Advancements and Current Developments in Integrated System Architectures of Lithium-Ion Batteries for Electric Mobility
Abstract
:1. Introduction
2. Literature Review
3. Optimizing E-Mobility Battery Systems: Energy Density, Degradation Mechanisms, and Fault Analysis
4. Overview of Lithium-Ion Battery System Architectures in the Context of E-Mobility
5. Present Status
6. Interdisciplinarity Challenges in LIB Design for E-Mobility
7. Novel Designs, Materials, and Manufacturing Techniques
8. Challenges and Future Directions
9. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- I.—International Energy Agency, CO2 Emissions in 2023. 2023. Available online: https://www.iea.org (accessed on 23 April 2024).
- IPCC. Global Warming of 1.5 °C; Cambridge University Press (CUP): Cambridge, UK, 2022. [Google Scholar] [CrossRef]
- Global Energy Trends. Enerdata. Available online: https://www.enerdata.net/publications/reports-presentations/world-energy-trends.html (accessed on 8 June 2024).
- Jackson, R.B.; Friedlingstein, P.; Le Quéré, C.; Abernethy, S.; Andrew, R.M.; Canadell, J.G.; Ciais, P.; Davis, S.J.; Deng, Z.; Liu, Z.; et al. Global fossil carbon emissions rebound near pre-COVID-19 levels. Environ. Res. Lett. 2022, 17, 031001. [Google Scholar] [CrossRef]
- Hossain, M.S.; Kumar, L.; Assad, M.E.H.; Alayi, R. Advancements and Future Prospects of Electric Vehicle Technologies: A Comprehensive Review. Complexity 2022, 2022, 3304796. [Google Scholar] [CrossRef]
- Das, K.; Kumar, R. Assessment of Electric Two-Wheeler Ecosystem Using Novel Pareto Optimality and TOPSIS Methods for an Ideal Design Solution. World Electr. Veh. J. 2023, 14, 215. [Google Scholar] [CrossRef]
- Bouter, A.; Guichet, X. The greenhouse gas emissions of automotive lithium-ion batteries: A statistical review of life cycle assessment studies. J. Clean. Prod. 2022, 344, 130994. [Google Scholar] [CrossRef]
- Haghani, M.; Sprei, F.; Kazemzadeh, K.; Shahhoseini, Z.; Aghaei, J. Trends in electric vehicles research. Transp. Res. D Transp. Environ. 2023, 123, 103881. [Google Scholar] [CrossRef]
- Das, K.; Kumar, R.; Krishna, A. Supervised Learning and Data Intensive Methods for the Prediction of Capacity Fade of Lithium-ion Batteries under Diverse Operating and Environmental Conditions. Water Energy Int. 2023, 66, 53–59. Available online: https://www.indianjournals.com/ijor.aspx?target=ijor:wei&volume=66r&issue=1&article=008 (accessed on 29 August 2023).
- United states council for automotive research(USCAR). Development of Lithium Electrode-Based Cell and Manufacturing for Automotive Traction Applications. Available online: https://uscar.org/wpfd_file/development-of-lithium-electrode-based-cell-and-manufacturing-for-automotive-traction-applications/ (accessed on 10 August 2024).
- International Energy Agency. Global EV Outlook 2023: Catching up with Climate Ambitions. 2023. Available online: https://www.iea.org (accessed on 12 May 2024).
- Fleischmann, J.; Hanicke, M.; Horetsky, E.; Ibrahim, D.; Jautelat, S.; Linder, M.; Schaufuss, P.; Torscht, L.; van de Rijt, A. Battery 2030: Resilient, Sustainable, and Circular; McKinsey & Company: Chicago, IL, USA, 2023. [Google Scholar]
- Masias, A.; Marcicki, J.; Paxton, W.A. Opportunities and Challenges of Lithium Ion Batteries in Automotive Applications. ACS Energy Lett. 2021, 6, 621–630. [Google Scholar] [CrossRef]
- Li, M.; Lu, J.; Chen, Z.; Amine, K. 30 Years of Lithium-Ion Batteries. Adv. Mater. 2018, 30, 1800561. [Google Scholar] [CrossRef]
- Link, S.; Neef, C.; Wicke, T. Trends in Automotive Battery Cell Design: A Statistical Analysis of Empirical Data. Batteries 2023, 9, 261. [Google Scholar] [CrossRef]
- Brighente, A.; Conti, M.; Donadel, D.; Poovendran, R.; Turrin, F.; Zhou, J. Electric Vehicles Security and Privacy: Challenges, Solutions, and Future Needs. arXiv 2023, arXiv:2301.04587. Available online: http://arxiv.org/abs/2301.04587 (accessed on 7 April 2024).
