Charging and discharging Control of Plug-in Electric Vehicles with uncertainties via Robust Model Predictive Control Method
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- 英文论文题名：Charging and discharging Control of Plug-in Electric Vehicles with uncertainties via Robust Model Predictive Control Method
- 论文作者：Peng Wang ; Long Ran ; Yunfeng Shao ; Suli Zou ; Zhongjing Ma
- 英文论文作者：Peng Wang ; Long Ran ; Yunfeng Shao ; Suli Zou ; Zhongjing Ma ; School of Automation ; Beijing Institute of Technology ; State Grid Chongqing ELectric Power Co.Electric Power Research Institute ; State Grid Lvliang Power Supply Company
- 年：2017
- 作者机构：School of Automation,Beijing Institute of Technology;State Grid Chongqing ELectric Power Co.Electric Power Research Institute;State Grid Lvliang Power Supply Company;
- 英文论文关键词：PEV ; RMPC ; uncertainty ; disturbance invariant set
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：U469.72
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：7
- 文件大小：294k
- 原文格式：D
- 会议级别：全国
摘要
With the development of the Plug-in Electric Vehicles(PEVs), it has become a significant part of electric load. The optimal PEVs' charging and discharging scheduling is a vital problem when PEVs connect into the smart grid. In most literatures,the charging and discharging procedure is assumed idealized. In reality, many uncertainties are exist during the procedure, such as the conversion efficiency, temperature and other aspects, but they are not easy to be expressed as a fixed term. In this paper,we use a random but bounded uncertainties to describe the uncertainties. Meanwhile, based on robust model predictive control(RMPC) method, we introduce the disturbance invariant set to solve the PEVs scheduling problem with uncertainties and design a feedback control law to guarantee the feasibility of it. A distributed method to reduce the computation complexity of the uncertain problem. At last, some simulations demonstrate the feasibility of the proposed centralized and distributed methods.
With the development of the Plug-in Electric Vehicles(PEVs), it has become a significant part of electric load. The optimal PEVs' charging and discharging scheduling is a vital problem when PEVs connect into the smart grid. In most literatures,the charging and discharging procedure is assumed idealized. In reality, many uncertainties are exist during the procedure, such as the conversion efficiency, temperature and other aspects, but they are not easy to be expressed as a fixed term. In this paper,we use a random but bounded uncertainties to describe the uncertainties. Meanwhile, based on robust model predictive control(RMPC) method, we introduce the disturbance invariant set to solve the PEVs scheduling problem with uncertainties and design a feedback control law to guarantee the feasibility of it. A distributed method to reduce the computation complexity of the uncertain problem. At last, some simulations demonstrate the feasibility of the proposed centralized and distributed methods.
With the development of the Plug-in Electric Vehicles(PEVs), it has become a significant part of electric load. The optimal PEVs' charging and discharging scheduling is a vital problem when PEVs connect into the smart grid. In most literatures,the charging and discharging procedure is assumed idealized. In reality, many uncertainties are exist during the procedure, such as the conversion efficiency, temperature and other aspects, but they are not easy to be expressed as a fixed term. In this paper,we use a random but bounded uncertainties to describe the uncertainties. Meanwhile, based on robust model predictive control(RMPC) method, we introduce the disturbance invariant set to solve the PEVs scheduling problem with uncertainties and design a feedback control law to guarantee the feasibility of it. A distributed method to reduce the computation complexity of the uncertain problem. At last, some simulations demonstrate the feasibility of the proposed centralized and distributed methods.
引文
[1]Zhongjing Ma,D.Callaway,and I.Hiskens.Decentralized charging control for large populations of plug-in electric vehicles.In IEEE International Conference on Control Applications,pages 191-195,2010.
[2]Yifeng He,B.Venkatesh,and Ling Guan.Optimal scheduling for charging and discharging of electric vehicles.IEEETransactions on Smart Grid,3(3):1095-1105,2012.
[3]Jonglak Pahasa and Issarachai Ngamroo.PHEVs bidirectional charging/discharging and So C control for microgrid frequency stabilization using multiple mpc.IEEE Transactions on Smart Grid,6(2):526-533,2015.
[4]Sonja Stdli and Emanuele Crisostomi.Optimal real-time distributed V2G and G2V management of electric vehicles.International Journal of Control,87(6):1153-1162,2014.
[5]Wladislaw Waag,Christian Fleischer,and Dirk Uwe Sauer.Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles.Journal of Power Sources,258(14):321-339,2014.
[6]Yuan Zou,Hu Xiaosong,Ma Hongmin,and Li Shengbo Eben.Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles.Journal of Power Sources,273:793-803,2015.
