Latif Rozaqi
Papers
3
Total Citations
27
H-Index
3
About
Latif Rozaqi’s research sits at the intersection of energy storage systems, power electronics, and rehabilitation robotics, with a focus on practical, real-world applications. His most impactful work, “RLS with Optimum Multiple Adaptive Forgetting Factors for SoC and SoH Estimation of Li-Ion Battery” (2017), has garnered 21 citations and addresses a critical challenge in battery management for electric vehicles and renewable energy systems. By improving upon traditional recursive least square methods with multiple adaptive forgetting factors, Rozaqi enhanced the accuracy of state-of-charge and state-of-health estimation—key to extending battery life and ensuring system reliability. In power electronics, his work on “Buck Converter Control for Lead Acid Battery Charger using Peak Current Mode” (2017) demonstrates his ability to design robust, efficient charging solutions for diverse applications, from robotics to smart grids. Beyond energy systems, Rozaqi has contributed to medical technology, as seen in his 2019 paper on “Experimental Performance Evaluation of Computer Vision for an Upper Limbs Rehabilitation Robot,” which explores computer vision to enhance robotic therapy for patients. With a total of 27 citations across his top papers, Rozaqi’s multidisciplinary approach—spanning estimation algorithms, converter control, and assistive robotics—highlights his commitment to solving practical engineering problems that impact both industry and healthcare.
Research Focus
Key Achievements
Top Papers
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