Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation

Year : 2026 | Volume : 13 | Issue : 02 | Page : 1 5
    By

    Rupali Sanjay Firke,

  • Mukesh Kumar Gupta,

  • Amit Tiwari,

  1. Research Scholar, Department of Computer Science and Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
  2. Professor, Department of Electrical Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India
  3. Assistant Professor, Department of Mechanical Engineering, Suresh Gyan Vihar University, Jaipur, Rajasthan, India

Abstract

Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, we have proposed a Particle Swarm Optimization (PSO)-based Support Vector Regression (SVR) framework that estimates SOC and SOH using only easily measured variables, namely ambient temperature, charge current, voltage, and cycle count. Our primary objectives are to improve estimation stability and reduce prediction error through optimal tuning of the SVR hyperparameters using PSO. PSO searches the validation set to find the optimal values of the penalty factor, kernel width, and epsilon-sensitive loss, thereby minimizing the root-mean-square error (RMSE). Convergence plots, residual curves, residual histograms, and confusion-matrix-based classification tests of the resulting SVR models are evaluated. Experimental results indicate that PSO converges reliably, achieving a minimum RMSE of approximately 26.6 for SOC and 9.17 for SOH. The residual analysis shows that SOH errors are predominantly within the range of +15, whereas SOC errors may reach up to +40, reflecting the higher dynamic complexity of SOC. Further analysis using the confusion matrix shows stronger consistency in SOH classification, with 17 of the moderate-SOH samples correctly classified. Overall, the PSOSVR framework is more robust and converges better for battery state estimation. These results indicate that PSO is efficient for hyperparameter optimization and that more time-dependent features are necessary to enhance the accuracy of SOC prediction.

Keywords: Particle Swarm Optimization, State of Charge Estimation, State of Health Prediction, Support Vector Regression, Battery Management System, Machine Learning Optimization

[This article belongs to Journal of Automobile Engineering and Applications ]

How to cite this article:
Rupali Sanjay Firke, Mukesh Kumar Gupta, Amit Tiwari. Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation. Journal of Automobile Engineering and Applications. 2026; 13(02):1-5.
How to cite this URL:
Rupali Sanjay Firke, Mukesh Kumar Gupta, Amit Tiwari. Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation. Journal of Automobile Engineering and Applications. 2026; 13(02):1-5. Available from: https://journals.stmjournals.com/joaea/article=2026/view=238895


