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.

Novel intelligent battery systems are gaining importance with functional hardware on the cell level. Cell-level hardware allows for advanced battery state monitoring and thermal management, but also leads to additional thermal interactions. In this work, an electro-thermal framework for the modeling of these novel intelligent battery cells is provided. Thereby, a lumped thermal model, as well as a novel neural network, are implemented in the framework as thermal submodels. For the first time, a direct comparison of a physics-based and a data-driven thermal battery model is performed in the same framework. The models are compared in terms of temperature estimation with regard to accuracy. Both models are very well suited to represent the thermal behavior in novel intelligent battery cells. In terms of accuracy and computation time, however, the data-driven neural network approach with a Nonlinear AutoregRessive network with eXogeneous input (NARX) shows slight advantages. Finally, novel applications of temperature prediction in battery electric vehicles are presented and the applicability of the models is illustrated. Thereby, the conventional prediction of the state of power is extended by simultaneous temperature prediction. Additionally, temperature forecasting is used for pre-conditioning by advanced cooling system regulation to enable energy efficiency and fast charging.

In electric vehicles with lithium-ion battery systems, the temperature of the battery cells has a great impact on performance, safety, and lifetime. Therefore, developing thermal models of lithium-ion batteries to predict and investigate the temperature development and its impact is crucial. Commonly, models are validated with experimental data to ensure correct model behaviour. However, influences of experimental setups or comprehensive validation concepts are often not considered, especially for the use case of prismatic cells in a battery electric vehicle. In this work, a 3D electro–thermal model is developed and experimentally validated to predict the cell’s temperature behaviour for a single prismatic cell under battery electric vehicle (BEV) boundary conditions. One focus is on the development of a single cell’s experimental setup and the investigation of the commonly neglected influences of an experimental setup on the cell’s thermal behaviour. Furthermore, a detailed validation is performed for the laboratory BEV scenario for spatially resolved temperatures and heat generation. For validation, static and dynamic loads are considered as well as the detected experimental influences. The validated model is used to predict the temperature within the cell in the BEV application for constant current and Worldwide harmonized Light vehicles Test Procedure (WLTP) load profile.

The recursive least squares algorithm for online parameter identification of induction machines has a high potential to serve as a basis for an innovative electric vehicle diagnosis concept. Commonly used for control parameter tuning, this approach is established in numerous industrial applications. However, in the automotive environment, special machine designs are used and highly dynamic operation takes place in a wide speed and load range. This results in fast transient parameter behavior, which is challenging in terms of online parameter identification. Therefore, the algorithm performance must be rated in our field of application with suitable dynamic test profiles. In this work, we compare several algorithm extensions to a novel recursive least squares algorithm with multiple variable forgetting factors for induction machines. The algorithms are analyzed regarding their handling of the associated transient parameter behavior. In addition, different identification model structures are considered to deal with the dynamic speed operation and the associated transient iron losses. Special attention is given to the real-time performance of the overall identification algorithms as this is a major requirement for implementation in automotive embedded systems. For validation, both, simulation and experimental results are presented, and associated configuration recommendations are provided.