Towards a smarter hybrid energy storage system based on battery and ultracapacitor - A critical review on topology and energy management

Abstract Hybrid Energy Storage System (HESS) can well solve the problems faced by alternative single energy storage system in terms of meeting the needs of high specific power and high specific energy simultaneously for plug-in hybrid electric vehicles (HEVs). A HESS containing battery and ultracapacitor (UC) has drawn much attention. However, there have been relatively few reviews on its structures and energy management strategies (EMSs). Based on the summary and analysis from the existing publications, this paper reviews and discusses the structures and the EMSs of HESSs comprised of battery and UC. Focusing on energy management research, a detailed discussion of rules-based control algorithms, optimization-based control algorithms and intelligent-based control algorithms is presented. Several typical implementations and applications are presented in detail, and a comparative evaluation of these methods can help researchers select the appropriate method to develop EMSs for HESSs. Finally, the paper also highlights a number of key factors and challenges, and presents the possible recommendations for the development of big data and machine learning-based algorithm for the energy management of the HESSs.

[1]  Ozan Erdinc,et al.  Energy management of an FC/UC hybrid vehicular power system using a combined neural network-wavelet transform based strategy , 2010 .

[2]  Lech M. Grzesiak,et al.  Fuzzy logic based power management strategy using topographic data for an electric vehicle with a battery-ultracapacitor energy storage , 2015 .

[3]  Shuo Zhang,et al.  Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming , 2015 .

[4]  Horng-Wen Wu.  A review of recent development: Transport and performance modeling of PEM fuel cells , 2016 .

[5]  Mazhar Moshirvaziri,et al.  Power-mix optimization for a hybrid ultracapacitor/battery pack in an electric vehicle using real-time GPS data , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[6]  Lino Guzzella,et al.  Implementation of Dynamic Programming for $n$-Dimensional Optimal Control Problems With Final State Constraints , 2013, IEEE Transactions on Control Systems Technology.

[7]  J. Wang,et al.  Driving cycle recognition neural network algorithm based on the sliding time window for hybrid electric vehicles , 2015 .

[8]  Andreas A. Malikopoulos.  Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey , 2014, IEEE Transactions on Intelligent Transportation Systems.

[9]  Feng Xiao,et al.  Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles , 2017 .

[10]  Alireza Khaligh,et al.  Design and Real-Time Controller Implementation for a Battery-Ultracapacitor Hybrid Energy Storage System , 2016, IEEE Transactions on Industrial Informatics.

[11]  Ching Chuen Chan,et al.  Emerging Energy-Efficient Technologies for Hybrid Electric Vehicles , 2007, Proceedings of the IEEE.

[12]  Jianqiu Li,et al.  Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles , 2014 .

[13]  O. Trescases,et al.  Predictive Algorithm for Optimizing Power Flow in Hybrid Ultracapacitor/Battery Storage Systems for Light Electric Vehicles , 2013, IEEE Transactions on Power Electronics.

[14]  Vassilios G. Agelidis,et al.  A Model Predictive Control System for a Hybrid Battery-Ultracapacitor Power Source , 2014, IEEE Transactions on Power Electronics.

[15]  Hongwen He,et al.  Energy management strategy research on a hybrid power system by hardware-in-loop experiments , 2013 .

[16]  Omar Z. Sharaf,et al.  An overview of fuel cell technology: Fundamentals and applications , 2014 .

[17]  Kamal Al-Haddad,et al.  A comprehensive review of Flywheel Energy Storage System technology , 2017 .

[18]  Ion Etxeberria-Otadui,et al.  Optimal Energy Management and Sizing of a Battery--Supercapacitor-Based Light Rail Vehicle With a Multiobjective Approach , 2016, IEEE Transactions on Industry Applications.

[19]  Wen-Yen Chen,et al.  A Reinforcement Learning Based Dynamic Power Management for Fuel Cell Hybrid Electric Vehicle , 2016, 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) and 17th International Symposium on Advanced Intelligent Systems (ISIS).

