Robust control for electric vehicle powertrains

This paper considers the application of robust control methods ($mu$- and ${rm H}_{infty}$-synthesis) to the speed and acceleration control problem encountered in electric vehicle powertrains. To this end, we consider a two degree of freedom control structure with a reference model. The underlying powertrain model is derived and combined into the corresponding interconnected system required for $mu$- and ${rm H}_{infty}$-synthesis. The closed-loop performance of the resulting controllers are compared in a detailed simulation analysis that includes nonlinear effects. It is observed that the $mu$-controller offers performance advantages in particular for the acceleration control problem, but at the price of a high-order controller.     

Optimal comfortability control of hybrid electric powertrains in acceleration mode

This paper presents a control design approach for optimizing the comfortability of hybrid electric powertrains in acceleration mode.A parallel hybrid electric v...     

综合的访问控制策略及其应用研究

提出了一种综合的访问控制(HAC)策略,它继承和汲取了几种主流的访问控制技术的优点,有效地解决了信息安全领域的访问控制问题和有效地保护数据的保密性和完整性,保证授权主体能访问客体和拒绝非授权访问.HAC具有良好的灵活性、可维护性、可管理性、更细粒度的访问控制性和更高的安全性,给信息系统设计人员和开发人员提供了访问控制安全功能的解决方案.举例说明了HAC在信息安全中的应用,结果表明HAC是实用的、有效的.     

面向业务流程访问控制策略及决策优化方法

在分析业务流程访问控制策略需求的基础上,对经典的XACML策略实施框架进行了扩展,提出一种能够根据业务流程执行状态管理策略的实施框架。通过在策略模式中引入元素和定义元素的语义,使其能够描述访问策略和委托策略,并支持任务级最小特权的实现。给出了两种策略决策优化方法,针对策略集中无效策略数量过多的问题,采用逐步裁减法减少策略元素比对的次数,针对策略集中委托策略数量过多且需要验证可信性的问题,采用信任关联法减少策略匹配的次数,有效地提高了策略决策的效率。     

Explicit solution to forward and backward stochastic differential equations with state delay and its application to optimal control

This paper is concerned with linear forward–backward stochastic differential equations (FBSDEs) with state delay, thesolvability which is much more complex than the case of no delay or input delay caused by the prediction of the backwardprocesses of the future time. To overcome this difficulty, we innovatively establish the non-homogeneous relationship betweenthe backward and forward processes with the help of the corresponding discrete-time system. The main contribution is togive the explicit solution to the FBSDEs with state delay in terms of partial Riccati equations for the first time. The presentedresults form the basis to solve the challenging problem of linear quadratic optimal control for multiplicative-noise stochasticsystems with state delay.     

Integrated modeling and analysis of dynamics for electric vehicle powertrains

Powertrain of an electric vehicle (EV) is a compound system with an electrical sub-system, such as batteries, inverters, and electrical motors, as well as a mechanical sub-system, including transmissions, differential, and wheels. Since the electrical systems directly affect the vehicle driving performance and dynamics of an EV, integrated modeling considering both the mechanical and electrical systems is essential to assess ultimate kinetic and dynamic characteristics of an EV in terms of input electrical quantities. In this paper, an entire analytic model for the powertrain of EVs is developed to describe EV dynamics with respect to electrical signals, in consideration of both mechanical and electrical systems. Theoretical models based on mathematical expressions, combining the mechanical power system and the electrical power systems, are derived for predicting the final vehicle driving performance as a function of electrical quantities. In addition, a Matlab model of an EV is developed to verify the derived mathematical analysis model. Based on the theoretical model of the powertrain, a variety of relationships between electrical quantities and vehicle dynamics, such as velocity, acceleration, and forces of the EVs, are finally investigated and analyzed.     

一种灵活的访问控制策略及其应用研究*

提出了一种声明式的、面向对象的、灵活访问控制策略的形式化描述、决策算法和实施框架。与传统访问控制策略相比,它包含授权类型、主体、权限、限制、影响等策略元素,能够更加精确地描述各类控制需求;同时,提出的策略决策算法和实施框架能适应决策环境的动态变化,有效满足复杂分布式系统的访问控制需求;最后,作为应用的例子,介绍了这种策略和实施框架在分布式园区网中的实现和应用情况。     

Design, implementation, and experimental validation of optimal power split control for hybrid electric trucks

Hybrid electric vehicles require an algorithm that controls the power split between the internal combustion engine and electric machine(s), and the opening and closing of the clutch. Optimal control theory is applied to derive a methodology for a real-time optimal-control-based power split algorithm. The presented strategy is adaptive for vehicle mass and road elevation, and is implemented on a standard Electronic Control Unit of a parallel hybrid electric truck. The implemented strategy is experimentally validated on a chassis dynamo meter. The fuel consumption is measured on 12 different trajectories and compared with a heuristic and a non-hybrid strategy. The optimal control strategy has a fuel consumption lower (up to 3%) than the heuristic strategy on all trajectories that are evaluated, except one. Compared to the non-hybrid strategy the fuel consumption reduction ranged from 7% to 16%.     

