Chemnitz Researcher Receives Best Paper Award at International Conference in Catania

Publication by researchers from the Chair of Measurement and Sensor Technology at Chemnitz University of Technology receives Best Paper Award at SSD’26 – the awarded method makes high-dimensional impedance analysis real-time capable in embedded systems, running more than 500 times faster than comparable approaches

At the 23rd International Multi-Conference on Systems, Signals and Devices (SSD’26), held March 30 to April 2, 2026, in Catania, Italy, Dr. Ahmed Yahia Kallel, research associate and group leader for Impedance Spectroscopy and Measurement Systems at the Chair of Measurement and Sensor Technology (headed by Prof. Dr. Olfa Kanoun) at Chemnitz University of Technology, received the Best Paper Award.

Lithium-ion batteries are the backbone of electric mobility and stationary energy storage. To reliably determine their state of charge, researchers use Electrochemical Impedance Spectroscopy (EIS), a method that measures a battery’s electrical behavior across different frequencies and generates up to 480 data points per measurement – far too many for the small microcontrollers in battery management systems to process in real time. The award-winning paper “Fast A*-mRMR for Model-Aware Feature Selection in EIS-Based Battery State of Charge Estimation” addresses this challenge through intelligent pre-selection of the most informative data points. The method transfers the A* algorithm – a proven tool in robotics and game development for efficiently finding optimal paths through complex environments – to the domain of feature selection. Rather than exhaustively testing every possible feature combination, the algorithm identifies the best selection directly and efficiently, much like a navigation system finds the shortest route without driving down every road. Two criteria are considered simultaneously: how informative a data point is for predicting the state of charge, and how much it overlaps with already-selected points (Minimum Redundancy Maximum Relevance, mRMR).

Tested on physics-based digital twin data from four battery cells across 17 state-of-charge levels and three temperatures (756 measurement points in total), Fast A*-mRMR achieves a prediction accuracy of 3.62 to 3.93 % error with a computation time of just 1.27 seconds – more than 500 times faster than comparable methods while matching or exceeding their accuracy. The work also yields a counterintuitive insight: allowing a controlled degree of similarity between selected data points can actually improve predictions, contrary to the prevailing assumption that redundancy should always be minimized. “Our method enables the practical use of high-dimensional impedance data in resource-constrained embedded systems, laying an important foundation for more efficient and reliable battery management systems,” explains Dr. Ahmed Yahia Kallel. “The Best Paper Award is a gratifying recognition that our work in intelligent sensor systems and sustainable energy technologies is resonating with the international research community,” adds Prof. Dr. Olfa Kanoun.

The SSD is an established IEEE international multi-conference founded in 2001, bringing together research in the fields of systems, signal processing, and devices, with an H-index of 35. The award was presented at the sub-conference Power Systems & Smart Energies (PSE). The proceedings are published in IEEEXplore and indexed in Scopus and Web of Science.

Contact: Prof. Dr. Olfa Kanoun, Chair of Measurement and Sensor Technology, email olfa.kanoun@etit.tu-chemnitz.de

Citation: Kallel, A.Y., Kanoun, O.: “Fast A*-mRMR for Model-Aware Feature Selection in EIS-Based Battery State of Charge Estimation.” Proc. 23rd Int. Multi-Conf. on Systems, Signals and Devices (SSD’26), Catania, Italy, 2026. Best Paper Award.

(Author: Prof. Dr. Olfa Kanoun)

Mario Steinebach
16.04.2026

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