Control Barrier Functions for State of Power Estimation in Lithium-Ion Battery Management

Abstract This paper presents a novel application of control barrier functions (CBFs) for estimating state of power (SOP) during charging and discharging cycles of lithium-ion batteries. We define SOP as the maximum amount of power that can be maintained over a specified time period. The proposed algorithm predicts the maximum achievable power level within a constraint set defining the operational boundaries of the cell, namely, state of charge (SOC), voltage, and core temperature. To demonstrate the efficacy of this approach, we simulate battery performance under the Urban Dynamometer Driving Schedule (UDDS), a representative profile of city driving conditions. Comparisons with i) model predictive control (MPC) and ii) a conventional bisection-based approach illustrate the merits of the CBF-based method in terms of practicality for real-world automotive applications. The aim of this study is to advance efforts to create safer and more effective battery management solutions for electric vehicles and energy storage technologies.