J Tian, R Xiong, W Shen, et al. Electrode ageing estimation and open circuit voltage reconstruction for lithium ion batteries. Energy Storage Materials, 2021, 37: 283–295.
Article
Google Scholar
J Tian, R Xiong, W Shen, et al. State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach. Applied Energy, 2021, 291: 116812.
Article
Google Scholar
V Etacheri, R Marom, R Elazari, et al. Challenges in the development of advanced Li-ion batteries: A review. Energy & Environmental Science, 2011, 4(9): 3243–3262.
Article
Google Scholar
L Lu, X Han, J Li, et al. A review on the key issues for lithium-ion battery management in electric vehicles. Journal of Power Sources, 2013, 226: 272–288.
Article
Google Scholar
H Dai, B Jiang, X Hu, et al. Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends. Renewable and Sustainable Energy Reviews, 2021, 138: 110480.
Article
Google Scholar
Y Wang, J Tian, Z Sun, et al. A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renewable and Sustainable Energy Reviews, 2020, 131: 110015.
Article
Google Scholar
W Zhang, W Cai, J Min, et al. 5G and AI technology application in the AMTC learning factory. Procedia Manufacturing, 2020, 45: 66–71.
Article
Google Scholar
J Wang, Y Ma, L Zhang, et al. Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 2018, 48: 144–156.
Article
Google Scholar
M Syafrudin, G Alfian, N Fitriyani, et al. Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors, 2018, 18(9): 2946.
Article
Google Scholar
C M Ezhilarasu, I K Jennions, Z Skaf. Understanding the role of a Digital Twin in the field of Integrated Vehicle Health Management (IVHM). IEEE International Conference on Systems, Man, and Cybernetics, 2019, 1484–1491.
C Li, S Mahadevan, Y Ling, et al. Dynamic Bayesian network for aircraft wing health monitoring digital twin. AIAA Journal, 2017, 55(3): 930–941.
Article
Google Scholar
L Deng, W Shen, H Wang, et al. A rest-time-based prognostic model for remaining useful life prediction of lithium-ion battery. Neural Computing and Applications, 2020, 33(6): 2035–2046.
Article
Google Scholar
G Xia, L Cao, G Bi. A review on battery thermal management in electric vehicle application. Journal of Power Sources, 2017, 367: 90–105.
Article
Google Scholar
M Grieves. Digital twin: Manufacturing excellence through virtual factory replication. 2018.
E Tuegel, A Ingraffea, T Eason, et al. Reengineering aircraft structural life prediction using a digital twin. International Journal of Aerospace Engineering, 2011, 2011: 154798.
Article
Google Scholar
K Funk, G Reinhart. Digital twins at the crossroad of production, product and technology. MikroSystemTechnik, Congress. VDE, 2018: 1–4.
GE Plans Software Platform For Creating “Digital Twins.” 2016. https://www.plantservices.com/industrynews/2016/ge-plans-software-platform-for-creating-digital-twins/. Accessed 5 May 2021.
Fei Tao, Ying Cheng, Jiangfeng Cheng, et al. Theories and technologies for cyber-physical fusion in digital twin shop-floor. Computer Integrated Manufacturing Systems, 2017, 23(8): 1603–1611. (in Chinese).
Google Scholar
K Panetta. Gartner top 10 strategic technology trends for 2017. 2016. https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018/. Accessed 5 May 2021.
K Panetta. Gartner top 10 strategic technology trends for 2018. 2017. https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2018/?utm_source=social&utm_campaign=sm-swg&utm_medium=social. Accessed 5 May 2021.
K Panetta. Gartner top 10 strategic technology trends for 2019. 2018. https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2019/. Accessed 5 May 2021.
C Cimino, E Negri, L Fumagalli. Review of digital twin applications in manufacturing. Computers in Industry, 2019, 113(6): 103130.
Article
Google Scholar
Y Zheng, S Yang, H Cheng. An application framework of digital twin and its case study. Journal of Ambient Intelligence and Humanized Computing, 2019, 10: 1141–1153.
