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.

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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.

The estimation of lithium ion capacity fade and impedance rise on real application is always a challenging work due to the associated complexity. This work envisages the study of the battery charging profile indicators (CPI) to estimate battery health indicators (capacity and resistance, BHI), for high energy density lithium-ion batteries. Different incremental capacity (IC) parameters of the charging profile will be studied and compared to the battery capacity and resistance, in order to identify the data with the best correlation. In this sense, the constant voltage (CV) step duration, the magnitudes of the IC curve peaks, and the position of these peaks will be studied. Additionally, the behaviour of the IC curve will be modeled to determine if there is any correlation between the IC model parameters and the capacity and resistance. Results show that the developed IC parameter calculation and the correlation strategy are able to evaluate the SOH with less than 1% mean error for capacity and resistance estimation. The algorithm has been implemented on a real battery module and validated on a real platform, emulating heavy duty application conditions. In this preliminary validation, 1% and 3% error has been quantified for capacity and resistance estimation.

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Background: The thermal management of a battery pack designed for an electric vehicle is a key to prevent accidental events and ensure a long lifespan of the batteries. A typical accident is a thermal runaway of one or more cells in the battery which can cause fire or explosion of the battery pack. This paper presents a numerical modelling of a battery pack (BP) and its heat exchanger (HE) for an electric vehicle. The heat produced in the battery is evacuated by the HE. Methods: Two different kinds of modelling have been realized: a computational fluid dynamic (CFD) modelling and a coarse (called MOD3 for 3D Model) modelling. The CFD modelling allows the creation of fine numerical simulations of a BP, but uses large meshes, therefore the cost of each calculation is important. In order to make a large number of quite long transient simulations, a second tool called MOD3, employing only a coarse mesh, was developed in this study at the Commissariat à l’énergie atomique et aux énergies alternatives (CEA). Results: Two measurement campaigns corresponding to two different versions of the HE have been conducted at CEA. The temperature measurements allow comparisons of MOD3 to a real battery pack and to fit some heat exchange coefficients. The cells temperatures as well as the cooling liquid temperature are compared. Conclusions: The MOD3 tool has been fitted partly on CFD calculations, and partly on experimental measurements. It will be integrated in a machine learning environment by CIDETEC to take into account the thermal management of the BP in real car simulations.

The control of the battery-thermal-management-system (BTMS) is key to prevent catastrophic events and to ensure long lifespans of the batteries. Nonetheless, to achieve a high-quality control of BTMS, several technical challenges must be faced: safe and homogeneous control in a multi element system with just one actuator, limited computational resources, and energy consumption restrictions. To address those challenges and restrictions, we propose a surrogate BTMS control model consisting of a classification machine-learning model that defines the optimum cooling-heating power of the actuator according to several temperature measurements. The la-belled-data required to build the control model is generated from a simulation environment that integrates model-predictive-control and linear optimization concepts. As a result, a controller that optimally controls the actuator with multi-input temperature signals in a multi-objective optimization problem is constructed. This paper benchmarks the response of the proposal using different classification machine-learning models and compares them with the responses of a state diagram controller and a PID controller. The results show that the proposed surrogate model has 35% less energy consumption than the evaluated state diagram, and 60% less energy consumption than a traditional PID controller, while dealing with multi-input and multi-objective systems.