- Zhu, P.; Slater, P.R.; Kendrick, E. Insights into architecture, design and manufacture of electrodes for lithium-ion batteries. Mater. Des. 2022, 223, 111208. [Google Scholar] [CrossRef]
- Löbberding, H.; Wessel, S.; Offermanns, C.; Kehrer, M.; Rother, J.; Heimes, H.; Kampker, A. From Cell to Battery System in BEVs: Analysis of System Packing Efficiency and Cell Types. World Electr. Veh. J. 2020, 11, 77. [Google Scholar] [CrossRef]
- Yang, B.; Wang, J.; Cao, P.; Zhu, T.; Shu, H.; Chen, J.; Zhang, J.; Zhu, J. Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: A critical comprehensive survey. J. Energy Storage 2021, 39, 102572. [Google Scholar] [CrossRef]
- Che, Y.; Deng, Z.; Tang, X.; Lin, X.; Nie, X.; Hu, X. Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method. Chin. J. Mech. Eng. 2022, 35, 4. [Google Scholar] [CrossRef]
- Pampel, F.; Pischinger, S.; Teuber, M. A systematic comparison of the packing density of battery cell-to-pack concepts at different degrees of implementation. Results Eng. 2022, 13, 100310. [Google Scholar] [CrossRef]
- Eshetu, G.G.; Zhang, H.; Judez, X.; Adenusi, H.; Armand, M.; Passerini, S.; Figgemeier, E. Production of high-energy Li-ion batteries comprising silicon-containing anodes and insertion-type cathodes. Nat. Commun. 2021, 12, 5459. [Google Scholar] [CrossRef]
- Feng, F.; Hu, X.; Hu, L.; Hu, F.; Li, Y.; Zhang, L. Propagation mechanisms and diagnosis of parameter inconsistency within Li-Ion battery packs. Renew. Sustain. Energy Rev. 2019, 112, 102–113. [Google Scholar] [CrossRef]
- Wang, J.; Deng, Z.; Peng, K.; Deng, X.; Xu, L.; Guan, G.; Abudula, A. Early Prognostics of Lithium-Ion Battery Pack Health. Sustainability 2022, 14, 2313. [Google Scholar] [CrossRef]
- Xue, Q.; Li, G.; Zhang, Y.; Shen, S.; Chen, Z.; Liu, Y. Fault diagnosis and abnormality detection of lithium-ion battery packs based on statistical distribution. J. Power Sources 2021, 482, 228964. [Google Scholar] [CrossRef]
- Zhang, Y.; Jiang, M.; Zhou, Y.; Zhao, S.; Yuan, Y. Towards High-Safety Lithium-Ion Battery Diagnosis Methods. Batteries 2023, 9, 63. [Google Scholar] [CrossRef]
- Gan, N.; Sun, Z.; Zhang, Z.; Xu, S.; Liu, P.; Qin, Z. Data-Driven Fault Diagnosis of Lithium-Ion Battery Overdischarge in Electric Vehicles. IEEE Trans. Power Electron. 2022, 37, 4575–4588. [Google Scholar] [CrossRef]
- Yao, L.; Xu, S.; Xiao, Y.; Hou, J.; Gong, X.; Fu, Z.; Tang, A. Fault Identification of Lithium-Ion Battery Pack for Electric Vehicle Based on GA Optimized ELM Neural Network. IEEE Access 2022, 10, 15007–15022. [Google Scholar] [CrossRef]
- Tran, M.-K.; Panchal, S.; Khang, T.D.; Panchal, K.; Fraser, R.; Fowler, M. Concept Review of a Cloud-Based Smart Battery Management System for Lithium-Ion Batteries: Feasibility, Logistics, and Functionality. Batteries 2022, 8, 19. [Google Scholar] [CrossRef] [PubMed]
- Messier, P.; LeBel, F.-A.; Rouleau, J.; Trovao, J.P.F. Multi-Cell Emulation for Battery Management System Validation. In Proceedings of the 2018 IEEE Vehicle Power and Propulsion Conference (VPPC), Chicago, IL, USA, 27–30 August 2018; pp. 1–6. [Google Scholar]
- Johannisson, A.; Jayasuriya, J.; Smajila, L. Outlook of EV Battery Pack Design Trends, Assessment of Trend Impact from a Recycling Perspective; KTH Royal Institute of Technology Depart. of Technology SE-10044: Stockholm, Sweden, 2023. [Google Scholar]
