Li-ion batteries are used in many industries, such as consumer electronics, electric vehicles, and internet-of-things. With the substantially increasing demand, sustainable battery technologies are desired. This talk explains several battery algorithms: adaptive charging, situational charging, context-based battery charging, etc. All algorithms extend battery longevity and require less battery replacement, thus contributing to sustainability enhancement.

Li-ion batteries are used in many industries, such as consumer electronics, electric vehicles, and internet-of-things. With the substantially increasing demand, sustainable battery technologies are desired. This talk explains several battery algorithms: adaptive charging, situational charging, context-based battery charging, etc. All algorithms extend battery longevity and require less battery replacement, thus contributing to sustainability enhancement.

Dynamic modeling, state estimation, and optimal charging control of Lithium-ion batteries are some of the primary challenges in advanced battery management. While model-based control and estimation algorithms have made a significant leap in the past decade, the integration of battery big data offers further insights into this highly-coupled and intricate electrochemical storage system. Mathematically, combining machine learning with physics is a trending approach for discovering unknown dynamics. This talk will highlight the application of physics-informed neural network and imitation learning in improving the real-time performance of batteries.

With increasing demands in application, balance between power and energy requirements at cell level is increasingly difficult to satisfy, particularly in respect of higher power. This work demonstrates hybridization of battery and EDLC in-electrode with active materials from both present in both electrodes, as opposed to one electrode from each as seen in lithium-ion capacitors, enabling the tuning of power and energy to application and also increasing in lifetime.

Thermal Runaway (TR) of LiBs is the key to 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 thermal propagation. At IFPEN, a Multiphysics Multiscale model is developed to be able to simulate the cell behavior under different initiation events (overheating, overcharging, short circuiting). The impacts of chemistry, SOC, and aging are studied.

Lithium-ion batteries have long been confined within narrow temperature thresholds, limiting their effectiveness and safety. This talk will explore a potential future where lithium-ion cells are intentionally engineered for higher operating temperatures and so revolutionize battery safety. By delving into cutting-edge advancements like solid-state batteries, which inherently thrive at elevated temperatures, we contemplate the possibility of harnessing higher operating temperatures as a design advantage rather than a limitation. We will also discuss the role that next-generation thermal management systems could play to facilitate this future.

Li-ion batteries (LIBs) offer diverse benefits for sustainable energy storage; they have a high storage capacity for their size, preserve good charge/discharge cycling stability over long periods, and have minimal charge loss during storage. However, like any energy storage technology, LIBs are not impeccable. Over time, battery deterioration can lead to safety and performance issues that shouldn’t be underestimated. One of the main issues is the gas generation during cell cycling, resulting in cell bulging in pouch cells, high internal pressure in cells with rigid metal housing, electrode delamination, and de-contacting of active materials. Identification of degradation products is the first step towards safer, longer-lasting, higher performing and more easily recyclable LIBs. This knowledge will help to understand the mechanistic pathways of degradation and how they can be inhibited. On the other hand, lithium-based batteries have the potential to undergo thermal runaway, during which mixtures of hazardous gases are. In the current study, a series of cylindrical lithium-ion cells was exposed to abuse conditions (thermal) to investigate the composition of the released gas mixture. Mass spectrometry has been used to detect the evolved gases in the swollen cells (off-line analysis) and during cycling of lab-made cells (on-line analysis).. Recently, we have introduced a new coupling of the mass spectrometry with the accelerating rate calorimetry (ARC) to monitor the emitted gases during the venting and thermal runaway of different LIB types.

Innate risk of propagation due to thermal runaway inhibits Lithium-ion battery application to safety-critical systems. Key design elements are necessary to eliminate risk of propagation. Fault Tree Analysis (FTA) was applied to development of a Passive Propagation Resistant (PPR) Lithium-ion battery system to drive design via probabilistic risk assessment. By characterizing the phenomena associated with single cell failure and propagation fault modes, each cause was designed out by employing multiple layers of fault-tolerant defense, allowing for safe & reliable shipboard deployment.

Safety has historically been deprioritized, often in favor of cost, resulting in energy storage systems that cannot respond in time to prevent catastrophic explosions and fires. However, safe lithium chemistry-agnostic battery deployment and operation is achievable and essential (particularly in urban areas where electricity demands are the greatest and safety is paramount) to propel the clean energy storage solutions that will enable the successful transformation from fossil fuel dependence.