At the heart of all lithium-ion batteries is a simple reaction: Lithium ions dissolved in an electrolyte solution “intercalate” or insert themselves into a solid electrode during battery discharge. When they de-intercalate and return to the electrolyte, the battery charges.

This process happens thousands of times throughout the life of a battery. The amount of power that the battery can generate, and how quickly it can charge, depend on how fast this reaction happens. However, little is known about the exact mechanism of this reaction, or the factors that control its rate.

In a study appearing in Science, MIT researchers have measured lithium intercalation rates in a variety of different battery materials and used that data to develop a new model of how the reaction is controlled. Their model suggests that lithium intercalation is governed by a process known as coupled ion-electron transfer, in which an electron is transferred to the electrode along with a lithium ion.

Insights gleaned from this model could guide the design of more powerful and faster charging lithium-ion batteries, the researchers say.

“What we hope is enabled by this work is to get the reactions to be faster and more controlled, which can speed up charging and discharging,” says Martin Bazant, the Chevron Professor of Chemical Engineering and a professor of mathematics at MIT.

The new model may also help scientists understand why tweaking electrodes and electrolytes in certain ways leads to increased energy, power, and battery life—a process that has mainly been done by trial and error.

“This is one of these papers where now we began to unify the observations of reaction rates that we see with different materials and interfaces, in one theory of coupled electron and ion transfer for intercalation, building up previous work on reaction rates,” says Yang Shao-Horn, the J.R. East Professor of Engineering at MIT and a professor of mechanical engineering, materials science and engineering, and chemistry.

Shao-Horn and Bazant are the senior authors of the paper. The paper’s lead authors are Yirui Zhang Ph.D., who is now an assistant professor at Rice University; Dimitrios Fraggedakis Ph.D., who is now an assistant professor at Princeton University; Tao Gao, a former MIT postdoc who is now an assistant professor at the University of Utah; and MIT graduate student Shakul Pathak.

Modeling lithium flow

For many decades, scientists have hypothesized that the rate of lithium intercalation at a lithium-ion battery electrode is determined by how quickly lithium ions can diffuse from the electrolyte into the electrode. This reaction, they believed, was governed by a model known as the Butler-Volmer equation, originally developed almost a century ago to describe the rate of charge transfer during an electrochemical reaction.

However, when researchers have tried to measure lithium intercalation rates, the measurements they obtained were not always consistent with the rates predicted by the Butler-Volmer equation.

Furthermore, obtaining consistent measurements across labs has been difficult, with different research teams reporting measurements for the same reaction that varied by a factor of up to 1 billion.

In the new study, the MIT team measured lithium intercalation rates using an electrochemical technique that involves applying repeated, short bursts of voltage to an electrode.

They generated these measurements for more than 50 combinations of electrolytes and electrodes, including lithium nickel manganese cobalt oxide, which is commonly used in electric vehicle batteries, and lithium cobalt oxide, which is found in the batteries that power most cell phones, laptops, and other portable electronics.

For these materials, the measured rates are much lower than has previously been reported, and they do not correspond to what would be predicted by the traditional Butler-Volmer model.

The researchers used the data to come up with an alternative theory of how lithium intercalation occurs at the surface of an electrode. This theory is based on the assumption that in order for a lithium ion to enter an electrode, an electron from the electrolyte solution must be transferred to the electrode at the same time.

“The electrochemical step is not lithium insertion, which you might think is the main thing, but it’s actually electron transfer to reduce the solid material that is hosting the lithium,” Bazant says. “Lithium is intercalated at the same time that the electron is transferred, and they facilitate one another.”

This coupled-electron ion transfer (CIET) lowers the energy barrier that must be overcome for the intercalation reaction to occur, making it more likely to happen. The mathematical framework of CIET allowed the researchers to make reaction rate predictions, which were validated by their experiments and substantially different from those made by the Butler-Volmer model.

Faster charging

In this study, the researchers also showed that they could tune intercalation rates by changing the composition of the electrolyte. For example, swapping in different anions can lower the amount of energy needed to transfer the lithium and electron, making the process more efficient.

“Tuning the intercalation kinetics by changing electrolytes offers great opportunities to enhance the reaction rates, alter electrode designs, and therefore enhance the battery power and energy,” Shao-Horn says.

Shao-Horn’s lab and their collaborators have been using automated experiments to make and test thousands of different electrolytes, which are used to develop machine-learning models to predict electrolytes with enhanced functions.

The findings could also help researchers to design batteries that would charge faster, by speeding up the lithium intercalation reaction. Another goal is reducing the side reactions that can cause battery degradation when electrons are picked off the electrode and dissolve into the electrolyte.

“If you want to do that rationally, not just by trial and error, you need some kind of theoretical framework to know what are the important material parameters that you can play with,” Bazant says. “That’s what this paper tries to provide.”

This story is republished courtesy of MIT News (web.mit.edu/newsoffice/), a popular site that covers news about MIT research, innovation and teaching.

