Health management can effectively extend the service life of proton exchange membrane fuel cells and reduce the maintenance cost. This paper proposes a health management method for the proton exchange membrane fuel cells based on an active fault tolerant control method. With the advantages of low cost and high speed, a stack voltage model is developed for the condition monitoring. The fast electrochemical impedance spectroscopy measurement is performed when the condition monitoring module detects faults. Then, the distribution of relaxation time is used to extract the characteristics of electrochemical impedance spectroscopy for fault diagnosis. Thereafter, based on the diagnosis results, the performance recovery strategies are incorporated to cope with the faults. This paper adopts three typical fuel cell faults, namely flooding, membrane drying, and air starvation, to demonstrate the effectiveness of the proposed health management. Experimental results show that the proposed health management can realize real-time health monitoring and rapid fault diagnosis, and achieve effective recovery of performance under faulty conditions, thereby significantly improving the reliability of the proton exchange membrane fuel cell system.

This article proposes an adaptive condition monitoring method for proton exchange membrane fuel cells based on fast electrochemical impedance spectroscopy and two-frequency impedance measurements. First, an impedance measurement system is developed to achieve fast electrochemical impedance spectroscopy and impedance measurements of single frequency. Second, two characteristic frequencies of the fuel cell stack are adaptively extracted from the impedance spectrum. With the two characteristic frequencies, an online state classification algorithm is proposed based on a multiclass linear discriminant classifier to realize the condition monitoring for fuel cells. Finally, the results are validated experimentally on a 3-kW and a 400-W fuel cell stack to verify the effectiveness, rapidity, and migrability of the proposed method.

This paper has propounded the notion of the design of cascaded integer order (IO) PID - fractional order (FO) PI^λD^μ controller by evolutionary multi-objective based optimization approach for a synchronous generator excitation system. The three contradicting performance indices have been framed in time domain as well as in frequency domain to minimize error, escalate the robust stability and to minimize the energy consumption. This paper propounded the issue of contradiction in minimizing error, escalation of robust stability and minimization of energy consumption by framing cascaded IO PID - FO PID controllers as multi-objective optimization problem. The optimization problem is solved to generate the design parameter that meets the competitive multi-objective specifications relating to performance, robust stability and to optimal control by making trade-off between them and respective weightage given to each objective function. The solution generates the non-dominated set of Pareto-optimal solutions and allows the designer to select a particular controller configuration with respective weightages. With the application of this proposed design to the excitation system of synchronous generator to a power plant’s, the dynamic robust stability enhanced explicitly with minimum energy consumption.

An optimal strategy for simultaneous charging of electric vehicle battery packs is proposed as a multiobjective optimization problem in this article. The charger consists of $\bm {n}$ constant voltage current sources for charging $\bm {n}$ batteries, respectively. Considering the energy loss and the charging criteria, a battery charging issue for the charging mode is formulated as a multiobjective optimization problem and the optimal solution is obtained with the application of quadratic programming. In addition, an adaptive momentum-based steepest descent algorithm is propounded to minimize the simultaneous charging time in the charging mode. Simulation and experimental results show that the actual state of charge converges to the same value within the theoretical shortest time for under the charging mode.