In conversation: Dr Richard Ahlfeld l H2D2 snow groomer dossier l Battery sealing focus l Coil windings l Electrogenic E-type conversion l Battery energy density l Thermal runaway prevention focus

63 Battery energy density | Deep insight in the US to nano-composite silicon into that anode. The Titan Silicon material will boost the volumetric energy density to 1000 Wh/L by 2030. “By integrating Sila’s groundbreaking battery material with our advanced cell-manufacturing capabilities, we believe we can address concerns such as range anxiety and charging time, and contribute to accelerating the adoption of EVs,” says Shoichiro Watanabe, executive vice-president of Panasonic Energy. “Panasonic is the world’s leading battery technology company, pushing the boundaries for performance, and we look forward to optimising Titan Silicon to help achieve these momentous goals,” says Gene Berdichevsky, co-founder and CEO of Sila. “This partnership represents a significant milestone for Sila, our customers and the industry at large, and will be key to accelerating consumer EV adoption.” Electrolytes Developing new electrolytes is where simulation has its limits, and this is where AI is finding gaps in data and forecasting new materials. This can be used to combine physics-based models with AI to extend the lifetime of cells. The physical models offer great insights to improve fast charging times and forecast how batteries age, but they cannot account for all the physics. About:Energy is working with Eatron to develop an AI ‘decision engine’ for a BMS that combines machine learning and the About:Energy electrochemical model, Voltt. The aiMAGINE project aims to use machine-learning frameworks in the BMS to extend the life of a battery pack in EVs and scooters, providing accurate assessments of SoC, SoH and patented remaining useful life (RUL) predictions. AI complements the electrochemical models, enhancing predictions by accounting for complex physical behaviours that cannot be modelled. This allows the AI-powered decision engine (AI-DE) to provide highly accurate operational parameters to the BMS, significantly increasing battery pack longevity and simplifying integration. “Implementing our novel, AI-powered, intelligent battery software layer with this revolutionary AI-DE can extend a battery pack’s first life by up to 20%,” says Dr Umut Genc, CEO of Eatron Technologies. “This makes it possible for OEMs to design optimally sized, more cost-effective battery packs.” O’Regan says: “The use of our advanced electrochemical models vastly streamlines AI model training, and this facilitates both ease of integration and a reduced time-to-market for OEMs and Tier 1s. The high-fidelity modelling reduces the need for physical experiments while delivering a clearer, more accurate picture of battery health. Armed with this information, an AI-DE-equipped BMS can deliver not just a longer battery lifetime, but faster charging times too.” About:Energy is also working with the universities of Portsmouth and Southampton on a more accurate approach to battery modelling, which will provide more data about the energy in the cells. The Voltt models can be downloaded to modelling and simulation tools from Comsol and Matlab, but About:Energy is developing a cloud-based tool to download the model and use the Python programming language to develop a drive cycle to assess the performance of the cells in specific applications, particularly automotive and aerospace. Future directions Boosting the energy density of an e-mobility system’s powertrain is a complex balance. Improvements in materials – whether for existing lithium NMC graphite cells, silicon anodes or lithium-metal solid state cells – can improve output, but the cell format, whether cylindrical or a mix of prismatic and pouch, can reduce wasted space and increase energy density. Then, cell-to-pack and other topologies can further reduce the passive elements in the system. When it comes to getting the most out of cells over the lifetime of a vehicle, new architectures and battery management systems can help. Accurately modelling the performance of a cell over the lifetime of operation can ensure power is delivered as expected. This can help avoid oversizing to meet future requirements and reduce the size of the pack overall to improve the energy density of the powertrain design. E-Mobility Engineering | May/June 2024 Thermal test rigs can provide the data for physical and AI models of battery cells to boost energy density (Image courtesy of About: Energy)

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