Category: Solar Power

Solar power is the conversion of the energy of the sun into electricity.

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Machine learning makes smarter batteries

Renewable energies, such as wind and solar energy are naturally intermittent. To balance their demand and supply, batteries of, for example, electric vehicles can be charged and act as an energy buffer for the power grid. Cars spend most of their time idle and could, at the same time, feed their electricity back into the grid. While this is still a dream of the future, commercialization of electric and hybrid vehicles is already creating a growing demand for long-lasting batteries, both for driving as well as grid buffering. Consequently, methods for evaluating the state of the battery will become increasingly important.

The long duration of battery health tests is a problem, hindering the rapid development of new batteries. Better battery life forcasting methods are therefore urgently needed but are extremely difficult to develop. Now, Severson and her colleagues report in the journal Nature Energy that machine learning can help to predict computer battery life by creating computer models. The published algorithms use data from early-stage charge and discharge cycles.

Normally, a figure of merit describes the health of a battery. It quantifies the ability of the battery to store energy relative to its original state. The health status is 100% when the battery is new and decreases with time. This is similar to the state of charge of a battery. Estimating the state of charge of a battery is, in turn, important to ensure safe and correct use. However, there is no consensus in the industry and science as to what exactly a battery’s health status is or how it should be determined.

The state of health of a battery reflects two signs of aging: progressive capacity decline and impedance increase (another measure of electrical resistance). Estimates of the state of charge of a battery must therefore take into account both the drop in capacity and the increase in impedance.

Lithium ion batteries, however, are complex systems in which both capacity fade and impedance increase are caused by multiple interacting processes. Most of these processes cannot be studied independently since they often occur in simultaneously. The state of health can therefore not be determined from a single direct measurement. Conventional health assessment methods include examining the interactions between the electrodes of a battery. Since such methods often intervene directly in the system “battery”, they make the battery useless, which is hardly desired.

A battery’s health status can also be determined in less invasive ways, for example using adaptive models and experimental techniques. Adaptive models learn from recorded battery performance data and adjust themselves. They are useful if system-specific battery information are not available. Such models are suitable for the diagnosis of aging processes. The main problem, however, is that they must be trained with experimental data before they can be used to determine the current capacity of a battery.

Experimental techniques are used to evaluate certain physical processes and failure mechanisms. This allows the rate of future capacity loss to be estimated. Unfortunately, these methods can not detect any intermittent errors. Alternative techniques use the rate of voltage or capacitance change (rather than raw voltage and current data). In order to accelerate the development of battery technology, further methods need to be found which can accurately predict the life of the batteries.

Severson and her colleagues have created a comprehensive data set that includes the performance data of 124 commercial lithium-ion batteries during their charge and discharge cycles. The authors used a variety of rapid charging conditions with identical discharge conditions. This method caused a change of the battery lives. The data covered a wide range of 150 to 2,300 cycles.

The researchers then used machine learning algorithms to analyze the data, creating models that can reliably predict battery life. After the first 100 cycles of each experimentally characterized battery their model already showed clear signs of a capacity fade. The best model could predict the lifetime of about 91% data sets studied in the study. Using the first five cycles, batteries could be classified into categories with short (<550 cycles) or long lifetimes.

The researchers’ work shows that data-driven modeling using machine learning allows forecasting the state of health of lithium-ion batteries. The models can identify aging processes that do not otherwise apparent in capacity data during early cycles. Accordingly, the new approach complements the previous predictive models. But at Frontis Energy, we also see the ability to combine generated data with models that predict the behavior of other complex dynamic systems.

(Photo: Wikipedia)

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Faster photoelectrical hydrogen

Achieving high current densities while maintaining high energy efficiency is one of the biggest challenges in improving photoelectrochemical devices. Higher current densities accelerate the production of hydrogen and other electrochemical fuels.

Now a compact, solar-powered, hydrogen-producing device has been developed that provides the fuel at record speed. In the journal Nature Energy, the researchers around Saurabh Tembhurne describe a concept that allows capturing concentrated solar radiation (up to 474 kW/m²) by thermal integration, mass transport optimization and better electronics between the photoabsorber and the electrocatalyst.

The research group of the Swiss Federal Institute of Technology in Lausanne (EPFL) calculated the maximum increase in theoretical efficiency. Then, they experimentally verified the calculated values ​​using a photoabsorber and an iridium-ruthenium oxide-platinum based electrocatalyst. The electrocatalyst reached a current density greater than 0.88 A/cm². The calculated conversion efficiency of solar energy into hydrogen was more than 15%. The system was stable under various conditions for more than two hours. Next, the researchers want to scale their system.

The produced hydrogen can be used in fuel cells for power generation, which is why the developed system is suitable for energy storage. The hydrogen-powered generation of electricity emits only pure water. However, the clean and fast production of hydrogen is still a challenge. In the photoelectric method, materials similar to those of solar modules were used. The electrolytes were based on water in the new system, although ammonia would also be conceivable. Sunlight reaching these materials triggers a reaction in which water is split into oxygen and hydrogen. So far, however, all photoelectric methods could not be used on an industrial scale.

