"Experimental HPPC and SOC\u2013OCV Dataset for the Panasonic NCR18650PF Lithium-Ion Battery for EV Applications"

"Accurate estimation of the State of Charge (SOC) and State of Energy (SOE) is fundamental to the safe, reliable, and energy-efficient operation of electric vehicles (EVs), where lithium-ion batteries (LIBs) serve as the primary energy storage system and exhibit strong nonlinear, rate-dependent, and dynamic characteristics under real-world driving conditions. The performance of SOC and SOE estimation algorithms directly depends on high-fidelity battery characterization data, particularly the SOC-open-circuit voltage (SOC-OCV) relationship for capturing the intrinsic nonlinear electrochemical behavior and Hybrid Pulse Power Characterization (HPPC) test data for quantifying internal resistance and transient polarization dynamics. This dataset provides experimentally obtained SOC-OCV curves and HPPC test measurements for a lithium-ion battery, enabling accurate parameter identification of standard RC-based equivalent circuit models commonly employed in battery management systems (BMS). The availability of both quasi-static and dynamic battery characteristics supports realistic modeling and validation of SOC and SOE estimation methods, including observer-based, Kalman-filter-based, robust, and adaptive techniques. Furthermore, the dataset is structured to facilitate battery circuit-model-based performance analysis under widely used and publicly available standard EV drive cycles such as UDDS, US06, LA92, and HWFET, thereby bridging experimental battery characterization with practical EV operating scenarios. Therefore, this SOC-OCV and HPPC dataset aims to promote reproducible research, fair benchmarking, performance comparison, and accelerated development of advanced battery modeling and estimation algorithms for next-generation EV applications."