A Game Theory Approach to Energy Management of An Engine–Generator/Battery/Ultracapacitor Hybrid Energy System

The complex configuration and behavior of multisource hybrid energy systems (HESs) present challenges to their energy management. For a balanced solution, it is especially important to represent and take advantage of the characteristics of each device and the interactive relationship among them. In this paper, multi-agent modeling and a game theory-based control strategy are proposed and combined for the energy management of an example engine-generator/battery/ultracapacitor (UC) HES. The three devices such as engine-generator unit, battery and UC packs are modeled and controlled as independent but related agents, through which the performance and requirements of the individual devices are fully respected. The energy management problem is then formulated as a noncooperative current control (NCC) game. The Nash equilibrium is analytically derived as a balanced solution that compromises the different preferences of the independent devices. The following simulation and experimental results validate the game theory-based control and its real-time implementation. The proposed approach could be further extended to become a general solution for the energy management and control of networked energy systems, in which again fully representing and balancing the different preferences of the components are important.

[1]  Clark Hochgraf,et al.  Effect of ultracapacitor-modified PHEV protocol on performance degradation in lithium-ion cells , 2014 .

[2]  Alireza Khaligh,et al.  A Supervisory Power-Splitting Approach for a New Ultracapacitor–Battery Vehicle Deploying Two Propulsion Machines , 2014, IEEE Transactions on Industrial Informatics.

[3]  Josep M. Guerrero,et al.  Advanced Control Architectures for Intelligent Microgrids—Part I: Decentralized and Hierarchical Control , 2013, IEEE Transactions on Industrial Electronics.

[4]  M. Gokasan,et al.  Sliding mode based powertrain control for efficiency improvement in series hybrid-electric vehicles , 2006, IEEE Transactions on Power Electronics.

[5]  K. T. Chau,et al.  Effective Charging Method for Ultracapacitors , 2005 .

[6]  He Yin,et al.  An adaptive fuzzy logic based energy management strategy for electric vehicles , 2014, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE).

[7]  Ilya V. Kolmanovsky,et al.  Game Theory Controller for Hybrid Electric Vehicles , 2014, IEEE Transactions on Control Systems Technology.

[8]  Enrique Kremers,et al.  Multi-agent modeling for the simulation of a simple smart microgrid , 2013 .

[9]  He Yin,et al.  Quantitative Evaluation of LiFePO$_4$ Battery Cycle Life Improvement Using Ultracapacitors , 2016, IEEE Transactions on Power Electronics.

[10]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[11]  A. Kuperman,et al.  Design of a Semiactive Battery-Ultracapacitor Hybrid Energy Source , 2013, IEEE Transactions on Power Electronics.

[12]  Wei Liang,et al.  Power Smoothing Energy Management and Its Application to a Series Hybrid Powertrain , 2013, IEEE Transactions on Control Systems Technology.

[13]  Sheldon S. Williamson,et al.  Power-Electronics-Based Solutions for Plug-in Hybrid Electric Vehicle Energy Storage and Management Systems , 2010, IEEE Transactions on Industrial Electronics.

[14]  Jean-Michel Vinassa,et al.  Characterization methods and modelling of ultracapacitors for use as peak power sources , 2007 .

[15]  Suleiman Abu-Sharkh,et al.  Rapid test and non-linear model characterisation of solid-state lithium-ion batteries , 2004 .

[16]  Robert T. Clemen,et al.  Making Hard Decisions with Decisiontools Suite , 2000 .

[17]  Konstantinos G. Arvanitis,et al.  A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids , 2015 .

[18]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[19]  Alon Kuperman,et al.  Battery–ultracapacitor hybrids for pulsed current loads: A review , 2011 .

[20]  Anirban Mahanti,et al.  Game Theoretic Model Predictive Control for Distributed Energy Demand-Side Management , 2015, IEEE Transactions on Smart Grid.

[21]  Lei Wang,et al.  Hardware-in-the-loop simulation for the design and verification of the control system of a series-parallel hybrid electric city-bus , 2012, Simul. Model. Pract. Theory.

[22]  S. Tisue NetLogo : Design and Implementation of a Multi-Agent Modeling Environment , 2004 .

[23]  Amir Ostadi,et al.  Optimal Sizing of Battery/Ultracapacitor-Based Energy Storage Systems in Electric Vehicles , 2015 .

[24]  Drew Fudenberg,et al.  Game theory (3. pr.) , 1991 .

[25]  T. S. Bhatti,et al.  A review on electrochemical double-layer capacitors , 2010 .

[26]  Ahmet Teke,et al.  A comprehensive overview of hybrid electric vehicle: Powertrain configurations, powertrain control techniques and electronic control units , 2011 .

[27]  Seung-Ki Sul,et al.  System Integration and Power-Flow Management for a Series Hybrid Electric Vehicle Using Supercapacitors and Batteries , 2008, IEEE Transactions on Industry Applications.

[28]  Jasbir S. Arora,et al.  Introduction to Optimum Design , 1988 .

[29]  Chen Zhao,et al.  Utility Function-Based Real-Time Control of A Battery Ultracapacitor Hybrid Energy System , 2015, IEEE Transactions on Industrial Informatics.

[30]  He Yin,et al.  Equivalent Series Resistance-Based Energy Loss Analysis of a Battery Semiactive Hybrid Energy Storage System , 2015, IEEE Transactions on Energy Conversion.

[31]  Pavol Bauer,et al.  Driving Range Extension of EV With On-Road Contactless Power Transfer—A Case Study , 2013, IEEE Transactions on Industrial Electronics.

[32]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[33]  Daniel Pérez Palomar,et al.  Demand-Side Management via Distributed Energy Generation and Storage Optimization , 2013, IEEE Transactions on Smart Grid.

[34]  Hamid Gualous,et al.  Design and New Control of DC/DC Converters to Share Energy Between Supercapacitors and Batteries in Hybrid Vehicles , 2008, IEEE Transactions on Vehicular Technology.

[35]  S.D.J. McArthur,et al.  Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges , 2007, IEEE Transactions on Power Systems.

[36]  Pierluigi Siano,et al.  A Review of Agent and Service-Oriented Concepts Applied to Intelligent Energy Systems , 2014, IEEE Transactions on Industrial Informatics.

[37]  Martin Mellincovsky,et al.  Supercapacitor Sizing Based on Desired Power and Energy Performance , 2014, IEEE Transactions on Power Electronics.

[38]  Alireza Khaligh,et al.  Optimization of Sizing and Battery Cycle Life in Battery/Ultracapacitor Hybrid Energy Storage Systems for Electric Vehicle Applications , 2014, IEEE Transactions on Industrial Informatics.

[39]  Jorge Moreno,et al.  Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks , 2006, IEEE Transactions on Industrial Electronics.

[40]  Peng Zhao,et al.  An Energy Management System for Building Structures Using a Multi-Agent Decision-Making Control Methodology , 2010, 2010 IEEE Industry Applications Society Annual Meeting.

[41]  Ali Emadi,et al.  Classification and Review of Control Strategies for Plug-In Hybrid Electric Vehicles , 2011, IEEE Transactions on Vehicular Technology.

[42]  Frank C. Walsh,et al.  Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic , 2011, IEEE Transactions on Vehicular Technology.