Characterisation and Dual-Output Estimation of Regenerative Braking Energy Recovery Under Battery Operating-Condition Variations

Regenerative braking recovers vehicle kinetic energy during deceleration, but realised battery-side recovery depends on braking demand and battery charge-acceptance conditions. This study characterises real-world regenerative braking behaviour and evaluates a dual-output event-level estimator using the public Real-World Electric Vehicle Data: Driving and Charging dataset. Negative-current intervals from eight drive records were segmented into 8181 valid events using hysteresis, short-gap bridging, acquisition-dropout splitting, and long-stretch exclusion. The events recovered 288.7 kWh and returned 683.4 Ah; the median event recovered 19.7 Wh over 3.6 s, and the maximum observed peak regenerative power was 224.1 kW. Event duration and peak current showed the strongest associations with recovered energy (Spearman rho = 0.802 and 0.871, respectively), while SoC, temperature, and voltage showed weak direct global associations. The maximum peak power decreased to 155.53 kW in the 90–100% SoC band, which indicates a constrained high-SoC charge-acceptance envelope. A 10-input, 12-hidden-neuron, two-output feedforward neural network (158 parameters) was optimised using HPWOA and benchmarked against PSO, WOA, and SFSA. On the reported held-out event set, HPWOA achieved R2 = 0.9784 and RMSE = 1.839 Wh for recovered energy and R2 = 0.9819 and RMSE = 4.891 kW for peak power. The framework is interpreted as retrospective event-level estimation rather than prebraking forecasting.