International audience

The growing demand for lithium-ion batteries (LIBs) in industries such as electric vehicles (EVs) and renewable energy storage underscores the need for tools that ensure transparent, sustainable, and compliant production processes. This paper presents the Battery Production Ontology (BPO), a comprehensive framework designed to standardize the representation and traceability of LIB production lifecycles. By modeling key aspects such as material flows, energy consumption, carbon emissions, and production activities, the BPO supports environmental impact assessments, supply chain transparency, and process optimization. The BPO is aligned with existing standards, including the EU’s digital battery passport requirements, ensuring interoperability across diverse systems. Developed using a structured methodology, the ontology underwent rigorous validation. Real-world case studies demonstrated its capacity to model emissions, trace materials, and represent production sequences, while quantitative assessments confirmed its scalability, reasoning efficiency, and accuracy for industrial applications. Additionally, the ontology integrates seamlessly with external standards like BONSAI, FOAF, Schema, and OWL Time, fostering semantic reuse and interoperability. By addressing the critical need for transparency and sustainability in LIB production, the BPO provides stakeholders with a robust tool to drive the green energy transition and achieve global sustainability goals. • A novel ontology models the lifecycle of lithium-ion battery (LIB) production. • Enhances traceability, sustainability, and compliance with EU battery regulations. • Integrates material flows, energy use, and emissions for lifecycle assessment (LCA). • Validated through case studies and quantitative performance evaluations. • Aligns with BONSAI, FOAF, Schema, and the EU Digital Battery Passport standards.

Monitoring complex systems is essential for preventing failures and ensuring operational safety. This paper proposes a method that combines ontology which provide structured knowledge representation with stream reasoning to enhance anomaly detection in dynamic environments. Unlike monitoring systems, our approach focuses on interpretable and scalable analysis of continuous data streams, enabling systematic identification of deviations from expected behavior. We demonstrate the applicability of this framework in monitoring lithium-ion batteries, where early detection of thermal anomalies is critical. By integrating a knowledge-driven model with data stream analysis, our method improves the reliability and safety of complex systems while offering explainable insights into detected anomalies.

In today’s energy-dependent world, ensuring the safety and efficiency of lithium-ion batteries is crucial. Early representation of anomalies becomes essential for optimizing performance, reducing disruptions, and prolonging battery lifetime in electric vehicle applications. This objective necessitates the integration of data from distributed and heterogeneous sources, a challenge traditionally tackled by semantic web technologies. In response, this paper introduces an ontology-based model that capitalizes on representing anomalies in lithium-ion batteries. Ontologies play a vital role in representing knowledge in a machine-interpretable format. Our approach enriches sensor data with contextual information, employing structured concepts, rules, and semantics specifically designed for representing anomalies in lithium-ion batteries.

This work aims at solving the torque ripple effect problem minimization in synchronous reluctance machines. The objective is realized by controlling the currents in the synchronous reluctance machine in respect to the reference current and as well, leads to the control of the torque of the machine thereby; reducing the torque ripple effect. In the first, Field -Oriented Control (FOC) is used to control the current of the machine in respect to the reference current. In the second, the Maximum Torque Per Ampere MTPA as well as the Optimal Current control approach are applied for the same purpose. The obtained results are analyzed and the best architecture is selected with the use of a Proportional Integral (PI) controller.

The battery/supercapacitor combination offers excellent performance for hybrid energy storage systems (HESS) in photovoltaic (PV) systems. This study involves a HESS composed of a battery and a supercapacitor (SC), which reduces the current demand on the battery. The performance of HESS in residential PV systems and its impact on energy efficiency are presented. First, the storage device is modeled and sized according to the specifications of the residential PV system under study. A frequency power-sharing strategy is then implemented to manage the power-sharing between the batteries and the supercapacitors. The batteries and PV modules are used to provide steady-state power, and the supercapacitors are used to support peak power. The performance waveforms of each storage device obtained by simulation look promising for the given improvement. The use of a battery/SC combination can significantly improve the performance of the PV system by reducing the battery current levels and lowering the state of charge (SOC) of the battery, thereby extending the overall battery life.

A critical aspect of managing lithium-ion battery packs in electric vehicle applications is accurately determining the State of Charge (SoC). There are several methods available to estimate it, including coulomb counting with direct evaluation, Open circuit voltage, kalman filter with adaptive approach, particle flter, as well as fuzzy logic and data-based approach. In this paper, we use the state of charge data already computed by a data-driven approach and combine it with an ontology of a battery pack. The built ontology models the battery pack, taking into account the topology, types of cells and their organization inside. To make an exact estimation, different strategies of balance control of the cells are considered. SWRL rules are used to compute the state of charge of the whole battery pack. Matlab Simulink multi-physics model of a lithium-ion battery is used to provide simulated data for the experiments. The given model is evaluated based on regression metrics showing its performance.

Lithium-ion batteries are used in most battery powered devices. Today’s research on Lithium-ion batteries mainly focuses on better energy management strategies and predictive maintenance. In this paper, a new approach based on auto-encoders and long short-term memory neural networks applied to usage data (voltage, current, temperature) is used to make a State of Health prediction. Encouraging results are obtained when conducting tests on various battery ageing datasets published by Sandia National Laboratories, the Massachusetts Institute of Technology and NASA’s Prognostics Center of Excellence.

Lithium Ion batteries are a core component of many lately designed devices. It is of crucial importance to be able to fully master the behaviour of batteries in order to meet the requirements in terms of safety, and performances. Predicting the Remaining Useful Life of batteries can help preventing a failure before it occurs, with an increased safety for the user and reduced costs linked to maintenance.The work described in this paper is an attempt to accurately predict the Remaining Useful Life of Li-Ion batteries using machine learning regression methods applied to a new set of ageing data published by the department of chemical engineering of the Massachusetts Institute of Technology. By changing the usual approach applied to data and feature management, very encouraging results were obtained. Compared with previous approaches in the literature using linear regression or Convolutional Neural Networks on the same dataset, our work on how to build a more efficient representation of ageing data combined with Artificial Neural Networks leads to more accurate predicting performances.

This paper proposes a control strategy to reduce speed ripples at low speed working conditions of Permanent Magnet Synchronous Machines (PMSMs) for Electric Vehicle Applications. The treated issue is related to the periodic torque ripples which induce speed oscillation that deteriorate the drive performance. To ensure a high-performance control regarding this issue, the reported work proposes an original control method based on Grey Wolf (GW) algorithm. The key idea of the proposed approach is to incorporate the benefit of fast optimization process of the GW optimizer in order to find input controls which satisfy the speed tracking and minimize speed ripples. The proposed method is described and the speed ripple issue is analyzed. Experimental results show that the proposed control method can be implemented in real-time on embedded hardware, offering high performance in both steady and transient states of the PMSM drives even at low speed range.