Article
Google Scholar
B Wu, W D Widanage, S Yang, et al. Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems. Energy and AI, 2020, 1: 100016.
Article
Google Scholar
X Qu, Y Song, D Liu, et al. Lithium-ion battery performance degradation evaluation in dynamic operating conditions based on a digital twin model. Microelectronics Reliability, 2020, 114: 113857.
Article
Google Scholar
W Li, M Rentemeister, J Badeda, et al. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. Journal of Energy Storage, 2020, 30: 101557.
Article
Google Scholar
M Shafto, M Conroy, R Doyle, et al. Modeling, simulation, information technology and processing roadmap. NASA Report, 2010.
R Rosen, G von Wichert, G Lo, et al. About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine, 2015, 48(3): 567–572.
Article
Google Scholar
M Schluse, J Rossmann. From simulation to experimentable digital twins: Simulation-based development and operation of complex technical systems. IEEE International Symposium on Systems Engineering, 2016, 273–278.
R Söderberg, K Wärmefjord, J S Carlson, et al. Toward a digital twin for real-time geometry assurance in individualized production. CIRP Annals, 2017, 66(1): 137–140.
Article
Google Scholar
Y Xu, Y Sun, X Liu, et al. A digital-twin-assisted fault diagnosis using deep transfer learning. IEEE Access, 2019, 7: 19990–19999.
Article
Google Scholar
P Wang, M Yang, Y Peng, et al. Sensor control in anti-submarine warfare—A digital twin and random finite sets based approach. Entropy, 2019, 21(6): 767–794.
Article
MathSciNet
Google Scholar
I A T Hashem, I Yaqoob, N B Anuar, et al. The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 2015, 47: 98–115.
Article
Google Scholar
U Gupta, A Gupta. Vision: A missing key dimension in the 5V big data framework. Journal of International Business Research and Marketing, 2015, 1: 40–47.
Article
Google Scholar
Fei Tao, Weiran Liu, Meng Zhang, et al. Five-dimension digital twin model and its ten applications. Computer Integrated Manufacturing Systems, 2019, 25(1): 1–18. (in Chinese).
Google Scholar
W Sun, Y Qiu, L Sun, et al. Neural network-based learning and estimation of battery state-of-charge: A comparison study between direct and indirect methodology. International Journal of Energy Research, 2020, 44(13): 10307–10319.
Article
Google Scholar
J Gubbi, R Buyya, S Marusic, et al. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 2013, 29: 1645–1660.
Article
Google Scholar
S Li, H He, J Li. Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology. Applied Energy, 2019, 242: 1259–1273.
Article
Google Scholar
K Christidis, M Devetsikiotis. Blockchains and smart contracts for the Internet of Things. IEEE Access, 2016, 4: 2292–2303.
Article
Google Scholar
Y Ma, X Li, G Li, et al. SOC Oriented electrochemical-thermal coupled modeling for lithium-ion battery. IEEE Access, 2019, 7: 156136–156149.
Article
Google Scholar
B Rajabloo, A Jokar, W Wakem, et al. Lithium iron phosphate electrode semi-empirical performance model. Journal of Applied Electrochemistry, 2018, 48(6): 663–674.
Article
Google Scholar
S M Rezvanizaniani, S Lee, J Lee. A comparative analysis of techniques for electric vehicle battery prognostics and health management (PHM). SAE Technical Papers, 2011. https://doi.org/10.4271/2011-01-2247.
Article
Google Scholar
W D Widanage, A Barai, G H Chouchelamane, et al. Design and use of multisine signals for Li-ion battery equivalent circuit modelling. Part 1: Signal design. Journal of Power Sources, 2016, 324: 70–78.
Article
Google Scholar
W D Widanage, A Barai, G H Chouchelamane, et al. Design and use of multisine signals for Li-ion battery equivalent circuit modelling. Part 2: Model estimation. Journal of Power Sources, 2016, 324: 61-69.