- Sulzer, V.; Mohtat, P.; Aitio, A.; Lee, S.; Yeh, Y.T.; Steinbacher, F.; Khan, M.U.; Lee, J.W.; Siegel, J.B.; Stefanopoulou, A.G.; et al. The challenge and opportunity of battery lifetime prediction from field data. Joule 2021, 5, 1934–1955. [Google Scholar] [CrossRef]
- Chang, C.; Wu, Y.; Jiang, J.; Jiang, Y.; Tian, A.; Li, T.; Gao, Y. Prognostics of the state of health for lithium-ion battery packs in energy storage applications. Energy 2022, 239, 122189. [Google Scholar] [CrossRef]
- Wang, G.; Gao, W.; He, X.; Peng, R.; Zhang, Y.; Dai, X.; Ping, P.; Kong, D. Numerical investigation on thermal runaway propagation and prevention in cell-to-chassis lithium-ion battery system. Appl. Therm. Eng. 2024, 236, 121528. [Google Scholar] [CrossRef]
- Zhong, H.; Lei, F.; Liu, J.; Ding, F.; Zhu, W.; Chi, X. Optimal design for efficiency enhanced hierarchical cell-to-cell equalization systems based on centralized model predictive control. Sustain. Energy Technol. Assess. 2023, 60, 103470. [Google Scholar] [CrossRef]
- Kim, K.; Choi, J.-I. Effect of cell-to-cell variation and module configuration on the performance of lithium-ion battery systems. Appl. Energy 2023, 352, 121888. [Google Scholar] [CrossRef]
- Manjunath, K.; Kalpana, R.; Singh, B.; Kiran, R. A Two-Stage Module Based Cell-to-Cell Active Balancing Circuit for Series Connected Lithium-Ion Battery Packs. IEEE Trans. Energy Convers. 2023, 38, 2282–2297. [Google Scholar] [CrossRef]
- Yu, Z.; Sun, Z.; Chang, L.; Ma, C.; Li, C.; Li, H.; Luan, C.; Al-Saidi, M.Y.M. Impact of Multiple Module Collectors on the Cell Current Distribution within the Battery Pack. Batteries 2023, 9, 501. [Google Scholar] [CrossRef]
- Gao, Y.; Liu, K.; Zhu, C.; Zhang, X.; Zhang, D. Co-Estimation of State-of-Charge and State-of- Health for Lithium-Ion Batteries Using an Enhanced Electrochemical Model. IEEE Trans. Ind. Electron. 2022, 69, 2684–2696. [Google Scholar] [CrossRef]
- Hu, C.; Ma, L.; Guo, S.; Guo, G.; Han, Z. Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols. Energy 2022, 246, 123404. [Google Scholar] [CrossRef]
- Cadini, F.; Sbarufatti, C.; Cancelliere, F.; Giglio, M. State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters. Appl. Energy 2019, 235, 661–672. [Google Scholar] [CrossRef]
- Yang, Z.; Wang, J.; Cui, C.; Jin, Y.; Zhang, G.; Zhou, H.; Qian, W. High power density & energy density Li-ion battery with aluminum foam enhanced electrode: Fabrication and simulation. J. Power Sources 2022, 524, 230977. [Google Scholar] [CrossRef]
- Jia, T.; Zhong, G.; Lv, Y.; Li, N.; Liu, Y.; Yu, X.; Zou, J.; Chen, Z.; Peng, L.; Kang, F.; et al. Prelithiation strategies for silicon-based anode in high energy density lithium-ion battery. Green Energy Environ. 2023, 8, 1325–1340. [Google Scholar] [CrossRef]
- Kirkaldy, N.; Samieian, M.A.; Offer, G.J.; Marinescu, M.; Patel, Y. Lithium-Ion Battery Degradation: Measuring Rapid Loss of Active Silicon in Silicon–Graphite Composite Electrodes. ACS Appl. Energy Mater. 2022, 5, 13367–13376. [Google Scholar] [CrossRef]
- Meng, Q.; An, Z.; Yu, X.; Wang, N.; Bai, D.; Liu, S. The Influence of Temperature on the Secondary Use of Lithium Iron Phosphate Power Battery. IOP Conf. Ser. Earth Environ. Sci. 2021, 791, 012129. [Google Scholar] [CrossRef]
- Fan, Y.; Bao, Y.; Ling, C.; Chu, Y.; Tan, X.; Yang, S. Experimental study on the thermal management performance of air cooling for high energy density cylindrical lithium-ion batteries. Appl. Therm. Eng. 2019, 155, 96–109. [Google Scholar] [CrossRef]
