Abstract
Lithium-ion batteries are an excellent choice for electric transportation because of their high energy density, minimum self-discharge, and prolonged cycle life. The performance of electric transportation depends on the battery management system (BMS) for efficient functioning in vehicles. The state of charge (SOC) is one of the crucial BMS parameters to indicate the available charge in the vehicle. A reliable and accurate SOC prediction is crucial for an effective electric vehicle operation but SOC estimation is challenging since it depends on multiple variables including ambient temperature, battery age, charging, and discharging current. The data-driven techniques use an approach to run sophisticated algorithms on a vast quantity of measured battery data to understand its behavior. Lithium-ion battery state of charge assessment poses a complex difficulty. Temperature and aging affect the non-linear connection between voltage and SOC, accurate current measurement is an essential parameter that requires rigorous calibration to manage inaccuracies. Estimation is further complicated by hysteresis effects during charge and discharge cycles, different C-rate dependencies, and state of health parameters. To solve critical challenges, the paper highlights the recent advancements in model-based approaches, coulomb counting techniques, and machine learning methodologies. By summarizing the basic principles and presenting a comprehensive overview of SOC estimation through deep learning, the review paper aims to serve as a valuable resource for researchers, and practitioners in the field of battery management systems for electric transportation applications.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
No data is generated or used in the research work and all the necessary information is available in the manuscript.
References
Saldaña, G., San-Martín, J. I., Zamora, I., Asensio, F. J., & Oñederra, O. (2019). Analysis of the current electric battery models for electric vehicle simulation. Energies, 12(14), 2750. https://doi.org/10.3390/en12142750
Noura, N., Boulon, L., & Jemeï, S. (2020). A review of battery state of health estimation methods: Hybrid electric vehicle challenges. World Electric Vehicle Journal, 11, 1–20. https://doi.org/10.3390/wevj11040066
Unterluggauer, T., Rich, J., Andersen, P. B., & Hashemi, S. (2022). Electric vehicle charging infrastructure planning for integrated transportation and power distribution networks: A review. eTransportation, 12, 100163. https://doi.org/10.1016/J.ETRAN.2022.100163
Kumar, R., Bansal, K., Kumar, A., Yadav, J., Gupta, M. K., & Singh, V. K. (2021). Renewable energy adoption: Design, development, and assessment of solar tree for the mountainous region. International Journal of Energy Research, 42(2), 1–17. https://doi.org/10.1002/er.7197
Lyu, P., et al. (2020). Recent advances of thermal safety of lithium ion battery for energy storage. Energy Storage Materials, 31, 195–220. https://doi.org/10.1016/j.ensm.2020.06.042
Chang, C., et al. (2022). Prognostics of the state of health for lithium-ion battery packs in energy storage applications. Energy, 239, 122189. https://doi.org/10.1016/J.ENERGY.2021.122189
Rajak, R., Kumar, S., Prakash, S., Rajak, N., & Dixit, P. (2023). A novel technique to optimize quality of service for directed acyclic graph (DAG) scheduling in cloud computing environment using heuristic approach. The Journal of Supercomputing, 79, 1956–1979. https://doi.org/10.1007/s11227-022-04729-4
Zhang, Y. Z., Xiong, R., He, H. W., Qu, X., & Pecht, M. (2019). Aging characteristics-based health diagnosis and remaining useful life prognostics for lithium-ion batteries. eTransportation, 1, 100004. https://doi.org/10.1016/J.ETRAN.2019.100004
Kumar, R., Pachauri, R. K., Badoni, P., Bharadwaj, D., Mittal, U., & Bisht, A. (2022). Investigation on parallel hybrid electric bicycle along with issuer management system for mountainous region. Journal of Cleaner Production, 362, 132430. https://doi.org/10.1016/j.jclepro.2022.132430
Das, K., & Kumar, R. (2023). Assessment of electric two-wheeler ecosystem using novel pareto optimality and TOPSIS methods for an ideal design solution. World Electric Vehicle Journal, 14, 215. https://doi.org/10.3390/wevj14080215
Krewer, U., Röder, F., Harinath, E., Braatz, R. D., Bedürftig, B., & Findeisen, R. (2018). Review—dynamic models of Li-ion batteries for diagnosis and operation: a review and perspective. Journal of the Electrochemical Society, 165, A3656–A3673. https://doi.org/10.1149/2.1061814jes
Singh, A., Prakash, S., & Singh, S. (2022). Optimization of reinforcement routing for wireless mesh network using machine learning and high-performance computing. Concurrency and Computation: Practice and Experience, 34, e6960. https://doi.org/10.1002/cpe.6960
Wu, X., Li, M., Du, J., & Hu, F. (2022). SOC prediction method based on battery pack aging and consistency deviation of thermoelectric characteristics. Energy Reports, 8, 2262–2272. https://doi.org/10.1016/j.egyr.2022.01.056
Wang, S. L., Fernandez, C., Zou, C. Y., Yu, C. M., Chen, L., & Zhang, L. (2019). A comprehensive working state monitoring method for power battery packs considering state of balance and aging correction. Energy, 171, 444–455. https://doi.org/10.1016/j.energy.2019.01.020
Vidal, C., Malysz, P., Naguib, M., Emadi, A., & Kollmeyer, P. J. (2022). Estimating battery state of charge using recurrent and non-recurrent neural networks. J. Energy Storage, 47, 103660. https://doi.org/10.1016/j.est.2021.103660
Das, K., Kumar, R., & Krishna, A. (2023). Supervised learning and data intensive methods for the prediction of capacity fade of lithium-ion batteries under diverse operating and environmental conditions. Water and Energy International, 66(1), 53–59.
