E-Mobility Engineering 016 l Aurora Powertrains eSled dossier l In Conversation: Thomas de Lange l Automated manufacturing focus l Torque sensing insight l Battery Show Europe 2022 report l Sodium batteries insight l User interfaces focus

Software predicts future cell health A machine learning algorithm could help reduce charging times and prolong battery life in EVs by predicting how different driving patterns affect battery performance (writes Nick Flaherty). Researchers from the University of Cambridge, in the UK, have developed a non-invasive impedance-based approach to get accurate data on the state of health (SOH) of a battery. The results were fed into a machine learning algorithm that can predict how different driving patterns will affect the future health of the battery. The algorithm could be used to recommend routes that get drivers from point to point in the shortest time without degrading the battery, for example, or recommend the fastest way to charge the battery without causing it to degrade. “Most methods of monitoring battery health assume that a battery is always used in the same way, but that’s not how we use batteries in real life – how you drive will affect how the battery degrades,” said Penelope Jones, a researcher from Cambridge’s Cavendish Laboratory, who worked on the project. The problem is that batteries with the same numerical SOH do not necessarily exhibit identical levels of each degradation process – lithium plating or electrode cracking, for example – yet the impact of future cell usage on the cell’s performance and degradation pathway depends significantly on the type of degradation that has already occurred. A monitoring system is therefore needed. The researchers’ non-invasive monitoring probe sends electrical pulses into a battery and measures the response, providing a series of ‘biomarkers’ of battery health. The method is gentle on the battery and doesn’t cause it to degrade any further. The electrical signals from the battery were converted into a description of the battery’s state, which was fed into the machine learning algorithm. The algorithm was able to predict how the battery would respond in the next charge-discharge cycle, depending on how quickly the battery was charged and how fast the car would be going the next time it was on the road. Tests with 88 commercial batteries showed that the algorithm did not require any information about previous usage of the battery to make an accurate prediction. The experiment focused on lithium cobalt oxide cells, which are widely used in rechargeable batteries, but the method is applicable across the different types of battery chemistries used in EVs. The method can be used by manufacturers, end-users or recyclers because it allows the health of the battery to be captured beyond a single number, and because it’s predictive. It could also reduce the time it takes to develop new types of batteries, allowing researchers to predict how they will degrade under different operating conditions. The researchers are now working with battery manufacturers on next- generation batteries and how the framework could be used to develop optimal fast-charging protocols to reduce EV charging times without causing degradation. BATTERY PERFORMANCE Solid-state cell with 3-minute charge Researchers in the US have developed a solid-state lithium metal battery for use in future EVs that would fully charge in as little as 3 minutes (writes Nick Flaherty). The technology, which has a lifetime of 10,000 cycles, has been licensed by Harvard University to Adden Energy to scale up to pouch and then prismatic cells for EV designs in the next 3 to 5 years. The design tackles the problem of lithium dendrites that grow with fast charging and reduce the lifetime. “If you want to electrify vehicles, a solid- state battery is the way to go,” said Xin Li, a scientific advisor to Adden Energy and Associate Professor of Materials Science at the Harvard John A Paulson School of Engineering and Applied Sciences. “We have achieved 5000 to 10,000 charge cycles in the lab in a battery’s lifetime, compared with 2000 to 3000 charging cycles for even the best in class now, and we don’t see any fundamental limit to scaling up our battery technology.” The cell has a specific power of 110.6 kW/kg and a specific energy of up to 631.1 Wh/kg. “Typically, lithium metal anodes in other solid-state designs develop dendrites, but we defeat their growth before they can cause damage,” said Luhan Ye, researcher at Harvard, who is now chief technology officer of Adden Energy. “As a result, the device can sustain its high performance over a long lifetime.” Tiny cracks in the ceramic layers can be generated during battery assembly or long-time cycling, and once they form, lithium dendrite penetration is inevitable. The researchers therefore use a hierarchy of layers to achieve a high current density with no lithium dendrite penetration. The cycling performance of the lithium metal anode paired with a LiNi0.8Mn0.1Co0.1O2 cathode showed an 82% capacity retention after 10,000 cycles at a 20C rate and 81.3% capacity retention after 2000 cycles at a 1.5C rate. BATTERIES Winter 2022 | E-Mobility Engineering 7 TheGrid