2 H2O → 2 H2 + O2; ∆G°’ = +237 kJ/mol (H2)

The newly developed system absorbed more than 400 times the amount of solar energy that normally shines on a given area. The researchers used high-power lamps to provide the necessary “solar energy”. Existing solar systems concentrate solar energy to a similar degree with the help of mirrors or lenses. The waste heat is used to accelerate the reaction.

The team predicts that the test equipment, with a footprint of approximately 5 cm, can produce an estimated 47 liters of hydrogen gas in six hours of sunshine. This is the highest rate per area for such solar powered electrochemical systems. At Frontis Energy we hope to be able to test and offer this system soon.

(Photo: Wikipedia)

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Better heat exchangers for concentrated solar power

Solar thermal systems are a good example of the particle-wave dualism expressed in Planck’s constant h: E = hf. Where h is the Planck constant, f is the frequency of the light and E is the resulting energy. Thus, the higher the frequency of the light, the higher the amount of energy. Solar thermal metal collectors transform the energy of high-frequency light by generating them to an abundance of low-frequencies through Compton shifts. Glass or ceramic coatings with high visible and UV transmittance absorb the low frequency light generated by the metal because they effectively absorb infrared light (so-called heat blockers). The efficiency of the solar thermal system improves significantly with increasing size, which is also the biggest advantage of such systems compared to photovoltaic generators. One disadvantage, however, is the downstream transformation of heat into electricity with the help of heat exchangers and turbines − a problem not only in solar thermal systems.

To provide the hot gas (supercritical CO2) to the turbines, heat exchangers are necessary. These heat exchangers transfer the heat energy generated by a power plant to the working fluid in a heat engine (usually a steam turbine) that converts the heat into mechanical energy. Then, the mechanical energy is used to generate electricity. These heat exchangers are operated at ~800 Kelvin and could be more efficient if the temperature were at >1,000 Kelvin. The entire process of converting heat into electricity is called a power cycle and is a critical process in power generation by solar thermal plants. Obviously, heat exchangers are pivotal elements in this process.

Ceramics are a great material material for heat exchanger because they can withstand extreme temperature fluctuations. However, unlike metals, ceramics are not easy to shape. Relatively coarse shapes, in turn, are made quickly and easily. In contrast, metals can be easily formed and have a high mechanical strength. Metals and ceramics have been valued for centuries for their distinctive properties. For example, bronze and iron have good impact resistance and are so malleable that they have been made into complex shapes such as weapons and locks. Ceramics, like those used to make pottery, have been formed into simpler shapes. Their resistance to heat and corrosion made ceramics a valued material. A new composite of metal and ceramic (a so-called cermet) combines these properties in amazing ways. A research group led by Mario Caccia reported now in the prestigious journal Nature about a cermet with properties that makes it usable for heat exchangers in solar thermal systems.

The history of such composites goes back to the middle of the 20th century. The advent of jet engines has created a need for materials with high resistance to heat and oxidation. On top of that, they had to deal with rapid temperature changes. Their excellent mechanical strength, which often surpassed that of existing metals, was highly appreciated by the newly created aerospace industry. Not surprisingly, the US Air Force funded more research into the production of cermets. Cermets have since been developed for multiple applications, but in most cases have been used for small parts or surfaces. The newly released composite withstands extreme temperatures, high pressures and rapid temperature changes. It could increase the efficiency of heat exchangers in solar thermal systems by 20%.

To produce the composite, the authors first produced a precursor, which was subject to further processing, comparable to potting the unfired version of a clay pot. The authors compacted tungsten carbide powder into the approximate shape of the desired article (the heat exchanger) and heated it at 1,400 °C for 2 minutes to bond the parts together. They then further processed this porous preform to produce the desired final shape.

Next, the authors heated the preform in a chemically reducing atmosphere (a mixture of 4% hydrogen in argon) at 1,100 °C. At the same temperature, they immersed the preform in a tank of liquid zirconium and copper (Zr2Cu). Finally, the preform was removed by heating to 1,350 °C. In this process, the zirconium displaces the tungsten from the tungsten carbide, producing zirconium carbide (ZrC) as well as tungsten and copper. The liquid copper is displaced from the ZrC matrix as the material solidifies. The final object consists of ~58% ZrC ceramic and ~36% tungsten metal with small amounts of tungsten carbide and copper. The beauty of the method is that the porous preform is converted into a non-porous ZrC / tungsten composite of the same dimensions. The total volume change is about 1-2%.

The elegant manufacturing process is complemented by the robustness of the final product. At 800 °C, the ZrC / tungsten cermet conducts heat 2 to 3 times better than nickel based iron alloys. Such alloys are currently used in high-temperature heat exchangers. In addition to the improved thermal conductivity, the mechanical strength of the ZrC / tungsten composite is also higher than that of nickel alloys. The mechanical properties are not affected by temperatures of up to 800 ° C, even if the material has previously been subjected to heating, e.g. for cooling cycles between room temperature and 800 °C. In contrast, iron alloys, e.g. stainless steels, and nickel alloys loose at least 80% of their strength.

(Photo: Wikipedia)