References

  1. Al-Hashimi, Z., Khamis, T., Al Kouzbary, M., Arifin, N., Mokayed, H., & Abu Osman, N. A. (2025). A decade of machine learning in lithium-ion battery state estimation: a systematic review. Ionics, 31(3), 2351-2377.
  2. Bokstaller, J., Schneider, J., & vom Brocke, J. (2023). Estimating SoC, SoH, or RuL of rechargeable batteries via IoT: A review. IEEE Internet of Things Journal, 11(5), 7559-7582.
  3. Qu, J., Liu, F., Ma, Y., & Fan, J. (2019). A neural-network-based method for RUL prediction and SOH monitoring of lithium-ion battery. IEEE access, 7, 87178-87191.
  4. Sekhar, J. C., Domathoti, B., & Santibanez Gonzalez, E. D. (2023). Prediction of battery remaining useful life using machine learning algorithms. Sustainability, 15(21), 15283.
  5. Bansal, P., Sharma, M., Dutta, R., & Tiwari, A. (2024, October). Battery performance tester for lithium-ion technology with Arduino Uno & MCP. In 2nd International Conference on Pervasive Computing Advances and Applications (PerCAA 2024)(Vol. 2024, pp. 258-263). IET.
  6. Kamişli, K., & Taş, İ. Ç. (2025). Estimation of Battery Remaining Life-time with Deep Learning Methods. International Journal of New Findings in Engineering, Science and Technology, 3(1), 32-42.
  7. Dini, P., Colicelli, A., & Saponara, S. (2024). Review on modeling and soc/soh estimation of batteries for automotive applications. Batteries, 10(1), 34.
  8. Tiwari, A. (2025). Design of Advanced AZ61 Magnesium Composites with Enhanced Strength and Ductility Using ANN-Based Computational Modeling. In Next-Generation Computational Intelligence: Trends and Technologies(pp. 209-231). Cham: Springer Nature Switzerland.
  9. R. Patni, N. P. Dabe, S. S. Kadlag, A. Dandotia, M. K. Gupta and A. Tiwari, “Electrical Fault Analysis and Fault Location Prediction in Power Systems Using Generative Adversarial Networks and Deep Learning,” 2025 Second International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM), Coimbatore, India, 2025, pp. 1-5, doi: 10.1109/iTechSECOM64750.2025.11307547.
  10. Tiwari, A., Vasnani, H., Kumar, N., & Labana, M. (2017). A Review on Waste Heat Recovery and Reused of Exhaust Gases from Diesel Engines. International Journal of Advance Reaserch in Science and Engineeing, Volume, (08).
  11. Jain, A. Tiwari, P. Jain, H. Vasnani, P. Bansal and B. Sharma, “Synergistic Effects of Additives in PCM Composites: A Pathway to Enhanced Thermal Energy Storage,” 2025 International Conference on Sustainability, Innovation & Technology (ICSIT), Nagpur, India, 2025, pp. 1-6, doi: 10.1109/ICSIT65336.2025.11293930.
  12. Ayob, A., Ansari, S., Lipu, M. S. H., Hussain, A., & Saad, M. H. M. (2022). SOC, SOH and RUL estimation for supercapacitor management system: Methods, implementation factors, limitations and future research improvements. Batteries, 8(10), 189.
  13. P. Kichara, A. Tiwari, M. K. Gupta, H. Vasnani, N. Pandel and M. Mathur, “Computational Design and Simulation of Diabetes Risk Assessment Models Based on NSGA-II Integrated Artificial Neural Networks,” 2025 IEEE 7th International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India, 2025, pp. 1-6, doi: 10.1109/ICCCA66364.2025.11325497.
  14. S. Patil, D. S. Raskar, M. K. Gupta, A. Tiwari, P. A. Thakare and S. Nayak, “Metaheuristic-Driven Predictive Modeling for Remaining Useful Life Optimization of Li-Ion Batteries,” 2025 IEEE 2nd International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS), Bangalore, India, 2025, pp. 1-6, doi: 10.1109/ICITEICS64870.2025.11341529.
  15. Tiwari, A. (2024). Prediction model for hardness and tensile strength of graphene reinforced AZ 61 alloy based composite using Metaheuristic Algorithm. Global Journal of Engineering and Technology Advances, 20(03), 042-052.
