The concept of Industry 4.0 originates from the will to introduce the benefits of digital computation into new and existing industrial plants to save time, materials and energy. The digital transformation requires that all machinery of the production line are connected together and with the enterprise applications, to capture and analyze data across all manufacturing stages. Then, such collected data can be exploited to take strategic decision on the production and to monitor it, reacting to unexpected behaviors and thus reducing downtime and maintenance costs. This article aims at supporting production engineers approaching digital transformation by exemplifying its key elements on a real life scenario, the Industrial Computer Engineering laboratory of the University of Verona. First of all, the article discusses network design, as communication is an enabler of the other technologies. Network is realized through automatic network synthesis from requirements and characteristics of the production line data flow. Then, the paper discusses data collection and the construction of a digital twin monitoring power consumption of the production line, with the goal of detecting any discrepancy between real time data and digital twin data. This allows to trigger an early intervention on the line, to guarantee an effective maintenance.

The global shift towards electric vehicles (EVs) represents a critical strategy for fighting climate change and reducing both reliance on fossil fuels and $CO_{2}$ emissions. In this scenario, battery management systems (BMS) become crucial in EVs to ensure battery safety, reliability, and efficiency. Recently, data-driven estimation techniques have been proposed to estimate battery-related metrics, such as remaining capacity or aging condition. These techniques emerged as an answer to batteries' intrinsic variability, but they rely primarily on full charge/discharge cycles, overlooking the nuances of partial charging, which is prevalent in real-world usage. This paper presents a novel BMS architecture for EVs, leveraging digital twin technology and data-driven modeling to address these challenges. The proposed dynamic dual-model approach seamlessly integrates real-time monitoring with cloud-based analytics to continuously evaluate and predict battery capacity degradation. Key innovations include sophisticated feature engineering and segmentation strategies, which enable precise state of health (SoH) estimation across a wide range of driving conditions. Additionally, the architecture incorporates a dynamically retraining State of Charge/Energy (SoC/SoE) model that adapts to battery aging, thereby maintaining high accuracy throughout the battery's life cycle. Extensive validation using datasets from public institutions demonstrates the effectiveness and robustness of the proposed system.

The aging of rechargeable batteries, with its associated replacement costs, is one of the main issues limiting the diffusion of electric vehicles (EVs) as the future transportation infrastructure. An effective way to mitigate battery aging is to act on its charge cycles, more controllable than discharge ones, implementing so-called battery-aware charging protocols. Since one of the main factors affecting battery aging is its average state of charge (SOC), these protocols try to minimize the standby time, i.e., the time interval between the end of the actual charge and the moment when the EV is unplugged from the charging station. Doing so while still ensuring that the EV is fully charged when needed (in order to achieve a satisfying user experience) requires a “just-in-time” charging protocol, which completes exactly at the plug-out time. This type of protocol can only be achieved if an estimate of the expected plug-in duration is available. While many previous works have stressed the importance of having this estimate, they have either used straightforward forecasting methods, or assumed that the plug-in duration was directly indicated by the user, which could lead to sub-optimal results. In this paper, we evaluate the effectiveness of a more advanced forecasting based on machine learning (ML). With experiments on a public dataset containing data from domestic EV charge points, we show that a simple tree-based ML model, trained on each charge station based on its users’ behaviour, can reduce the forecasting error by up to 4× compared to the simple predictors used in previous works. This, in turn, leads to an improvement of up to 50% in a combined aging-quality of service metric.

The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject to aging and performance deterioration over time. For this reason, monitoring the battery State Of Charge (SOC) and State Of Health (SOH) during the EV lifetime is a very relevant problem. This work proposes a battery digital twin structure designed to accurately reflect battery dynamics at the run time. To ensure a high degree of correctness concerning non-linear phenomena, the digital twin relies on data-driven models trained on traces of battery evolution over time: a SOH model, repeatedly executed to estimate the degradation of maximum battery capacity, and a SOC model, retrained periodically to reflect the impact of aging. The proposed digital twin structure will be exemplified on a public dataset to motivate its adoption and prove its effectiveness, with high accuracy and inference and retraining times compatible with onboard execution.