Health-aware model predictive energy management for fuel cell electric vehicle based on hybrid modeling method

Fuel cell lifetime is strongly affected by dynamic conditions. Most existing energy management works only focus on the fuel cell durability protection from the perspective of output power slope, without deeply considering the influence of the important parameters inside the stack. However, considering the variation of stack internal parameters (mechanism analysis) is more significant for fuel cell lifetime evaluation. In this paper, a health -aware model predictive control (HA-MPC) energy management strategy is proposed for fuel cell electric vehicle. A fuel cell health state model is established from the perspective of stack hydrogen excess ratio (HER), oxygen excess ratio (OER) and humidity through the hybrid modeling method. The fuel cell mechanism model and the low-dimensional data-driven model are established through the grey-box model estimation method and genetic algorithm-based radial basis function (GA-RBF) neural network. Then the objective function of energy management strategy is developed considering the total equivalent hydrogen consumption and stack improper parameter changes of HER, OER and humidity. Comparing with model predictive control strategy based on the typical power cost function, the HA-MPC can effectively reduce the steep drop of HER and OER in low power region by 3.58% and 4.41%, which can protect the fuel cell system lifetime.

Automated summary

In this paper, a health-aware model predictive control (HA-MPC) energy management strategy is proposed for fuel cell electric vehicles. By considering the important parameters inside the fuel cell stack and establishing a health state model, the proposed strategy can accurately evaluate the fuel cell lifetime. Compared with conventional strategies, the HA-MPC can effectively reduce the steep drop of impedance in low power region, thereby protecting the fuel cell system lifetime.