  16. Swarnkar, H., Jain, R., & Tiwari, A. (2024). Comparative Energy and Exergy Performance of Box-Type Solar Cookers Using Various Pot Materials. International Journal of Machine Systems and Manufacturing Technology, 2(01), 9-15.
  17. Tiwari, A., Vasnani, H., Bansal, P., & Yadav, V. Optimizing rail steel strength prediction: a novel genetic algorithm-ANN approach for microstructure analysis of pearlite phase. In Recent Trends in Data Analytics and Computing(pp. 12-18). CRC Press.
  18. Jain, R., Tiwari, A., Jain, P., Vasnani, H., Bansal, P., & Sharma, B. (2025, August). Synergistic Effects of Additives in PCM Composites: A Pathway to Enhanced Thermal Energy Storage. In 2025 International Conference on Sustainability, Innovation & Technology (ICSIT)(pp. 1-6). IEEE.
  19. K. Sikarwal, M. K. Gupta, A. Gupta, M. Bhardwaj, A. Tiwari and H. Vasnani, “Adaptive Federated Learning for Resource-Constrained Edge Devices: Enhancing Convergence, Communication Efficiency, and Privacy-Utility Trade-Offs,” 2025 International Conference on Future Technologies (ICFT), Sangli, India, 2025, pp. 1-6, doi: 10.1109/ICFT66708.2025.11336786.
  20. Tiwari, A., Jain, R., & Swarnkar, H. (2025). Optimization of Performance Analysis of IoT-Based Temperature Monitoring Box Type Solar Cooker. In Evolving Landscapes of Research and Development: Trends, Challenges, and Opportunities(pp. 261-290). IGI Global Scientific Publishing.
  21. Tang, R., Zhang, P., Ning, S., & Zhang, Y. (2023). Prediction of battery SOH and RUL based on cooperative characteristics in voltage-temperature-time dimensions. Journal of The Electrochemical Society, 170(6), 060535.
  22. Tiwari, N. S. Gaira, R. Tiwari, A. Singh, A. K. Yadav and S. Khanduri, “AI-Driven Design of Composite Materials for Aerospace Engineering,” 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES), Lucknow, India, 2024, pp. 1-6, doi: 10.1109/IC3TES62412.2024.10877546.
  23. Tiwari, A., Kumar, N., & Banerjee, M. K. (2024). Ageing Characteristics of Stir Cast AZ 61 Alloy with Minor Additions. Recent Patents on Engineering, 18(6), 67-82.
  24. Pandey, S. Mishra, A. Tiwari and P. Bansal, “An Analytical Approach to Social Progress: Genetic Algorithm Optimization and Statistical Assessment of Social Entrepreneurship,” 2025 International Conference on Sustainability, Innovation & Technology (ICSIT), Nagpur, India, 2025, pp. 1-6, doi: 10.1109/ICSIT65336.2025.11295610.
  25. Agarwal, S. Mishra, A. Tiwari, H. Vasnani, S. Arora and N. Saini, “Neuro-Sentiment Framework: Leveraging ANN to Decode Social Media Impact on Stock Price Fluctuations,” 2025 International Conference on Sustainable Technologies for Humanity and Smart World (HSWTech), Pune, India, 2025, pp. 1-5, doi: 10.1109/HSWTech64936.2025.11277417.
  26. Tiwari, A., & Kumar, A. (2025). Synergistic Effects of Sc and Ca on the Performance of Modified AZ61 Mg Alloy in Defense Application. In Innovative Materials for Next-Generation Defense Applications: Cost, Performance, and Mass Production(pp. 315-352). IGI Global Scientific Publishing.
  27. Jain, R., Tiwari, A., Jain, P., Vasnani, H., Varshney, P., & Bansal, P. (2025, July). Performance Evaluation of IoT-Enabled PV Panels Using Graphene-Paraffin Composite Thermal Management. In 2025 International Conference on Communication and Smart Devices (ICCoSD)(Vol. 1, pp. 1-6). IEEE.
  28. K. Singh, S. Mishra, A. Tiwari, H. Vasnani, S. Tiwari and R. Sharma, “Hybrid ANN–GA Predictive Modeling of Psychological Barriers in Female Healthcare Consumers,” 2025 IEEE International Conference on Compute, Control, Network & Photonics (ICCCNP), Bangalore, India, 2025, pp. 1-6, doi: 10.1109/ICCCNP63914.2025.11233440.
  29. Tiwari, P. Bansal, H. Vasnani and N. Pandel, “Non Dominated Sorting Genetic Algorithm (NSGA-II) Modelling for AZ 31 Magnesium Alloy Composite,” 2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), Greater Noida, India, 2025, pp. 268-272, doi: 10.1109/IC3ECSBHI63591.2025.10991085.