Deng, K., et al. (2021). An adaptive PMP-based model predictive energy management strategy for fuel cell hybrid railway vehicles. eTransportation, 7, 100094. https://doi.org/10.1016/j.etran.2020.100094
Kumar, R., Kumar, A., Gupta, M. K., Yadav, J., & Jain, A. (2022). Solar tree-based water pumping for assured irrigation in sustainable Indian agriculture environment. Sustainable Production and consumption, 33, 15–27. https://doi.org/10.1016/j.spc.2022.06.013
Yang, S., Zhang, C., Jiang, J., Zhang, W., Zhang, L., & Wang, Y. (2021). Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications. Journal of Cleaner Production, 314, 128015. https://doi.org/10.1016/J.JCLEPRO.2021.128015
Ren, X., Liu, S., Yu, X., & Dong, X. (2021). A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM. Energy, 234, 121236. https://doi.org/10.1016/j.energy.2021.121236
Kumar, R., Ahuja, N. J., Saxena, M., & Kumar, A. (2016). Modelling and simulation of object detection in automotive power window. Indian Journal of Science and Technology. https://doi.org/10.17485/ijst/2016/v9i43/104393
Almaita, E., Alshkoor, S., Abdelsalam, E., & Almomani, F. (2022). State of charge estimation for a group of lithium-ion batteries using long short-term memory neural network. Journal of Energy Storage, 52, 104761. https://doi.org/10.1016/j.est.2022.104761
Unhelkar, B., Joshi, S., Sharma, M., Prakash, S., Mani, A. K., & Prasad, M. (2022). Enhancing supply chain performance using RFID technology and decision support systems in the industry 4.0–A systematic literature review. International Journal of Information Management Data Insights, 2, 100084. https://doi.org/10.1016/J.JJIMEI.2022.100084
Agrawal, A., Ghune, N., Prakash, S., & Ramteke, M. (2021). Evolutionary algorithm hybridized with local search and intelligent seeding for solving multi-objective Euclidian TSP. Expert Systems with Applications, 181, 115192. https://doi.org/10.1016/J.ESWA.2021.115192
Liu, T., Yang, X.-G., Ge, S., Leng, Y., & Wang, C.-Y. (2021). Ultrafast charging of energy-dense lithium-ion batteries for urban air mobility. ETransportation, 7, 100103. https://doi.org/10.1016/j.etran.2021.100103
Almeida, G. C. S., de Souza, A. C. Z., & Ribeiro, P. F. (2020). A neural network application for a lithium-ion battery pack state-of-charge estimator with enhanced accuracy (p. 33) (2020). https://doi.org/10.3390/wef-06915
Hong, J., Wang, Z., Chen, W., Wang, L. Y., & Qu, C. (2020). Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles. Journal of Energy Storage, 30, 1–21. https://doi.org/10.1016/j.est.2020.101459
Wang, Z., Li, X., & Wang, Y. (2021). State of charge estimation of lithium-ion battery based on improved recurrent neural network. Journal of Physics: Conference Series, 2109, 7323–7332. https://doi.org/10.1088/1742-6596/2109/1/012005
Bonfitto, A., Feraco, S., Tonoli, A., Amati, N., & Monti, F. (2019). Estimation accuracy and computational cost analysis of artificial neural networks for state of charge estimation in lithium batteries. Batteries, 5, 47. https://doi.org/10.3390/batteries5020047
Herle, A., Channegowda, J., & Prabhu, D. (2020) A temporal convolution network approach to state-of-charge estimation in li-ion batteries. In 2020 IEEE 17th India Council International Conference INDICON 2020, no. 1. https://doi.org/10.1109/INDICON49873.2020.9342315.
