Maral Partovibakhsh
Papers
4
Total Citations
338
H-Index
4
About
Maral Partovibakhsh is a researcher specializing in state estimation, battery management systems, and autonomous mobile robot control, with particular expertise in advanced Kalman filtering techniques. Her most significant contribution lies in the development of adaptive unscented Kalman filtering (AUKF) methods for real-time estimation of lithium-ion battery parameters and state-of-charge (SoC), a critical challenge in powering autonomous mobile robots operating in unpredictable environments. Her 2014 paper on this topic has garnered an impressive 297 citations, establishing it as a landmark reference in the battery estimation field. Building on earlier foundational work from 2012, Partovibakhsh demonstrated that adaptive noise covariance adjustment yields more robust and accurate SoC predictions, directly improving power management reliability. Beyond battery systems, she has extended her AUKF expertise to wheeled mobile robot locomotion, developing algorithms for online slip ratio estimation and sliding mode control across varying terrain conditions. Her integrated approach — combining probabilistic state estimation with practical robotics control — bridges theoretical signal processing and real-world autonomous systems engineering. Her body of work reflects a consistent focus on making mobile robotic platforms more intelligent, energy-aware, and terrain-adaptive, contributing meaningfully to the broader field of autonomous systems research.
Research Focus
Key Achievements
Top Papers
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