Power/Performance Bits: July 10

Heating up EV batteries; adaptive sampling algorithm; co-designing antennas and electronics.


Heating up EV batteries
Researchers from Pennsylvania State University developed a self-heating battery that can charge rapidly in cold conditions, a step they hope could spread adoption of electric vehicles.

“Electric vehicles are popular on the west coast because the weather is conducive,” said Xiao-Guang Yang, assistant research professor in mechanical engineering, Penn State. “Once you move them to the east coast or Canada, then there is a tremendous issue. We demonstrated that the batteries can be rapidly charged independently of outside temperature.”

Lithium-ion batteries degrade when rapidly charged under 50 degrees Fahrenheit because, rather than the lithium ions smoothly integrating with the carbon anodes, the lithium deposits in spikes on the anode surface. This lithium plating reduces cell capacity, but also can cause electrical spikes and unsafe battery conditions. Currently, long, slow charging is the only way to avoid lithium plating under 50 degrees F.

Previously, the researchers developed a battery that could self-heat to avoid below-freezing power drain. Now, the same principle is being applied to batteries to allow 15-minute rapid charging at all temperatures, even as low as minus 45 degrees F.

A fast charging battery for all outside temperatures that rapidly heats up internally prior to charging battery materials. (Source: Chao-Yang Wang / Penn State)

The self-heating battery uses a thin nickel foil with one end attached to the negative terminal and the other extending outside the cell to create a third terminal. A temperature sensor attached to a switch causes electrons to flow through the nickel foil to complete the circuit when the temperature is below room temperature. This rapidly heats up the nickel foil through resistance heating and warms the inside of the battery. Once the battery’s internal temperature is above room temperature, the switch turns opens and the electric current flows into the battery to rapidly charge it.

“One unique feature of our cell is that it will do the heating and then switch to charging automatically,” said Chao-Yang Wang, professor of chemical engineering and of materials science and engineering at Penn State. “Also, the stations already out there do not have to be changed. Control of heating and charging is within the battery, not the chargers.”

The self-heating battery was able to withstand 4,500 cycles of 15-minute charging at 32 degrees F with a 20% capacity loss. This provides approximately 280,000 miles of driving and a lifetime of 12.5 years, longer than most warranties. A conventional battery tested under the same conditions lost 20% capacity in 50 charging cycles.

Adaptive sampling algorithm
Computer scientists at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) developed a new algorithm with the potential to exponentially speed up computation by dramatically reducing the number of parallel steps required to reach a solution.

The researchers looked at optimization problems, which find the best solution from all possible solutions, relying on sequential algorithms with a number of steps proportional to the size of the data. “These optimization problems have a diminishing returns property,” said Yaron Singer, Assistant Professor of Computer Science at SEAS. “As an algorithm progresses, its relative gain from each step becomes smaller and smaller.”

In contrast, this new algorithm samples a variety of directions in parallel. Based on that sample, the algorithm discards low-value directions from its search space and chooses the most valuable directions to progress towards a solution.

The team gives the example of movie recommendation, where rather than sequentially adding every movie with similar attributes, it samples a group of movies at random and discards dissimilar ones, adding batches until it has enough recommendations. They say this process of adaptive sampling is key to the algorithm’s ability to make the right decision at each step.

The black line shows the number of steps a traditional algorithm takes to solve a problem while the red line demonstrates the number of steps the new algorithm needs. (Source: Harvard SEAS)

“Traditional algorithms for this class of problem greedily add data to the solution while considering the entire dataset at every step,” said Eric Balkanski, graduate student at SEAS. “The strength of our algorithm is that in addition to adding data, it also selectively prunes data that will be ignored in future steps.”

In experiments, Singer and Balkanski demonstrated that their algorithm could sift through a data set which contained 1 million ratings from 6,000 users on 4,000 movies and recommend a personalized and diverse collection of movies for an individual user 20 times faster than the state-of-the-art.

The researchers also tested the algorithm on a taxi dispatch problem, where there are a certain number of taxis and the goal is to pick the best locations to cover the maximum number of potential customers. Using a data set of two million taxi trips from the New York City taxi and limousine commission, the adaptive-sampling algorithm found solutions 6 times faster.

“This gap would increase even more significantly on larger scale applications, such as clustering biological data, sponsored search auctions, or social media analytics,” said Balkanski.

“This research is a real breakthrough for large-scale discrete optimization,” said Andreas Krause, professor of Computer Science at ETH Zurich, who was not involved in the research. “One of the biggest challenges in machine learning is finding good, representative subsets of data from large collections of images or videos to train machine learning models. This research could identify those subsets quickly and have substantial practical impact on these large-scale data summarization problems.”

The team is continuing to work with practitioners on implementing the algorithm.

Co-designing antennas and electronics
Researchers at Georgia Tech propose a new co-design technique that allows for simultaneous optimization of antennas and electronics in millimeter wave transmitters. The co-design scheme allows fabrication of multiple transmitters and receivers on the same IC chip or the same package, potentially enabling multiple-input-multiple-output (MIMO) systems as well as boosting data rates and link diversity.

The team recently completed a recent proof-of-concept antenna-based outphasing transmitter.

“Our electronics and antenna were designed so that they can work together to achieve a unique on-antenna outphasing active load modulation capability that significantly enhances the efficiency of the entire transmitter,” said Hua Wang, an assistant professor in Georgia Tech’s School of Electrical and Computer Engineering. “This system could replace many types of transmitters in wireless mobile devices, base stations and infrastructure links in data centers.”

The new design is capable of maintaining a high-energy efficiency regardless whether the device is operating at its peak or average output power.

“We are combining the output power though a dual-feed loop antenna, and by doing so with our innovation in the antenna and electronics, we can substantially improve the energy efficiency,” said Wang. “The innovation in this particular design is to merge the antenna and electronics to achieve the so-called outphasing operation that dynamically modulates and optimizes the output voltages and currents of power transistors, so that the millimeter wave transmitter maintains a high energy efficiency both at the peak and average power.”

One of the packaged millimeter wave transmitters with antenna-electronics co-designed collaboratively by the Georgia Tech researchers. The ultra-miniaturized IC chip contains on-chip antenna and all the required electronics for millimeter wave signal generation and transmitting. Multiple IC chips can be tiled together to form a large array for 5G MIMO applications. (Source: Allison Carter, Georgia Tech)

The co-design also facilitates spectrum efficiency by allowing more complex modulation protocols, according to the team. That will enable transmission of a higher data rate within the fixed spectrum allocation that poses a significant challenge for 5G systems. “Within the same channel bandwidth, the proposed transmitter can transmit six to ten times higher data rate,” Wang said.

Multi-feed antennas are key to the work. “An antenna structure with multiple feeds allows us to use multiple electronics to drive the antenna concurrently. Different from conventional single-feed antennas, multi-feed antennas can serve not only as radiating elements, but they can also function as signal processing units that interface among multiple electronic circuits,” Wang explained. “This opens a completely new design paradigm to have different electronic circuits driving the antenna collectively with different but optimized signal conditions, achieving unprecedented energy efficiency, spectral efficiency and reconfigurability.”

The new designs have been implemented in 45nm CMOS SOI IC devices and flip-chip packaged on high-frequency laminate boards, where testing has confirmed a minimum two-fold increase in energy efficiency.

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