Fuel efficient power management strategy for fuel cell hybrid powertrains

A real time control strategy for fuel cell hybrid vehicles is proposed. The objective is to reduce the hydrogen consumption by using an efficient power sharing strategy between the fuel cell system (FCS) and the energy buffer (EB). The energy buffer (battery or supercapacitor) is charge-sustained (no plug-in capabilities). The real time control strategy is derived from a non-causal optimization algorithm based on optimal control theory. The strategy is validated experimentally with a hardware-in-the-loop (HiL) test bench based on a 600 W fuel cell system.     

Hardware-in-the-loop validation of a power management strategy for hybrid powertrains

Previously, a hybrid powertrain management strategy was developed that controls the power sources based on frequency content, mitigating aggressive engine transients. This article presents a hardware-in-the-loop validation of this strategy, with a real engine and battery integrated into a diesel hybrid electric vehicle simulation, thereby allowing for a realistic evaluation of fuel economy, soot emissions, and battery life. Considering an aggressive drive cycle and a state-of-charge regulation strategy as a benchmark, the frequency-based strategy yields 5.9% increase in fuel economy, 62.7% decrease in soot emissions, and 23% reduction in effective Amp-hours processed, which should yield an increase in battery life.     

The optimal robust control policy for uncertain semi-Markov control processes

The optimization problems of Markov control processes (MCPs) with exact knowledge of system parameters, in the form of transition probabilities or infinitesimal transition rates, can be solved by using the concept of Markov performance potential which plays an important role in the sensitivity analysis of MCPs. In this paper, by using an equivalent infinitesimal generator, we first introduce a definition of discounted Poisson equations for semi-Markov control processes (SMCPs), which is similar to that for MCPs, and the performance potentials of SMCPs are defined as solution of the equation. Some related optimization techniques based on performance potentials for MCPs may be extended to the optimization of SMCPs if the system parameters are known with certainty. Unfortunately, exact values of the distributions of the sojourn times at some states or the transition probabilities of the embedded Markov chain for a large-scale SMCP are generally difficult or impossible to obtain, which leads to the uncertainty of the semi-Markov kernel, and thereby to the uncertainty of equivalent infinitesimal transition rates. Similar to the optimization of uncertain MCPs, a potential-based policy iteration method is proposed in this work to search for the optimal robust control policy for SMCPs with uncertain infinitesimal transition rates that are represented as compact sets. In addition, convergence of the algorithm is discussed.     

Dynamic state feedback and its application to linear optimal control

We investigate the linear optimal control problems in the context of dynamic state feedback configuration. The dynamic state feedback is a dual structure of the dynamic observer. In conjunction with the well known arguments on linear matrix differential and Lyapunov equations, we elicit the fact that the quadratic performance index is always computable with this configuration. Based on this property, we suggest a nonlinear optimization programming method to get suboptimal or near optimal time-invariant dynamic state feedback controls. One can also evaluate the efficacy of pre-designed dynamic state feedback controllers utilizing this property. Two illustrative examples are given to show the effectiveness of the proposed approach.     

基于伪谱法的自由采样实时最优反馈控制及应用

利用高斯伪谱法收敛速率快、精度高的特点,基于通用伪谱优化软件包在线求解非线性系统的最优控制问题.将伪谱反馈控制理论与非线性最优控制理论结合起来,给出了一种自由采样实时最优反馈控制算法,该算法通过连续在线生成开环最优控制的方式提供闭环反馈.考虑计算误差、模型参数不确定性和干扰的作用,假定系统状态方程右侧的非线性向量函数关于状态、控制和系统参数是Lipschitz连续的,利用Bellman最优性原理对闭环控制系统的有界稳定性进行了分析和理论证明.最后,以高超声速再入飞行器为应用对象,研究了其再入制导问题,仿真结果验证了该算法的可行性和有效性.     

