PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Rechargeable Lithium-ion batteries have been widely utilized in diverse mobility applications, including electric vehicles (EVs), due to their high energy density and prolonged lifespan. However, the performance characteristics of those batteries, in terms of stability, efficiency, and life cycle, greatly affect the overall performance of the EV. Therefore, a battery management system (BMS) is required to manage, monitor and enhance the performance of the EV battery pack. For that purpose, a variety of Artificial Intelligence (AI) techniques have been proposed in the literature to enhance BMS capabilities, such as monitoring, battery state estimation, fault detection and cell balancing. This paper explores the state-of-the-art research in AI techniques applied to EV BMS. Despite the growing interest in AI-driven BMS, there are notable gaps in the existing literature. Our primary output is a comprehensive classification and analysis of these AI techniques based on their objectives, applications, and performance metrics. This analysis addresses these gaps and provides valuable insights for selecting the most suitable AI technique to develop a reliable BMS for EVs with efficient energy management.

• Abulifa, A. A., Soh, A. C., Hassan, M. K., Ahmad, R. M. K. R., & Radzi, M. A. M. (2019). Energy management system in battery electric vehicle based on fuzzy logic control to optimize the energy consumption in HVAC system. International Journal of Integrated Engineering, 11(4), 11-20. https://doi.org/10.30880/ijie.2019.11.04.002

• Ahmed, M., Zheng, Y., Amine, A., Fathiannasab, H., & Chen, Z. (2021). The role of artificial intelligence in the mass adoption of electric vehicles. Joule, 5(9), 2296-2322. https://doi.org/10.1016/j.joule.2021.07.012

• Andrea, D. (2010). Battery Management Systems for Large Lithium Battery Packs. Artech House.

• Ardeshiri, R., Balagopal, B., Alsabbagh, A., Ma, C., & Chow, M. (2020). Machine learning approaches in battery management systems: State of the art: Remaining useful life and fault detection. In 2020 2nd IEEE International conference on industrial electronics for sustainable energy systems (IESES) (Vol. 1, pp. 61-66). IEEE Publishing. https://doi.org/10.1109/ieses45645.2020.9210642

• Awad, M., & Khanna, R. (2015). Efficient learning machines: Theories, concepts, and applications for engineers and system designers. Springer nature. https://doi.org/10.1007/978-1-4302-5990-9

• Azzeh, M., Nassif, A. B., & Banitaan, S. (2018). Comparative analysis of soft computing techniques for predicting software effort based use case points. IET Software, 12(1), 19-29. https://doi.org/10.1049/iet-sen.2016.0322

• Baveja, R., Bhattacharya, J., Panchal, S., Fraser, R., & Fowler, M. (2023). Predicting temperature distribution of passively balanced battery module under realistic driving conditions through coupled equivalent circuit method and lumped heat dissipation method. Journal of Energy Storage, 70, Article 107967. https://doi.org/10.1016/j.est.2023.107967

• Bhatti, G., Mohan, H., & Singh, R. R. (2021). Towards the future of smart electric vehicles: Digital twin technology. Renewable and Sustainable Energy Reviews, 141, Article 110801. https://doi.org/10.1016/j.rser.2021.110801

• Bonfitto, A. (2020). A method for the combined estimation of battery state of charge and state of health based on artificial neural networks. Energies, 13(10), Article 2548. https://doi.org/10.3390/en13102548

• Chandran, V., Patil, C. K., Karthick, A., Ganeshaperumal, D., Rahim, R., & Ghosh, A. (2021). State of charge estimation of lithium-ion battery for electric vehicles using machine learning algorithms. World Electric Vehicle Journal, 12(1), Article 38. https://doi.org/10.3390/wevj12010038

• Cui, X., Panda, B., Chin, C. M. M., Sakundarini, N., Wang, C. T., & Pareek, K. (2020). An application of evolutionary computation algorithm in multidisciplinary design optimization of battery packs for electric vehicle. Energy Storage, 2(3), Article e158. https://doi.org/10.1002/est2.158

• Cui, Z., Wang, L., Li, Q., & Wang, K. (2022). A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network. International Journal of Energy Research, 46(5), 5423-5440. https://doi.org/10.1002/er.7545