Ali, M. U., et al. (2022). An adaptive state of charge estimator for lithium-ion batteries. Energy Science & Engineering. https://doi.org/10.1002/ese3.1141
Dhawankar, P., et al. (2021). Next-generation indoor wireless systems: compatibility and migration case study. IEEE Access, 9, 156915–156929. https://doi.org/10.1109/ACCESS.2021.3126827
Chen, Z., Zhao, H., Shu, X., Zhang, Y., Shen, J., & Liu, Y. (2021). Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter. Energy, 228, 120630. https://doi.org/10.1016/j.energy.2021.120630
Das, A. S., Dwivedi, P. K., Mondal, A. K., Kumar, R., Reddy, R. M., & Kumar, A. (2017). Storage optimization of automated storage and retrieval systems using breadth-first search algorithm. In Proceedings of the international conference on nano-electronics, circuits & communication systems (pp. 229–238). Springer. https://doi.org/10.1007/978-981-10-2999-8_18.
You, H., Zhu, J., Wang, X., Jiang, B., et al. (2022). Nonlinear health evaluation for lithium-ion battery within full-lifespan. Journal of Energy Chemistry, 72, 333–341. https://doi.org/10.1016/j.jechem.2022.04.013
Singh, A., Singh, S., & Prakash, S. (2023). Critical comparative analysis and recommendation in MAC protocols for wireless mesh networks using multi-objective optimization and statistical testing. Wireless Personal Communications, 129, 2319–2344. https://doi.org/10.1007/s11277-023-10228-3
How, D. N. T., Hannan, M. A., Lipu, M. S. H., Sahari, K. S. M., Ker, P. J., & Muttaqi, K. M. (2020). State-of-charge estimation of Li-ion battery in electric vehicles: A deep neural network approach. IEEE Transactions on Industry Applications, 56, 5565–5574. https://doi.org/10.1109/TIA.2020.3004294
Trivedi, V., Prakash, S., & Ramteke, M. (2017). Optimized on-line control of MMA polymerization using fast multi-objective DE. Materials and Manufacturing Processes, 32, 1144–1151. https://doi.org/10.1080/10426914.2016.1257802
Prakash, S., Trivedi, V., & Ramteke, M. (2016). An elitist non-dominated sorting bat algorithm NSBAT-II for multi-objective optimization of phthalic anhydride reactor. International Journal of Systems Assurance Engineering and Management, 7, 299–315. https://doi.org/10.1007/s13198-016-0467-6
Li, X., Yuan, C., & Wang, Z. (2020). Multi-time-scale framework for prognostic health condition of lithium battery using modified Gaussian process regression and nonlinear regression. Journal of Power Sources, 467, 228358. https://doi.org/10.1016/j.jpowsour.2020.228358
Cadini, F., Sbarufatti, C., Cancelliere, F., & Giglio, M. (2019). State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters. Applied Energy, 235(2018), 661–672. https://doi.org/10.1016/j.apenergy.2018.10.095
Che, Y., Deng, Z., Tang, X., Lin, X., Nie, X., & Hu, X. (2022). Lifetime and aging degradation prognostics for lithium-ion battery packs based on a cell to pack method. Chinese Journal of Mechanical Engineering (English Edition), 35, 1–16. https://doi.org/10.1186/s10033-021-00668-y
Srivastava, S., Kumar, A., Singh, A., Prakash, S., & Kumar, A. (2022). An improved approach towards biometric face recognition using artificial neural network. Multimedia Tools and Applications, 81, 8471–8497. https://doi.org/10.1007/s11042-021-11721-2
Cong, X., Zhang, C., Jiang, J., Zhang, W., & Jiang, Y. (2020). A hybrid method for the prediction of the remaining useful life of lithium-ion batteries with accelerated capacity degradation. IEEE Transactions on Vehicular Technology, 69, 12775–12785. https://doi.org/10.1109/TVT.2020.3024019
Hannan, M. A., et al. (2021). Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model. Science and Reports, 11, 19541. https://doi.org/10.1038/s41598-021-98915-8
Kumar, C., Bharti, T. S., & Prakash, S. (2023). A hybrid data-driven framework for spam detection in online social network. Procedia Comput. Sci., 218, 124–132. https://doi.org/10.1016/j.procs.2022.12.408
Kumar, R., Dwivedi, P. K., Praveen Reddy, D., & Das, A. S. (2014). Design and implementation of hydraulic motor based elevator system. In 2014 IEEE 6th India international conference on power electronics (IICPE), Kurukshetra, India (pp. 1–6). https://doi.org/10.1109/IICPE.2014.7115821.
