State Estimation Models of Lithium-Ion Batteries for Battery Management System: Status, Challenges, and Future Trends
Source abstract
Abstract
The state estimation technology of lithium-ion batteries is one of the core functions elements of the battery management system (BMS), and it is an academic hotspot related to the functionality and safety of the battery for electric vehicles. This paper comprehensively reviews the research status, technical challenges, and development trends of state estimation of lithium-ion batteries. First, the key issues and technical challenges of battery state estimation are summarized from three aspects of characteristics, models, and algorithms, and the technical challenges in state estimation are deeply analyzed. Second, four typical battery states (state of health, state of charge, state of energy, and state of power) and their joint estimation methods are reviewed, and feasible estimation frameworks are proposed, respectively. Finally, the development trends of state estimation are prospected. Advanced technologies such as artificial intelligence and cloud networking have further reshaped battery state estimation, bringing new methods to estimate the state of the battery under complex and extreme operating conditions. The research results provide a valuable reference for battery state estimation in the next-generation battery management system.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Electric Vehicles and Infrastructure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
Funding
National Natural Science Foundation of China (NSFC) · Shanghai Science and Technology Development Fund
References (100+)
Show source-provided references
1. Comprehensively analysis the failure evolution and safety evaluation of automotive lithium ion battery10.1016/j.etran.2021.100140 ↗
2. Plug-in hybrid electric vehicle utility factor in China cities: Influencing factors, empirical research, and energy and environmental application10.1016/j.etran.2021.100138 ↗
3. Optimization & validation of Intelligent Energy Management System for pseudo dynamic predictive regulation of plug-in hybrid electric vehicle as donor clients10.1016/j.etran.2020.100050 ↗
4. Nonlinear optimization strategy for state of power estimation of lithium-ion batteries: A systematical uncertainty analysis of key impact parameters10.1109/TII.2021.3111539 ↗
5. Houache, M.S., Yim, C.-H., Karkar, Z., and Abu-Lebdeh, Y. (2022). On the Current and Future Outlook of Battery Chemistries for Electric Vehicles—Mini Review. Batteries, 8.10.3390/batteries8070070 ↗
6. An overview of global power lithium-ion batteries and associated critical metal recycling10.1016/j.jhazmat.2021.127900 ↗
7. Sustainable use of spilled turbinable energy in Ecuador: Three different energy storage systems10.1016/j.rser.2021.112005 ↗
8. Influence of the connection topology on the performance of lithium-ion battery pack under cell-to-cell parameters variations10.1016/j.est.2021.102896 ↗
9. Effect of parallel connection topology on air-cooled lithium-ion battery module: Inconsistency analysis and comprehensive evaluation10.1016/j.apenergy.2022.118758 ↗
10. State estimation for advanced battery management: Key challenges and future trends10.1016/j.rser.2019.109334 ↗
11. Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends10.1016/j.rser.2020.110480 ↗
12. Towards a smarter battery management system: A critical review on battery state of health monitoring methods10.1016/j.jpowsour.2018.10.019 ↗
13. A strong tracking adaptive fading-extended Kalman filter for the state of charge estimation of lithium-ion batteries10.1002/er.8307 ↗
14. A balancing current ratio based state-of-health estimation solution for lithium-ion battery pack10.1109/TIE.2021.3108715 ↗
15. A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries10.1016/j.apenergy.2022.119502 ↗
16. A data-model fusion method for online state of power estimation of lithium-ion batteries at high discharge rate in electric vehicles10.1016/j.energy.2022.124270 ↗
17. Remaining available energy prediction for lithium-ion batteries considering electrothermal effect and energy conversion efficiency10.1016/j.est.2021.102728 ↗
18. Calculation of the state of safety (SOS) for lithium ion batteries10.1016/j.jpowsour.2016.05.068 ↗
19. Safety assessment of electrically cycled cells at high temperatures under mechanical crush loads10.1016/j.etran.2020.100087 ↗
20. Joint estimation method for maximum available energy and state-of-energy of lithium-ion battery under various temperatures10.1016/j.jpowsour.2021.230132 ↗
21. State of health estimation for lithium-ion batteries based on temperature prediction and gated recurrent unit neural network10.1016/j.jpowsour.2021.230892 ↗
22. Load-responsive model switching estimation for state of charge of lithium-ion batteries10.1016/j.apenergy.2019.01.057 ↗
23. A method for state-of-charge estimation of LiFePO4 batteries based on a dual-circuit state observer10.1016/j.jpowsour.2015.07.028 ↗
24. Observer based battery SOC estimation: Using multi-gain-switching approach10.1016/j.apenergy.2017.03.079 ↗
25. A capacity prediction framework for lithium-ion batteries using fusion prediction of empirical model and data-driven method10.1016/j.energy.2021.121556 ↗
