This paper develops a multi-objective co-design optimization framework for the optimal sizing and selection of battery and power electronics in hybrid battery energy storage systems (HBESSs) connected to the grid. The co-design optimization approach is crucial for such a complex system with coupled subcomponents. To this end, a nondominated sorting genetic algorithm (NSGA-II) is used for optimal sizing and selection of technologies in the design of the HBESS, considering design parameters such as cost, efficiency, and lifetime. The interoperable framework is applied considering three first-life battery cells and one second-life battery cell for forming two independent battery packs as a hybrid battery unit and considers two power conversion architectures for interfacing the hybrid battery unit to the grid with different power stages and levels of modularity. Finally, the globally best HBESS system obtained as the output of the framework is made up of LTO first-life and LFP second-life cells and enables a total cost of ownership (TCO) reduction of 29.6% compared to the baseline.

Battery health data from real-world applications are vital for optimizing and predicting battery lifetime. This study presents the design and verification of an active battery-diagnostic system and method to collect such data. The system measures battery pack capacity and resistance by applying a diagnostic protocol via a bi-directional charger. This was demonstrated on a stationary-energy-storage application, under real-world conditions, to verify the system’s design requirements. Measurements at the start and the end of the demonstration period of a month resulted in an observed degradation of 1.79 ± 0.34% battery capacity and an increase of 1.42 ± 0.75% in battery resistance. The successful measurements of capacity and resistance prove the compatibility of the system with real-world battery systems and confirm the design requirements were met. The system was able to perform autonomous and in situ measurements while only requiring the addition of software to the battery management system and by using the bi-directional charger of the energy storage system. By repeatedly applying the same diagnostic protocol over time, this system enables consistent tracking of battery health.