Power distribution optimization of a fully active hybrid energy storage system configuration for vehicular applications
Tài liệu tham khảo
Cui, 2022, Battery electric vehicle usage pattern analysis driven by massive real-world data, Energy, 250, 10.1016/j.energy.2022.123837
Adu-Gyamfi, 2022, Determinants of adoption intention of battery swap technology for electric vehicles, Energy, 251, 10.1016/j.energy.2022.123862
Samdhyan, 2022, Development of carbon-based copper sulfide nanocomposites for high energy supercapacitor applications: a comprehensive review, J. Energy Storage, 46, 10.1016/j.est.2021.103886
Kumar, 2022, Microflowers of Sn-Co-S derived from ultra-thin nanosheets for supercapacitor applications, J. Energy Storage, 49, 10.1016/j.est.2022.104084
Ren, 2022, Full current-type control-based hybrid energy storage system, Energies, 15, 2910, 10.3390/en15082910
Abadi, 2022, A model predictive control strategy for performance improvement of hybrid energy storage systems in DC microgrids, IEEE Access, 10, 25400, 10.1109/ACCESS.2022.3155668
Shi, 2022, Energy management strategy for battery/supercapacitor hybrid electric city bus based on driving pattern recognition, Energy, 243, 10.1016/j.energy.2021.122752
Yang, 2020, Applications of battery/supercapacitor hybrid energy storage systems for electric vehicles using perturbation observer based robust control, J. Power Sources, 448, 10.1016/j.jpowsour.2019.227444
Zhang, 2020, A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles, J. Energy Storage, 31, 10.1016/j.est.2020.101721
Xiao, 2022, Multiobjective optimization for a Li-ion battery and supercapacitor hybrid energy storage electric vehicle, Energies, 15, 2821, 10.3390/en15082821
Yuhimenko, 2015, DC active power filter-based hybrid energy source for pulsed power loads, IEEE J. Emerg. Sel. Top Power Electron, 3, 1001, 10.1109/JESTPE.2015.2421305
Wang, 2019, Comparison of decomposition levels for wavelet transform based energy management in a plug-in hybrid electric vehicle, J. Clean. Prod., 210, 1085, 10.1016/j.jclepro.2018.11.082
Asensio, 2018, Efficiency and Performance Analysis of Battery-Ultracapacitor based Semi-active Hybrid Energy Systems for Electric Vehicles, IEEE Lat. Am. Trans., 16, 2581, 10.1109/TLA.2018.8795138
Tavakol-Sisakht, 2016, Energy management using fuzzy controller for hybrid electrical vehicles, J. Intell. Fuzzy Syst., 30, 1411, 10.3233/IFS-152054
Yu, 2015, A novel fuzzy-logic based control strategy for a semi-active battery/super-capacitor hybrid energy storage system in vehicular applications, J. Intell. Fuzzy Syst., 29, 2575, 10.3233/IFS-151960
Chen, 2016, Industrial information integration—A literature review 2006-2015, J. Ind. Inf. Integr., 2, 30
Xu, 2020, Industrial information integration - An emerging subject in industrialization and informatization process, J. Ind. Inf. Integr., 17
Chen Y. A Survey on Industrial Information Integration 2016–2019, Journal of Industrial Integration and Management, 5(1): 33–163. DOI 10.1142/S2424862219500167.
Xu, 2016, Inaugural issue editorial, J. Ind. Inf. Integr., 1, 1
Chi, 2016, An optimal two-tier fuzzified control scheme for energy efficiency management of parallel hybrid vehicles, J. Ind. Inf. Integr., 4, 1
Pradhan, 2018, Antlion optimizer tuned PID controller based on Bode ideal transfer function for automobile cruise control system, J. Ind. Inf. Integr., 9, 45
Choi, 2012, Energy management optimization in a battery/supercapacitor hybrid energy storage system, IEEE Trans. Smart Grid, 3, 463, 10.1109/TSG.2011.2164816
Wegmann, 2017, Optimized operation of hybrid battery systems for electric vehicles using deterministic and stochastic dynamic programming, J. Energy Storage, 14, 22, 10.1016/j.est.2017.09.008
Li, 2021, Sizing optimization and energy management strategy for hybrid energy storage system using multiobjective optimization and random forests, IEEE Trans. Power Electron., 36, 11421, 10.1109/TPEL.2021.3070393
Uebel, 2018, Optimal energy management and velocity control of hybrid electric vehicles, IEEE Trans. Veh. Technol., 67, 327, 10.1109/TVT.2017.2727680
Hredzak, 2014, A model predictive control system for a hybrid battery-ultracapacitor power source, IEEE Trans. Power Electron., 29, 1469, 10.1109/TPEL.2013.2262003
Asensio, 2022, Short-term power demand prediction for energy management of an electric vehicle based on batteries and ultracapacitors, Energy, 247, 10.1016/j.energy.2022.123430
Wieczorek, 2017, A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithm, Appl. Energy, 192, 222, 10.1016/j.apenergy.2017.02.022
Bhattacharjee, 2019, Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance, Energy, 183, 235, 10.1016/j.energy.2019.06.115