Guo, J., Li, Z., & Li, M. (2020). A review on prognostics methods for engineering systems. IEEE Transactions on Reliability, 69, 1110–1129. https://doi.org/10.1109/TR.2019.2957965
Severson, K. A., et al. (2019). Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 4(5), 383–391. https://doi.org/10.1038/s41560-019-0356-8
Lyu, Z., Wang, G., & Gao, R. (2021). Li-ion battery prognostic and health management through an indirect hybrid model. Journal of Energy Storage, 42, 102990. https://doi.org/10.1016/J.EST.2021.102990
Tran, M. K., & Fowler, M. (2020). A review of lithium-ion battery fault diagnostic algorithms: Current progress and future challenges. Algorithms, 13, 62. https://doi.org/10.3390/a13030062
Kumar, R., Ahuja, N. J., Saxena, M., & Kumar, A. (2020). Automotive power window communication with DTC algorithm and hardware-in-the loop testing. Wireless Personal Communications, 114, 3351–3366. https://doi.org/10.1007/s11277-020-07535-4
Kumar, A., Bansal, K., Kumar, D., Devrari, A., Kumar, R., & Mani, P. (2020). FPGA application for wireless monitoring in power plant. Nuclear Engineering and Technology, 53, 1167–1175. https://doi.org/10.1016/j.net.2020.09.003
Gupta, M. K., Kumar, R., Verma, V., & Sharma, A. (2021). Robust control based stability analysis and trajectory tracking of triple link robot manipulator. J. Eur. Systèmes Autom, 54, 641–647. https://doi.org/10.18280/jesa.540414
Kumar, R., Divyanshu, & Kumar, A. (2021). Nature based self-learning mechanism and simulation of automatic control smart hybrid antilock braking system. Wireless Personal Communications, 116, 3291–3308. https://doi.org/10.1007/s11277-020-07853-7
Dubarry, M., & Baure, G. (2020). Perspective on commercial Li-ion battery testing, best practices for simple and effective protocols. Electronics, 9, 152. https://doi.org/10.3390/electronics9010152
Rajak, N., Rajak, R., & Prakash, S. (2022). A workflow scheduling method for cloud computing platform. Wireless Personal Communications, 126, 3625–3647. https://doi.org/10.1007/s11277-022-09882-w
Edge, J. S., et al. (2021). Lithium ion battery degradation: What you need to know. Physical Chemistry Chemical Physics: PCCP, 23, 8200–8221. https://doi.org/10.1039/d1cp00359c
Haidri, R. A., Alam, M., Shahid, M., Prakash, S., & Sajid, M. (2022). A deadline aware load balancing strategy for cloud computing. Concurrency and Computation: Practice and Experience, 34, e6496. https://doi.org/10.1002/cpe.6496
Li, W., Limoge, D. W., Zhang, J., Sauer, D. U., & Annaswamy, A. M. (2021). Estimation of potentials in lithium-ion batteries using machine learning models. IEEE Transactions on Control Systems Technology, 30, 680–695. https://doi.org/10.1109/TCST.2021.3071643
Ansean, D., et al. (2019). Lithium-ion battery degradation indicators via incremental capacity analysis. IEEE Transactions on Industry Applications, 55, 2992–3002. https://doi.org/10.1109/TIA.2019.2891213
Barai, A., et al. (2019). A comparison of methodologies for the non-invasive characterisation of commercial Li-ion cells. Progress in Energy and Combustion Science, 72, 1–31. https://doi.org/10.1016/j.pecs.2019.01.001
Ma, Y., Shan, C., Gao, J., & Chen, H. (2022). A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction. Energy, 251, 123973. https://doi.org/10.1016/j.energy.2022.123973
Armand, M., et al. (2020). Lithium-ion batteries—Current state of the art and anticipated developments. Journal of Power Sources, 479, 228708. https://doi.org/10.1016/j.jpowsour.2020.228708
Meng, H., & Li, Y. F. (2019). A review on prognostics and health management (PHM) methods of lithium-ion batteries. Renewable and Sustainable Energy Reviews, 116, 109405. https://doi.org/10.1016/j.rser.2019.109405
Liu, D., et al. (2019). Review of recent development of in situ/operando characterization techniques for lithium battery research. Advanced Materials, 31, 1–57. https://doi.org/10.1002/adma.201806620
Bian, X., Liu, L., & Yan, J. (2019). A model for state-of-health estimation of lithium ion batteries based on charging profiles. Energy, 177, 57–65. https://doi.org/10.1016/J.ENERGY.2019.04.070
Kaiwartya, O., et al. (2018). virtualization in wireless sensor networks: Fault tolerant embedding for internet of things. IEEE Internet of Things Journal, 5, 571–580. https://doi.org/10.1109/JIOT.2017.2717704
Pal, A., Kumar, R., & Kumar, V. R. S. (2015). Conceptual design of an automatic fluid level controller for aerospace applications. In 2015 international conference on soft-computing and networks security (ICSNS) (pp. 1–8). https://doi.org/10.1109/ICSNS.2015.7292433.
