E-Mobility Engineering 017 l ECE Doosan electric excavators dossier l In Conversation: Matt Faulks l Battery testing focus l Battery Show North America 2022 report l Ariel Hipercar digest l Cathode materials insight l Thermal management focus

Acknowledgements The author would like to thank Johannes Eschenauer at Hitachi High-Tech Analytical Science and Jari Granath at Proventia for their help with researching this article. The technique sends electrical pulses into the cell and measures the response. The measurements are then processed by an ML algorithm to predict the battery’s health and useful lifespan. The key factor is that the method is non-invasive and is a simple add-on to any existing battery system. Predicting the state of health and the remaining useful lifespan of lithium-ion batteries is one of the major limits on the widespread adoption of EVs. Over time, battery performance is degraded owing to a range of chemical processes. Individually, each process doesn’t have much effect on battery performance, but collectively they can severely shorten a battery’s performance and lifespan. Current methods for predicting battery health are based on tracking the current and voltage during battery charging and discharging, but that misses important indicators of battery health. Tracking the many processes taking place in a battery requires new ways of probing batteries in action, as well as new algorithms that can detect subtle signals as they are charged and discharged. Improving the software that monitors charging and discharging, and using data-driven software to control the charging process, provides a major improvement in battery performance. The technique combines electrochemical impedance spectroscopy (EIS) with Gaussian process machine learning. EIS is a real-time, non-invasive and information-rich measurement that has not previously been used for batteries. More than 20,000 EIS spectra of commercial LCO/graphite lithium-ion batteries were collected at different states of health, states of charge and temperatures. The Gaussian process model takes the entire spectrum as its input, without further modification, and automatically determines which spectral features predict degradation. The model then accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery. Importantly, the model learns how to distinguish important signals from irrelevant noise. This approach can also give hints about the physical mechanism of degradation. It can estimate the capacity and health of batteries cycled at three constant temperatures, at any point in its life, from a single impedance measurement. The model can also indicate which electrical signals are most correlated with ageing, which in turn allows specific experiments to probe why and how batteries with particular chemistries degrade. Modelling Another way around the challenges of testing cells is to use modelling, which is becoming increasingly important as the size and complexity of e-mobility platforms increases. It is a particular issue for applications such as off-road and construction equipment, which have a wide variety of designs. One battery integrator uses 20 Ah LTO cells for high power output with extensive simulation based on testing the cells. Powertrains are so complex that it is not viable to use a trial-and-error approach, so before building prototypes good simulation models of the powertrains and the battery systems are essential. These are electrochemical, thermal and electrodynamic models for a standard base LTO 1 kW, 46 V module that can be used to build 400 or 800 V battery systems for hundreds of kilowatts. The models are built by the integrator, taking cell-level measurements to turn cell-level simulation models into a module model; the system model is then a repetition of the module models. This has to be supported with real- world testing to validate the models, including thermal testing, in-house. Conclusion Many different techniques can be used for non-invasive analysis of materials and cells in battery packs. X-rays are increasingly popular, providing data for modelling and ML frameworks. This increased accuracy of data is helping provide more information about the reliability and lifetime of all kinds of cell chemistries for all sorts of e-mobility applications. Focus | Battery testing A battery test system (Courtesy of Proventia Test Solutions) 42 January/February 2023 | E-Mobility Engineering