[7]Jana Kalawoun,Krystyna Biletska,Frdric Suard,and Maxime Montaru.From a novel classification of the battery state of charge estimators toward a conception of an ideal one.Journal of Power Sources,279:694-706,2015.
[8]Sabine Piller,Marion Perrrin,and Andreas Jossen.Methods for state-of-charge determination and their applications.96:113-120,2001.
[9]Mehmet Ugras Cuma and Tahsin Koroglu.A comprehensive review on estimation strategies used in hybrid and battery electric vehicles.Renewable&Sustainable Energy Reviews,42:517-531,2015.
[10]M.Shehab,El Din,M.F.Abdel-Hafez,and A.A.Hussein.Enhancement in li-ion battery cell state-of-charge estimation under uncertain model statistics.IEEE Transactions on Vehicular Technology,pages 1-1,2016.
[11]Hao Liang and Weihua Zhuang.Stochastic modeling and optimization in a microgrid:A survey.Energies,7(4):2027-2050,2014.
[12]Hans Georg Beyer and Bernhard Sendhoff.Robust optimization:A comprehensive survey.Computer Methods in Applied Mechanics and Engineering,196(33-34):3190-3218,2007.
[13]Virginie Gabrel,Ccile Murat,and Aurlie Thiele.Recent advances in robust optimization:An overview.European Journal of Operational Research,235(3):471-483,2014.
[14]D.R.Ramirez and E.F.Camacho.Characterization of minmax mpc with bounded uncertainties and a quadratic criterion.pages 358-363,2002.
[15]D.R.Ramirez,T.Alamo,and E.F.Camacho.Computational burden reduction in mincmax mpc.Journal of the Franklin Institute,348(9):2430-2447,2011.
[16]D.Q.Mayne,M.M.Seron,and S.V.Rakovic.Robust model predictive control of constrained linear systems with bounded disturbances.Automatica,41:219-224,2005.
[17]Soon Ng Kong,Chin Sien Moo,Yi Ping Chen,and Yao Ching Hsieh.Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries.Applied Energy,86(9):1506-1511,2009.
[18]GL Plett.Extended kalman filtering for battery management systems of lipb-based hev battery packs-part 2.modeling and identification.Journal of Power Sources,134(2):262-276,2004.
[19]Jonghoon Kim,Seongjun Lee,and Bohyung Cho.Discharging/charging voltage-temperature pattern recognition for improved soc/capacity estimation and soh prediction at various temperatures.Journal of Power Electronics,12(1):1-9,2012.
[20]Languang Lu,Xuebing Han,Jianqiu Li,Jianfeng Hua,and Minggao Ouyang.A review on the key issues for lithium-ion battery management in electric vehicles.Journal of Power Sources,226(3):272-288,2013.
[21]Seyed Mohammad Rezvanizaniani,Zongchang Liu,Yan Chen,and Jay Lee.Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle(ev)safety and mobility.Journal of Power Sources,256(12):110-124,2014.
[22]W.Yao,J.Zhao,F.Wen,and Y.Xue.A hierarchical decomposition approach for coordinated dispatch of plug-in electric vehicles.IEEE Transactions on Power Systems,28(3):2768-2778,2013.
[23]Z Ma,S Zou,and X Liu.A distributed charging coordination for large-scale plug-in electric vehicles considering battery degradation cost.IEEE Transactions on Control Systems Technology,23(5):1-1,2015.
[24]Zhongjing Ma,Duncan S.Callaway,and Ian A.Hiskens.Decentralized charging control of large populations of plugin electric vehicles.IEEE Transactions on Control Systems Technology,21(1):67-78,2013.
[25]I.Kolmanovsky and E.C.Gilbert.Theory and computation of distrubance invariant sets for discrete-time linear systems.Mathematical Problems in Engineering,4:317-367,1998.
[26]S.V.Rakovic,E.C.Kerrigan,K.I.Kouramas,and D.Q.Mayne.Invariant approximations of robustly positively invariant sets for constrained lineardiscrete-time systems subject to bounded disturbances,January,2004.
[27]J B Rawlings and D Q Mayne.Model predictive control:Theory and design.pages 3430-3433,2009.
[2]Yifeng He,B.Venkatesh,and Ling Guan.Optimal scheduling for charging and discharging of electric vehicles.IEEETransactions on Smart Grid,3(3):1095-1105,2012.
[3]Jonglak Pahasa and Issarachai Ngamroo.PHEVs bidirectional charging/discharging and So C control for microgrid frequency stabilization using multiple mpc.IEEE Transactions on Smart Grid,6(2):526-533,2015.
