Key result
Low-cost battery platform achieves ~0.28% maximum state-of-charge error using a hybrid EKF-ANN method.
Why the study?
Commercial battery cyclers and high-end instrumentation can be limited in academic and applied research environments, motivating low-cost, reproducible test benches for lithium-ion battery characterization and estimator validation.
The proposed low-cost experimental platform provides an accessible and reproducible framework for lithium-ion battery testing, equivalent circuit model identification, and state-of-charge estimator evaluation.
Answers are grounded in this paper and cite their sources.
Enables accessible Li-ion testing in resource-limited labs; leaves open validation against commercial standards before broad adoption.
Reliable experimental platforms are essential for lithium-ion (Li-Ion) battery characterization, equivalent circuit model (ECM) identification, and state-of-charge (SOC) estimator validation. However, access to commercial battery cyclers and high-end instrumentation can be limited in academic and applied research environments, which motivates the development of low-cost and reproducible test benches. This work presents the development and validation of a low-cost experimental platform for Li-Ion battery characterization, SOC-dependent ECM identification, voltage model validation, and SOC estimator assessment. The proposed platform integrates constant-current–constant-voltage (CC-CV) charging, a controlled-current electronic load implemented on a printed circuit board (PCB), ESP32-based embedded acquisition, and MATLAB-based data processing. A Samsung INR18650-35E cell was characterized through full-discharge tests at different C-rates, pulse discharge tests (PDTs), and dynamic current profiles. The measured capacity at 0.2C was 3345.1 mAh, showing close agreement with the manufacturer-reported minimum nominal capacity of 3350 mAh. First- and second-order Thévenin ECMs were identified from PDT data, parameterized as SOC-dependent models, and validated under Scaled Dynamic Stress Test (DST) and Modified Pulsed Dynamic Stress Test (P-DST) profiles. The second-order ECM identified from the most complete PDT dataset achieved voltage RMSE values of 23.24 mV and 12.14 mV under the DST and P-DST profiles, respectively. The platform was further used to evaluate SOC estimators based on extended Kalman filters (EKF) and a hybrid Extended Kalman Filter-Artificial Neural Network (EKF-ANN) residual correction method. The EKF based on the second-order ECM achieved SOC RMSE values of 0.5088% and 1.0890% under the complete dynamic profiles, while the hybrid EKF-ANN reduced the RMSE to 0.2276% and 0.2788% over the dynamic test blocks. These results show that the proposed platform provides an accessible experimental framework for connecting battery testing, ECM identification, voltage validation, and BMS-oriented SOC estimator evaluation within a single reproducible workflow.
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Valderrama-Vélez et al. (2026) studied Lithium-ion battery characterization. Low-cost experimental platform was evaluated on Voltage and SOC RMSE. A low-cost experimental platform successfully characterized a Li-Ion battery, with a hybrid EKF-ANN SOC estimator achieving RMSE values of 0.2276% and 0.2788% over dynamic test blocks.
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