Residential heating, ventilation, and air conditioning (HVAC) systems constitute a significant proportion of energy usage in buildings, necessitating energy management optimization. In this context, occupancy-aware HVAC control is a promising option with 20–50% energy savings in homes. However, occupancy sensing technology suffers from long payback times, privacy issues, and poor comfort. Moreover, there is an increasing need for further advanced technologies that help regulate indoor air quality in addition to energy control.

To meet these expectations, scientists have recently turned to intelligent control methods such as quantum reinforcement learning (QRL)-based on quantum computing principles. Such approaches can notably accelerate the machine learning process as well as handle the complexity of real-world building dynamics.

In a new study, a group of researchers from the Republic of Korea, led by Sangkeum Lee, Assistant Professor of Computer Engineering at Hanbat National University, have presented the first demonstration of continuous-variable, quantum-enhanced reinforcement learning for residential HVAC and home power management. Their findings are published in the journal Energy and AI.

Dr. Lee says, “Unlike conventional reinforcement learning techniques, QRL leverages quantum computing principles to efficiently handle high dimensional state and action spaces, enabling more precise HVAC control in multi-zone residential buildings. Our framework integrates real-time occupancy detection using deep learning with operational data, including power consumption patterns, air conditioner control data, and external temperature variations.”

Furthermore, the proposed technology integrates features such as multi-zone cooling—to control the temperature of individual zones in a building—and clustering—to group similar data points and adjust cooling. In this way, a single controller jointly optimizes comfort, energy cost, and carbon signals in real time.

The researchers performed simulations based on real world data from 26 residential households over a three-month period. They found that QRL HVAC control significantly outperforms deep deterministic policy gradient method as well as proximal policy optimization algorithm, while maintaining thermal comfort, achieving 63% and 62.4% reductions in power consumption, respectively, and 64.4% and 62.5% decrease in electricity costs, respectively.

The present approach comes with many more benefits. It is retrofit-friendly and works with standard temperature, occupancy, and CO₂ sensors and common HVAC equipment and thermostats. It is also robust to uncertainty, easily handling noisy forecasts on weather and occupancy and device constraints. In addition, it has a generalizable framework that can be extended from apartments to small buildings and microgrids.

Dr. Lee says, “It can be utilized in smart thermostats and autonomous home energy management systems that co-optimize comfort, bills, and emissions without manual tuning and rooftop photovoltaics and home battery scheduling. Our framework is also useful for utility demand-response and time-of-use programs with automated control.”

QRL-based HVAC control can notably be applied at community or campus scale through grid-interactive efficient buildings and virtual power plants (VPPs). Herein, millions of homes can coordinate as VPPs to stabilize renewables-heavy grids. It can also ensure personalized indoor environmental quality within carbon budgets and integrate advanced intelligent control options.

As hardware matures in the coming years, quantum-accelerated policy research could facilitate faster training for complex multi-energy systems such as HVAC, electric vehicles, and energy storage systems. In the long term, this work is expected to guide the path toward standardized secure controllers that can be certified and deployed at a wide scale.

Provided by
Hanbat National University Industry–University Cooperation Foundation

The PSNI has commissioned a senior lawyer to review whether there was any misconduct by police officers following an independent review that found police unlawfully monitored journalists’ phone data, but found no ‘widespread and systemic’ surveillance.

Jon Boutcher, chief constable of the Police Service of Northern Ireland, told the Northern Ireland Policing Board that he had appointed an “eminent” legal counsel, John Beggs KC, to review a 200 page report on PSNI surveillance and report back to confirm there was no misconduct or wrong-doing by police officers.

Beggs, a specialist in police misconduct cases, represented the police commanders at the 2016 Hillsborough inquests, and is the co-author of Police Misconduct, Complaints, and Public Regulation

Separately, the police force has referred itself to the Information Commissioner’s Office (ICO), to investigate whether a “defensive operation” by the PSNI to gather journalist’s phone numbers to and compare them to internal phone records to identify PSNI staff who may have passed information to journalists was lawful.

Boutcher was speaking following the publication of a 200 page review by Angus McCullough KC, which found that the PSNI had made 21 phone data applications to identify journalist’s confidential sources, collated a secret register of over 1000 journalists phone numbers, and identified four cases where the PSNI had used “directed surveillance” for investigations involving journalists and one involving a lawyer.

Sinn Féin representative Gerry Kelly, pressed the chief constable on whether he stood by his public statement that there were no issues of misconduct, criminality or unlawfulness revealed by the McCullough report.

Kellly said there were “unlawful retentions” of two journalists data, despite clear court orders that the data should be destroyed, that there were 21 cases of the unlawful use of covert powers to identify journalists sources, and a “washing through” operation to identify PSNI employees who had phone contact with  journalists that was likely in breach of human rights laws.

 “I just think for you to come in and to say that there’s no issue here, I just find hard,” he told Boutcher.