Yadav, J., Kurre, S. K., Kumar, A., & Kumar, R. (2021). Nonlinear dynamics of controlled release mechanism under boundary friction. Results Engineering, 11, 100265. https://doi.org/10.1016/j.rineng.2021.100265
Yadav, R., et al. (2021). Smart healthcare: RL-based task offloading scheme for edge-enable sensor networks. IEEE Sensors Journal, 21, 24910–24918. https://doi.org/10.1109/JSEN.2021.3096245
Baure, G., & Dubarry, M. (2019). Synthetic vs. real driving cycles: A comparison of electric vehicle battery degradation. Batteries, 5, 42. https://doi.org/10.3390/BATTERIES5020042
Liu, Y., Zhang, C., Jiang, J., Zhang, L., Zhang, W., et al. (2022). Deduction of the transformation regulation on voltage curve for lithium-ion batteries and its application in parameters estimation. eTransportation, 12, 100164. https://doi.org/10.1016/j.etran.2022.100164
Kumar, R., Ahuja, N. J., & Saxena, M. (2018). Improvement and approval of impediment recognition and activity for power window. In Intelligent communication, control and devices: Proceedings of ICICCD 2017, (pp. 855–864). https://doi.org/10.1007/978-981-10-5903-2_89
Khaleghi, S., et al. (2022). Developing an online data-driven approach for prognostics and health management of lithium-ion batteries. Applied Energy, 308, 118348. https://doi.org/10.1016/J.APENERGY.2021.118348
Kong, J., Yang, F., Zhang, X., Pan, E., Peng, Z., & Wang, D. (2021). Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries. Energy, 223, 120114. https://doi.org/10.1016/j.energy.2021.120114
Hong, S., Hwang, H., Kim, D., Cui, S., & Joe, I. (2021). Real driving cycle-based state of charge prediction for ev batteries using deep learning methods. Applied Sciences, 11, 11285. https://doi.org/10.3390/app112311285
Singh, A., Prakash, S., Kumar, A., & Kumar, D. (2022). A proficient approach for face detection and recognition using machine learning and high-performance computing. Concurrency and Computation: Practice and Experience, 34, e6582. https://doi.org/10.1002/cpe.6582
Acknowledgements
The University of Petroleum and Energy Studies, India, provided a state-of-the-art electric and hybrid vehicle laboratory facility for the authors to ideate, execute, and evaluate the performance of the system.
Funding
No funding received from any private or public organizations.
Author information
Authors and Affiliations
Contributions
KD: Formal analysis, collected the data, wrote the draft paper and RK: Conceived idea, analysis, contributed data, final paper writing and revision.
Corresponding author
Ethics declarations
Competing interests
The authors declare that they have no conflict of interest regarding this manuscript.
Consent for publication
All the authors mutually agreed to publish in the journal.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Das, K., Kumar, R. A Comprehensive Review of Categorization and Perspectives on State-of-Charge Estimation Using Deep Learning Methods for Electric Transportation. Wireless Pers Commun 133, 1599–1618 (2023). https://doi.org/10.1007/s11277-023-10830-5
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-023-10830-5