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This dataset contains experimental data of capacity and electrochemical impedance of five Lithium Polymer (LiPo) batteries (model LP-503562-IS-3 manufactured by BAK Technology). All batteries have been subjected to hundreds of charge-discharge cycles to obtain their characteristics at different states-of-health. Capacities have been measured under both standard and stress conditions. At fixed intervals (45 cycles in most cases) batteries have been subjected to partial discharge cycles to measure impedance spectra at different values of the state-of-charge. Impedance spectra have been fitted by using an equivalent circuit model; estimated circuit parameters are included in the dataset.

The evolution of rechargeable battery characteristics have led to their use in almost every device in our everyday life. This importance has also increased the relevance of estimating the remaining battery charge (state of charge, SOC) and their health (state of health, SOH). One of the methods for the estimation of these parameters is based on the impedance spectroscopy obtained from the battery output impedance measured at multiple frequencies. This paper proposes an embedded measurement system capable of measuring the battery output impedance while in operation (either charging or supplying power to the intended device). The developed system generates a small amplitude stimulus that is added to the battery current. The system then measures the battery voltage and current to estimate the impedance at the stimulus frequencies. Three batteries were measured at different SOC levels, demonstrating the system principle of operation. Complementarily, a battery impedance equivalent circuit was used, together with genetic algorithms, to estimate the circuit parameters and assess their dependence on the battery SOC.

This dataset consists of electrochemical impedance spectroscopy measurements on commonly-used batteries, namely Samsung ICR18650-26J cylindrical Lithium-Ion cells. The complex impedance of the batteries was measured at a set of fourteen different frequencies from 0.05 Hz to 1000 Hz, using a random-phase multi-sine excitation signal. For each excited frequency, the current amplitude was 50 mA, resulting in a measurement uncertainty of approximately 0.1 mΩ.Six measurement repetitions are provided at ten different states-of-charge of four different brand-new batteries. Repeated EIS measurement results were obtained, for each individual battery cell, from six separate discharge cycles. All measurements were performed with the battery placed in a temperature-controlled chamber at 25 ± 1 °C. Batteries were allowed to thermalize before each measurement.

Electrochemical impedance spectroscopy (EIS) is a powerful tool for monitoring rechargeable batteries through state of charge and state of health estimation. In practical applications, EIS may be performed using ad-hoc electronics embedded in the final product. The accuracy of these custom impedance measurement systems must be validated against laboratory instrumentation. This paper presents the software tool EasyEIS, which we developed to perform EIS using a standard commercial source measure unit on a lithium-ion battery to validate results from a custom impedance measurement system. EasyEIS also includes a software-based sinusoidal waveform generator function that allows cost-effective EIS also in instruments that do not offer any hardware sinusoidal function generation feature.

In this paper, a feasibility study of a shuntless coulomb counting method for estimating the state of charge (SOC) of a battery is presented. Contrary to conventional coulomb counting, the proposed method does not require an external resistive shunt; it instead only requires voltage measurements performed on the battery under test while it is operating. The current is measured indirectly using the battery’s equivalent series resistance (ESR). The method consists of a preliminary calibration phase where the ESR and the open-circuit voltage of the battery are measured for different SOCs and stored in look-up tables (LUTs). Then, in the subsequent operational phase, the method uses these LUTs together with the measured voltage at the battery terminals to estimate the SOC. The performance of the proposed method is evaluated on a sample lithium polymer (LiPo) battery, using a realistic current profile derived from the Worldwide Harmonized Light-Duty Vehicles Test Procedure (WLTP). The results of this experimental evaluation demonstrate a SOC estimation root-mean-square error of 0.82% and a maximum SOC error of 1.45%. These results prove that the proposed method is feasible in a practical scenario.

Accurate information concerning the position of users, devices, mobile robots, or systems is crucial for the safe and efficient operation of applications in the industrial, commercial, and consumer fields [...]

Meaningful information on the internal state of a battery can be derived by measuring its impedance. Accordingly, battery management systems based on electrochemical impedance spectroscopy are now recognized as a feasible solutions for online battery control and diagnostic. Since the impedance of a battery is always changing along with its state of charge and aging effects, it is important to have a stable impedance reference in order to calibrate and test a battery management system. In this work we propose a programmable impedance emulator that in principle could be used for the calibration of any battery management system based on electrochemical impedance spectroscopy. A digital finite-impulse-response filter is implemented, whose frequency response is programmed so as to reproduce exactly the impedance of a real battery in the frequency domain. The whole design process of the filter is presented in detail. An analytical expression for the impedance of real battery in the frequency domain is derived from an equivalent circuit model. The model is validated both through numerical simulations and experimental tests. In particular, the filter is implemented on a low-cost microcontroller unit, and the emulated impedance is measured by means of a custom-made electrochemical impedance spectroscopy measuring system, and verified by using standard commercial bench instruments. Results on this prototype show the feasibility of using the proposed emulator as a fully controllable and low-cost reference for calibrating battery impedance measurement systems.

