Predictive Energy Management Strategy for Fuel Cell Heavy‐Duty Truck Based on Multizone Tube Model Predictive Control

Fuel cell heavy‐duty trucks (FCHDTs) have large cargo capacity, and their power fluctuation range can reach 3–5 times that of ordinary passenger cars. As a result, fuel cells are more prone to continuous high load, rapid load change and other deteriorated conditions, which poses a major challenge to energy management strategies. To address this issue, this paper proposes a predictive energy management strategy based on multizone Tube model predictive control (MPC). First, a multizone velocity predictor is introduced, utilizing improved transformer and K‐means clustering techniques. This method includes multiple prediction submodels to handle the wide power range of FCHDTs. Then a two‐stage Tube predictive control method is proposed. In the first stage of predictive control, a multizone maximal robust positive invariant set is constructed to obtain tube constraints. The multiobjective function is designed to balance fuel economy with durability and generate the optimal reference power trajectory. In the second stage, during online operation, MPC technology with short time steps is used to adjust instantaneous power in real‐time, tracing the reference trajectory established in the first stage. Results demonstrate that this method effectively smooths the output power fluctuations of the fuel cell and reduces the total operating cost of FCHDTs; specifically, compared to single‐stage MPC, the dual‐stage MPC strategy reduces the standard deviation of fuel cell power by 0.43, 2.82, 4.86, and 6.49 under lithium battery power boundaries of ±24, ±48, ±72, and ±96 kW, respectively.