In BPL case we would be using the secondary lead which we get from scrap batteries which we have been collecting over the years.

Most of the lead produced comes from secondary sources. Lead scrap includes lead-acid batteries, cable coverings, pipes, sheets and lead coated, or terne bearing, metals. Solder, product waste and dross may also be recovered for its small lead content. Most secondary lead is used in batteries.

To recover lead from a battery, the battery is broken, and the components are classified. The lead containing components are processed in blast furnaces for hard lead or rotary reverberatory furnaces for fine particles. The blast furnace is similar in structure to a cupola furnace used in iron foundries. The furnace is charged with slag, scrap iron, limestone, coke, oxides, dross, and reverberatory slag. The coke is used to melt and reduce the lead. Limestone reacts with impurities and floats to the top. This process also keeps the lead from oxidizing. The molten lead flows from the blast furnace into holding pots. Lead may be mixed with alloys, including antimony, tin, arsenic, copper, and nickel. It is then cast into ingots.

Smelting of Lead helps in putting recycling process, whereby instead of just throwing away scrap batteries, these scrap can be put into good use of getting the ingots and individuals getting some money instead of just discarding their scrap batteries.

At the heart of most electronics, today are rechargeable lithium-ion batteries (LIBs). But their energy storage capacities are not enough for large-scale energy storage systems (ESSs). Lithium-sulfur batteries (LSBs) could be useful in such a scenario due to their higher theoretical energy storage capacity. They could even replace LIBs in other applications like drones, given their lightweight and lower cost.

But the same mechanism that is giving them all this power is keeping them becoming a widespread practical reality. Unlike LIBs, the reaction pathway in LSBs leads to an accumulation of solid lithium sulfide (Li₂S₆) and liquid lithium polysulfide (LiPS), causing a loss of active material from the sulfur cathode (positively charged electrode) and corrosion of the lithium anode (negatively charged electrode). To improve battery life, scientists have been looking for catalysts that can make this degradation efficiently reversible during use.

In a new study published in ChemSusChem, scientists from Gwangju Institute of Technology (GIST), Korea, report their breakthrough in this endeavor. “While looking for a new electrocatalyst for the LSBs, we recalled a previous study we had performed with cobalt oxalate (CoC₂O₄) in which we had found that negatively charged ions can easily adsorb on this material’s surface during electrolysis. This motivated us to hypothesize that CoC₂O₄ would exhibit similar behavior with sulfur in LSBs as well,” explains Prof. Jaeyoung Lee from GIST, who led the study.

To test their hypothesis, the scientists constructed an LSB by adding a layer of CoC₂O₄ on the sulfur cathode.

Sure enough, observations and analyses revealed that CoC₂O₄‘s ability to adsorb sulfur allowed the reduction and dissociation of Li₂S₆ and LiPS. Further, it suppressed the diffusion of LiPS into the electrolyte by adsorbing LiPS on its surface, preventing it from reaching the lithium anode and triggering a self-discharge reaction. These actions together improved sulfur utilization and reduced anode degradation, thereby enhancing the longevity, performance, and energy storage capacity of the battery.

Charged by these findings, Prof. Lee envisions an electronic future governed by LSBs, which LIBs cannot realize. “LSBs can enable efficient electric transportation such as in unmanned aircrafts, electric buses, trucks and locomotives, in addition to large-scale energy storage devices,” he observes. “We hope that our findings can get LSBs one step closer to commercialization for these purposes.”

Perhaps, it’s only a matter of time before lithium-sulfur batteries power the world.

ML And Battery Life Cycle

The research team has been working to develop a long-lasting electric vehicle battery that can be charged in 10 minutes.

“Battery technology is important for any type of electric powertrain. By understanding the fundamental reactions that occur within the battery we can extend its life, enable faster charging and ultimately design better battery materials. We look forward to building on this work through future experiments to achieve lower-cost, better-performing batteries,” said Patrick Herring, a senior scientist of Toyota Research Institute.

Earlier studies used more conventional machine learning forms to accelerate battery testing and find out the best charging method. Though the studies made major progress in determining battery lifetime, they did not reveal much about the science behind why a few batteries last longer than the others.

The current research teaches machines how to learn a new type of failure mechanism to design better and safer fast-charging batteries. In general, fast charging stresses and damages the battery. Better practices would help battery technology and fight climate change, the team said. Further, this approach can be used for developing grid-scale battery systems for producing wind and solar electricity.

The team was able to optimize the fast charging protocol for lithium-ion batteries within a month using machine learning. Without ML, this would usually take two years. “At the end of the day, we see our job as accelerating the pace of battery R&D. Whether it’s discovering new chemistry or finding a way to make a safer battery, it’s all very time-consuming. We’re trying to save time,” said Will Chueh, an associate professor at Stanford University, who also led the study. 

The Experiment

For this experiment, the team took a closer look at the Lithium ions movement between the cathode and anode — made of nano-sized grains lumped together as particles — during charging and discharging. In particular, the behavior of cathode particles, comprising nickel, manganese, and cobalt (NMC), were observed in detail. NMC is the most widely used material in electric vehicle batteries. The particles absorb lithium ions when the battery is discharging and release them when the battery is charging.

The team got an overall look at the particles when the battery was being fast charged using X-rays from SLAC’s Stanford Synchrotron Radiation Lightsource. The same particles were later examined with scanning X-ray transmission microscopy, which focuses on individual particles. The data obtained from these experiments; information from mathematical models on fast charging; and physics and chemistry equations were used in the scientific machine learning algorithm. The team said this is the first time scientific machine learning has been used in battery technology.