With the rapid advancement in electric vehicle (EV) technology, efficient battery management has become crucial for enhancing performance, safety, and longevity. This research integrates Internet of Things (IoT) and artificial intelligence (AI) technology to provide a revolutionary solution to battery management in electric vehicles. Our suggested method uses IoT sensors integrated inside the EV battery to gather data in real-time while keeping an eye on many factors like voltage, temperature, and current. The system can learn from past data and adjust to changing situations thanks to the integration of AI, which increases forecast accuracy and battery management efficiency. By continuously analyzing data and adjusting parameters in real-time, the system enhances battery performance, extends lifespan, and ensures safety by identifying potential issues before they escalate. This data is then processed using a neural network-based algorithm to predict battery health, optimize charging protocols, and forecast remaining useful life. Battery parameters such as temperature, voltage, and current are collected from the sensors, such as temperature sensor, current sensor, and voltage sensor. These values are updated to the ESP 32 controller and the IOT (Internet of Things) cloud as thing speak. The battery parameters are stored in the Raspberry Pi controller. The support vector machine (SVM) will analyse the battery parameters to produce a better output. The SVM produced accuracy, precision, recall and F1-score of 90%, 80%, 78%, and 81%, respectively.
Fayyazi M, Sardar P, Thomas SI, Daghigh R, Jamali A, Esch T, et al. Artificial Intelligence/machine learning in energy management systems, control, and optimization of hydrogen fuel cell Vehicles. Sustainability. 2023 Mar 15;15(6):5249.
2.
Shwetha K, Shahar Banu S. Deep SVM-driven predictive analytics for improved decision-making in e-learning. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2025;16(2):564–78.
3.
Abu SM, Hannan MA, Lipu MH, Mannan M, Ker PJ, Hossain MJ, et al. State of the art of lithium-ion battery material potentials: An analytical evaluations, issues and future research directions. Journal of Cleaner Production. 2023 Mar 25;394:136246.
4.
Tabatabaei SM, Khozeymehnezhad H, Akbarpour A, Varjavand P. Investigating Effects of obstacles Arrangement on the velocity of Density Current in experimental conditions. Journal of Science and Engineering. 2017;4(1):53–64.
5.
Xu Y, Zhang H, Yang Y, Zhang J, Yang F, Yan D, et al. Optimization of energy management strategy for extended range electric vehicles using multi-island genetic algorithm. Journal of Energy Storage. 2023 May 1;61:106802.
6.
Huy DT. The Risk Level of Viet Nam Commercial Electric Industry under Financial Leverage during and after the Global Crisis 2009-2011. International Academic Journal of Organizational Behavior and Human Resource Management. 2018;5(1):109–21.
7.
Khawaja Y, Shankar N, Qiqieh I, Alzubi J, Alzubi O, Nallakaruppan MK, et al. Battery management solutions for li-ion batteries based on artificial intelligence. Ain Shams Engineering Journal. 2023 Dec 1;14(12):102213.
8.
Farhan MN. Estimation of the production function of Diyala State company for Electrical Industries for the period 2010-2019. International Academic Journal of Social Sciences. 2022;9(1):1–8.
9.
Ma Y, Chen X, Wang L, Yang J. Study on smart home energy management system based on artificial intelligence. Journal of Sensors. 2021;2021(1).
10.
Bhattacharya R, Kapoor T. Advancements in Power Electronics for Sustainable Energy Systems: A Study in the Periodic Series of Multidisciplinary Engineering. Smart Grid Integration. 2024:19–25.
11.
Ghalkhani M, Habibi S. Review of the Li-ion battery, thermal management, and AI-based battery management system for EV application. Energies. 2022 Dec 24;16(1):185.
12.
Zhang X, Rodriguez S. Advanced Optimization Techniques for Vehicle Dynamics in Robotics. Association Journal of Interdisciplinary Technics in Engineering Mechanics. 2023 Oct 9;1(1):1–3.
13.
Yan W, Li MJ, Mei N, Qu CY, Wang Y, Liu LP. Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm. Measurement and Control. 2023 May;56(5–6):1026–36.
14.
Billert AM, Erschen S, Frey M, Gauterin F. Predictive battery thermal management using quantile convolutional neural networks. Transportation Engineering. 2022 Dec 1;(10):100150.
15.