[20]  Alireza Khaligh,et al.  A Supervisory Energy Management Control Strategy in a Battery/Ultracapacitor Hybrid Energy Storage System , 2015, IEEE Transactions on Transportation Electrification.

[21]  Stefano Longo,et al.  A review on electric vehicle battery modelling: From Lithium-ion toward Lithium–Sulphur , 2016 .

[22]  Chengxiong Mao,et al.  Optimal control of state-of-charge of superconducting magnetic energy storage for wind power system , 2014 .

[23]  Huang Xiaoliang,et al.  Energy Management Strategy based on frequency-varying filter for the battery supercapacitor hybrid system of Electric Vehicles , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

[24]  Jianqiu Li,et al.  The optimization of a hybrid energy storage system at subzero temperatures: Energy management strategy design and battery heating requirement analysis , 2015 .

[25]  Zeyu Chen,et al.  Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions , 2016 .

[26]  Samir Jemei,et al.  Nonlinear autoregressive neural network in an energy management strategy for battery/ultra-capacitor hybrid electrical vehicles , 2016 .

[27]  Maciej Wieczorek,et al.  A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm , 2017 .

[28]  Lei Yang,et al.  A Fuzzy-Logic Power Management Strategy Based on Markov Random Prediction for Hybrid Energy Storage Systems , 2016 .

[29]  S. Masoud Barakati,et al.  Fuzzy energy management in electrical vehicles with different hybrid energy storage topologies , 2015, 2015 4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS).

[30]  M. Verbrugge,et al.  Cycle-life model for graphite-LiFePO 4 cells , 2011 .

[31]  Li Quan,et al.  Multimode Optimization Research on a Multiport Magnetic Planetary Gear Permanent Magnet Machine for Hybrid Electric Vehicles , 2018, IEEE Transactions on Industrial Electronics.

[32]  Kalyanmoy Deb,et al.  Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems , 1999, Evolutionary Computation.

[33]  Binggang Cao,et al.  Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles , 2017 .

[34]  Heath Hofmann,et al.  Energy management strategies comparison for electric vehicles with hybrid energy storage system , 2014 .

[35]  Lech M. Grzesiak,et al.  A lithium battery and ultracapacitor hybrid energy source for an urban electric vehicle , 2012 .

[36]  Hanmin Liu,et al.  Improvement on the Cold Cranking Capacity of Commercial Vehicle by Using Supercapacitor and Lead-Acid Battery Hybrid , 2009, IEEE Transactions on Vehicular Technology.

[37]  Joao P. Trovao,et al.  A multi-level energy management system for multi-source electric vehicles – An integrated rule-based meta-heuristic approach , 2013 .

[38]  Chee Wei Tan,et al.  A review of energy sources and energy management system in electric vehicles , 2013 .

[39]  Jiayi Cao,et al.  Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle , 2018 .

[40]  Jian Zhao,et al.  Optimal fuzzy logic based energy management strategy of battery/supercapacitor hybrid energy storage system for electric vehicles , 2016, 2016 12th World Congress on Intelligent Control and Automation (WCICA).

[41]  Vahid Esfahanian,et al.  Optimum sizing and optimum energy management of a hybrid energy storage system for lithium battery life improvement , 2013 .

[42]  Bo Gao,et al.  Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective , 2017, IEEE Transactions on Vehicular Technology.

[43]  Manuel Moreno-Eguilaz,et al.  Drive Cycle Identification and Energy Demand Estimation for Refuse-Collecting Vehicles , 2015, IEEE Transactions on Vehicular Technology.

[44]  Chen Shi,et al.  Research on Reinforcement Learning Technology: A Review , 2004 .

[45]  Wenlong Jing,et al.  Battery-supercapacitor hybrid energy storage system in standalone DC microgrids: areview , 2017 .

[46]  Slawomir Koziel,et al.  Computational Optimization, Methods and Algorithms , 2016, Computational Optimization, Methods and Algorithms.

