A complementary cathode design with LiNi0.8Mn0.1Co0.1O2(NMC) and LiMnxFeyPO4(LMFP) as active material facilitates not only stable cycling of lithium metal batteries, but also outstanding thermostability. Although the use of NMC addressed high energy, the high reactivity of it results in severe parasitic side reactions. Furthermore, the O2 release during cycling arise the thermostability concern in batteries. When the NMC is partially replaced by LMFP, it displays excellent electrochemical and thermal performance.

In this work, an intelligent battery thermal management (iBTM) technology utilizes phase change composite (PCC) for thermal management and safety of Li-ion batteries. It integrates advanced battery management system (BMS) to monitor battery temperature, state of health (SOH), state of charge (SOC), and PCC matrix thermal state of charge (TSoC) to enable real-time dynamic fast charging. Initial results demonstrated 15-20 minutes rapid charge of high energy Type 21700 commercial cells.

The need for low-temperature power is a principal driver in the design of many battery systems. In this talk, we will explore the mechanism by which the thermally modulated cell design eliminates this concern and the resulting landscape it creates for improved thermal stability, safety, and performance.

In a thermal management system where total heat generation during cycling is one of the most salient features and controls the temperature of each cell/battery pack is of interest. Therefore, in this respect, total heat generations were investigated by a 3D-Calvet MS80 calorimeter (Setaram, France) under isothermal conditions at 25 °C in sodium and magnesium batteries. The analysis had shown that the dominant heat sources during charging-discharging were caused by mainly joule heat. Thermal data and various resistances by electrochemical impedance spectroscopy give a relationship to calculate the individual heat.

Thermal Runaway (TR) of LiBs is the key of the safety. It involves multi-scale phenomena ranging from internal physic-chemical mechanisms to battery components including safety features (CID, Pressure disk, vent) and further to the thermal propagation. At IFPEN a Multiphysics Multiscale model is developed able to simulate the cell behavior under different initiation event (overheat, overcharge, short circuit). The impact of chemistry, SOC and aging are studied.

Safety and durability concerns caused by surface and interface instabilities of high-surface-activity energy materials are challenging modern electrochemical energy conversion and storage systems. I will present our most recent representative works on the surface and interface engineering of functional materials for energy devices with greatly improved safety under harsh operating conditions such as fast charging at high currents.

Herein, we report a tunable mass loading and dense silicon nanowire (SiNWs) growth on a highly conductive, flexible, fire-resistant, and mechanically robust 3D interwoven stainless steel fiber cloth (SSFC).  Galvanostatic cycling of the Si NWs@SSFC anode with a mass loading of 1.32 mg. cm -2 achieves a stable areal capacity of ~2 mAh cm-2 at 0.2C after 200 cycles. This approach is viable for practical applications in high-energy-density LIBs.

Safety is the primary concern in lithium-ion(LIB) battery-powered EVs and ESSs. Replacement of highly flammable traditional liquid electrolyte with nonflammable liquid electrolyte is the first step toward prevention of fire event of LIBs. Stabilization of solid electrolyte interphase(SEI) layer at anode is a must for lithium dendrite-free LIBs. I will show that well-designed nonflammable liquid electrolyte including 1.0 M lithium salt yields a safe and long-cycled Ni-rich NCM chemistry LIBs.

With new regulations on electric vehicle safety, a warning 5 minutes prior to the evolution of hazardous conditions caused by thermal runaway becomes mandatory to enable passengers to exit safely. To reduce the risk of battery fires, hot particle filters can be integrated into venting units. Latest progress in this field will be shared in this presentation, and next steps toward a holistic battery pack safety management will be discussed.

Fast and full battery charging enhances user experience in consumer electronics, electric vehicles and IOT devices. However, it accelerates battery degradation, resulting in less battery longevity. This session talks about machine-learning/deep-learning application to battery charging, optimizing charging scheme and extending battery longevity while maintaining user experience. The technology is also expected to enhance sustainability and reduce the cost of ownership as it requires less battery replacement.

New machine learning (ML) approaches offer a route to accelerated materials discovery, process optimization, and cell design by training predictive models on existing experimental data and then using these models to future experiments. In this talk, we present our research in using ML to accelerate each of these areas, discuss best practices for the application of ML to materials design, and highlight the Aionics materials design software platform.

This talk will review past and ongoing research studies on battery capacity forecasting and early life prediction and discuss the long-term testing and methodology development efforts led by a team of researchers at the University of Connecticut and Iowa State University.