Power-demand-informed robust power coordination for series hybrid electric vehicles with prescribed-performance constraints

Reliable power-following capability is essential for series hybrid electric vehicles (SHEVs), yet practical discrepancies between actual load-side demand and source-side generation can produce source-load power mismatch, thereby degrading charge-sustaining performance. Additionally, the ability of the power coordination strategy to reconcile transient maneuverability with steady-state fuel economy is also critical for unlocking the full potential of SHEVs. To address these challenges, this paper proposes a power-demand-informed robust power coordination strategy for SHEVs with prescribed-performance constraints. First, source-load power mismatch is formulated as an equivalent battery-port residual and mapped into an additive disturbance, which connects practical unmodeled powertrain uncertainties with the control-oriented model. Second, a power-demand-informed factor is constructed to adaptively blend economy-oriented and dynamic oriented engine speeds, thereby accommodating diverse driving demands. Third, an extended state observer is designed to estimate the disturbance. Then, its bounded estimation error is derived and incorporated as a robustness margin into the transformation from prescribed-performance requirements to battery power constraints. The resulting nonlinear model predictive control problem is efficiently solved with constrained iterative linear quadratic regulator. Simulation and hardware-in-the-loop (HIL) tests are conducted to validate the performance of the proposed strategy. Compared with the baseline strategies, the proposed strategy achieves a favorable compromise among fuel economy, engine speed stability, and SoC robustness across both testing scenarios. The root mean square error of SoC is reduced by 97.778% and 97.297%, respectively, demonstrating its robustness against source-load power mismatch. The HIL test results further confirm the practical application potential of the strategy.