Shortest path stochastic control for hybrid electric vehicles

When a hybrid electric vehicle (HEV) is certified for emissions and fuel economy, its power management system must be charge sustaining over the drive cycle, meaning that the battery state of charge (SOC) must be at least as high at the end of the test as it was at the beginning of the test. During the test cycle, the power management system is free to vary the battery SOC so as to minimize a weighted combination of fuel consumption and exhaust emissions. This paper argues that shortest path stochastic dynamic programming (SP‐SDP) offers a more natural formulation of the optimal control problem associated with the design of the power management system because it allows deviations of battery SOC from a desired setpoint to be penalized only at key off. This method is illustrated on a parallel hybrid electric truck model that had previously been analyzed using infinite‐horizon stochastic dynamic programming with discounted future cost. Both formulations of the optimization problem yield a time‐invariant causal state‐feedback controller that can be directly implemented on the vehicle. The advantages of the shortest path formulation include that a single tuning parameter is needed to trade off fuel economy and emissions versus battery SOC deviation, as compared with two parameters in the discounted, infinite‐horizon case, and for the same level of complexity as a discounted future‐cost controller, the shortest‐path controller demonstrates better fuel and emission minimization while also achieving better SOC control when the vehicle is turned off. Linear programming is used to solve both stochastic dynamic programs. Copyright © 2007 John Wiley & Sons, Ltd.     

Simulation and control of hybrid electric vehicles

This research develops a typical model for a parallel hybrid electric vehicle. Model predictive controllers and simulations for this model have been built to verify the ability of the system to control the speeds and to handle the transitional period for the clutch engagement from the pure electrical driving to the hybrid driving. If the output constraints are the measured speeds and the unmeasured torques which are not strictly imposed and can be violated somewhat during the clutch engagements, a modified model predictive controller with soften output constraints has been developed. Simulations show that the new model predictive controller can control the speeds very well for rapid clutch engagements, which enhance the driving comfort and reduce the jerk on the parallel hybrid electric vehicles.     

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).     

Logic-based solution methods for optimal control of hybrid systems

Combinatorial optimization over continuous and integer variables is a useful tool for solving complex optimal control problems of hybrid dynamical systems formulated in discrete-time. Current approaches are based on mixed-integer linear (or quadratic) programming (MIP), which provides the solution after solving a sequence of relaxed linear (or quadratic) programs. MIP formulations require the translation of the discrete/logic part of the hybrid problem into mixed-integer inequalities. Although this operation can be done automatically, most of the original symbolic structure of the problem (e.g., transition functions of finite state machines, logic constraints, symbolic variables, etc.) is lost during the conversion, with a consequent loss of computational performance. In this paper, we attempt to overcome such a difficulty by combining numerical techniques for solving convex programming problems with symbolic techniques for solving constraint satisfaction problems (CSP). The resulting "hybrid" solver proposed here takes advantage of CSP solvers for dealing with satisfiability of logic constraints very efficiently. We propose a suitable model of the hybrid dynamics and a class of optimal control problems that embrace both symbolic and continuous variables/functions, and that are tailored to the use of the new hybrid solver. The superiority in terms of computational performance with respect to commercial MIP solvers is shown on a centralized supply chain management problem with uncertain forecast demand.     

并联新能源汽车最优效率实时控制

为了提高并联混合动力汽车驱动系统的实时效率,降低燃油消耗,本文提出一种基于效率最优的协调控制策略.根据不同驱动模式下电池的充放电状态,建立了充放电状态下驱动系统的等效燃油消耗模型,在分析电池效率和发动机效率的基础上,得到驱动系统效率的统一表达式,进而通过建立不同功率需求不同荷电状态下系统最优效率的功率分配系数图谱,设计了系统效率最优的协调控制策略,协调控制策略根据优化的功率分配系数在发动机和电机间进行力矩分配,协调控制策略可以离线计算并实时执行.两种工况循环下的仿真结果表明效率最优控制策略能有效地提高混合动力系统实时效率和燃油经济性.     

Reasoning with advanced policy rules and its application to access control

This paper presents a formal framework to represent and manage advanced policy rules, which incorporate the notions of provision and obligation. Provisions are those conditions that need to be satisfied or actions that must be performed by a user or an agent before a decision is rendered, while obligations are those conditions or actions that must be fulfilled by either the user or agent or by the system itself within a certain period of time after the decision. This paper proposes a specific formalism to express provisions and obligations within a policy and investigates a reasoning mechanism within this framework. A policy decision may be supported by more than one rule-based derivation, each associated with a potentially different set of provisions and obligations (called a global PO set). The reasoning mechanism can derive all the global PO sets for each specific policy decision and facilitates the selection of the best one based on numerical weights assigned to provisions and obligations as well as on semantic relationships among them. The formal results presented in the paper hold for many applications requiring the specification of policies, but this paper illustrates the use of the proposed policy framework in the security domain only.