With the increasing demand for electric vehicles, the interest in battery systems is growing. In order to enable safe operation of these complex energy storage systems, methods of fault diagnosis are needed. Particularly reconfigurable battery systems (RBS) with switches are promising on the way to fault tolerance as they allow the system to be reconfigured in the event of a fault. In this work, a model-based fault diagnosis algorithm is developed and validated that uses the switches of a RBS to improve the fault isolability. Since the algorithm changes the structure of the system in order to differentiate between non-isolable faults, it is classified as an active fault diagnosis algorithm. The deviations between sensor measurements and model, called residuals, are stochastically analyzed. For fault isolation a fuzzy clustering approach is used. A constrained sigma-point Kalman filter minimizes model uncertainties and therefore increases the sensitivity and robustness of the fault diagnosis approach. Furthermore, the filter allows estimating the fault amplitude in case of a fault. Based on active sequential hypothesis testing, a policy to calculate the next switch position is proposed and investigated. It is shown simulatively and experimentally that additional faults are isolated by the presented active approach

This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development.

In order to improve the efficiency of battery electric vehicles, the utilization of reconfigurable batteries and multilevel inverters based on low‐voltage Si MOSFETs gains growing attention. Focusing on the half‐bridge as basic switching unit, this paper examines voltage and current waveforms during switching. It is shown that the current commutation speed depends on various factors such as sign and amplitude of the current as well as the particular switching scenario. For instance, turning off a positive cell current takes 0.21 $\mu$s while turning it on takes 0.9 $\mu$s. In addition, it is revealed that depending on the switching scenario and current direction, the lithium‐ion cell supports or opposes the remagnetization of the parasitic inductances and therefore the switching losses of the MOSFETs. Switching a positive load current of 125 A from active to bypass converts 276 $\mu$J into heat while switching from bypass to active converts merely 32.5 $\mu$J into heat.

Intelligent battery cells follow the approach of having greater knowledge of the single cell state in a battery system by sensors on cell level. The further-reaching approach of reconfigurable battery systems pursues the principle of controlling each cell in a system independently by switches to maximize power and safety of the whole battery system. In the literature, different approaches for sensing, communication and battery management functionality are proposed. However, less attention has been paid to the power loss of such intelligent cells and the resulting thermal effects. Especially for large prismatic cells in an automotive application the temperature is important for power, safety and life-time. Therefore, in this work, an intelligent cell prototype is realized based on a prismatic 25 Ah Li-ion cell. In addition, a 3D thermal model of the intelligent cell is developed and validated experimentally. The model is used to investigate the thermal behavior of an intelligent cell under automotive conditions in detail. The results reveal that an useful application with an temperature increase in the jelly roll of 3 K is only possible with a reduced power loss of the electronics and a well thought-out design for an intelligent cell.