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Optimal energy management of a series hybrid vehicle with combined fuel economy and low-emission objectives

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2014年第12期228卷 1424-1439页

作者： Johri, Rajit Filipi, Zoran Univ Michigan Ann Arbor MI 48109 USA Ford Motor Co Dearborn MI 48121 USA Clemson Univ Greenville SC 29607 USA

A series hybrid powertrain provides ultimate freedom in controlling the engine. The flexibility enabled with hybridization creates chances for a synergistic approach, in which the hybrid supervisory control can be augmented to address both the emissions and the efficiency. In this paper, two policy optimization techniques are proposed, namely stochastic dynamic programming and neurodynamic programming, for designing power management controllers. These controllers are then compared with a baseline rule-based controller. The intention is to investigate the additional benefits possible through application of policy optimization algorithms and a systematic framework capable of representing complex system-level effects. The power management of a series hydraulic hybrid vehicle is pursued as a sequential decision-making problem under uncertainty (stochastic control). The low energy density of the hydraulic accumulator adds to the control challenge. First, stochastic dynamic programming and neurodynamic programming are applied to design a controller based on the fuel economy objective. The problem is subsequently expanded to include minimization of transient diesel engine emissions. This poses additional challenges due to the increased state space. The problem is computationally intractable by stochastic dynamic programming and is solved using the newly proposed neurodynamic programming framework. Finally, the supervisory controllers are implemented and evaluated using simulations and an engine-in-the-loop facility. It is shown that, by designing an intelligent multi-objective controller, significant reduction in both the fuel consumption and the emissions can be achieved compared with strategies which focus solely on the fuel consumption.

关键词： Hybrid vehicle supervisory control neurodynamic programming stochastic dynamic programming emissions engine-in-the-loop system

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Pricing American-style derivatives with European call options

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MANAGEMENT SCIENCE 2006年第1期52卷 95-110页

作者： Laprise, SB Fu, MC Marcus, SI Lim, AEB Zhang, HJ Adv Informat Technol BAE Syst Arlington VA 22203 USA Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA

We present a new approach to pricing American-style derivatives that is applicable to any Markovian setting (i.e., not limited to geometric Brownian motion) for which European call-option prices are readily available. By approximating the value function with an appropriately chosen interpolation function, the pricing of an American-style derivative with arbitrary payoff function is converted to the pricing of a portfolio of European call options, leading to analytical expressions for those cases where analytical European call prices are available (e.g., the Merton jump-diffusion process). Furthermore, in many settings, the approach yields upper and lower analytical bounds that provably converge to the true option price. We provide computational results to illustrate the convergence and accuracy of the resulting estimators.

关键词： American-style derivatives American options European options call options early exercise stochastic dynamic programming

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Two-step optimal energy management strategy for single-shaft series-parallel powertrain

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MECHATRONICS 2016年 36卷 147-158页

作者： Li, Liang Yan, Bingjie Song, Jian Zhang, Yuanbo Jiang, Guirong Li, Lei Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Yanshan Univ Coll Mech Engn Qinhuangdao 066004 Hebei Peoples R China Suzhou Green Control Transmiss Technol Co Ltd Suzhou Jiangsu Peoples R China

The series-parallel architecture of the plug-in hybrid electric powertrain has attracted wide attentions in recent years for its flexible and highly efficient operating modes. However, despite the improvement of the vehicle fuel economy, it has been gradually facing more challenges on the design of the optimal controller due to the complex structure and the fast depletion of the battery. In this paper, a two-step optimal energy management strategy is proposed for a novel single-shaft series-parallel powertrain. In the first step, an equivalent method is adopted, in which two motors are equivalently regarded as one. After detailed analysis of the operating modes, various objective functions are established to pre-optimize the power split between engine and motor or two motors. In the second step, a stochastic dynamic programming (SDP) is adopted to optimize the power split between the engine and the equivalent motor, and the optimal combination of the operating modes. Then coupled with the pre-optimized results of the first step, the optimal power split among the engine and two motors could be obtained and then constructed as simple lookup-tables, which have great potential for practical applications. Finally, the preliminary test about the real-time performance of the optimal results is developed on the hardware-in-the-loop (HIL) system. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Energy management strategy Plug-in hybrid electric powertrain Two step method stochastic dynamic programming HIL

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A NEW CONDITION FOR THE EXISTENCE OF OPTIMAL STATIONARY POLICIES IN AVERAGE COST MARKOV DECISION-PROCESSES

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OPERATIONS RESEARCH LETTERS 1986年第1期5卷 17-23页

作者： SENNOTT, LI Department of Mathematics Illinois State University Normal IL 61761 USA

Discrete time countable state Markov decision processes with finite decision sets and bounded costs are considered. Conditions are given under which an unbounded solution to the average cost optimality equation exists and yields an optimal stationary policy. A new form of the optimality equation is derived for the case in which every stationary policy gives rise to an ergodic Markov chain.