- Rizk, R.; Louahlia, H.; Gualous, H.; Schaetzel, P. Passive Cooling of High Capacity Lithium-Ion Batteries. In Proceedings of the 2018 IEEE International Telecommunications Energy Conference (INTELEC), Turino, Italy, 7–11 October 2019; pp. 1–4. [Google Scholar]
- Xiong, R.; Pan, Y.; Shen, W.; Li, H.; Sun, F. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives. Renew. Sustain. Energy Rev. 2020, 131, 110048. [Google Scholar] [CrossRef]
- Alipour, M.; Ziebert, C.; Conte, F.V.; Kizilel, R. A review on temperature-dependent electrochemical properties, aging, and performance of lithium-ion cells. Batteries 2020, 6, 35. [Google Scholar] [CrossRef]
- Chen, W.; Liang, J.; Yang, Z.; Li, G. A Review of Lithium-Ion Battery for Electric Vehicle Applications and Beyond. Energy Procedia 2019, 158, 4363–4368. [Google Scholar] [CrossRef]
- Swornowski, P.J. Destruction mechanism of the internal structure in Lithium-ion batteries used in aviation industry. Energy 2017, 122, 779–786. [Google Scholar] [CrossRef]
- Wu, C.; Lou, J.; Zhang, J.; Chen, Z.; Kakar, A.; Emley, B.; Ai, Q.; Guo, H.; Liang, Y.; Lou, J.; et al. Current status and future directions of all-solid-state batteries with lithium metal anodes, sulfide electrolytes, and layered transition metal oxide cathodes. Nano Energy 2021, 87, 106081. [Google Scholar] [CrossRef]
- Dall-Orsoletta, A.; Ferreira, P.; Dranka, G.G. Low-carbon technologies and just energy transition: Prospects for electric vehicles. Energy Convers. Manag. X 2022, 16, 100271. [Google Scholar] [CrossRef]
- Jeevanantham, B.; Shobana, M.K. Enhanced cathode materials for advanced lithium-ion batteries using nickel-rich and lithium/manganese-rich LiNi MnCoO2. J. Energy Storage 2022, 54, 105353. [Google Scholar] [CrossRef]
- Kim, J.S.; Lee, D.C.; Lee, J.J.; Kim, C.W. Optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experiments. Sci. Rep. 2020, 1, 15586. [Google Scholar] [CrossRef]
- Huang, C.-S.; Cheng, Z.; Chow, M.-Y. A Robust and Efficient State-of-Charge Estimation Methodology for Serial-Connected Battery Packs: Most Significant Cell Methodology. IEEE Access 2021, 9, 74360–74369. [Google Scholar] [CrossRef]
- Saw, L.H.; Poon, H.M.; Thiam, H.S.; Cai, Z.; Chong, W.T.; Pambudi, N.A.; King, Y.J. Novel thermal management system using mist cooling for lithium-ion battery packs. Appl. Energy 2018, 223, 146–158. [Google Scholar] [CrossRef]
- Saqli, K.; Bouchareb, H.; M’Sirdi, N.K.; Oudghiri, M. Battery Pack Thermal Modeling, Simulation and electric model Identification. In Proceedings of the 2021 9th International Renewable and Sustainable Energy Conference (IRSEC), Tetouan, Morocco, 23–27 November 2021. [Google Scholar]
- Zhang, C.; Cheng, G.; Ju, Q.; Zhang, W.; Jiang, J.; Zhang, L. Study on Battery Pack Consistency Evolutions during Electric Vehicle Operation with Statistical Method. Energy Procedia 2017, 105, 3551–3556. [Google Scholar] [CrossRef]
- Hu, L.; Ye, Y.; Bo, Y.; Huang, J.; Tian, Q.; Yi, X.; Li, Q. Performance evaluation strategy for battery pack of electric vehicles: Online estimation and offline evaluation. Energy Rep. 2022, 8, 774–784. [Google Scholar] [CrossRef]
