a new approach to robustness analysis in multi-objective optimization problems aimed at obtaining the most robust Pareto front solutions and distributing the solutions along the most robust regions of the optimal Pareto set. A new set of test problems accounting for the different types of robustness cases is presented in this study. Non-dominated solutions are classified according to their degree of robustness and are distributed along the Pareto front according to specific algorithm parameter values. Verification of the proposed method is carried out using the developed test problems and artificial and real world benchmark test problems present in the literature.     

SICE benchmark problem: starting speed control of SI engines

Starting the engine and quickly adjusting the engine speed to the target value after that will always be a challenge for vehicles with an internal combustion engines at development sites in the automotive industry. The SICE Research Committee on Advanced Powertrain Control Theory provided this task as a benchmark problem with the engine model. Just as control developers in the industry are provided actual engines, many of the academic experts have acquired engine models, analyzed behavior and constructed controls, and solved control tasks. We summarize this activity by explaining the methods by many challengers that achieved the target performance.