关键词： Markov processes stochastic dynamic programming

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Fully polynomial time (Sigma, Pi)-approximation schemes for continuous nonlinear newsvendor and continuous stochastic dynamic programs

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MATHEMATICAL programming 2022年第1-2期195卷 183-242页

作者： Halman, Nir Nannicini, Giacomo Bar Ilan Univ Ramat Gan Israel IBM TJ Watson Res Ctr IBM Quantum Yorktown Hts NY USA

We study the nonlinear newsvendor problem concerning goods of a non-discrete nature, and a class of stochastic dynamic programs with several application areas such as supply chain management and economics. The class is characterized by continuous state and action spaces, either convex or monotone cost functions that are accessed via value oracles, and affine transition functions. We establish that these problems cannot be approximated to any degree of either relative or additive error, regardless of the scheme used. To circumvent these hardness results, we generalize the concept of fully polynomial-time approximation scheme allowing arbitrarily small additive and multiplicative error at the same time, while requiring a polynomial running time in the input size and the error parameters. We develop approximation schemes of this type for the classes of problems mentioned above. In light of our hardness results, such approximation schemes are "best possible". A computational evaluation shows the promise of this approach.

关键词： Newsvendor problem stochastic inventory control Hardness of approximation Approximation algorithms stochastic dynamic programming K -approximation sets and functions

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Adaptive water infrastructure planning for nonstationary hydrology

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ADVANCES IN WATER RESOURCES 2018年 118卷 83-94页

作者： Hui, Rui Herman, Jonathan Lund, Jay Madani, Kaveh Univ Calif Davis Ctr Watershed Sci Davis CA 95616 USA Univ Calif Davis Dept Civil & Environm Engn Davis CA 95616 USA Imperial Coll London Ctr Environm Policy London SW7 2AZ England Stockholm Univ Dept Phys Geog Stockholm Sweden

The uncertainty of a changing climate raises challenges for water infrastructure planning and design. Not accounting for nonstationarity may result in under-designed structures that fail too frequently, or over-designed structures that are economically inefficient. This concern is magnified by uncertainty in the long-term frequency and magnitude of future extreme events. Planning strategies that allow adaptations over a structure's life could improve both reliability and economic efficiency. This study develops a method to inform adaptive water infrastructure planning with uncertain hydrologic and other forms of nonstationarity, applied to levee system planning. A stochastic dynamic programming model including a Markov process is developed for infrastructure planning with uncertain nonstationarity in flood frequency. Bayes' theorem is used to update peak flow probabilities conditioned on observed past peak flows and to update expected residual flood damages over time. A levee system planning problem with a numerical example from California illustrates the approach to derive optimal levee heights over time, and economic values of adapting to uncertain nonstationary flood risk. The projected range of probabilistic hydrology scenarios affects the optimal results, particularly in later planning stages as hydrology scenarios diverge with time. Adaptive planning strategies allowing more levee upgrades over time slightly lowers the overall cost and provides better flood protection than one-time construction under nonstationary hydrology for any climate in the example. Compared to a known future nonstationary hydrology, incorporating uncertain nonstationary climate results in higher levees being planned for observed severe hydrology scenarios in later stages. The overall present value cost with uncertain nonstationary climate depends on rates of change in peak flow distribution parameters in future hydrology scenarios.