- Balagopal, B.; Huang, C.S.; Chow, M.-Y. Sensitivity Analysis of Lithium Ion Battery Parameters to Degradation of Anode Lithium Ion Concentration. In Proceedings of the IECON 2019—45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal, 14–17 October 2019; pp. 4543–4548. [Google Scholar]
- Burow, D.; Sergeeva, K.; Calles, S.; Schorb, K.; Börger, A.; Roth, C.; Heitjans, P. Inhomogeneous degradation of graphite anodes in automotive lithium ion batteries under low-temperature pulse cycling conditions. J. Power Sources 2016, 307, 806–814. [Google Scholar] [CrossRef]
- Zhang, G.; Wei, X.; Han, G.; Dai, H.; Zhu, J.; Wang, X.; Tang, X.; Ye, J. Lithium plating on the anode for lithium-ion batteries during long-term low temperature cycling. J. Power Sources 2021, 484, 229312. [Google Scholar] [CrossRef]
- Pavlovskii, A.A.; Pushnitsa, K.; Kosenko, A.; Novikov, P.; Popovich, A.A. Organic anode material for Lithium-ion batteries: Recent Progress and Challenges. Materials 2023, 16, 177. [Google Scholar] [CrossRef] [PubMed]
- Wang, L.; Qiu, J.; Wang, X.; Chen, L.; Cao, G.; Wang, J.; Zhang, H.; He, X. Insights for understanding multiscale degradation of LiFePO4 cathodes. eScience 2022, 2, 125–137. [Google Scholar] [CrossRef]
- Gao, J.-Y.; Yang, Y.-F.; Zhang, X.-K.; Li, S.-L.; Hu, P.; Wang, J.-S. A review on recent progress of thermionic cathode. Tungsten 2020, 2, 289–300. [Google Scholar] [CrossRef]
- Zhang, C.; Wang, Y.; Gao, Y.; Wang, F.; Mu, B.; Zhang, W. Accelerated fading recognition for lithium-ion batteries with Nickel-Cobalt-Manganese cathode using quantile regression method. Appl. Energy 2019, 256, 113841. [Google Scholar] [CrossRef]
- Wu, X.; Ruan, D.; Tan, S.; Feng, M.; Li, B.; Hu, G. Effect of stirring environment humidity on electrochemical performance of nickel-rich cathode materials as lithium ion batteries. Ionics 2020, 26, 5427–5434. [Google Scholar] [CrossRef]
- Dixon, B.; Mason, A.; Sahraei, E. Effects of electrolyte, loading rate and location of indentation on mechanical integrity of li-ion pouch cells. J. Power Sources 2018, 396, 412–420. [Google Scholar] [CrossRef]
- Gauthier, R.; Luscombe, A.; Bond, T.; Bauer, M.; Johnson, M.; Harlow, J.; Louli, A.; Dahn, J.R. How do Depth of Discharge, C-rate and Calendar Age Affect Capacity Retention, Impedance Growth, the Electrodes, and the Electrolyte in Li-Ion Cells? J. Electrochem. Soc. 2022, 169, 020518. [Google Scholar] [CrossRef]
- Li, W.; Zhang, H.; van Vlijmen, B.; Dechent, P.; Sauer, D.U. Forecasting battery capacity and power degradation with multi-task learning. Energy Storage Mater. 2022, 53, 453–466. [Google Scholar] [CrossRef]
- Johnen, M.; Pitzen, S.; Kamps, U.; Kateri, M.; Dechent, P.; Sauer, D.U. Modeling long-term capacity degradation of lithium-ion batteries. J. Energy Storage 2021, 34, 102011. [Google Scholar] [CrossRef]
- Baskin, I.; Ein-Eli, Y. Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology. Adv. Energy Mater. 2022, 12, 2202380. [Google Scholar] [CrossRef]
- Shen, X.; Zhang, X.-Q.; Ding, F.; Huang, J.-Q.; Xu, R.; Chen, X.; Yan, C.; Su, F.-Y.; Chen, C.-M.; Liu, X.; et al. Advanced Electrode Materials in Lithium Batteries: Retrospect and Prospect. Energy Mater. Adv. 2021, 2021, 1205324. [Google Scholar] [CrossRef]
- Thackeray, M.M.; Wolverton, C.; Isaacs, E.D. Electrical energy storage for transportation—Approaching the limits of, and going beyond, lithium-ion batteries. Energy Environ. Sci. 2012, 5, 7854–7863. [Google Scholar] [CrossRef]