Mathankumar M, Gunapriya B, Guru RR, Singaravelan A, Sanjeevikumar P. AI and ML powered IoT applications for energy management in electric vehicles. Wireless Personal Communications. 2022 Sep;126(2):1223–39.
16.
Vidal C, Malysz P, Kollmeyer P, Emadi A. Machine Learning applied to electrified vehicle battery state of charge and state of health estimation: State-of-the-art. Ieee Access. Mar 16;8:52796–814.
17.
Umathe S, Hiware R. Artificial intelligence and IoT based smart battery management system for electric vehicle. In2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON). 2022 Dec 23 (pp. 1-7). IEEE.
18.
Krishna G, Singh R, Gehlot A, Akram SV, Priyadarshi N, Twala B. Digital technology implementation in battery-management systems for sustainable energy storage: review, challenges, and recommendations. Electronics. 2022 Aug 27;11(17):2695.
19.
Buidin TI, Mariasiu F. Battery thermal management systems: Current status and design approach of cooling technologies. Energies. 2021 Aug 10;14(16):4879.
20.
Kleiner J, Stuckenberger M, Komsiyska L, Endisch C. Advanced Monitoring and prediction of the thermal state of intelligent battery cells in electric vehicles by physics-based and data-driven modeling. Batteries. 2021 May 11;7(2):31.
21.
Kaleem MB, He W, Li H. Machine learning driven digital twin model of Li-ion batteries in electric vehicles: a review. Artificial Intelligence and Autonomous Systems. 2023 May 14;1(1):1–2.
22.
Afzal MZ, Aurangzeb M, Iqbal S, Pushkarna M, Rehman AU, Kotb H, et al. A Novel electric vehicle battery management system using an artificial neural network-based adaptive droop control theory. International Journal of Energy Research. 2023;2023(1):2581729.
23.
Shi D, Zhao J, Eze C, Wang Z, Wang J, Lian Y, et al. Cloud-based artificial intelligence framework for battery management system. Energies. 2023 May 30;16(11):4403.
24.
Jaliliantabar F, Mamat R, Kumarasamy S. Prediction of lithium-ion battery temperature in different operating conditions equipped with passive battery thermal management system by artificial neural networks. Materials Today: Proceedings. 2022 Jan 1;48:1796–804.
25.
Kumar VS, Begum AY, Moniruzzaman M, Sagar KV, Rao LM. Battery Management in Electrical Vehicles Using Machine Learning Techniques. Journal of Pharmaceutical Negative Results. 2022 Oct 7;13.
26.
Billert AM, Frey M, Gauterin F. A method of developing quantile convolutional neural networks for electric vehicle battery temperature prediction trained on cross-domain data. IEEE Open Journal of Intelligent Transportation Systems. 2022 May 23;3:411–25.
27.
Li W, Cui H, Nemeth T, Jansen J, Ünlübayir C, Wei Z, et al. Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning. Applied Energy. 2021 Jul 1;293:116977.
28.
Lipu MH, Miah MS, Jamal T, Rahman T, Ansari S, Rahman MS, et al. Artificial Intelligence approaches for advanced battery management system in electric vehicle applications: A statistical analysis towards future research opportunities. Vehicles. 2023 Dec 25;6(1):22–70.
29.
Wang Z, Jochem P, Yilmaz HU, Xu L. Integrating vehicle‐to‐grid technology into energy system models: Novel methods and their impact on greenhouse gas emissions. Journal of Industrial Ecology. 2022 Apr;26(2):392–405.
30.
Macharia VM, Garg VK, Kumar D. A review of electric vehicle technology: Architectures, battery technology and its management system, relevant standards, application of artificial intelligence, cyber security, and interoperability challenges. IET Electrical Systems in Transportation. 2023 Jun;13(2):e12083.
31.
Jain R, Chakravarthi MK, Kumar PK, Hemakesavulu O, Ramirez-Asis E, Pelaez-Diaz G, et al. Internet of Things-based smart vehicles design of bio-inspired algorithms using artificial intelligence charging system. Nonlinear Engineering. 2022 Oct 26;11(1):582–9.
32.
Arya A, Arya M, Bhagat A, Paliwal P, Thakur T, Warudkar V. Artificial intelligence-based smart electric vehicle battery management system. Espacenet 2021.
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