Energies MDPI’s Post

🚨 Exciting News! 🚨 I am excited to announce that our latest research article, “Lithium-Ion Battery Health Management and State of Charge (SOC) Estimation Using Adaptive Modelling Techniques,” has been published in the renowned journal Energies by MDPI. This work presents a comprehensive health management system (HMS) for lithium-ion batteries, introducing innovative methods to improve state of charge (SOC) and state of health (SOH) estimation and ensure the safety and longevity of batteries, especially in electric vehicles. 🔬 Key Highlights: • Developed an Improved Online Battery Model (IOBM) for enhanced SOC and SOH estimation accuracy. • Utilized Forgetting Factor Recursive Least Squares (FFRLS) for real-time parameter updates and an Adaptive Nonlinear Sliding Mode Observer (ANSMO) for precise SOC estimation. • Integrated an LSTM neural network to dynamically adjust battery capacity based on operating conditions. • Achieved outstanding results in validation using the Urban Dynamometer Driving Schedule (UDDS) test. This innovative approach showcases how advanced modeling and estimation techniques can enhance the reliability of battery management systems, mitigate risks, and ensure balanced performance in electrified transportation systems. I want to adress my sincere gratitude to the whole team, to my supervisors Pr. Nacer Kouider M’sirdi, and Pr. Mohammed Oudghiri Bentaie, and to my colleague Khadija Saqli for their collaboration and contributions to this work. 📚 Read the full paper here: https://lnkd.in/eXWFAt9V 🔗 DOI: 10.3390/en17225746 #BatteryManagement #LithiumIonBatteries #StateOfCharge #ElectricVehicles #BatteryModelling #EnergyInnovation #Research Energies MDPI