• Duraisamy, T., & Kaliyaperumal, D. (2021). Machine learning-based optimal cell balancing mechanism for electric vehicle battery management system. IEEE Access, 9, 132846-132861. https://doi.org/10.1109/access.2021.3115255

• Gabbar, H. A., Othman, A. M., & Abdussami, M. R. (2021). Review of battery management systems (BMS) development and industrial standards. Technologies, 9(2), Article 28. https://doi.org/10.3390/technologies9020028

• Ghazali, A. K., Hassan, M. K., Radzi, M. A. M., & As’arry, A. (2020). Integrated braking force distribution for electric vehicle regenerative braking system. Pertanika Journal of Science & Technology, 28(S2), 173-182. https://doi.org/10.47836/pjst.28.s2.14

• Hemeida, A. M., Hassan, S. A., Mohamed, A. A. A., Alkhalaf, S., Mahmoud, M. M., Senjyu, T., & El-Din, A. B. (2020). Nature-inspired algorithms for feed-forward neural network classifiers: A survey of one decade of research. Ain Shams Engineering Journal, 11(3), 659-675. https://doi.org/10.1016/j.asej.2020.01.007

• Hojjati, A., Monadi, M., Faridhosseini, A., & Mohammadi, M. (2018). Application and comparison of NSGA-II and MOPSO in multi-objective optimization of water resources systems. Journal of Hydrology and Hydromechanics, 66(3), 323-329. https://doi.org/10.2478/johh-2018-0006

• How, D. N., Hannan, M. A., Lipu, M. S. H., Sahari, K. S., Ker, P. J., & Muttaqi, K. M. (2020). State-of-charge estimation of Lithium-Ion battery in electric vehicles: A deep neural network approach. IEEE Transactions on Industry Applications, 56(5), 5565-5574. https://doi.org/10.1109/tia.2020.3004294

• Irsoy, O., & Cardie, C. (2014). Deep recursive neural networks for compositionality in language. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27 (pp. 1-9). DBLP Publishing.

• Jose, P. S., Jose, P., Wessley, G., & Rajalakshmy, P. (2022). Environmental impact of electric vehicles. In E-Mobility (pp. 31-42). Springer. https://doi.org/10.1007/978-3-030-85424-9_2

• Kamal, E., & Adouane, L. (2018). Hierarchical energy optimization strategy and its integrated reliable battery fault management for hybrid hydraulic-electric vehicle. IEEE Transactions on Vehicular Technology, 67(5), 3740-3754. https://doi.org/10.1109/tvt.2018.2805353

• Kara, A. (2021). A data-driven approach based on deep neural networks for lithium-ion battery prognostics. Neural Computing and Applications, 33(20), 13525-13538. https://doi.org/10.1007/s00521-021-05976-x

• Karahoca, A. (2012). Advances in data mining knowledge discovery and applications. BoD–Books on Demand.

• Kaur, K., Garg, A., Cui, X., Singh, S., & Panigrahi, B. K. (2021). Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles. International Journal of Energy Research, 45(2), 3113-3128. https://doi.org/10.1002/er.6005

• Laadjal, K., & Cardoso, A. J. M. (2021). Estimation of lithium-ion batteries state-condition in electric vehicle applications: Issues and state of the art. Electronics, 10(13), Article 1588. https://doi.org/10.3390/electronics10131588

• Lee, M. (2020). An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data. World Patent Information, 63, Article 102002. https://doi.org/10.1016/j.wpi.2020.102002

• Li, S., & Zhao, P. (2021). Big data driven vehicle battery management method: A novel cyber-physical system perspective. Journal of Energy Storage, 33, Article 102064. https://doi.org/10.1016/j.est.2020.102064

• Li, W., Rentemeister, M., Badeda, J., Jöst, D., Schulte, D., & Sauer, D. U. (2020). Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. Journal of Energy Storage, 30, Article 101557. https://doi.org/10.1016/j.est.2020.101557

• Li, X., Wang, T., Wu, C., Tian, J., & Tian, Y. (2021). Battery pack state of health prediction based on the electric vehicle management platform data. World Electric Vehicle Journal, 12(4), Article 204. https://doi.org/10.3390/wevj12040204

• Liang, X., Bao, N., Zhang, J., Garg, A., & Wang, S. (2018). Evaluation of battery modules state for electric vehicle using artificial neural network and experimental validation. Energy Science & Engineering, 6(5), 397-407. https://doi.org/10.1002/ese3.214

