Benchmarking Advanced Control and Estimation Techniques for Smart Energy and Measurement Systems

The convergence of smart sensing, renewable energyconversion, and advanced battery management is reshapingthe landscape of modern electrical infrastructure. This paperpresents a unified benchmarking study spanning five tightly cou-pled technical domains: (i) smart contactless height measurementusing an Arduino-based ultrasonic sensing platform with mobileapplication integration; (ii) next-generation solar grid integrationusing a 400 kW Modular Multilevel Converter (MMC) coupledwith Maximum Power Point Tracking (MPPT) control; (iii) opti-mization of residential grid-connected photovoltaic (PV) systemsunder partial shading using fuzzy logic-based MPPT; (iv) logicalnumerical modeling of Battery Energy Storage Systems (BESS)for peak shaving, load shifting, and load leveling in smart gridscenarios; and (v) advanced nonlinear State-of-Charge (SOC) andcapacity estimation for degrading battery systems via UnscentedKalman Filtering (UKF). Collectively, these five contributionsform a coherent pipeline from physical measurement at thegrid edge through large-scale renewable energy conversion toprecise battery lifecycle management. Key findings confirm thatthe ultrasonic smart scale achieves sub-1% relative measurementerror across a range of 2 cm to 13 feet; the MMC-MPPTarchitecture achieves a Total Harmonic Distortion (THD) be-low 5% with output voltage stabilization within 0.8 seconds;the fuzzy logic MPPT recovers 48.3% additional power underpartial shading compared to local-maximum tracking; the BESSnumerical model successfully simulates grid-service scenarios inMATLAB/Simulink; and the UKF achieves SOC estimation ac-curacy within 0.6% of actual values, outperforming conventionalKalman filter approaches by approximately 10% in capacityestimation accuracy. These results collectively demonstrate thepotential of integrated hardware–software co-design for next-generation smart energy infrastructure.