The excellent fuel economy of fuel cell passenger vehicles has facilitated its applications. However, high operation costs including economic costs and degradation costs of power source make it uncompetitive. To minimize total operating costs, a new operation cost minimization online energy management strategy (OCMO-EMS) combining dynamic programming (DP) with control rule extraction is proposed in this work. Firstly, both the hydrogen consumption and degradation of power source are incorporated into multi-objective cost function, an improved DP is introduced to generate the globally optimal results. Then, for online application of globally optimal results, a new control rule extraction method that combines fixed thresholds with local working probability is raised. This method effectively extracts control rules to obtain near-optimal result and reduce calibration workload. In the end, offline and hardware in loop (HIL) simulations are carried out on China Light-duty vehicle Test Cycle-Passenger (CLTC_P). Compared with the power-following strategy, the OCMO-EMS reduces the total operating costs by 22.6%. Meanwhile, the total costs difference between DP and OCMO-EMS are within 4%. The HIL results are consistent with offline simulation. Therefore, the strategy proposed in this work can served as a theoretically reference for EMS design of fuel cell passenger vehicles.
The optimal tracking control of air pressure and air flow is an important guarantee to improve the output characteristics of fuel cells. However, under the load disturbances scenario, the optimal control effect is difficult to guarantee. In order to solve this problem, this paper proposes a new control method based on real-time disturbances observation and MPC optimal control. The decoupling of air pressure and air flow is realized by feedback linearization, and then an extended state observer is designed to achieve accurate estimation of load disturbances. Based on the principle of optimal output power of the fuel cell system, the reference trajectory of air pressure and air flow is obtained. Based on this, the optimal MPC controller is designed to achieve accurate tracking of air pressure and air flow by controlling the motor voltage of the air compressor and the opening of the back pressure valve. Under load disturbances, compared with feedback linearization control, improved tracking and robust performances of the proposed strategy can be exhibited through offline and online tests, the net power of PEMFCs is increased by 3%.
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