• Liu, K., Li, K., Ma, H., Zhang, J., & Peng, Q. (2018). Multi-objective optimization of charging patterns for lithium-ion battery management. Energy Conversion and Management, 159, 151-162. https://doi.org/10.1016/j.enconman.2017.12.092

• Liu, K., Li, K., Peng, Q., & Zhang, C. (2019). A brief review on key technologies in the battery management system of electric vehicles. Frontiers of Mechanical Engineering, 14(1), 47-64. https://doi.org/10.1007/s11465-018-0516-8

• Liu, X., Zheng, C., Wu, J., Meng, J., Stroe, D. I., & Chen, J. (2020). An improved state of charge and state of power estimation method based on genetic particle filter for lithium-ion batteries. Energies, 13(2), Article 478. https://doi.org/10.3390/en13020478

• López, O. A. M., López, A. M., & Crossa, J. (2022). Multivariate statistical machine learning methods for genomic prediction. Springer Nature. https://doi.org/10.1007/978-3-030-89010-0

• Ma, Y., Duan, P., Sun, Y., & Chen, H. (2018). Equalization of lithium-ion battery pack based on fuzzy logic control in electric vehicle. IEEE Transactions on Industrial Electronics, 65(8), 6762-6771. https://doi.org/10.1109/tie.2018.2795578

• Mawonou, K. S., Eddahech, A., Dumur, D., Beauvois, D., & Godoy, E. (2021). State-of-health estimators coupled to a random forest approach for lithium-ion battery aging factor ranking. Journal of Power Sources, 484, Article 229154. https://doi.org/10.1016/j.jpowsour.2020.229154

• Meng, J., Cai, L., Stroe, D. I., Luo, G., Sui, X., & Teodorescu, R. (2019). Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles. Energy, 185, 1054-1062. https://doi.org/10.1016/j.energy.2019.07.127

• Mitra, V., Wang, C. J., & Banerjee, S. (2007). Text classification: A least square support vector machine approach. Applied soft computing, 7(3), 908-914. https://doi.org/10.1016/j.asoc.2006.04.002

• Murnane, M., & Ghazel, A. (2017). A closer look at state of charge (SOC) and state of health (SOH) estimation techniques for batteries. Analog devices, 2, 426-436.

• Nagarale, S. D., & Patil, B. P. (2020). A review on AI based predictive battery management system for e-mobility. TEST Engineering & Management, 83, 15053-15064.

• Omariba, Z. B., Zhang, L., & Sun, D. (2019). Review of battery cell balancing methodologies for optimizing battery pack performance in electric vehicles. IEEE Access, 7, 129335-129352. https://doi.org/10.1109/access.2019.2940090

• Othman, B. M., Salam, Z., & Husain, A. R. (2022). A computationally efficient adaptive online state-of-charge observer for Lithium-ion battery for electric vehicle. Journal of Energy Storage, 49, Article 104141. https://doi.org/10.1016/j.est.2022.104141

• Park, S., Ahn, J., Kang, T., Park, S., Kim, Y., Cho, I., & Kim, J. (2020). Review of state-of-the-art battery state estimation technologies for battery management systems of stationary energy storage systems. Journal of Power Electronics, 20(6), 1526-1540. https://doi.org/10.1007/s43236-020-00122-7

• Purohit, K., Srivastava, S., Nookala, V., Joshi, V., Shah, P., Sekhar, R., Panchal, S., Fowler, M., Fraser, R., Tran, M. K., & Shum, C. (2021). Soft sensors for state of charge, state of energy, and power loss in formula student electric vehicle. Applied System Innovation, 4(4), Article 78. https://doi.org/10.3390/asi4040078

• Qiu, Z., & Qian, H. (2018). Adaptive genetic particle filter and its application to attitude estimation system. Digital Signal Processing, 81, 163-172. https://doi.org/10.1016/j.dsp.2018.06.015

• Rahimifard, S., Ahmed, R., & Habibi, S. (2021). Interacting multiple model strategy for electric vehicle batteries state of charge/health/power estimation. IEEE Access, 9, 109875-109888. https://doi.org/10.1109/access.2021.3102607

