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.

Battery system engineers face the challenge of balancing competing requirements regarding performance, maintainability, sustainability, safety, and cost—especially in the automotive industry. IBS potentially offer a solution with fewer trade-offs. They feature a battery management system with advanced sensing and data analysis capabilities that facilitate improved battery monitoring and operation. Reconfigurable energy storage units enable sophisticated operating strategies, including complete cell state control, full energy content utilization, and a measured response to faults. This article presents the design, development, and operation of a full-scale intelligent battery system prototype comprising 324 automotive lithium-ion cells with a nominal voltage of 400$\mathrm{V}$. The system exhibits a modular single cell architecture and an advanced centralized battery management system. We detail the system architecture, hardware and software component design, and system integration. Initial tests demonstrate the battery’s operability, extended functionality, and enhanced safety. Our analysis shows that the additional losses introduced by reconfigurability are more than offset by the benefits of full energy utilization—even for new cells, with increasing advantage as aging progresses. The results underscore the potential of intelligent battery systems and motivate further research and development toward economic assessment and industrial adoption.

Today’s electric vehicles commonly comprise battery management systems in which model-based functionalities are applied. To keep the incorporated models up to date state and parameter estimation is crucial, but only with sufficient excitation adequate estimates are achievable. In recent years, reconfigurable battery systems became more and more prominent in research, whereby switches allow to change the power path reversibly during operation. Within this work, the impact of switching actions of reconfigurable battery systems on state and parameter estimation is investigated. For this purpose, suitable switching patterns and strategies are introduced and their benefit on estimation accuracy is evaluated based on validation experiments. A reconfigurable battery pack consisting of twelve lithium-ion cells in mixed topology is excited by different current profiles whereby varying switching actions are applied. Results show that switching operation significantly improves the estimation accuracy, particularly in cases where the native current excitation is of low dynamics. Furthermore, a suitable threshold for situation-based switching is determined which uses the Goertzel algorithm to assess the current excitation.

In the field of signal filtering sensor noise variance estimation is of high interest. Various approaches exist for different applications. Within this work, we propose a novel online noise variance estimation scheme based on an online algorithm with exponential forgetting. The approach serves as an extension of a sensor data fusion algorithm that was presented earlier for the application within multi-cell battery systems equipped with cell-individual sensors. Utilizing measurements of electrical-linked sensors the signals and their noises are separated, and the noise variance is adaptively determined. Experiments show that sensor data fusion is equivalent to common methods like low-pass filtering to gain the target signal. Consequently, the variance is estimated with high accuracy especially with regard to signals featuring high dynamic range. Moreover, the results are on a par with difference-based noise estimation. Furthermore, the influence of relevant parameters on the method is investigated namely the adaptivity of the algorithm and the necessary number of involved sensors. As a result, with just eight sensors decent results are achieved within an exemplary application.