  30. Tiwari, A., Bansal, P., Pandel, N., Vasnani, H., Amrousse, R., & Azat, S. (2025). AI-Driven Design of AZ61 Magnesium Alloy for Enhanced Performance in Industrial Automation. In Advancing Cybersecurity in Smart Factories Through Autonomous Robotic Defenses(pp. 153-172). IGI Global Scientific Publishing.
  31. S. Bartakke, P. S. Maruti, M. K. Gupta, A. Tiwari, H. Vasnani and N. Pandel, “A Random Forest Approach for Intelligent Vehicle Anomaly Detection and Safety Assurance,” 2025 Second International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM), Coimbatore, India, 2025, pp. 1-6, doi: 10.1109/iTechSECOM64750.2025.11307576.
  32. Tiwari, A., Bansal, P., Vasnani, H., & Pandel, N. (2025, January). Non Dominated Sorting Genetic Algorithm (NSGA-II) Modelling for AZ 31 Magnesium Alloy Composite. In 2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI)(pp. 268-272). IEEE.
  33. CL, S., JN, C. S., Alluraiah, N. C., C, D., Kumar Pujari, H., & Khan, B. (2025). An Overview of Remaining Useful Life Prediction of Battery Using Deep Learning and Ensemble Learning Algorithms on Data‐Dependent Models. International Transactions on Electrical Energy Systems, 2025(1), 2242749.
  34. Tiwari, A., Vasnani, H., Jain, R., Swarnkar, H., Tanwar, H., & Srivastava, S. (2024). Fabrication of low-cost metal polishing machine for preparation of microstructure samples of lightweight alloy and composite material. Global Journal of Engineering and Technology Advances, 19(3), 112-123.
  35. Kumar, Y., Tiwari, A., Vasnani, H., & Kumar, N. (2018). Investigation of mechanical behavior of Al 6063 & SiC composite materials. International Journal of Advanced Technology and Engineering Exploration, 5(47), 376-384.
  36. Tiwari, P. Badhani, P. Jaiswal, A. Singh, N. Tripathi and N. S. Gaira, “Machine Learning for Predicting Fatigue Life of Mechanical Components,” 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM), Dehradun, India, 2024, pp. 1-5, doi: 10.1109/ICACCM61117.2024.11059219.
  37. K. Sikarwal, S. D. Raskar, M. K. Gupta, A. Gupta, M. Bhardwaj and A. Tiwari, “An Adaptive Communication Efficient Federated Learning Framework for Edge Intelligence under Resource and Privacy Constraints,” 2025 IEEE 7th International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India, 2025, pp. 1-6, doi: 10.1109/ICCCA66364.2025.11325591.
  38. Tiwari, A., Jain, R., Swarnkar, H., Bansal, P., Mathur, M., & Vasnani, H. (2025). Effect of graphene-based paraffin composite on performance enhancement of IoT-integrated box-type solar cooker. Environmental Science and Pollution Research, 1-24.
  39. Sharma, P., Patel, V., Singh, A. K., & Tiwari, A. (2024). Phase Transformation of Manganese Steel: A Role of Alloying Elements. , 14(2), 15-20.
  40. Swarnkar, H., Jain, R., Tiwari, A., & Vasnani, H. (2024). Phase change material application in solar cooking for performance enhancement through storage of thermal energy: A future demand. World Journal of Advanced Engineering Technology and Sciences, 12(01), 306-330.
  41. Kaur, T., Tiwari, A., Mathur, M., Sharma, N., Ganesan, S., Ahlawat, Y. K., … & Gakhar, D. (2025). Nanomaterials for Wheat Stress Tolerance: Trade‐Off on Underlying Mechanisms and Latest Breakthroughs Under Changing Climate. Journal of Agronomy and Crop Science, 211(4), e70096.
  42. Patni, Y. R., Dabe, N. P., Kadlag, S. S., Dandotia, A., Gupta, M. K., & Tiwari, A. (2025, October). Electrical Fault Analysis and Fault Location Prediction in Power Systems Using Generative Adversarial Networks and Deep Learning. In 2025 Second International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM)(pp. 1-5). IEEE.
  43. Singh, M. K., Mishra, S., Tiwari, A., Vasnani, H., Tiwari, S., & Sharma, R. (2025, September). Hybrid ANN–GA Predictive Modeling of Psychological Barriers in Female Healthcare Consumers. In 2025 IEEE International Conference on Compute, Control, Network & Photonics (ICCCNP)(pp. 1-6). IEEE.