Article
Google Scholar
L Zhang, H Peng, Z Ning, et al. Comparative research on RC equivalent circuit models for lithium-ion batteries of electric vehicles. Applied Sciences, 2017, 7(10): 1002.
Article
Google Scholar
S Nejad, D T Gladwin, D A Stone. A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states. Journal of Power Sources, 2016, 316: 183–196.
Article
Google Scholar
R Xiong, J Tian, W Shen, et al. A novel fractional order model for state of charge estimation in lithium ion batteries. IEEE Transactions on Vehicular Technology, 2019, 68(5): 4130–4139.
Article
Google Scholar
C Zhang, Z Yayun, G Dong, et al. Data-driven lithium-ion battery states estimation using neural networks and particle filtering. International Journal of Energy Research, 2019, 43(14): 8230–8241.
Google Scholar
J Newman, W Tiedemann. Porous-electrode theory with battery applications. AIChE Journal, 1975, 21(1): 25–41.
Article
Google Scholar
M Doyle, T Fuller, J Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of The Electrochemical Society, 1993, 140(6): 1526–1533.
Article
Google Scholar
V Subramanian, J Ritter, R White. Approximate solutions for galvanostatic discharge of spherical particles I. Constant diffusion coefficient. Journal of the Electrochemical Society, 2001, 148(11): E444–E449.
Article
Google Scholar
M Guo, G Sikha, R White. Single-particle model for a lithium-ion cell: Thermal behavior. Journal of The Electrochemical Society, 2011, 158(2): A122–A132.
Article
Google Scholar
S Santhanagopalan, Q Guo, P Ramadass, et al. Review of models for predicting the cycling performance of lithium ion batteries. Journal of Power Sources, 2006, 156(2): 620–628.
Article
Google Scholar
J Li, D Wang, M Pecht. An electrochemical model for high C-rate conditions in lithium-ion batteries. Journal of Power Sources, 2019, 436: 226885.
Article
Google Scholar
M Ecker, T Tran, P Dechent, et al. Parameterization of a physico-chemical model of a lithium-ion battery: I. Determination of parameters. Journal of the Electrochemical Society, 2015, 162(9): A1836–A1848.
Article
Google Scholar
J Schmalstieg, C Rahe, M Ecker, et al. Full cell parameterization of a high-power lithium-ion battery for a physico-chemical model: Part I. Physical and electrochemical parameters. Journal of The Electrochemical Society, 2018, 165(16): A3799–A3810.
Article
Google Scholar
J Schmalstieg, D Sauer. Full cell parameterization of a high-power lithium-ion battery for a physico-chemical model: Part II. Thermal parameters and validation. Journal of The Electrochemical Society, 2018, 165(16): A3811–A3819.
Article
Google Scholar
W Li, D Cao, D Jöst, et al. Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries. Applied Energy, 2020, 269: 115104.
Article
Google Scholar
M A Rahman, S Anwar, A Izadian, Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method. Journal of Power Sources, 2016, 307: 86–97.
Article
Google Scholar
J Forman, S Bashash, J Stein, et al. Reduction of an electrochemistry-based li-ion battery model via quasi-linearization and padé approximation. Journal of The Electrochemical Society, 2011, 158(2): A93–A101.
Article
Google Scholar
Y Ma, J Ru, M Yin, et al. Electrochemical modeling and parameter identification based on bacterial foraging optimization algorithm for lithium-ion batteries. Journal of Applied Electrochemistry, 2016, 46(11): 1119–1131.
Article
Google Scholar
Y Fang, R Xiong, J Wang. Estimation of lithium-ion battery state of charge for electric vehicles based on dual extended Kalman filter. Energy Procedia, 2018, 152: 574–579.
Article
Google Scholar
J Wang, R Xiong, L Li, et al. A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach. Applied Energy, 2018, 229: 648–659.
Article
Google Scholar
B Xia, H Wang, Y Tian, et al. State of charge estimation of lithium-ion batteries using an adaptive cubature Kalman filter. Energies, 2015, 8(6): 5916–5936.
