Citation:

Hari Prasad Bhupathi, Srikiran Chinta, 2024. "Battery Health Monitoring With AI: Creating Predictive Models to Assess Battery Performance and Longevity", ESP Journal of Engineering & Technology Advancements  4(4): 103-112.

Abstract:

The rapid adoption of battery-powered devices, particularly in electric vehicles and energy storage systems, has highlighted the need for efficient and accurate battery health monitoring methods. Traditional approaches, based on basic parameters like voltage and capacity, are limited in their ability to predict long-term performance and degradation. This research explores the application of artificial intelligence (AI) in developing predictive models to assess battery health and longevity. By analysing real-time data from various battery parameters, machine learning algorithms, including decision trees, neural networks, and support vector machines, are utilized to predict battery performance over time. The models aim to provide accurate forecasts of remaining useful life (RUL), failure prediction, and performance degradation. The findings demonstrate that AI-based predictive models offer significant improvements over conventional methods, enabling more proactive maintenance, extended battery life, and optimized usage. The study also discusses the challenges and potential future directions for enhancing the accuracy and applicability of AI in battery health monitoring.

References:

[1] Zhao, Z., Liu, Z., & Zhang, L. (2020). A Deep Learning Approach for Predicting Battery Health and Degradation in Electric Vehicles. Journal of Power Sources, 448, 227437. https://doi.org/10.1016/j.jpowsour.2019.227437

[2] He, H., & Xie, Y. (2019). Data-driven Health Prognosis of Lithium-ion Batteries Using Long Short-Term Memory Networks. Journal of Energy Storage, 24, 100758. https://doi.org/10.1016/j.est.2019.100758

[3] Hari Prasad Bhupathi, Srikiran Chinta, 2021. "Integrating AI with Renewable Energy for EV Charging: Developing Systems That Optimize the Use of Solar or Wind Energy for EV Charging", ESP Journal of Engineering and Technology Advancements (ESP-JETA), 2(1): 260-271.

[4] Li, J., & Yang, X. (2021). Predicting the Remaining Useful Life of Lithium-ion Batteries Using Random Forest and Support Vector Machine Algorithms. Applied Energy, 280, 115963. https://doi.org/10.1016/j.apenergy.2020.115963

[5] Hari Prasad Bhupathi, Srikiran Chinta, 2022. "Smart Charging Revolution: AI and ML Strategies for Efficient EV Battery Use", ESP Journal of Engineering & Technology Advancements (ESP-JETA), 2 (2): 154-167.

[6] Khaled, A., & Omar, N. (2020). Battery Health Management in Electric Vehicles: A Review of Recent Advances in AI Approaches. Energy Reports, 6, 1309–1322. https://doi.org/10.1016/j.egyr.2020.06.030

[7] Hussain, A., & Ghosal, A. (2018). Predicting the Degradation of Lithium-ion Batteries: A Review of Approaches Using Machine Learning and AI Techniques. International Journal of Energy Research, 42(9), 3084-3099. https://doi.org/10.1002/er.4153

[8] Zhao, S., & Wang, Z. (2021). A Hybrid Deep Learning Model for Battery Lifetime Prediction in Electric Vehicles. Journal of Power Sources, 487, 229371. https://doi.org/10.1016/j.jpowsour.2020.229371

[9] Hari Prasad Bhupathi, Srikiran Chinta, 2022. "Predictive Algorithms for EV Charging: AI Techniques for Battery Optimization", ESP Journal of Engineering & Technology Advancements (ESP-JETA), 2(4): 165-178.

[10] Wang, W., & Liu, T. (2020). Battery Management System and SOC Development: A Machine Learning Approach. Journal of Energy Engineering, 146(6), 04020088. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000735

[11] Hari Prasad Bhupathi, 2023. "Deep Learning and EV Charging: Battery Life and Performance" ESP International Journal of Advancements in Science & Technology (ESP-IJAST) Volume 1, Issue 1: 29-46.

[12] Liu, W., & Li, X. (2019). Artificial Neural Networks for Predicting the State of Health of Lithium-Ion Batteries in Electric Vehicles. Energy, 165, 300–308. https://doi.org/10.1016/j.energy.2018.09.102

[13] Hari Prasad Bhupathi, Srikiran Chinta, 2023. "Optimizing EV Ecosystems: AI and Machine Learning in Battery Charging", ESP International Journal of Advancements in Science & Technology (ESP-IJAST), Volume 1, Issue 3: 84-96.

