Researcher Developing Smart Chips to Address Battery Safety

Battery development firm KVI is working on smart chips to improve the safety and performance of lithium-ion batteries, used in many devices today from electric vehicles to smartphones. (GETTY IMAGES)

By AI Trends Staff

The number one issue in lithium-ion batteries powering products from e-bikes and power tools for consumers, to self-driving cars and submarines, is to enhance battery safety, Dr. Rachid Yazami told an audience at the virtual International Battery Seminar from Cambridge EnerTech this week.

Dr. Yazami, founder of Singapore startup KVI, which is developing smart chips to enhance battery performance and safety, is known for his critical role in the development of lithium-ion batteries. In a talk on whether AI can help address battery issues, he outlined the challenges in a battery market that is projected to reach $35 billion in value by 2025. “The market is growing very fast,” he said.

Dr. Rachid Yazami, founder of KVI and contributor to development of the lithium-ion battery

Short circuits in lithium-ion batteries are caused when a thin slip of polypropylene that keeps electrodes from touching is breached, so that the electrodes come in contact and generate heat and possibly fire.

“We don’t want to see vehicles and mobile phones catching fire,” Dr. Yazami said. “These things we have to address very seriously. A lot of progress has been made, especially in increasing the quality of the batteries.”

Additional challenges include: reducing charging time currently ranging from 1.5 to eight hours for an electric vehicle depending on the battery, to less than one hour and even 30 minutes; increasing the driving range from 250 km to 500 km (150 to 300 miles) currently, to 900 km (560 miles); and extending the battery service life, currently at about two years, to 10 years.

In its work to address these challenges, KVI has developed two kinds of chips, one that combines material science with AI to “manage the battery performance in smarter ways,” and the other that helps to manage a new protocol for fast charging. Eventually the chips may be combined, “to get all the advantages.”  Dr. Yazami will be looking for manufacturers to embed the chips in new batteries once they are available,  which he estimated would be in 12 to 18 months.  “We already have working prototypes for ultrafast battery chargers,” he said in response to a question from AI Trends.

The fast-charging chip can measure data on entropy, a property of a thermodynamic system, to assess the state of the battery and of its safety. “We can adapt the charging protocol according to the state of charge of the battery, Dr. Yazami said. “We use the AI to optimize the data processing and the protocols we use to charge the battery,” allowing for optimal charging, not more than the battery can take. The system continues to learn as the battery is used and is aging.

One of the biggest reasons for fires in lithium-ion batteries are internal short circuits, “which happen when the separator breaks or there is a hot spot and it melts,” Dr Yazami said. “That can trigger events that end in a thermal runaway, sometimes fire and explosions.” Detecting this thermal runaway at an early stage is very difficult.

“We have developed some technology to detect it at a very early stage,” he said. He has found a linear relationship between battery entropy, charted on the Y axis, and battery enthalpy, a thermodynamic quantity equal to the total heat content of the system, on the X axis. “We have found very specific cell voltages where we can trigger an alarm.” This allows the researchers to adjust the charging voltages based on the state of health of the battery, using AI to help. “We are developing the software to follow the battery as it is aging.”

The team has also developed a non-linear fast charging solution, in which current and voltage is not constant, but adapts to the battery.

“We have developed solutions based on AI and thermodynamics data to monitor the state of battery safety,” Dr. Yazami said.

Asked by another researcher how he measures entropy and enthalpy for state of charge (SoC) detection, Dr. Yazami said the linear relationship depends on the chemistry and state of health of the battery. ”The linear coefficients evolve as the battery ages. The AI enables prediction of the coefficients to determine the state of health of the battery,” he said.

Learn more about Dr. Rachid Yazami.

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