Hand gesture recognition is a crucial task for the automated translation of sign language, which enables communication for the deaf. This work proposes the usage of a magnetic positioning system for recognizing the static gestures associated with the sign language alphabet. In particular, a magnetic positioning system, which is comprised of several wearable transmitting nodes, measures the 3D position and orientation of the fingers within an operating volume of about 30 × 30 × 30 cm, where receiving nodes are placed at known positions. Measured position data are then processed by a machine learning classification algorithm. The proposed system and classification method are validated by experimental tests. Results show that the proposed approach has good generalization properties and provides a classification accuracy of approximately 97% on 24 alphabet letters. Thus, the feasibility of the proposed gesture recognition system for the task of automated translation of the sign language alphabet for fingerspelling is proven.

Reliable diagnosis of early-stage Parkinson’s disease is an important task, since it permits the administration of a timely treatment, slowing the progression of the disease. Together with non-motor symptoms, other important signs of disease can be retrieved from the measurement of the movement trajectory and from tremor appearances. To measure these signs, the paper proposes a magnetic tracking system able to collect information about translational and vibrational movements in a spatial cubic domain, using a low-cost, low-power and highly accurate solution. These features allow the usage of the proposed technology to realize a portable monitoring system, that may be operated at home or in general practices, enabling telemedicine and preventing saturation of large neurological centers. Validation is based on three tests: movement trajectory tracking, a rest tremor test and a finger tapping test. These tests are considered in the Unified Parkinson’s Disease Rating Scale and are provided as case studies to prove the system’s capabilities to track and detect tremor frequencies. In the case of the tapping test, a preliminary classification scheme is also proposed to discriminate between healthy and ill patients. No human patients are involved in the tests, and most cases are emulated by means of a robotic arm, suitably driven to perform required tasks. Tapping test results show a classification accuracy of about 93% using a k-NN classification algorithm, while imposed tremor frequencies have been correctly detected by the system in the other two tests.

This paper describes the development of a distributed sensing system that can be disseminated in an environment of interest to monitor the vibration of a structure. This low-cost system consists of several sensor nodes and a central receiving node. All nodes are built using off-the-shelf electronic components. Each of the sensor nodes is battery-powered and equipped with a triaxial MEMS accelerometer, a wireless Long Range (LoRa) transceiver module for data transmission, a GPS module used for synchronization, and a microcontroller. The operation of the sensor node is validated by controlled laboratory tests where it is compared to a commercial reference accelerometer. Furthermore, the feasibility and potential benefits of the application of the proposed system to a structure in an archaeological site is investigated. Results show that the proposed sensor node could successfully monitor the vibration at several locations within the site. Therefore, it may be employed to detect the most relevant stresses to the structure, allowing for the identification of risks.

We present a short-range magnetic positioning system that can track in real-time both the position and attitude (i.e., the orientation of the principal axes of an object in space) of up to six moving nodes. Moving nodes are small solenoids coupled with a capacitor (resonant circuit) and supplied with an oscillating voltage. Active moving nodes are detected by measuring the voltage that they induce on a three-dimensional matrix of passive coils. Data on each receiving coil are acquired simultaneously by a distributed data-acquisition architecture. Then, they are sent to a computer that calculates the position and attitude of each moving node. The entire process is run in real-time: the system can perform 62 position and attitude measurements per second when tracking six nodes simultaneously and up to 124 measurements per second when tracking one node only. Different active nodes are identified using a frequency-division multiple access technique. The position and angular resolution of the system have been experimentally estimated by tracking active nodes along a reference trajectory traced by a robotic arm. The factors limiting the viability of upscaling the system with more than six active nodes are discussed.

In this paper, we consider magnetic positioning and tracking of objects and provide a comparison of the characteristics of two major measurement models: the magnetic dipole model and the mutual inductance model. The numerical results obtained by applying these models to a short-range position measurement application, with a maximum operating distance of approximately 50 cm, are compared. Based on the results of this comparison, a prototype 9-sensor array is developed, experimental tests are performed, and extensive measurement results are presented. Outcomes show the feasibility of tracking the position and orientation of a mobile coil in real time with a median positioning error below 1 cm and a worst-case error of about 2 cm and 11 degrees inside a spatial region of 30 × 30 × 30 cm3 operational volume.