Article
Google Scholar
Q Yu, R Xiong, C Lin, et al. Lithium-ion battery parameters and state-of-charge joint estimation based on H-Infinity and unscented Kalman filters. IEEE Transactions on Vehicular Technology, 2017, 66(10): 8693–8701.
Article
Google Scholar
B Liu, X Tang, F Gao. Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model. Electrochimica Acta, 2020, 344: 136098.
Article
Google Scholar
J C A Antón, P J GNieto, F G D Juez, et al. Battery state-of-charge estimator using the MARS technique. IEEE Transactions on Power Electronics, 2012, 28(8): 3798–3805.
Article
Google Scholar
C Fleischer, W Waag, Z Bai, et al. On-line self-learning time forward voltage prognosis for lithium-ion batteries using adaptive neuro-fuzzy inference system. Journal of Power Sources, 2013, 243: 728–749.
Article
Google Scholar
S Khaleghi, Y Firouz, J Mierlo, et al. Developing a real-time data-driven battery health diagnosis method, using time and frequency domain condition indicators. Applied Energy, 2019, 225:113813.
Article
Google Scholar
L Chen, W Lin, J Li, et al. Prediction of lithium-ion battery capacity with metabolic grey model. Energy, 2016, 106: 662–672.
Article
Google Scholar
C Chen, R Xiong, R Yang, et al. State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter. Journal of Cleaner Production, 2019, 234: 1153–1164.
Article
Google Scholar
Y Zhang, R Xiong, H He, et al. Aging characteristics-based health diagnosis and remaining useful life prognostics for lithium-ion batteries. eTransportation, 2019, 1(6): 100004.
Article
Google Scholar
K Severson, P Attia, N Jin, et al. Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 2019, 4(5): 383–391.
Article
Google Scholar
A Kuznietsov, T Happek, F L T Guefack. On-board state of health estimation of Li-Ion batteries packs using incremental capacity analysis with principal components. 2018 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC), 2018.
E Riviere, P Venet, A Sari, et al. LiFePO4 battery state of health online estimation using electric vehicle embedded incremental capacity analysis. 2015 IEEE Vehicle Power and Propulsion Conference (VPPC), 2015.
R Xiong, Y Pan, W Shen, et al. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives. Renewable and Sustainable Energy Reviews, 2020, 131: 110048.
Article
Google Scholar
Y Zhang, R Xiong, H He, et al. A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction. 2017 Prognostics and System Health Management Conference (PHM-Harbin), 2017, 1059–1062.
Y Zhang, R Xiong, H He, et al. Validation and verification of a hybrid method for remaining useful life prediction of lithium-ion batteries. Journal of Cleaner Production, 2019, 212: 240–249.
Article
Google Scholar
R Xiong, Y Zhang, J Wang, et al. Lithium-ion battery health prognosis based on a real battery management system used in electric vehicles. IEEE Transactions on Vehicular Technology, 2018, 68(5): 4110–4121.
Article
Google Scholar
Y Xing, E Ma, K-L Tsui, et al. An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectronics Reliability, 2013, 53(6): 811–820.
Article
Google Scholar
Q Miao, L Xie, H Cui, et al. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique. Microelectronics Reliability, 2013, 53(6): 805–810.
Article
Google Scholar
W Xian, B Long, M Li, et al. Prognostics of lithium-ion batteries based on the verhulst model, particle swarm optimization and particle filter. Instrumentation and Measurement, IEEE Transactions On, 2014, 63(1): 2–17.
Article
Google Scholar
F Yang, K L Tsui, Q Zhou, et al. Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter. IEEE Transactions on Instrumentation and Measurement, 2016, 65(6): 1282–1291.
Article
Google Scholar
Y Zhang, R Xiong, H He, et al. Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries. IEEE Transactions on Vehicular Technology, 2018, 67(7): 5695–5705.
Article
Google Scholar
X Hu, L Xu, X Lin, et al. Battery lifetime prognostics. Joule, 2020, 4(2): 310–346.
Article
Google Scholar
H Chun, J Kim, J Yu, et al. Real-time parameter estimation of an electrochemical lithium-ion battery model using a long short-term memory network. IEEE Access, 2020, 8: 81789–81799.
