Determining the available power of hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs) is crucial for their optimal and efficient operation. The available power, or State of Power (SOP), is defined as the maximum power that a battery can deliver or absorb within a specified time horizon during operation. Since SOP is not directly measurable and depends on internal battery states such as the State of Charge (SOC) and State of Health (SOH), its estimation is inherently complex. Furthermore, short-term high-power events such as sudden acceleration or regenerative braking occur at elevated current rates, where exceeding voltage, current, or SOC limits must be avoided, particularly when operating near these boundaries. The strong dependence of battery dynamics on current rate at low temperatures adds further challenges.
This work presents an online method for estimating the available battery power based on a Model Predictive Control (MPC) framework. In the proposed approach, the MPC formulation predicts the system behavior over a finite horizon to determine the maximum admissible power while ensuring that operational constraints are respected. Unlike conventional MPC, where the control input is iteratively applied to the system, the input current here only serves as a limit in both charge and discharge directions to prevent violation of the defined constraints. The battery dynamics are modeled using a 2-RC equivalent circuit model of a high-power NMC cell, extended to include hysteresis and current-rate dependency. Analysis of High-Power Pulse Characterization (HPPC) test data for the selected cell revealed a significant current dependence of the model parameters at temperatures below 0 °C, underscoring the necessity of including these effects in accurate power prediction.
Results of the MPC based power prediction are validated and compared against a naïve open-loop approach that assumes a constant current operation mode over the prediction horizon.