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).

Health-conscious battery management systems (BMSs) that rely on surface temperature measurements are insufficient for managing automotive lithium-ion batteries (LIBs). Experimental studies have shown temperature differences of up to $10~^{\circ }$ C between surface and core of cylindrical LIBs. BMSs that consider only surface temperature overlook critical thermal information. The missing monitoring can delay detecting thermal events within the cell, accelerating battery degradation and increasing the risk of thermal runaway. This article introduces two deep learning algorithms to address this: Kolmogorov-Arnold network (KAN) and interconnected long short-term memory (LSTM) network. Both approaches estimate the core temperature of LIBs without requiring surface temperature feedback to the neural network. Experimental validation revealed a core temperature mean absolute error (MAE) of $0.55~^{\circ }$ C with a computational cost of 2.9–3.2 ms for KAN. The proposed interconnected LSTM reached a MAE of $0.80~^{\circ }$ C. The performance of the two core temperature estimation techniques was further evaluated under dynamic loading profile using urban dynamometer driving schedule (UDDS) drive cycle. The KAN method achieved a MAE of $0.325~^{\circ }$ C, demonstrating its adaptability to dynamic operating conditions. The two proposed methods, primarily KAN, are both adaptive and computationally efficient, making them suitable for integrating onboard BMS and cloud-enabled digital-twin-based BMS systems.

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.

Effective operation of a cloud-based electric vehicle battery management system (BMS) and control of associated modular multilevel inverters (MMI) require real-time streaming of operational data. However, existing data specifications are only suitable for hard-wired battery configuration and battery testers to store long-term historical data. This paper fills this gap by introducing a standard data specification and cloud-based architecture to store and analyze operational data for implementing cloud-based BMS. The approach can simplify the implementation of cloud-based digital twinning of batteries and BMS. The proposed data specification will ease research and development in the battery platform. Additionally, the specification can accommodate the configuration and control parameters of MMI-based reconfigurable battery packs and is also suitable for real-time data streaming for cloud-based BMS. The effectiveness of the proposed data specification in cloud-based real-time data streaming and state estimation and the provision of accommodating measurement error is demonstrated through experimental data and analysis.

Towards smart batteries for electric vehicles (EVs) smart algorithms to control battery packs, mainly reconfigurable batteries, have to be developed. 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 RL algorithm, Amortized Q-learning (AQL) is implemented, which allows enourmous numbers of possible configurations of the reconfigurable battery to be controlled, as well as the combination of classical controlling approach and machine learning methods. This enables safety mechanisms in 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 compared to the classical. Nevertheless, AQL shows great potential to be applied for multiobjective optimizations as an applicable RL algorithm for control in power electronics.

Technologies like data-driven methods for battery state estimation, fleet monitoring and cloud-based BMSs are emerging. However, challenges in data compression and storage hinder their widespread adoption. This paper addresses these issues by proposing a novel, efficient lossy voltage data compression method for measurements in electric vehicles. The method is grounded in polynomial regression and enables the use of the adaptive method without the need for parameters or training of the model which, representing an improvement over existing techniques. At a compression rate of 99.75% in an ambient temperature of 25 °C on average across all drive cycles compared, the root mean square error (RMSE) was 5.62 mV. Impressively, at a compression rate of 99%, the RMSE decreased to 3.12 mV. Furthermore, an implementation on a low-power STM32 microcontroller can compress 600 data points in just 35 milliseconds, demonstrating its suitability for real-time applications. These results highlight the potential of our approach to significantly improve the efficiency and accuracy of voltage measurement compression in electric vehicles, paving the way for advancements in electric vehicle technology.