Article
Google Scholar
A Sancarlos, M Cameron, A Abel, et al. From ROM of electrochemistry to AI-based battery digital and hybrid twin. Archives of Computational Methods in Engineering, 2020, 28(3): 979–1015.
Article
MathSciNet
Google Scholar
J Ma, S Xu, P Shang, et al. Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method. Applied Energy, 2020, 262: 114490.
Article
Google Scholar
T Osterloh, J Rossmann. A rigid body dynamics simulation framework for the analysis of attitude control systems of modular satellite systems. 2019 IEEE International Systems Conference (SysCon), 2019.
M Baumann, S Rohr, M Lienkamp. Cloud-connected battery management for decision making on second-life of electric vehicle batteries. 2018 Thirteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2018.
D Ren, L Lu, P Shen, et al. Battery remaining discharge energy estimation based on prediction of future operating conditions. Journal of Energy Storage, 2019, 25: 100836.
Article
Google Scholar
R Xiong, S Ma, H Li, et al. Toward a safer battery management system: A critical review on diagnosis and prognosis of battery short circuit. IScience, 2020, 23(4): 101010.
Article
Google Scholar
X Feng, D Ren, X He, et al. Mitigating thermal runaway of lithium-ion batteries. Joule, 2020, 4(4): 743–770.
Article
Google Scholar
S McGlaun. BYD Blade Battery promises safer electric vehicles. 2020. https://www.slashgear.com/byd-blade-battery-promises-safer-electric-vehicles-30614808/. Accessed 5 May 2021.
Q Wang, P Ping, X Zhao, et al. Thermal runaway caused fire and explosion of lithium ion battery. Journal of Power Sources, 2012, 208: 210–224.
Article
Google Scholar
G-H Kim, A Pesaran, R Spotnitz. A three-dimensional thermal abuse model for lithium-ion cells. Journal of Power Sources, 2007, 170(2): 476–489.
Article
Google Scholar
D C Lee, C W Kim. Two-way nonlinear mechanical-electrochemical-thermal coupled analysis method to predict thermal runaway of lithium-ion battery cells caused by quasi-static indentation. Journal of Power Sources, 2020, 475: 228678.
Article
Google Scholar
X Feng, S Zheng, D Ren, et al. Key characteristics for thermal runaway of li-ion batteries. Energy Procedia, 2019, 158: 4684–4689.
Article
Google Scholar
M Nascimento, M S Ferreira, J L Pinto. Temperature fiber sensing of Li-ion batteries under different environmental and operating conditions. Applied Thermal Engineering, 2019, 149: 1236–1243.
Article
Google Scholar
M S K Mutyala, J Zhao, J Li, et al. In-situ temperature measurement in lithium ion battery by transferable flexible thin film thermocouples. Journal of Power Sources, 2014, 260: 43–49.
Article
Google Scholar
M Nascimento, S Novais, M S Ding, et al. Internal strain and temperature discrimination with optical fiber hybrid sensors in Li-ion batteries. Journal of Power Sources, 2019, 410–411: 1–9.
Article
Google Scholar
T Waldmann, M Wilka, M Kasper, et al. Temperature dependent ageing mechanisms in Lithium-ion batteries – A Post-Mortem study. Journal of Power Sources, 2014, 262: 129–135.
Article
Google Scholar
Y Gao, X Zhang, Q Cheng, et al. Classification and review of the charging strategies for commercial lithium-ion batteries. IEEE Access, 2019, 7: 43511–43524.
Article
Google Scholar
A Tomaszewska, Z Chu, X Feng, et al. Lithium-ion battery fast charging: A review. eTransportation, 2019, 1: 100011.
Article
Google Scholar
H Perez, S Dey, X Hu, et al. Optimal charging of li-ion batteries via a single particle model with electrolyte and thermal dynamics. Journal of The Electrochemical Society, 2017, 164(7): A1679–A1687.