26. Detection of Li-ion battery failure and venting with Carbon Dioxide sensors10.1016/j.etran.2020.100100 ↗
27. Quantifiability of inherent cell-to-cell variations of commercial lithium-ion batteries10.1016/j.etran.2021.100129 ↗
28. Artificial neural networks, gradient boosting and support vector machines for electric vehicle battery state estimation: A review10.1016/j.est.2022.105384 ↗
29. Online quantitative diagnosis of internal short circuit for lithium-ion batteries using incremental capacity method10.1016/j.energy.2021.123082 ↗
30. Data-driven fault diagnosis and thermal runaway warning for battery packs using real-world vehicle data10.1016/j.energy.2021.121266 ↗
31. The prismatic surface cell cooling coefficient: A novel cell design optimisation tool & thermal parameterization method for a 3D discretised electro-thermal equivalent-circuit model10.1016/j.etran.2020.100099 ↗
32. A review on recent progress, challenges and perspective of battery thermal management system10.1016/j.ijheatmasstransfer.2020.120834 ↗
33. Ultrafast charging of energy-dense lithium-ion batteries for urban air mobility10.1016/j.etran.2021.100103 ↗
34. Modeling strategy for progressive failure prediction in lithium-ion batteries under mechanical abuse10.1016/j.etran.2020.100098 ↗
35. The impact of V2X service under local climatic conditions within Canada on EV durability10.1016/j.etran.2021.100124 ↗
36. State-of-the-art vehicle-to-everything mode of operation of electric vehicles and its future perspectives10.1016/j.rser.2022.112574 ↗
37. Komsiyska, L., Buchberger, T., Diehl, S., Ehrensberger, M., Hanzl, C., Hartmann, C., Hölzle, M., Kleiner, J., Lewerenz, M., and Liebhart, B. (2021). Critical review of intelligent battery systems: Challenges, implementation, and potential for electric vehicles. Energies, 14.10.3390/en14185989 ↗
38. Implementation for a cloud battery management system based on the CHAIN framework10.1016/j.egyai.2021.100088 ↗
39. Towards long lifetime battery: AI-based manufacturing and management10.1109/JAS.2022.105599 ↗
40. Battery digital twins: Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems10.1016/j.egyai.2020.100016 ↗
41. An overview of cyber-physical security of battery management systems and adoption of blockchain technology10.1109/JESTPE.2020.2968490 ↗
42. Multi-time scale variable-order equivalent circuit model for virtual battery considering initial polarization condition of lithium-ion battery10.1016/j.energy.2021.123084 ↗
43. A novel online identification algorithm of lithium-ion battery parameters and model order based on a fractional order model10.1049/rpg2.12172 ↗
44. Fractional-order modeling and parameter identification for lithium-ion batteries10.1016/j.jpowsour.2015.05.059 ↗
45. Physics-based fractional-order model with simplified solid phase diffusion of lithium-ion battery10.1016/j.est.2020.101404 ↗
46. State of power estimation of lithium-ion battery based on fractional-order equivalent circuit model10.1016/j.est.2021.102954 ↗
47. CPSO-based parameter-identification method for the fractional-order modeling of lithium-ion batteries10.1109/TPEL.2021.3073810 ↗
48. State of charge estimation of Lithium-ion battery using an improved fractional-order extended Kalman filter10.1016/j.est.2022.104007 ↗
49. A new neural network model for the state-of-charge estimation in the battery degradation process10.1016/j.apenergy.2014.01.066 ↗
50. A modified-electrochemical impedance spectroscopy-based multi-time-scale fractional-order model for lithium-ion batteries10.1016/j.electacta.2021.139066 ↗
51. Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries10.1016/j.conengprac.2022.105176 ↗
52. Online accurate state of health estimation for battery systems on real-world electric vehicles with variable driving conditions considered10.1016/j.jclepro.2021.125814 ↗
53. Advanced diagnostics to evaluate heterogeneity in lithium-ion battery modules10.1016/j.etran.2020.100045 ↗
54. Guo, D., Yang, G., Zhao, G., Yi, M., Feng, X., Han, X., Lu, L., and Ouyang, M. (2020). Determination of the differential capacity of lithium-ion batteries by the deconvolution of electrochemical impedance spectra. Energies, 13.10.3390/en13040915 ↗
55. A review on state of health estimation for lithium ion batteries in photovoltaic systems10.1016/j.etran.2019.100028 ↗
56. A comparative investigation of aging effects on thermal runaway behavior of lithium-ion batteries10.1016/j.etran.2019.100034 ↗
57. A review of key issues for control and management in battery and ultra-capacitor hybrid energy storage systems10.1016/j.etran.2020.100064 ↗
58. A new state-of-health estimation method for lithium-ion batteries through the intrinsic relationship between ohmic internal resistance and capacity10.1016/j.measurement.2017.11.016 ↗
59. Aging performance characterization and state-of-health assessment of retired lithium-ion battery modules10.1016/j.est.2021.102743 ↗
60. Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications10.1016/j.jclepro.2021.128015 ↗
61. On-board capacity estimation of lithium iron phosphate batteries by means of half-cell curves10.1016/j.jpowsour.2016.05.041 ↗
62. Optimizing the operation of energy storage using a non-linear lithium-ion battery degradation model10.1016/j.apenergy.2019.114360 ↗
