Model predictive control with thermal constraints for fuel cell hybrid
electric vehicle based on speed prediction
Abstract
Because of the soft dynamic performance of the fuel cell stack,
the battery is usually integrated in the power system in fuel cell
hybrid electric vehicles. In this paper, a real time energy management
strategy considering thermal constraints based on speed prediction with
neuron network is proposed. The main principle of the proposed control
strategy is to get the future power requirement with model predictive
control based on the historic speed information, and then optimize the
objective function according to the state variables. The objective
function is set to minimize the equivalent fuel consumption of the
vehicle and extend the life span of the fuel cell stack based on thermal
constraints. Contrasting with the control strategy without thermal
constraints under the WLTC driving cycle, the proposed energy management
is 0.9% higher, but the temperature of the fuel cell stack and the
battery can be limited within an appropriate range. The total equivalent
fuel consumption is 3.9% lower than dynamic programming control
strategy, which proves the availability of the proposed control strategy
can reduce the equivalent fuel consumption while prolonging the fuel
cell stack life span. Hardware in loop (HIL) experiment is implemented
to testify the real time application of the proposed control strategy.
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