Mohammed, 2018, Economical evaluation and optimal energy management of a stand-alone hybrid energy system handling in genetic algorithm strategies, Electronics (Basel), 7, 233
Shen, 2016, Design and real-time controller implementation for a battery-ultracapacitor hybrid energy storage system, IEEE Trans. Ind. Inf., 12, 1910, 10.1109/TII.2016.2575798
Mesbahi, 2017, Combined optimal sizing and control of Li-ion battery/supercapacitor embedded power supply using hybrid particle swarm–nelder–mead algorithm, IEEE Trans. Sustainable Energy, 8, 59, 10.1109/TSTE.2016.2582927
Machado, 2016, Effectiveness of supercapacitors in pure electric vehicles using a hybrid metaheuristic approach, IEEE Trans. Veh. Technol., 65, 29, 10.1109/TVT.2015.2390919
Wang, 2017, Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles, Appl. Energy, 194, 596, 10.1016/j.apenergy.2016.05.030
Trovao, 2015, A real-time energy management architecture for multisource electric vehicles, IEEE Trans. Ind. Electron., 62, 3223, 10.1109/TIE.2014.2376883
Wang, 2017, Adaptive sliding-mode with hysteresis control strategy for simple multimode hybrid energy storage system in electric vehicles, IEEE Trans. Ind. Electron., 64, 1404, 10.1109/TIE.2016.2618778
Xiong, 2018, Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle, Appl. Energy, 211, 538, 10.1016/j.apenergy.2017.11.072
Lu, 2019, Multi-objective optimization-based real-time control strategy for battery/ultracapacitor hybrid energy management systems, IEEE Access, 7, 11640, 10.1109/ACCESS.2019.2891884
Nambisan, 2022, Optimal energy management of battery supercapacitor aided solar pv powered agricultural feed mill using Pontryagin's minimum principle, IEEE Trans. Power Electron., 37, 2216
Zhang, 2020, A real-time energy management and speed controller for an electric vehicle powered by a hybrid energy storage system, IEEE Trans. Ind. Inf., 16, 6272, 10.1109/TII.2020.2964389
Zhang, 2020, An adaptive energy management strategy for fuel cell/battery/supercapacitor hybrid energy storage systems of electric vehicles, Int. J. Electrochem. Sci., 15, 3410, 10.20964/2020.04.50
Liu, 2022, An adaptive energy management strategy of stationary hybrid energy storage system, IEEE Trans. Transport. Electrification, 8, 2261, 10.1109/TTE.2022.3150149
Zhang, 2021, Fuzzy adaptive filtering-based energy management for hybrid energy storage system, Comput. Syst. Sci. Eng., 36, 117, 10.32604/csse.2021.014081
Shaik, 2020, Application of adaptive neuro-fuzzy inference rule-based controller in hybrid electric vehicles, J. Electrical Eng. Technol., 15, 1937, 10.1007/s42835-020-00459-w
da Silva, 2022, Franco Giuseppe Dedini. Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle, Appl. Energy, 324, 10.1016/j.apenergy.2022.119723
Yang, 2020, Applications of battery/supercapacitor hybrid energy storage systems for electric vehicles using perturbation observer based robust control, J. Power Sources, 448, 10.1016/j.jpowsour.2019.227444
Yodwong, 2020, Differential flatness-based cascade energy/current control of battery/supercapacitor hybrid source for modern e-vehicle applications, Mathematics, 8, 704, 10.3390/math8050704
Xu, 2022, Hierarchical Q-learning network for online simultaneous optimization of energy efficiency and battery life of the battery/ultracapacitor electric vehicle, J. Energy Storage, 46, 10.1016/j.est.2021.103925
Wang, 2022, Power dynamic allocation strategy for urban rail hybrid energy storage system based on iterative learning control, Energy, 245, 10.1016/j.energy.2022.123263
Chen, 2022, Development of machine learning methods in hybrid energy storage systems in electric vehicles, Math. Probl. Eng., 2022
Yu, 2020, Efficient model predictive control for real-time energy optimization of battery: upercapacitors in electric vehicles, Int. J. Energy Res., 44, 7495, 10.1002/er.5473
Sellali, 2020, Hardware implementation of an improved control strategy for battery/super capacitor hybrid system in electric vehicles, IET Electrical Syst. Transport., 10, 204, 10.1049/iet-est.2019.0034
Hu, 2022, A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data, Renew. Sustainable Energy Rev., 162, 10.1016/j.rser.2022.112416
Wang, 2022, A multi-objective optimization energy management strategy for power split HEV based on velocity prediction, Energy, 238
Zhang, 2020, A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles, J. Energy Storage, 31, 10.1016/j.est.2020.101721
Eckert, 2020, Electric vehicle powertrain and fuzzy control multi-objective optimization, considering dual hybrid energy storage systems, IEEE Trans. Veh. Technol., 69, 3773, 10.1109/TVT.2020.2973601
Mirjalili, 2014, Grey Wolf Optimizer, Adv. Eng. Software, 69, 46, 10.1016/j.advengsoft.2013.12.007