- Mishra, A.; Mehta, A.; Basu, S.; Malode, S.J.; Shetti, N.P.; Shukla, S.S.; Nadagouda, M.N.; Aminabhavi, T.M. Electrode materials for lithium-ion batteries. Mater. Sci. Energy Technol. 2018, 1, 182–187. [Google Scholar] [CrossRef]
- Zhang, Y.; Tang, Q.; Zhang, Y.; Wang, J.; Stimming, U.; Lee, A.A. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning. Nat. Commun. 2020, 11, 1706. [Google Scholar] [CrossRef]
- Hosen, S.; Jaguemont, J.; Van Mierlo, J.; Berecibar, M. Battery lifetime prediction and performance assessment of different modeling approaches. iScience 2021, 24, 102060. [Google Scholar] [CrossRef]
- Zhang, Y.; Peng, Z.; Guan, Y.; Wu, L. Prognostics of battery cycle life in the early-cycle stage based on hybrid model. Energy 2021, 221, 119901. [Google Scholar] [CrossRef]
- Zeng, M.; Zhang, P.; Yang, Y.; Xie, C.; Shi, Y. SOC and SOH joint estimation of the power batteries based on fuzzy unscented Kalman filtering algorithm. Energies 2019, 12, 3122. [Google Scholar] [CrossRef]
- Gabbar, H.A.; Othman, A.M.; Abdussami, M.R. Review of Battery Management Systems (BMS) Development and Industrial Standards. Technologies 2021, 9, 28. [Google Scholar] [CrossRef]
- Sharma, A.; Zanotti, P.; Musunur, L.P. Enabling the Electric Future of Mobility: Robotic Automation for Electric Vehicle Battery Assembly. IEEE Access 2019, 7, 170961–170991. [Google Scholar] [CrossRef]
- Jin, T.; Singer, G.; Liang, K.; Yang, Y. Structural batteries: Advances, challenges and perspectives. Mater. Today 2023, 62, 151–167. [Google Scholar] [CrossRef]
- Belingardi, G.; Scattina, A. Battery Pack and Underbody: Integration in the Structure Design for Battery Electric Vehicles—Challenges and Solutions. Vehicles 2023, 5, 498–514. [Google Scholar] [CrossRef]
- Galos, J.; Pattarakunnan, K.; Best, A.S.; Kyratzis, I.L.; Wang, C.; Mouritz, A.P. Energy Storage Structural Composites with Integrated Lithium-Ion Batteries: A Review. Adv. Mater. Technol. 2021, 6, 2001059. [Google Scholar] [CrossRef]
- Li, Y.; Yang, J.; Liu, W.L.; Liao, C.L. Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery. Energies 2020, 13, 3791. [Google Scholar] [CrossRef]
- Park, S.; Kato, D.; Gima, Z.; Klein, R.; Moura, S. Optimal Experimental Design for Parameterization of an Electrochemical Lithium-Ion Battery Model. J. Electrochem. Soc. 2018, 165, A1309–A1323. [Google Scholar] [CrossRef]
- Sadeghi, E.; Zand, M.H.; Hamzeh, M.; Saif, M.; Alavi, S.M.M. Controllable Electrochemical Impedance Spectroscopy: From Circuit Design to Control and Data Analysis. IEEE Trans. Power Electron. 2020, 35, 9933–9942. [Google Scholar] [CrossRef]
- Jiaqiang, E.; Yue, M.; Chen, J.; Zhu, H.; Deng, Y.; Zhu, Y.; Zhang, F.; Wen, M.; Zhang, B.; Kang, S. Effects of the different air cooling strategies on cooling performance of a lithium-ion battery module with baffle. Appl. Therm. Eng. 2018, 144, 231–241. [Google Scholar] [CrossRef]
- Lu, M.; Zhang, X.; Ji, J.; Xu, X.; Zhang, Y. Research progress on power battery cooling technology for electric vehicles. J. Energy Storage 2019, 27, 101155. [Google Scholar] [CrossRef]
- Lipu, M.S.H.; Al Mamun, A.; Ansari, S.; Miah, S.; Hasan, K.; Meraj, S.T.; Abdolrasol, M.G.M.; Rahman, T.; Maruf, H.; Sarker, M.R.; et al. Battery Management, Key Technologies, Methods, Issues, and Future Trends of Electric Vehicles: A Pathway toward Achieving Sustainable Development Goals. Batteries 2022, 8, 119. [Google Scholar] [CrossRef]