63. End-to-end capacity estimation of Lithium-ion batteries with an enhanced long short-term memory network considering domain adaptation10.1016/j.jpowsour.2021.230823 ↗
64. Modeling capacity fade of lithium-ion batteries during dynamic cycling considering path dependence10.1016/j.est.2022.104718 ↗
65. Battery aging conscious intelligent energy management strategy and sensitivity analysis of the critical factors for plug-in hybrid electric buses10.1016/j.etran.2020.100061 ↗
66. Future ageing trajectory prediction for lithium-ion battery considering the knee point effect10.1109/TEC.2021.3130600 ↗
67. Diao, W., Saxena, S., Han, B., and Pecht, M. (2019). Algorithm to determine the knee point on capacity fade curves of lithium-ion cells. Energies, 12.10.3390/en12152910 ↗
68. Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells10.1016/j.egyai.2020.100006 ↗
69. An online SOC and capacity estimation method for aged lithium -ion battery pack considering cell inconsistency10.1016/j.est.2020.101250 ↗
70. A method for SOC estimation based on simplified mechanistic model for LiFePO4 battery10.1016/j.energy.2016.08.080 ↗
71. An overview and comparison of online implementable SOC estimation methods for lithium-ion battery10.1109/TIA.2017.2775179 ↗
72. Parameter identification and SOC estimation of a battery under the hysteresis effect10.1109/TIE.2019.2956394 ↗
73. Zhou, W., Zheng, Y., Pan, Z., and Lu, Q. (2021). Review on the battery model and SOC estimation method. Processes, 9.10.3390/pr9091685 ↗
74. State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach10.1016/j.energy.2021.120116 ↗
75. A review on various temperature-indication methods for Li-ion batteries10.1016/j.apenergy.2019.02.078 ↗
76. A novel practical state of charge estimation method: An adaptive improved ampere-hour method based on composite correction factor10.1002/er.5758 ↗
77. Liu, Z., Li, Z., Zhang, J., Su, L., and Ge, H. (2019). Accurate and efficient estimation of lithium-ion battery state of charge with alternate adaptive extended Kalman filter and ampere-hour counting methods. Energies, 12.10.3390/en12040757 ↗
78. State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting10.1016/j.electacta.2014.11.011 ↗
79. A fast capacity estimation method based on open circuit voltage estimation for LiNixCoyMn1-x-y battery assessing in electric vehicles10.1016/j.est.2020.101830 ↗
80. State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures10.1016/j.apenergy.2013.07.008 ↗
81. A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm10.1016/j.apenergy.2016.01.010 ↗
82. A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries10.1016/j.energy.2017.12.061 ↗
83. Online Estimation of Lithium Polymer Batteries State-of-Charge Using Particle Filter-Based Data Fusion with Multimodels Approach10.1109/TIA.2016.2524438 ↗
84. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs-Part 1. Background10.1016/j.jpowsour.2004.02.031 ↗
85. Robustness analysis of State-of-Charge estimation methods for two types of Li-ion batteries10.1016/j.jpowsour.2012.06.005 ↗
86. Improved battery SOC estimation accuracy using a modified UKF with an adaptive cell model under real EV operating conditions10.1109/TTE.2018.2802043 ↗
87. A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF10.1016/j.energy.2022.124933 ↗
88. State of charge estimation based on a simplified electrochemical model for a single LiCoO2 battery and battery pack10.1016/j.energy.2017.05.158 ↗
89. Simplified electrochemical lithium-ion battery model with variable solid-phase diffusion and parameter identification over wide temperature range10.1016/j.jpowsour.2021.229900 ↗
90. Open-Circuit Voltage-Based State of Charge Estimation of Lithium-ion Battery Using Dual Neural Network Fusion Battery Model10.1016/j.electacta.2015.12.001 ↗
91. State-of-charge estimation for battery management system using optimized support vector machine for regression10.1016/j.jpowsour.2014.07.016 ↗
92. Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries10.1109/TIE.2017.2787586 ↗
93. Semi-empirical long-term cycle life model coupled with an electrolyte depletion function for large-format graphite/LiFePO4 lithium-ion batteries10.1016/j.jpowsour.2017.08.094 ↗
94. A physics-based aging model for lithium-ion battery with coupled chemical/mechanical degradation mechanisms10.1016/j.electacta.2021.139133 ↗
95. Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part II: Pseudo-two-dimensional model simplification and state of charge estimation10.1016/j.jpowsour.2014.08.089 ↗
96. Control-oriented thermal-electrochemical modeling and validation of large size prismatic lithium battery for commercial applications10.1016/j.energy.2020.119057 ↗
97. A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model10.1016/j.apenergy.2019.113327 ↗
98. Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method10.1016/j.apenergy.2020.114490 ↗
99. A semi-empirical, electrochemistry-based model for Li-ion battery performance prediction over lifetime10.1016/j.est.2019.100819 ↗
100. Enhancing the estimation accuracy in low state-of-charge area: A novel onboard battery model through surface state of charge determination10.1016/j.jpowsour.2014.07.090 ↗