With the increasing number of sold electric vehicles, the diagnosis of lithium ion traction batteries will become an important topic. For the development of diagnostic concepts, it is of high importance to have a good overview and knowledge of the internal aging mechanisms which happen inside a single cell. This paper presents a review of existing literature on this topic and summarizes the known aging mechanisms using the FMEA method (Failure Mode and Effects Analysis) in order to categorize them and establish the relationship between failure effects and causes. Based on the summarized information, new diagnostic concepts can be developed.

This paper presents a multiphysical proton exchange membrane fuel cell (PEMFC) stack model. The stack model is divided into three submodels describing the different physical domains, namely, electrical, fluidic, and thermal. The stacking method has been used to model the fuel cell stack from a single-cell model. The proposed model has been validated against a 1.2-kW commercial PEMFC stack with excellent agreement between simulation and experimentation. Based on the simulation results, a novel model reduction method is proposed. The reduced model is suitable for real-time simulation purpose. Moreover, a real-time-model-based fuel cell emulator is introduced. The emulator has three real-time computation cores with different rates. The three computation cores are interconnected with a digital communication bus. A dc/dc buck converter is designed in order to receive the model-predicted stack power conditions and emulate the real fuel cell stack power output. The experimental test results show that such an emulator is suitable for fuel cell system hardware-in-the-loop applications.

The genetic algorithm (GA) is one of the most used methods to identify the parameters of Li-ion battery models. However, the parametrization of the GA method is not straightforward and can lead to poor accuracy and/or long calculation times. The Taguchi design method provides an approach to optimize GA parameters, achieving a good balance between accuracy and calculation time. The Taguchi design method is thus used to define the most adapted GA parameters to identify the parameters of model of Li-ion batteries for household applications based on static and dynamic tests in the time domain. The results show a good compromise between calculation time and accuracy (RMSE less than 0.6). This promising approach could be applied to other Li-ion battery applications, resulting from measurements in the frequency domain or different kinds of energy storage.