[47]  Eleni Ampatzi,et al.  Characteristics of electrical energy storage technologies and their applications in buildings , 2013 .

[48]  Ion Etxeberria-Otadui,et al.  Optimal energy management of a battery-supercapacitor based light rail vehicle using genetic algorithms , 2015, 2015 IEEE Energy Conversion Congress and Exposition (ECCE).

[49]  Olle Sundström,et al.  A generic dynamic programming Matlab function , 2009, 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC).

[50]  Aldo Sorniotti,et al.  Power split strategies for hybrid energy storage systems for vehicular applications , 2014 .

[51]  Alexandre De Bernardinis,et al.  Comparative analysis of two hybrid energy storage systems used in a two front wheel driven electric vehicle during extreme start-up and regenerative braking operations , 2017 .

[52]  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.

[53]  Keith Robert Pullen,et al.  Review of battery electric vehicle propulsion systems incorporating flywheel energy storage , 2015 .

[54]  Ozan Erdinc,et al.  A wavelet-fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid vehicular power system , 2009 .

[55]  Jun Guo,et al.  Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[56]  Qi Li,et al.  Development of energy management system based on a power sharing strategy for a fuel cell-battery-supercapacitor hybrid tramway , 2015 .

[57]  A. Emadi,et al.  A New Battery/UltraCapacitor Hybrid Energy Storage System for Electric, Hybrid, and Plug-In Hybrid Electric Vehicles , 2012, IEEE Transactions on Power Electronics.

[58]  A. Halvaei Niasar,et al.  Energy management of dual-source propelled electric vehicle using fuzzy controller optimized via genetic algorithm , 2016, 2016 7th Power Electronics and Drive Systems Technologies Conference (PEDSTC).

[59]  Alireza Khaligh,et al.  Battery, Ultracapacitor, Fuel Cell, and Hybrid Energy Storage Systems for Electric, Hybrid Electric, Fuel Cell, and Plug-In Hybrid Electric Vehicles: State of the Art , 2010, IEEE Transactions on Vehicular Technology.

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

[61]  Da Deng,et al.  Li‐ion batteries: basics, progress, and challenges , 2015 .

[62]  Steffen Kutter,et al.  Integrated thermal and energy management of plug-in hybrid electric vehicles , 2012 .

[63]  R Bellman,et al.  DYNAMIC PROGRAMMING AND LAGRANGE MULTIPLIERS. , 1956, Proceedings of the National Academy of Sciences of the United States of America.

[64]  Hak-Man Kim,et al.  Robustness Improvement of Superconducting Magnetic Energy Storage System in Microgrids Using an Energy Shaping Passivity-Based Control Strategy , 2017 .

[65]  Tao Zhu,et al.  Comparison Study of Two Semi-Active Hybrid Energy Storage Systems for Hybrid Electric Vehicle Applications and Their Experimental Validation , 2017 .

[66]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[67]  Nasser L. Azad,et al.  Real-Time Nonlinear Model Predictive Control of a Battery–Supercapacitor Hybrid Energy Storage System in Electric Vehicles , 2017, IEEE Transactions on Vehicular Technology.

[68]  Xavier Kestelyn,et al.  Adaptive Energy Management System Based on a Real-Time Model Predictive Control With Nonuniform Sampling Time for Multiple Energy Storage Electric Vehicle , 2017, IEEE Transactions on Vehicular Technology.

[69]  Haisheng Chen,et al.  Progress in electrical energy storage system: A critical review , 2009 .

[70]  Qinpu Wang,et al.  Survey on Energy Management Strategy for Plug-in Hybrid Electric Vehicles , 2017 .

[71]  Qingfeng Wang,et al.  A review of developments in energy storage systems for hybrid excavators , 2017 .

[72]  Xiaowu Zhang,et al.  A comparison study of different semi-active hybrid energy storage system topologies for electric vehicles , 2015 .

[73]  Reinhard Madlener,et al.  Economics of centralized and decentralized compressed air energy storage for enhanced grid integration of wind power , 2013 .