  44. Tiwari, A. (2026). Role of Magnesium and Its Composite for Biomedical Application: A Future Demand. In Biomedical Applications and Toxicity of Polymers, Nanoparticles, Biomaterials, and Metal Ions(pp. 321-364). IGI Global Scientific Publishing.
  45. Pandel, N., Dash, S. K., Tiwari, A., & Bansal, P. (2026). Spin Bias Analysis Detection on Text Publishing in Media Using Recurrent. In Proceedings of International Conference on Innovations in Data Science: ICIDS 2024(p. 163). Springer Nature.
  46. Pandey, S., Mishra, S., Tiwari, A., & Bansal, P. (2025, August). An Analytical Approach to Social Progress: Genetic Algorithm Optimization and Statistical Assessment of Social Entrepreneurship. In 2025 International Conference on Sustainability, Innovation & Technology (ICSIT)(pp. 1-6). IEEE.
  47. Tiwari, A., Pandel, N., Bansal, P., & Varshney, P. (2026). Microstructure Morphology Analysis for Strengthening Mechanism of AZ 61. In Proceedings of International Conference on Innovations in Data Science: ICIDS 2024(p. 55). Springer Nature.
  48. Patil, P. S., Raskar, D. S., Gupta, M. K., Tiwari, A., Thakare, P. A., & Nayak, S. (2025, August). Metaheuristic-Driven Predictive Modeling for Remaining Useful Life Optimization of Li-Ion Batteries. In 2025 IEEE 2nd International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS)(pp. 1-6). IEEE.
  49. Agnihotri, A., Vasudev, H., Singh, G., Singh, S., Hosalli, R., Veeresha, G., … & Tiwari, A. (2025). Evaluation of HVOF Sprayed and Burnished WC-Co-Cr Coatings on Boiler Steel: Mechanical and Microstructural Properties. Journal of Bio-and Tribo-Corrosion, 11(2), 1-12.
  50. Gabra, M. H., Jain, R. K., & Tiwari, A. (2017). Energy efficient cupola furnace via hybridization with a biomass gasifier.  J. Emerg. Technol. Eng. Res, 5, 54-62.
  51. Swarnkar, H., Pandey, R. K., Prakash, V. V., Nayak, S., Vasnani, H., Jain, R., & Tiwari, A. (2024). Performance Analysis of IoT-based Temperature Monitoring Box Type Solar Cooker: A Multi-objective Optimization Approach. Recent Patents on Mechanical Engineering.
  52. Pandel, N., Tiwari, A., Bansal, P., Nayak, S., Amrousse, R., & Azat, S. (2025). Deep Learning for Lightweight Materials: CNN-Driven Microstructure Optimization in AZ61 Alloy. In Deep Learning Innovations for Securing Critical Infrastructures(pp. 545-576). IGI Global Scientific Publishing.
  53. Soni, P. K., Tiwari, A., Kosuru, S. N. V. J., Dhandapani, S., Kumar, R. S., Deshmukh, K., … & Hailu, N. (2025). An analytical approach to study the mechanical and temperature resistance of Cr2C3 reinforced with cobalt-based advanced composite coatings on Inconel 713 substrate. Journal of Materials Science: Materials in Engineering, 20(1), 1-15.
  54. Tiwari, A., & Mathur, M. (2025). A Novel Approach Toward Computational Image Processing and Strategies for Precision Agriculture. Computational Intelligence and Image Processing in Agriculture: Applications and Innovations, 73-96.
  55. Natesh, C. P., Siddeshkumar, N. G., Srinivasa, G., Shivaramakrishna, A., Pruthvi, H. M., Prasad, C. D., … & Aden, A. A. (2025). Multi-objective optimization of surface roughness and MRR in AISI 316L stainless steel processed by MQL end milling using taguchi, RSM, ANN, and RFR methods. Scientific Reports, 15(1), 36583.
  56. Pandel, N., Tiwari, A., Bansal, P., & Amrousse, R. (2025). Unlocking the Potential of AI in Healthcare: A Critical Review of Innovations Barriers and the Path Forward. Integrating AI With Haptic Systems for Smarter Healthcare Solutions, 467-492.
  57. Soni, P. K., Prasad, C. D., Manjunath, H. R., Tiwari, A., Raju, V. P., Athithan, S., … & Kumar, A. (2025). Comparison of tribological behavior of hard faced Fe-based metallic composite coatings sprayed using HVOF. Journal of Bio-and Tribo-Corrosion, 11(1), 5.
