Mahdi Yousef, MostafaShaterabadi, MohammadKarimi, Houshang2026-09-242026-09-242025M. M. Yousef, M. Shaterabadi and H. Karimi, "An Adaptive Kalman-Guided Soft Sensor Using Feedforward Neural Networks for SOC Estimation in Lithium-Ion Batteries," 2025 IEEE 34th International Symposium on Industrial Electronics (ISIE), Toronto, ON, Canada, 2025, pp. 1-6, doi: 10.1109/ISIE62713.2025.1112474197983503748032163-5145https://hdl.handle.net/10315/44079https://doi.org/10.1109/ISIE62713.2025.11124741© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This accepted conference paper is published as M. M. Yousef, M. Shaterabadi and H. Karimi, "An Adaptive Kalman-Guided Soft Sensor Using Feedforward Neural Networks for SOC Estimation in Lithium-Ion Batteries," 2025 IEEE 34th International Symposium on Industrial Electronics (ISIE), Toronto, ON, Canada, 2025, pp. 1-6, doi: 10.1109/ISIE62713.2025.11124741.Accurate State-of-Charge (SOC) estimation is crucial for ensuring the battery’s safe operation and prolonged lifespan, but it remains a challenge due to sensor noise and system nonlinearity. This paper proposes a novel covariance-adaptive hybrid approach that interprets neural network outputs as virtual measurements and dynamically integrates them with the a priori state predictions from a Discrete Kalman filter to yield refined a posteriori estimates. The introduced adaptive scheme uses exogenous inputs derived from spectral analysis to provide past information to the Feedforward Neural Network (FNN) to perform soft sensing and generate network-aided measurements. The novel processing occurs after this step by adaptive integration with Kalman filter results. The developed hybrid architecture is tested on real experimental battery datasets for LG 18650 HG2, and the results prove its superior performance against existing state-of-the-art algorithms. Experimental results averaged over twenty Monte Carlo trials demonstrated nearly 28% and 39% improvement in the mean absolute error and root mean square error, respectively, compared to the baseline results.enTemperature measurementLithium-ion batteriesAdaptive systemsSoft sensorsEstimationBattery management systemsBattery charge measurementFeedforward neural networksKalman filtersState of chargeState of charge estimationCovariance-adaptive hybrid approachDiscrete Kalman filterNeural network virtual measurementsConference Paper