• Sanguesa, J. A., Torres-Sanz, V., Garrido, P., Martinez, F. J., & Marquez-Barja, J. M. (2021). A review on electric vehicles: Technologies and challenges. Smart Cities, 4(1), 372-404. https://doi.org/10.3390/smartcities4010022

• Shen, M., & Gao, Q. (2019). A review on battery management system from the modeling efforts to its multiapplication and integration. International Journal of Energy Research, 43(10), 5042-5075. https://doi.org/10.1002/er.4433

• Shu, X., Li, G., Shen, J., Lei, Z., Chen, Z., & Liu, Y. (2020). A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization. Energy, 204, Article 117957. https://doi.org/10.1016/j.energy.2020.117957

• Talele, V., Moralı, U., Patil, M. S., Panchal, S., & Mathew, K. (2023). Optimal battery preheating in critical subzero ambient condition using different preheating arrangement and advance pyro linear thermal insulation. Thermal Science and Engineering Progress, 42, Article 101908. https://doi.org/10.1016/j.tsep.2023.101908

• Tan, K. K. H., Wong, Y. W., & Nugroho, H. (2022). Image classification for edge-cloud setting: A comparison study for OCR application. Pertanika Journal of Science & Technology, 30(2), 1157 - 1170. https://doi.org/10.47836/pjst.30.2.17

• Tran, M. K., Panchal, S., Chauhan, V., Brahmbhatt, N., Mevawalla, A., Fraser, R., & Fowler, M. (2022). Python‐based scikit‐learn machine learning models for thermal and electrical performance prediction of high‐capacity lithium‐ion battery. International Journal of Energy Research, 46(2), 786-794. https://doi.org/10.1002/er.7202

• Tran, M. K., Panchal, S., Khang, T. D., Panchal, K., Fraser, R., & Fowler, M. (2022). Concept review of a cloud-based smart battery management system for lithium-ion batteries: Feasibility, logistics, and functionality. Batteries, 8(2), Article 19. https://doi.org/10.3390/batteries8020019

• Vidal, C., Malysz, P., Kollmeyer, P., & Emadi, A. (2020). Machine learning applied to electrified vehicle battery state of charge and state of health estimation: State-of-the-art. IEEE Access, 8, 52796-52814. https://doi.org/10.1109/access.2020.2980961

• Wang, Y., Xu, R., Zhou, C., Kang, X., & Chen, Z. (2022). Digital twin and cloud-side-end collaboration for intelligent battery management system. Journal of Manufacturing Systems, 62, 124-134. https://doi.org/10.1016/j.jmsy.2021.11.006

• Xuan, L., Qian, L., Chen, J., Bai, X., & Wu, B. (2020). State-of-charge prediction of battery management system based on principal component analysis and improved support vector machine for regression. IEEE Access, 8, Article 164693-164704. https://doi.org/10.1109/access.2020.3021745

• Yang, S., He, R., Zhang, Z., Cao, Y., Gao, X., & Liu, X. (2020). CHAIN: Cyber hierarchy and interactional network enabling digital solution for battery full-lifespan management. Matter, 3(1), 27-41. https://doi.org/10.1016/j.matt.2020.04.015

• Yang, S., Zhang, Z., Cao, R., Wang, M., Cheng, H., Zhang, L., Jiang, Y., Li, Y., Chen, B., Ling, H., Lian, Y., We, B., & Liu, X. (2021). Implementation for a cloud battery management system based on the CHAIN framework. Energy and AI, 5, Article 100088. https://doi.org/10.1016/j.egyai.2021.100088

• Zhang, F., & O’Donnell, L. J. (2020). Support vector regression. In Machine Learning (pp. 123-140). Academic Press. https://doi.org/10.1016/b978-0-12-815739-8.00007-9

• Zhang, M., & Fan, X. (2020). Review on the state of charge estimation methods for electric vehicle battery. World Electric Vehicle Journal, 11(1), Article 23. https://doi.org/10.3390/wevj11010023

• Zhao, F., Li, Y., Wang, X., Bai, L., & Liu, T. (2020). Lithium-ion batteries State of Charge prediction of electric vehicles using RNNs-CNNs neural networks. IEEE Access, 8, 98168-98180. https://doi.org/10.1109/access.2020.2996225