The end-of-life event of the battery system of an electric vehicle is defined by a fixed end-of-life threshold value. However, this kind of end-of-life threshold does not capture the application and battery characteristics and, consequently, it has a low accuracy in describing the real end-of-life event. This paper proposes a systematic methodology to determine the end-of-life threshold that describes accurately the end-of-life event. The proposed methodology can be divided into three phases. In the first phase, the health indicators that represent the aging behavior of the battery are defined. In the second phase, the application specifications and battery characteristics are evaluated to generate the end-of-life criteria. Finally, in the third phase, the simulation environment used to calculate the end-of-life threshold is designed. In this third phase, the electric-thermal behavior of the battery at different aging conditions is simulated using an electro-thermal equivalent circuit model. The proposed methodology is applied to a high-energy electric vehicle application and to a high-power electric vehicle application. The stated hypotheses and the calculated end-of-life threshold of the high-energy application are empirically validated. The study shows that commonly assumed 80 or 70% EOL thresholds could lead to mayor under or over lifespan estimations.

The estimation of lithium ion capacity fade and impedance rise on real application is always a challenging work due to the associated complexity. This work envisages the study of the battery charging profile indicators (CPI) to estimate battery health indicators (capacity and resistance, BHI), for high energy density lithium-ion batteries. Different incremental capacity (IC) parameters of the charging profile will be studied and compared to the battery capacity and resistance, in order to identify the data with the best correlation. In this sense, the constant voltage (CV) step duration, the magnitudes of the IC curve peaks, and the position of these peaks will be studied. Additionally, the behaviour of the IC curve will be modeled to determine if there is any correlation between the IC model parameters and the capacity and resistance. Results show that the developed IC parameter calculation and the correlation strategy are able to evaluate the SOH with less than 1% mean error for capacity and resistance estimation. The algorithm has been implemented on a real battery module and validated on a real platform, emulating heavy duty application conditions. In this preliminary validation, 1% and 3% error has been quantified for capacity and resistance estimation.

Lithium-ion batteries are being used everywhere in a great variety of applications. There are some mayor applications such as the ones related to road transportation which have consolidated and well-known standards and regulations to guide battery designers. Nonetheless, the design standards and regulations of battery systems for non-common applications such as off-road vehicles, maritime type vessels or stationary applications are either generic from road transportation applications or simply unknown. Consequently, a study of standards for non-common three use cases has been performed. The performed study collects and classifies the most interesting standards that will guide battery system and sub-system designers on the development of battery systems for off-road use cases, marine use cases and stationary use cases. In addition to this, representative electric power profiles for each of these three non-common use cases are presented as alternatives to he well-stablished standardized driving cycles. As result, a list of standards that will guide battery system and sub-system designers have been provided along with a driving cycle that captures properly the characteristics electrical stresses of each of these non-common applications.