- Cosley, M.R.; Garcia, M.P. Battery thermal management system. In Proceedings of the INTELEC, International Telecommunications Energy Conference (Proceedings), Chicago, IL, USA, 19–23 September 2004; pp. 38–45. [Google Scholar]
- Han, W.; Wik, T.; Kersten, A.; Dong, G.; Zou, C. Next-Generation Battery Management Systems: Dynamic Reconfiguration. IEEE Ind. Electron. Mag. 2020, 14, 20–31. [Google Scholar] [CrossRef]
- Gaines, L. The future of automotive lithium-ion battery recycling: Charting a sustainable course. Sustain. Mater. Technol. 2014, 1–2, 2–7. [Google Scholar] [CrossRef]
- Akhmetov, N.; Manakhov, A.; Al-Qasim, A.S. Li-Ion Battery Cathode Recycling: An Emerging Response to Growing Metal Demand and Accumulating Battery Waste. Electronics 2023, 12, 1152. [Google Scholar] [CrossRef]
- Hassan, M.U.; Saha, S.; Haque, E.; Islam, S.; Mahmud, A.; Mendis, N. A comprehensive review of battery state of charge estimation techniques. Sustain. Energy Technol. Assess. 2022, 54, 102801. [Google Scholar] [CrossRef]
- Wei, Z.; Hu, J.; Li, Y.; He, H.; Li, W.; Sauer, D.U. Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries. Appl. Energy 2022, 307, 118246. [Google Scholar] [CrossRef]
- Kumar, R.; Pachauri, R.K.; Badoni, P.; Bharadwaj, D.; Mittal, U.; Bisht, A. Investigation on parallel hybrid electric bicycle along with issuer management system for mountainous region. J. Clean. Prod. 2022, 362, 132430. [Google Scholar] [CrossRef]
- Cheng, J.; Shuai, S.; Tang, Z.; Changfa, T. Thermal performance of a lithium-ion battery thermal management system with vapor chamber and minichannel cold plate. Appl. Therm. Eng. 2023, 222, 119694. [Google Scholar] [CrossRef]
- Alzwayi, A.; Paul, M.C. Heat transfer enhancement of a lithium-ion battery cell using vertical and spiral cooling fins. Therm. Sci. Eng. Prog. 2024, 47, 102304. [Google Scholar] [CrossRef]
- Yu, H.; Mu, X.; Zhu, Y.; Liao, C.; Han, L.; Wang, J.; Cai, W.; Kan, Y.; Song, L.; Hu, Y. Sandwich structured ultra-strong-heat-shielding aerogel/copper composite insulation board for safe lithium-ion batteries modules. J. Energy Chem. 2023, 76, 438–447. [Google Scholar] [CrossRef]
- Wang, S.; Wu, F.; Takyi-Aninakwa, P.; Fernandez, C.; Stroe, D.-I.; Huang, Q. Improved singular filtering-Gaussian process regression-long short-term memory model for whole-life-cycle remaining capacity estimation of lithium-ion batteries adaptive to fast aging and multi-current variations. Energy 2023, 284, 128677. [Google Scholar] [CrossRef]
- Pejman, R.; Gorman, J.; Najafi, A.R. Multi-physics design of a new battery packaging for electric vehicles utilizing multifunctional composites. Compos. Part B Eng. 2022, 237, 109810. [Google Scholar] [CrossRef]
- Sui, X.; He, S.; Meng, J.; Stroe, D.-I.; Huang, X.; Teodorescu, R. Optimization of the discharge cut-off voltage in LiFePO4 battery packs. In Proceedings of the 2019 21st European Conference on Power Electronics and Applications (EPE ’19 ECCE Europe), Genova, Italy, 3–5 September 2019. [Google Scholar]
- Das, K.; Kumar, R.; Krishna, A. Analyzing electric vehicle battery health performance using supervised machine learning. Renew. Sustain. Energy Rev. 2024, 189, 113967. [Google Scholar] [CrossRef]
- Steffen, L.; Catherine, J.A. Open-Source Database for Lithium-Ion Batteries Released. Available online: https://www.isi.fraunhofer.de (accessed on 21 May 2024).
