Abstract
A Cyber-Physical System for In-operando Monitoring and Diagnostics of Battery Cells According to the EU long-term vision, the Europe of the future would be a low-carbon and healthy continent, based on clean energy sources and efficient transportation, thanks to full exploitation of renewable energies, electrification, smart mobility and digitalization. The electrochemical battery, regardless of its chemical compounds, is one key technology to realize the above vision. To make the battery of the future the sustainable pillar for the development of a strategic technological asset, the battery innovation has to face challenges aiming at extending the lifetime of the battery cell and at making it safer and more economically viable. The Project described in this document aims at achieve these goals by developing a multi-parameter smart sensor network (including related models/algorithms for the interpretation/exploitation of data in terms of cell operation/state) to be deployed on each single battery cell and capable of measuring online different physical quantities in the time/space/frequency domains. This system may shut down the cell when required to lower the risks and the collection of data may foster the penetration of the second-life usage, causing a net reduction in the cost of ownership. This Project will include the design, development, deployment, and test of a Cyber-Physical System (CPS) supporting the concept of Smart Battery Cell. Accordingly, a chemical neutral architecture will be researched and proposed that is able to measure, monitor and diagnose the battery cell status, in-situ and while in-operation, through real-time online electrochemical impedance spectroscopy (EIS), distributed temperature and strain measurements. This multi-parameter approach is integrated in a model-based data-processing framework that collects data and sensor information to provide knowledge about the battery cell state-of-charge, state-of-health, and state-of-safety. A fully integrated EIS measurement system will be designed and fabricated to allow for dedicated low-power signal processing. The thermo-mechanical sensors will be based on wafer-level vacuum packaged silicon MEMS resonators to complement the set of sensors interfaced with the battery cell. The Project Partnership comprehends expertise in all needed development areas, such as VLSI design and fabrication, MEMS sensor design and fabrication, signal processing, modeling, system identification, reliability, and safety testing capabilities and facilities. The strong background of the proponents in the area of Instrumentation & Measurement will assure that all metrological attributes of the smart battery cell will be taken into consideration to provide instrumentation-grade performance to the realized prototypes. Description of the activity of UNIBO-DEI Unit and Expected Results Accurate measurements of battery parameters are the basis for most of the data processing and management done by standard BMSs. Typically, the BMS relies on global current, voltage, and temperature measurements carried out at the pack/module level, which provide only average information. Geometrically distributed and in-operando estimations of critical quantities at single cell level would instead provide much more insight and reliable information about the dynamic behavior of the battery during its whole life cycle, allowing for the implementation of next generation adaptive Battery Management Systems, fault warning solutions, and real-time diagnostics. In this Project, UNIBO-DEI will contribute to a novel monitoring approach based on an integrated multi-parameter sensing system at cell level. The approach is application-independent and chemistry-independent, in the sense that it is not specifically targeted towards a battery category and can be applied to any rechargeable battery cell (e.g., automotive, stationary storage, industrial rovers and drones). According to the Smart Cell concept, the sensing should be performed while the battery is working and it is connected to its load and should provide essential data to the algorithms for the real-time estimation of the most important state parameters. To this end, UNIBO-DEI will develop an integrated sensor monitoring the time-evolution of the electrochemical impedance at the electrical port of the cell, to be supplemented by the sub-system monitoring the thermal gradients throughout the external surface of the cell, and the surface distributed mechanical strain (role of the other Units). Electrochemical Impedance Spectroscopy (EIS) is a standard and well-established technique for the characterization of batteries in laboratories. The electrochemical impedance spectrum of a battery cell encompasses a large amount of information about the general behavior of the cell and its health. Depending on the physical variable of interest or the target state parameter, a specific bandwidth of the spectrum can be analyzed to estimate the value of the inherent variable. For instance, State-of-Charge (SoC) and ageing effects could be estimated from an analysis of the impedance spectrum in the charge-transfer and mass-transport regions. There exist in the literature some examples of EIS measurement systems for battery applications, but they are not specifically designed to be integrated within the battery cell. Thus, they either require off-the-shelf components to be adapted to the application or the presence of a DC-DC converter for generating the excitation signal. UNIBO-DEI will design and experimentally validate a EIS system integrated into a silicon chip of few squared millimetre size, and capable of vector measurement of low impedances (down to 0.1 mohm) in the 0.1-Hz – 10-kHz frequency range, with a 10-s sampling period and accuracy of at least 5% of the reading, while the cell is operating. The sensor will be part of the overall multi-parameter real-time monitoring system/network at cell level, that will also include a Thermo-mechanical (T-M) 2-D sensor array applied on the cell surface with temperature resolution of 0.1 K and strain resolutions of 1 nε/50 nε for the 500 με/25 mε dynamic range versions of the system, respectively. To enable in-operando cell parameter estimation and diagnosis, a custom diagnostic framework is required. Based on raw data collected by the multi-parameter sensor network, the diagnostic framework must estimate cell state parameters such as SoC and SoH and detect faults, to ensure efficiency and safety. The most common types of battery faults are internal, such as overcharge, overdischarge, short circuit, and thermal runaway, or external, including sensor, cooling system, and connection faults. Many methods have been proposed for specific types of faults, such as overcharge and overdischarge. The main classes of fault diagnosis methods are: -Model-Based: a battery model is used to compute residuals, which are then used to detect faults, by comparison with thresholds or boundary conditions. The most common battery models are Equivalent Circuit Models, which are often used in conjunction with tracking algorithms such as the Kalman filter to achieve the fault diagnosis functionality. -Non-Model-Based: signal processing and knowledge-based techniques are used to detect faults based on sensor data. Signal processing methods utilize metrics computed directly from data like entropy, the Z-score, or the correlation coefficient. Moreover, knowledge-based methods include machine learning techniques that require training datasets, such as random forest classifiers for detecting specific faults or neural networks. UNIBO-DEI will cooperate with the other Units for the development of both model- and non-model-based approaches, that will be investigated and compared in order to develop the cyber part of the Smart Cell Cyber-Physical System, capable of estimating cell state parameters and performing in-operando diagnostics. Batteries are exploited in different applications with extremely various environments and specifications. Therefore, it is important to define a general testing procedure unrelated to the final application but specific for the validation of the proposed concepts in several real working scenarios, with also the implementation of critical events. In this Project, UNIBO-DEI will cooperate with the other Units for the development of automatic testing, capable of allowing for both standard charge/discharge (constant current/constant voltage, constant power) profiles, as well as for ad-hoc defined charge/discharge dynamic profiles. The latter ones are aimed at out-of-standard test in-operando conditions, such as those encountered in possible real-life scenarios, e.g., electrical vehicles and unmanned air vehicles.. The testing setups will be equipped with gold standard instruments for the monitoring of the main electrical parameters to be used as reference values. The automatic testing setups will be capable of validating the Project Proof of Concept (PoC) in the -20 °C – 60 °C temperature range, at SoC levels from 0% to 100%, with charging/discharging rate up to 10 C, and capable of monitoring the swelling of the battery cell and environmental/mechanical parameters.
Dettagli del progetto
Responsabile scientifico: Pier Andrea Traverso
Strutture Unibo coinvolte:
Dipartimento di Ingegneria dell'Energia Elettrica e dell'Informazione "Guglielmo Marconi"
Coordinatore:
Università degli Studi di PERUGIA(Italy)
Contributo totale Unibo: Euro (EUR) 70.725,00
Durata del progetto in mesi: 24
Data di inizio
28/09/2023
Data di fine:
28/02/2026