[14] Shao, S., & Wang, X. (2021). Real-time Health Monitoring of Lithium-Ion Batteries Using Recurrent Neural Networks for Electric Vehicles. Computers in Industry, 126, 103375. https://doi.org/10.1016/j.compind.2020.103375

[15] Chen, J., & Zhao, Y. (2020). A Review of AI and Machine Learning Applications in Battery Management Systems. Energy Science & Engineering, 8(3), 1153–1167. https://doi.org/10.1002/ese3.735

[16] "Investigation of WBG based Power Converters used in E-Transportation, Hari Prasad Bhupathi, B.P. Bobba, Ummidisetty Niharika, Sattur Akshitha, K. Sardina, B. Lingam and M.M. Adnan, E3S Web Conf., 552 (2024) 01145, DOI: https://doi.org/10.1051/e3sconf/202455201145"

[17] "Effect of Inverter Voltage Levels on Torque Ripples of PMSM Drive Vinodh Kumar Pandraka, Ramamohan Rao Tinnavelli, Rajagiri Anil Kumar, Hari Prasad Bhupathi and Sree Lakshmi Gundebommu E3S Web of Conf., 547 (2024) 02016 DOI: https://doi.org/10.1051/e3sconf/202454702016"

[18] Kumar, Krishna. , Kiran, Surya. , , R.. , K, Aby. , Venkata, Peruri. , Prasad, Hari. Optimizing Sensor Localization and Cluster Performance in Wireless Sensor Networks through Internet of Thing (IoT) and Boosted Weight Centroid Algorithm. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2024, pp. 223-230. DOI: https://doi.org/10.54216/JISIoT.130218

[19] "Simultaneous Wireless Power and Data Transfer in Different Applications Phaneendra Babu Bobba, Mohd Hamza Anwar, Hari Prasad Bhupathi, P. Shirisha and Laeth Hamza E3S Web Conf., 552 (2024) 01147 DOI: https://doi.org/10.1051/e3sconf/202455201147"

[20] "Review on Charging Methods and Charging Solutions for Electric Vehicles B.K. Chakravarthy, G. Sree Lakshmi and Hari Prasad Bhupathi E3S Web of Conf., 547 (2024) 03001 DOI: https://doi.org/10.1051/e3sconf/202454703001"

[21] "Performance analysis of solid-state batteries in Electric vehicle applications Phaneendra Babu Bobba, Lakshmi Sri Harshitha Yerraguntla, Sathvika Pisini, H.P. Bhupathi, Srinivas D and M.M. Hassan E3S Web Conf., 552 (2024) 01149 DOI: https://doi.org/10.1051/e3sconf/202455201149"

[22] H. P. Bhupathi, S. L. Gundebommu and V. K. Pandraka, "Performance Comparison of Three-level Diode Clamped and T-type Multilevel Inverter," 2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT), Greater Noida, India, 2024, pp. 1-5, doi: 10.1109/ICEECT61758.2024.10739197.

[23] R. Thati et al., "Efficient Multi-Port Power Converter Design Using a Single Inductor for Electric Vehicle Systems," 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET), Hyderabad, India, 2024, pp. 1-7, doi: 10.1109/SEFET61574.2024.10717991.

[24] P. S. Devi et al., "Visualization Studies of the Various Wireless Power Transfer Topologies," 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET), Hyderabad, India, 2024, pp. 1-6, doi: 10.1109/SEFET61574.2024.10718109.

[25] P. B. Bobba et al., "Comparative Analysis of Material Choices for Enhanced Efficiency in Wireless Power Transfer," 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET), Hyderabad, India, 2024, pp. 1-6, doi: 10.1109/SEFET61574.2024.10717487.

[26] P. B. Bobba et al., "Comprehensive Analysis of Various Coil Geometries in Single Transmitter and Multi-Receiver Wireless Power Transmission," 2024 IEEE 4th International Conference on Sustainable Energy and Future Electric Transportation (SEFET), Hyderabad, India, 2024, pp. 1-7, doi: 10.1109/SEFET61574.2024.10718276.

[27] S. Chapala, P. R. Kumar, G. S. Lakshmi and H. P. Bhupathi, "Implementation of Fuzzy MPPT for Grid Connected PV – Wind – Battery System," 2024 2nd World Conference on Communication & Computing (WCONF), RAIPUR, India, 2024, pp. 1-7, doi: 10.1109/WCONF61366.2024.10691968.

Keywords:

: Battery Health Monitoring, Predictive Models, Artificial Intelligence (AI), Machine Learning, Battery Degradation, Battery Life Prediction, Performance Forecasting, Remaining Useful Life (RUL), Data Preprocessing, Battery Management Systems (BMS), AI in Energy Storage, Predictive Maintenance.