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This paper presents nonlinear model predictive control based adaptive equivalent consumption minimization strategy for fuel cell hybrid electric bus. The proposed strategy considers the average travel speed profile of road segments in route of fuel cell hybrid electric bus. The proposed nonlinear model predictive control based adaptive equivalent consumption minimization strategy determines the optimal control input by considering the battery current-rate and the fuel cell balance-of-plant. The nonlinear model predictive control based adaptive equivalent consumption minimization strategy consists of three main stages: the data pre-processing stage, the nonlinear model predictive control stage, and the adaptive equivalent consumption minimization strategy stage. In the data preprocessing stage, reference power trajectory is generated while considering the response time of the fuel cell hybrid electric bus. In the nonlinear model predictive control stage, the reference state of charge trajectory is generated by considering the battery current-rate and the fuel cell balance-of-plant. In the adaptive equivalent consumption minimization strategy stage, the optimal control input is determined to minimize instantaneous equivalent fuel consumption by considering the reference state of charge trajectory. The proposed energy management system is compared with dynamic programming using actual bus route based real-driving scenarios. The comparison results demonstrate that the proposed energy management system can generate control inputs that are similar to the global optimal solution calculated by dynamic programming with a reasonable computation time.

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Nonlinear Model Predictive Control Based Adaptive Equivalent Consumption Minimization Strategy for Fuel Cell Electric Bus Considering Average Travel Speed