Article
Google Scholar
W Mai, A Colclasure, K Smith. Model-instructed design of novel charging protocols for the extreme fast charging of lithium-ion batteries without lithium plating. Journal of the Electrochemical Society, 2020, 167(8): 080517.
Article
Google Scholar
M S Wu, P C J Chiang, J C Lin. Electrochemical investigations on advanced lithium-ion batteries by three-electrode measurements. Journal of The Electrochemical Society, 2005, 152(1): A47–A52.
Article
Google Scholar
J Zhao, J Jiang, L Niu. A novel charge equalization technique for electric vehicle battery system. The Fifth International Conference on Power Electronics and Drive Systems, 2003. PEDS 2003, 2003, 2: 853–857.
Article
Google Scholar
R Xiong, W Sun, Q Yu, et al. Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles. Applied Energy, 2020, 279: 115855.
Article
Google Scholar
J Kim, J Shin, C Chun, et al. Stable configuration of a li-ion series battery pack based on a screening process for improved voltage/SOC balancing. IEEE Transactions on Power Electronics, 2012, 27(1): 411–424.
Article
Google Scholar
M A Hannan, M M Hoque, S E Peng, et al. Lithium-ion battery charge equalization algorithm for electric vehicle applications. 2016 IEEE Industry Applications Society Annual Meeting, 2016, pp. 1–8.
Y S Lee, M W Cheng. Intelligent control battery equalization for series connected lithium-ion battery strings. IEEE Transactions on Industrial Electronics, 2005, 52(5): 1297–1307.
Article
Google Scholar
H Qian, J Zhang, J-S Lai, et al. A high-efficiency grid-tied battery energy storage system. IEEE Transactions on Power Electronics, 2011, 26(3): 886–896.
Article
Google Scholar
M Einhorn, F V Conte, C Kral, et al. A method for online capacity estimation of lithium ion battery cells using the state of charge and the transferred charge. IEEE International Conference on Sustainable Energy Technologies (ICSET), 2012, 48(2): 736–741.
Google Scholar
Y Zheng, M Ouyang, L Lu, et al. On-line equalization for lithium-ion battery packs based on charging cell voltages: Part 1. Equalization based on remaining charging capacity estimation. Journal of Power Sources, 2014, 247: 676–686.
Article
Google Scholar
Y Zheng, M Ouyang, L Lu, et al. On-line equalization for lithium-ion battery packs based on charging cell voltages: Part 2. Fuzzy logic equalization. Journal of Power Sources, 2014, 247: 460–466.
Article
Google Scholar
Q Lin, J Wang, R Xiong, et al. Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries. Energy, 2019, 183: 220–234.
Article
Google Scholar
X Hu, Y Zheng, D A Howey, et al. Battery warm-up methodologies at subzero temperatures for automotive applications: Recent advances and perspectives. Progress in Energy and Combustion Science, 2020, 77: 100806.
Article
Google Scholar
J Lv, W Song, L Shili, et al. Influence of equalization on LiFePO 4 battery inconsistency. International Journal of Energy Research, 2017, 41(8): 1171–1181.
Article
Google Scholar
R Xiong, Q Yu, W Shen, et al. A sensor fault diagnosis method for a lithium-ion battery pack in electric vehicles. IEEE Transactions on Power Electronics, 2019, 34(10): 9709–9718.
Article
Google Scholar
J Ma, P Shang, X Zou, et al. A hybrid transfer learning scheme for remaining useful life prediction and cycle life test optimization of different formulation Li-ion power batteries. Applied Energy, 2021, 282: 116167.
Article
Google Scholar
I R Aenugu, G Bere, J Ochoa, et al. Battery data management and analytics platform using blockchain technology. 2020 IEEE Transportation Electrification Conference & Expo (ITEC), 2020: 153–157.
Yang Gao, Xing He, Qian Ai. Multi Agent Coordinated Optimal Control Strategy for Smart Microgrid Based on Digital Twin Drive. Power System Technology, 2021. https://doi.org/10.13335/j.1000-3673.pst.2020.2278. (in Chinese).
Article
Google Scholar