关键词： Nonstationary hydrology Uncertain nonstationarity Water infrastructure planning stochastic dynamic programming Risk-based analysis

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dynamic Project Expediting: A stochastic Shortest-Path Approach

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MANAGEMENT SCIENCE 2023年第6期70卷 3748-3768页

作者： Bertazzi, Luca Mogre, Riccardo Trichakis, Nikolaos Univ Brescia Dept Econ & Management I-25122 Brescia Italy Univ Durham Business Sch Durham DH1 3LB England MIT Operat Res Ctr Cambridge MA 02142 USA MIT Sloan Sch Management Cambridge MA 02142 USA

We deal with the problem of managing a project or a complex operational process by controlling the execution pace of the activities it comprises. We consider a setting in which these activities are clearly defined, are subject to precedence constraints, and progress randomly. We formulate a discrete-time, infinite-horizon Markov decision process in which the manager reviews progress in each period and decides which activities to expedite to balance expediting costs with delay costs. We derive structural properties for this dynamic project expediting problem. These enable us then to devise exact solution methods that we show to reduce computational burden significantly. We illustrate how our method generalizes and can be used to tackle a wide range of so-called stochastic shortestpath problems that are characterized by an intuitive property and can capture other applications, including medical decision-making and disease-modeling problems. Moreover, we also deal with the state identification issue for our problem, which is a challenging task in and of itself, owing to precedence constraints. We complement our analytical results with numerical experiments, demonstrating that both our solution and state identification methods significantly outperform extant methods for a supply chain example and for various randomly generated instances.

关键词： project management project risk Markov decision process stochastic dynamic programming stochastic shortest path

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A sequential dynamic pricing model and its applications

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NAVAL RESEARCH LOGISTICS 2004年第4期51卷 501-521页

作者： Lin, KY Virginia Polytech Inst & State Univ Grado Dept Ind & Syst Engn Blacksburg VA 24061 USA

Consider a sequential dynamic pricing model where a seller sells a given stock to a random number of customers. Arriving one at a time, each customer will purchase one item if the product price is lower than her personal reservation price. The seller's objective is to post a potentially different price for each customer in order to maximize the expected total revenue. We formulate the seller's problem as a stochastic dynamic programming model, and develop an algorithm to compute the optimal policy. We then apply the results from this sequential dynamic pricing model to the case where customers arrive according to a continuous-time point process. In particular, we derive tight bounds for the optimal expected revenue, and develop an asymptotically optimal heuristic policy. (C) 2004 Wiley Periodicals, Inc.

关键词： dynamic pricing inventory control stochastic dynamic programming asymptotically optimal policy

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On the existence of stationary optimal policies for partially observed MDPs under the long-run average cost criterion

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SYSTEMS & CONTROL LETTERS 2006年第2期55卷 165-173页

作者： Hsu, SP Chuang, DM Arapostathis, A Univ Texas Austin TX 78712 USA Natl Chi Nan Univ Puli 545 Nantou Taiwan

This paper studies the problem of the existence of stationary optimal policies for finite state controlled Markov chains, with compact action space and imperfect observations, under the long-run average cost criterion. It presents sufficient conditions for existence of solutions to the associated dynamic programming equation, that strengthen past results. There is a detailed discussion comparing the different assumptions commonly found in the literature. (c) 2005 Elsevier B.V. All rights reserved.

关键词： Markov decision processes optimal control stochastic dynamic programming imperfect information

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Evaluation of optimization operation models for cascaded hydropower reservoirs to utilize medium range forecasting inflow

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Science China(Technological Sciences) 2013年第10期56卷 2540-2552页

作者： XU Wei PENG Yong WANG BenDe School of Hydraulic Engineering Dalian University of Technology

This paper evaluates the performances of the models that incorporate forecasting inflow for cascaded hydropower reservoirs operation. These models are constructed separately on the concepts of explicit stochastic optimization （ESO） and implicit sto- chastic optimization （ISO） as well as parametefization-simulation-optimization （PSO）. Firstly, the aggregation-disaggregation method is implemented in ESO models to reduce the complexity of stochastic dynamic programming （SDP）. And the aggre- gate flow SDP （AF-SDP） and aggregation-disaggregation SDP （AD-SDP） are constructed respectively. Secondly, in ISO mod- el, decision tree is the well-known and widespread algorithm. The algorithm C 5.0 is selected to extract the if-then-else rules for reservoir operation. Thirdly, based on the PSO model, the hedging rule curves （HRCs） are pre-defined by fusing the storage and inflow as state variable. The parameters of the HRCs are determined by using the simulation-optimization model. Finally, China＇s Hun River cascade hydropower reservoirs system is taken as an example to illustrate the efficiency and reliability of the models. In addition, the values of quantitative precipitation forecasts of the global forecast system （10 days lead-time） are implemented to forecast the 10 days inflow.

关键词： cascaded hydropower reservoirs forecasting inflow stochastic dynamic programming decision tree hedging rule

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