[74]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[75]  Yuan Zou,et al.  Reinforcement Learning of Adaptive Energy Management With Transition Probability for a Hybrid Electric Tracked Vehicle , 2015, IEEE Transactions on Industrial Electronics.

[76]  Vassilios G. Agelidis,et al.  Application of explicit model predictive control to a hybrid battery-ultracapacitor power source , 2015 .

[77]  Jianqiu Li,et al.  Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach , 2015 .

[78]  Kamal Al-Haddad,et al.  Neural Network Controller to Manage the Power Flow of a Hybrid Source for Electric Vehicles , 2015, 2015 IEEE Vehicle Power and Propulsion Conference (VPPC).

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

[80]  Stefano Di Cairano,et al.  Cloud-Based Velocity Profile Optimization for Everyday Driving: A Dynamic-Programming-Based Solution , 2014, IEEE Transactions on Intelligent Transportation Systems.

[81]  Li Quan,et al.  Active Disturbance Rejection Controller for Speed Control of Electrical Drives Using Phase-Locking Loop Observer , 2019, IEEE Transactions on Industrial Electronics.

[82]  Rui Xiong,et al.  Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle , 2018 .

[83]  Jianqiu Li,et al.  The influence of driving cycle characteristics on the integrated optimization of hybrid energy storage system for electric city buses , 2017 .

[84]  Gang Xu,et al.  Simultaneous Optimization for Hybrid Electric Vehicle Parameters Based on Multi-Objective Genetic Algorithms , 2011 .

[85]  Guizhou Ren,et al.  Review of electrical energy storage system for vehicular applications , 2015 .

[86]  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).

[87]  Xiaofeng Lin,et al.  Battery-supercapacitor electric vehicles energy management using DP based predictive control algorithm , 2014, CIVTS.

[88]  Shuo Zhang,et al.  Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system , 2017 .

[89]  David G. Dorrell,et al.  A review of supercapacitor modeling, estimation, and applications: A control/management perspective , 2018 .

[90]  Vassilios G. Agelidis,et al.  Integrated Reconfigurable Configuration for Battery/Ultracapacitor Hybrid Energy Storage Systems , 2016, IEEE Transactions on Energy Conversion.

[91]  Hongwen He,et al.  A comparative study on the applicability of ultracapacitor models for electric vehicles under different temperatures , 2017 .

[92]  Shuo Zhang,et al.  Comparison of the topologies for a hybrid energy-storage system of electric vehicles via a novel optimization method , 2015 .

[93]  Jialu Li,et al.  RETRACTED: Energy recovery data characteristics extraction of flywheel energy storage control system for vehicular applications , 2017 .

[94]  Jun Guo,et al.  Cross-modal subspace learning for fine-grained sketch-based image retrieval , 2017, Neurocomputing.

[95]  Walter Lhomme,et al.  Comparison of energy management strategies of a battery/supercapacitors system for electric vehicle under real-time constraints , 2016 .

[96]  Yi Lu Murphey,et al.  Intelligent power management in a vehicular system with multiple power sources , 2011 .

[97]  Qiao Zhang,et al.  An Adaptive Energy Management System for Electric Vehicles Based on Driving Cycle Identification and Wavelet Transform , 2016 .

[98]  Ding Chen,et al.  The Application Research of Operating Vehicle GPS Big Data Mining , 2015 .

[99]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[100]  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.

[101]  Haoran Zhao,et al.  Review of energy storage system for wind power integration support , 2015 .

[102]  N. Omar,et al.  Lithium iron phosphate based battery: Assessment of the aging parameters and development of cycle life model , 2014 .

[103]  Hedayat Saboori,et al.  Emergence of hybrid energy storage systems in renewable energy and transport applications – A review , 2016 .

[104]  Weiwen Deng,et al.  Power Management for Hybrid Energy Storage System of Electric Vehicles Considering Inaccurate Terrain Information , 2017, IEEE Transactions on Automation Science and Engineering.