- Silva, G.; Dutra, T.A.; Nunes-Pereira, J.; Silva, A.; Silva, G.; Dutra, T.A.; Nunes-Pereira, J.; Silva, A. Coupled and decoupled structural batteries: A comparative analysis. J. Power Sources 2024, 604, 234392. [Google Scholar] [CrossRef]
- Waldmann, T.; Scurtu, R.-G.; Richter, K.; Wohlfahrt-Mehrens, M. 18650 vs. 21700 Li-ion cells—A direct comparison of electrochemical, thermal, and geometrical properties. J. Power Sources 2020, 472, 228614. [Google Scholar] [CrossRef]
- Chen, Y.; Ye, C.; Zhang, N.; Liu, J.; Li, H.; Davey, K.; Qiao, S.Z. Prospects for practical anode-free sodium batteries. Mater. Today 2024, 73, 260–274. [Google Scholar] [CrossRef]
- Ank, M.; Sommer, A.; Gamra, K.A.; Schöberl, J.; Leeb, M.; Schachtl, J.; Streidel, N.; Stock, S.; Schreiber, M.; Bilfinger, P.; et al. Lithium-Ion Cells in Automotive Applications: Tesla 4680 Cylindrical Cell Teardown and Characterization. J. Electrochem. Soc. 2023, 170, 120536. [Google Scholar] [CrossRef]
Description | 18650 | 21700 | 4680 [110] |
---|---|---|---|
Electrode thicknesses (double side coated) (anode) | ~126 μm | ~126 μm | ~121 µm |
Electrode thicknesses (double side coated) (cathode) | 125 μm | 125 μm | ~85 µm |
Current collector foil thicknesses (anode) | 10 μm (Cu) | 10 μm (Cu) | 8 µm (Cu) |
Current collector foil thicknesses (cathode) | 20 μm (Al) | 20 μm (Al) | 10 µm (Al) |
Areal capacity (cathode) | 5.00 mAh/cm2 | 5.00 mAh/cm2 | 4.9 mAh/cm2 |
Areal capacity (anode) | 4.12 mAh/cm2 | 4.12 mAh/cm2 | 5.5 mAh/cm2 |
N/P ratio (cathode) | 1.21 | 1.21 | ~1.08 |
Separator thickness | 16 μm | 9 μm | |
Cell capacity | 1.625 ± 0.007 Ah | 2.457 ± 0.040 Ah | 26.136 ± 0.040 Ah |
Weight | 40.99 ± 0.03 g | 59.71 ± 0.10 g | 355 g |
Internal resistance (50% SOC and 25 °C) | 25.0 ± 0.2 mΩ | 13.2 ± 0.1 mΩ | 5.77 ± 0.1 mΩ |
Electrolyte | ~12 wt.-% | ||
Separator | ~15 wt.-% | ||
Housing | 20 wt.-% | 18 wt.-% | 23 wt.-% |
Weight of the electrodes | 53 wt.-% | 55 wt.-% | 55 wt.-% |
Number of windings | 21 | 26 | |
Electrolyte | 5 mL | 7 mL | 60 mL |
Cell case | Nickel-plated steel or stainless | ||
Cell case thickness | ~0.22 to 0.28 mm | ~0.30 mm | |
Energy density (Wh/L) | ~575 | ~629 | 670–700 |
Spec. energy (Wh/kg) | 280–310 | 280–310 | 272–300 |
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© 2024 by the authors. Published by MDPI on behalf of the World Electric Vehicle Association. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Rawat, S.; Choudhury, S.; Saini, D.K.; Gupta, Y.C. Advancements and Current Developments in Integrated System Architectures of Lithium-Ion Batteries for Electric Mobility. World Electr. Veh. J. 2024, 15, 394. https://doi.org/10.3390/wevj15090394
Rawat S, Choudhury S, Saini DK, Gupta YC. Advancements and Current Developments in Integrated System Architectures of Lithium-Ion Batteries for Electric Mobility. World Electric Vehicle Journal. 2024; 15(9):394. https://doi.org/10.3390/wevj15090394
Chicago/Turabian StyleRawat, Sandeep, Sushabhan Choudhury, Devender Kumar Saini, and Yogesh Chandra Gupta. 2024. "Advancements and Current Developments in Integrated System Architectures of Lithium-Ion Batteries for Electric Mobility" World Electric Vehicle Journal 15, no. 9: 394. https://doi.org/10.3390/wevj15090394
APA StyleRawat, S., Choudhury, S., Saini, D. K., & Gupta, Y. C. (2024). Advancements and Current Developments in Integrated System Architectures of Lithium-Ion Batteries for Electric Mobility. World Electric Vehicle Journal, 15(9), 394. https://doi.org/10.3390/wevj15090394