This work presents an approach to monitoring the State-Of-Charge of Lithium-Ion battery cells via piezo disc-based ultrasonic Time-Of-Flight measurement by measuring the traveling time of a mechanical pulse through the cell between two surface-mounted sensors. The main advantage of this approach is the simplicity and the resulting low cost, which makes it suitable for future application in battery management systems. In detail, the excitation of the piezo actuator is done using a single semiconductor switch instead of a power amplifier, and the received signal is processed with an amplifier and Schmitttrigger combination to condition the signal for the microprocessor, which is part of a battery management system. Both the functionality and the limits of the design are evaluated with a high energy density Lithium-Ion pouch cell under different operational scenarios. Several parameters such as temperature, current rates, and excitation frequency are varied to prove the design concept. For validation purposes, an estimation function is generated and a real-world driving cycle applied. An estimation with an error of 1.29% of the Time-Of-Flight total value or 16.8%of the State-Of-Charge value under challenging conditions is achieved with the current setup.

Thanks to its exceptional performance in terms of high energy and power density as well as long lifespan, the lithium-ion secondary battery is the most relevant electrochemical energy storage technology to meet the requirements for partial or full electrification of vehicles (plug-in hybrids or pure electric vehicles), and thanks to decreasing cost and ongoing technical improvements, it will maintain this role in the near to mid-term future. This study benchmarks eight different (five 21700 and three 18650 format) high-energy cylindrical cells concerning their suitability for automotive applications and aims to give a holistic overview and comparison between them. Therefore, an ante-mortem material analysis, a benchmark of electrical and thermal values as well as a cycle life study were carried out. The results show that even when applying similar concepts like Nickel-rich cathodes with graphite-based anodes, the cells show wide variations in their performance under the same test conditions.

The use of equivalent circuit models for simulating the operating behaviour of Lithium-Ion batteries is well established in the automotive and the renewable energy sector. However, finding the correct parameter set for these models is still a challenging task. This manuscript proposes a comprehensive methodology for estimating the required, temperature dependent simulation parameters from battery measurements. Based on a specific load current and prior system knowledge, an algorithm first analyses the correlation between current steps and the measured terminal voltage. Secondly, a combination of Particle Swarm Optimisation and Gauss-Newton algorithm fits the initially estimated parameters from the first algorithm to the measurement data. Finally, the dependency of each simulation parameter on both the state of charge and the battery temperature is determined. As this contribution aims at modelling reversible effects of Lithium-Ion batteries, ageing effects are neglected. The validation against measurement data proves that the generated parameter set enables the user to accurately reproduce and investigate the operating behaviour of the chosen battery. Applied to an LFP cell, the comparison between measurements and simulations in standardised real-life automotive driving cycles (Artemis, FTP75 and WLTC) shows a terminal voltage error of less than 1.09% within the typical operational window between SOC0.15 and SOC0.95.

The end-of-life event of the battery system of an electric vehicle is defined by a fixed end-of-life threshold value. However, this kind of end-of-life threshold does not capture the application and battery characteristics and, consequently, it has a low accuracy in describing the real end-of-life event. This paper proposes a systematic methodology to determine the end-of-life threshold that describes accurately the end-of-life event. The proposed methodology can be divided into three phases. In the first phase, the health indicators that represent the aging behavior of the battery are defined. In the second phase, the application specifications and battery characteristics are evaluated to generate the end-of-life criteria. Finally, in the third phase, the simulation environment used to calculate the end-of-life threshold is designed. In this third phase, the electric-thermal behavior of the battery at different aging conditions is simulated using an electro-thermal equivalent circuit model. The proposed methodology is applied to a high-energy electric vehicle application and to a high-power electric vehicle application. The stated hypotheses and the calculated end-of-life threshold of the high-energy application are empirically validated. The study shows that commonly assumed 80 or 70% EOL thresholds could lead to mayor under or over lifespan estimations.

During the charge and discharge process, lithium-ion batteries change their mechanical properties due to internal structural changes caused by intercalation and de-intercalation of the ions in the anode and cathode. Furthermore, the behavior changes over the lifetime of the battery due to several degradation mechanisms. The mechanical properties of the cell hold valuable information for monitoring these changes and additionally provide data for mechanical construction and further optimization of battery systems. Hence, in this manuscript, the mechanical frequency response function is investigated as a non-destructive method to determine parameters such as stiffness and damping of pouch cells and their correlation with the state of charge (SOC), the state of health (SOH), and the temperature of the cell. Using a mechanical shaker and an impedance head, it is shown that low amplitude forces of only a few Newton and a low frequency region of several hundred Hertz already suffice to show differences in the state of charge and state of health as well as in mechanical properties and the dependencies on temperature. Also the limitations of the method are shown, as the frequency response is not distinct for each parameter and thus, at the moment, does not allow absolute determination of a single value without prior system knowledge.