A modern battery management system (BMS) in electric vehicles plays a crucial role in enhancing battery pack safety, reliability, and performance, particularly in e-transportation applications. To achieve more accurate estimation methods, combining battery digital twinning with cloud computing for computational power and data storage capabilities proves beneficial. Over the last decade, various data-driven state-of-charge (SOC) estimation methods, such as machine learning and deep learning approaches, have been introduced to provide highly precise estimations. The widely used SOC estimation method in the industry is the extended Kalman filter (EKF). To explore and analyze the potential use of SOC estimation in a cloud platform, this paper develops and conducts a comparative analysis of four SOC estimation methods: EKF, Feedforward Neural Network (FNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). These models are deployed in two cloud computing infrastructures, and their accuracy and computing time are thoroughly examined in this study. The study concludes that the EKF method is the fastest and most accurate among all considered methods. It boasts an average execution time of 54.8 ms and a mean absolute error of $\mathbf {2\times 10^{-4}}$ when measured over a physical distance of approximately 450 km via the mobile network Long-Term Evolution (LTE).

In the context of the electrification of the mobility sector, smart algorithms have to be developed to control battery packs. Smart and reconfigurable batteries are a promising alternative to conventional battery packs and offer new possibilities for operation and condition monitoring. This work proposes a reinforcement learning (RL) algorithm to balance the State of Charge (SoC) of reconfigurable batteries based on the topologies half-bridge and battery modular multilevel management (BM3). As an RL algorithm, Amortized Q-learning (AQL) is implemented, which enables the control of enormous numbers of possible configurations of the reconfigurable battery as well as the combination of classical controlling approaches and machine learning methods. This enhances the safety mechanisms during control. As a neural network of the AQL, a Feedforward Neuronal Network (FNN) is implemented consisting of three hidden layers. The experimental evaluation using a 12-cell hybrid cascaded multilevel converter illustrates the applicability of the method to balance the SoC and maintain the balanced state during discharge. The evaluation shows a 20.3% slower balancing process compared to a conventional approach. Nevertheless, AQL shows great potential for multiobjective optimizations and can be applied as an RL algorithm for control in power electronics.

For enhanced safety and efficiency, it is crucial to accurately monitor and determine the condition of a lithium-ion battery (LIB). Because of dynamic operating conditions and several diverse aging mechanisms, it is necessary to further improve the algorithms of the battery management system. In this paper, machine learning (ML) is used to estimate the state of charge (SOC). As data-driven models highly depend on their data basis and battery tests are time and cost expensive, the data is enriched by simulation and augmented data. Based on this data, a linear regression, a support vector machine, and different types of artificial neural networks are trained. All models are tested on a real world data set. A lowest test error is obtained using the convolutional neural network with a test RMSE of 1.74 %. The results highlight the accuracy of data-driven models for the prediction of the battery state and the potential to improve the models by using data augmentation techniques.

The battery-based multilevel inverter has grown in popularity due to its ability to boost a system’s safety while increasing the effective battery life. Nevertheless, the system’s high degree of freedom, induced by a large number of switches, provides difficulties. In the past, central computation systems that needed extensive communication between the master and the slave module on each cell were presented as a solution for running such a system. However, because of the enormous number of slaves, the bus system created a bottleneck during operation. As an alternative to conventional multilevel inverter systems, which rely on a master–slave architecture for communication, decentralized controllers represent a feasible solution for communication capacity constraints. These controllers operate autonomously, depending on local measurements and decision-making. With this approach, it is possible to reduce the load on the bus system by approximately 90 percent and to enable a balanced state of charge throughout the system with an absolute maximum standard deviation of $1.1\times {10}^{-5}$. This strategy results in a more reliable and versatile multilevel inverter system, while the load on the bus system is reduced and more precise switching instructions are enabled.