[4]Sonja Stdli and Emanuele Crisostomi.Optimal real-time distributed V2G and G2V management of electric vehicles.International Journal of Control,87(6):1153-1162,2014.
[5]Wladislaw Waag,Christian Fleischer,and Dirk Uwe Sauer.Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles.Journal of Power Sources,258(14):321-339,2014.
[6]Yuan Zou,Hu Xiaosong,Ma Hongmin,and Li Shengbo Eben.Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles.Journal of Power Sources,273:793-803,2015.
[7]Jana Kalawoun,Krystyna Biletska,Frdric Suard,and Maxime Montaru.From a novel classification of the battery state of charge estimators toward a conception of an ideal one.Journal of Power Sources,279:694-706,2015.
[8]Sabine Piller,Marion Perrrin,and Andreas Jossen.Methods for state-of-charge determination and their applications.96:113-120,2001.
[9]Mehmet Ugras Cuma and Tahsin Koroglu.A comprehensive review on estimation strategies used in hybrid and battery electric vehicles.Renewable&Sustainable Energy Reviews,42:517-531,2015.
[10]M.Shehab,El Din,M.F.Abdel-Hafez,and A.A.Hussein.Enhancement in li-ion battery cell state-of-charge estimation under uncertain model statistics.IEEE Transactions on Vehicular Technology,pages 1-1,2016.
[11]Hao Liang and Weihua Zhuang.Stochastic modeling and optimization in a microgrid:A survey.Energies,7(4):2027-2050,2014.
[12]Hans Georg Beyer and Bernhard Sendhoff.Robust optimization:A comprehensive survey.Computer Methods in Applied Mechanics and Engineering,196(33-34):3190-3218,2007.
[13]Virginie Gabrel,Ccile Murat,and Aurlie Thiele.Recent advances in robust optimization:An overview.European Journal of Operational Research,235(3):471-483,2014.
[14]D.R.Ramirez and E.F.Camacho.Characterization of minmax mpc with bounded uncertainties and a quadratic criterion.pages 358-363,2002.
[15]D.R.Ramirez,T.Alamo,and E.F.Camacho.Computational burden reduction in mincmax mpc.Journal of the Franklin Institute,348(9):2430-2447,2011.
[16]D.Q.Mayne,M.M.Seron,and S.V.Rakovic.Robust model predictive control of constrained linear systems with bounded disturbances.Automatica,41:219-224,2005.
[17]Soon Ng Kong,Chin Sien Moo,Yi Ping Chen,and Yao Ching Hsieh.Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries.Applied Energy,86(9):1506-1511,2009.
[18]GL Plett.Extended kalman filtering for battery management systems of lipb-based hev battery packs-part 2.modeling and identification.Journal of Power Sources,134(2):262-276,2004.
[19]Jonghoon Kim,Seongjun Lee,and Bohyung Cho.Discharging/charging voltage-temperature pattern recognition for improved soc/capacity estimation and soh prediction at various temperatures.Journal of Power Electronics,12(1):1-9,2012.
[20]Languang Lu,Xuebing Han,Jianqiu Li,Jianfeng Hua,and Minggao Ouyang.A review on the key issues for lithium-ion battery management in electric vehicles.Journal of Power Sources,226(3):272-288,2013.
[21]Seyed Mohammad Rezvanizaniani,Zongchang Liu,Yan Chen,and Jay Lee.Review and recent advances in battery health monitoring and prognostics technologies for electric vehicle(ev)safety and mobility.Journal of Power Sources,256(12):110-124,2014.
[22]W.Yao,J.Zhao,F.Wen,and Y.Xue.A hierarchical decomposition approach for coordinated dispatch of plug-in electric vehicles.IEEE Transactions on Power Systems,28(3):2768-2778,2013.
[23]Z Ma,S Zou,and X Liu.A distributed charging coordination for large-scale plug-in electric vehicles considering battery degradation cost.IEEE Transactions on Control Systems Technology,23(5):1-1,2015.
[24]Zhongjing Ma,Duncan S.Callaway,and Ian A.Hiskens.Decentralized charging control of large populations of plugin electric vehicles.IEEE Transactions on Control Systems Technology,21(1):67-78,2013.
[25]I.Kolmanovsky and E.C.Gilbert.Theory and computation of distrubance invariant sets for discrete-time linear systems.Mathematical Problems in Engineering,4:317-367,1998.
[26]S.V.Rakovic,E.C.Kerrigan,K.I.Kouramas,and D.Q.Mayne.Invariant approximations of robustly positively invariant sets for constrained lineardiscrete-time systems subject to bounded disturbances,January,2004.
[27]J B Rawlings and D Q Mayne.Model predictive control:Theory and design.pages 3430-3433,2009.