  58. Kasbi, Y., Remissa, I., Toshtay, K., Mabrouk, A., Bachar, A., Azat, S., … & Amrousse, R. (2025). H2O2 and HAN Green Monopropellants—A State-of-the-Art Review on Their Recent Development, Corresponding Synthesized Catalysts, and Their Possible Use as Thrusters. Catalysts, 15(2), 183.
  59. Tiwari, G., Yadav, V., Bansal, P., & Tiwari, A. (2026). Data-Driven Approach to Breast Cancer Detection: EDA on the MIAS Mammography Dataset. In Intelligent Systems Using Semiconductors for Robotics and IoT(pp. 500-505). CRC Press.
  60. Sachin, B., Rao, C. M., Prasad, C. D., Tiwari, A., Ravikiran, Raghavendra, T., … & Madhusudhana, R. (2024). Interactive design and development of an intelligent vision-driven 3D printed precision sorting mechanism for silk cocoons. International Journal on Interactive Design and Manufacturing (IJIDeM), 1-10.
  61. Bartakke, C. S., Maruti, P. S., Gupta, M. K., Tiwari, A., Vasnani, H., & Pandel, N. (2025, October). A Random Forest Approach for Intelligent Vehicle Anomaly Detection and Safety Assurance. In 2025 Second International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems (iTech SECOM)(pp. 1-6). IEEE.
  62. Prasad, C. D., UB, P. K., Tiwari, A., C, V., BN, D. K., Nayak, H., … & Hailu, N. (2025). Characterization and wear behavior of plasma-sprayed hard-faced silicon carbide coatings on AISI 410 steel substrate. Journal of Materials Science: Materials in Engineering, 20(1), 89.
  63. Chopra, A., Jain, T. K., Saxena, N., & Tiwari, A. (2023). Predictive Analytics of a Community Survey Data by Artificial Neural Network-A Subset of Machine Learning. Indian Journal of Science and Technology, 16(34), 2778-2788.
  64. Agarwal, R., Mishra, S., Tiwari, A., Vasnani, H., Arora, S., & Saini, N. (2025, September). Neuro-Sentiment Framework: Leveraging ANN to Decode Social Media Impact on Stock Price Fluctuations. In 2025 International Conference on Sustainable Technologies for Humanity and Smart World (HSWTech)(pp. 1-5). IEEE.
  65. Patel, V., Sharma, P., Tiwari, A., & Vasnani, H. (2024). Magnesium alloys in the automotive industry: Alloying elements and their impact on material performance. Global Journal of Engineering and Technology Advances, 21(01), 130-145.
  66. Sikarwal, P. K., Raskar, S. D., Gupta, M. K., Gupta, A., Bhardwaj, M., & Tiwari, A. (2025, November). An Adaptive Communication Efficient Federated Learning Framework for Edge Intelligence under Resource and Privacy Constraints. In 2025 IEEE 7th International Conference on Computing, Communication and Automation (ICCCA)(pp. 1-6). IEEE.
  67. Tiwari, A., Bansal, P., Amrousse, R., & SeitkhanAzat. (2026). Integrating Deep Learning in Rolling Process Design for Nanocomposites: A Novel Approach to Strength Prediction. Enhancing Hybrid Nanodevice Fabrication Efficiency Using Machine Learning, 41-61.
  68. Xia, F., Yu, Y., & Chen, J. (2024). SOH and RUL prediction of lithium batteries based on fusions of RLOESS filtered electrochemical and thermal features by bidirectional gated recurrent unit network. Journal of Energy Storage, 102, 114134.
  69. Tiwari, A., Soni, P. K., & Nayak, S. (2025). GA Optimization for Performance and Emission Characteristics of SI Engine via Ethanol-Gasoline Blend. In Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications(pp. 317-348). IGI Global Scientific Publishing.
  70. Dineva, A. (2024). Evaluation of advances in battery health prediction for electric vehicles from traditional linear filters to latest machine learning approaches. Batteries, 10(10), 356.
Regular Issue Subscription Original Research
Volume 13
Issue 02
Received 04/02/2026
Accepted 10/02/2026
Published 10/03/2026
Publication Time 34 Days
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