Powertrain Fuel Consumption Modeling and Benchmark Analysis of a Parallel P4 Hybrid Electric Vehicle Using Dynamic Programming

The goal of this work is to develop a hybrid electric vehicle model that is suitable for use in a dynamic programming algorithm that provides the benchmark for optimal control of the hybrid powertrain. The benchmark analysis employs dynamic programming by backward induction to determine the globally optimal solution by solving the energy management problem starting at the final timestep and proceeding backwards in time. This method requires the development of a backwards facing model that propagates the wheel speed of the vehicle for the given drive cycle through the driveline components to determine the operating points of the powertrain. Although dynamic programming only searches the solution space within the feasible regions of operation, the benchmarking model must be solved for every admissible state at every timestep leading to strict requirements for runtime and memory. The backward facing model employs the quasi-static assumption of powertrain operation to reduce the fidelity of the model to accommodate these requirements. Verification and validation testing of the dynamic programming algorithm is conducted to ensure successful operation of the algorithm and to assess the validity of the determined control policy against a high-fidelity forward-facing vehicle model with a percent difference of fuel consumption of 1.2%. The benchmark analysis is conducted over multiple drive cycles to determine the optimal control policy that provides a benchmark for real-time algorithm development and determines control trends that can be used to improve existing algorithms. The optimal combined charge sustaining fuel economy of the vehicle is determined by the dynamic programming algorithm to be 32.99 MPG, a 52.6% increase over the stock 3.6 L 2019 Chevrolet Blazer.

Stochastic Maximum Principle for Forward-Backward Regime Switching Jump Diffusion Systems and Applications to Finance

The authors prove a sufficient stochastic maximum principle for the optimal control of a forward-backward Markov regime switching jump diffusion system and show its connection to dynamic programming principle. The result is applied to a cash flow valuation problem with terminal wealth constraint in a financial market. An explicit optimal strategy is obtained in this example.

带无向环优先级的单机总加权完成时间调度问题

单机调度是生产调度领域的一个经典问题,研究了工件间有加工优先级要求的单机总加权完成时间调度问题,考虑了若将工件优先级关系构成的优先级图视为无向图,包含有环的情况。针对该问题,设计了结合双向动态规划的拉格朗日松弛算法进行求解,使得可以求解一个工件可能有多个紧前或紧后工件的情况。大量实验测试结果表明,该算法能够在较短时间内得到令人满意的近优解。

基于MATLAB的动态规划常用算法的实现

运用MATLAB编程实现了动态规划的逆序、顺序、双向混合算法,并分别应用于求解几类典型问题,验证了该方法的有效性,同时表明该程序对求解动态规划多类典型问题是通用的,丰富了MATLAB优化工具箱,具有一定的应用价值.

工件优先级图为非连接图且含环的单机总加权拖期调度问题

为满足实际生产环境对工件加工顺序和工件到达时间的要求,提出了具有新特征的单机总加权拖期调度问题,其特点体现在:工件有动态到达时间,且由工件优先级关系构成的优先级图为非连接图且存在环的情况,对该问题建立数学规划模型,在扩展Tang和Xuan等的基础上,提出了结合双向动态规划的拉格朗日松弛算法求解该问题。在该算法的设计中,提出双向动态规划算法求解拉格朗日松弛问题,使得它可处理优先级图中一个工件可能有多个紧前或紧后工件的情况,采用次梯度算法更新拉格朗日乘子,基于拉格朗日松弛问题的解设计启发式算法构造可行解。实验测试结果显示,所设计的拉格朗日松弛算法能够在较短的运行时间内得到令人满意的近优解,为更复杂的调度问题的求解提供了思路。

基于ATCQAM系统和HARQ协议的渐进图像鲁棒编码传输方案

研究了无线衰落信道下的渐进图像传输问题，针对现有基于不等重功率分配（UPA）传输优化方案的不足，提出了一种基于自适应网格编码调制（ATCQAM）系统和混合自动重传（HARQ）协议的新型编码传输方案。不同于以往鲁棒传输方案，该方案充分考虑了反馈信道提供的自动重传及信道状态信息。为了减小图像传输的端对端失真，该方案采用逆向动态规划算法对待传图像压缩码流的传输功率和编码调制模式进行联合优化。仿真实验表明，该方案相对于前人不考虑反馈信息的编码传输方案，能显著提高接收端图像的重建质量。在高斯白噪声信道和准静态衰落信道下，峰值信噪比的平均增益可分别达到3．6dB和14．4dB。

带Poisson跳跃的正倒向随机微分对策的最大值原理与动态规划之间的关系

本文研究了带Poisson跳跃的零和正倒向随机微分对策的最大值原理与动态规划之间的关系;在一定的可微性假设下,建立了对偶过程、广义Hamilton函数和值函数之间的联系;作为主要结果的应用,讨论了金融市场中一类带有模型不确定性的递归效用投资组合优化问题.

完全耦合正倒向随机控制系统的动态规划原理与最大值原理之间的联系

研究了完全耦合正倒向随机控制系统的动态规划原理和最大值原理之间的联系,其递归效用泛函由受控完全耦合的正倒向随机微分方程的解给出。主要结果是在一定的光滑性假设下,给出了最优值函数、广义哈密顿函数和对偶过程之间的联系,但正向方程的扩散项不含变量z。一般情形的结果仍是公开问题。最后给出一个线性例子来解释理论结果。