Scientists develop method to convert discarded plastic water bottles into high-performance supercapacitors, potentially reducing pollution while advancing clean energy.

Recently, the selection of machine learning model based on only the data distribution without concerning the noise of the data. This study aims to distinguish, which models perform well under noisy data, and establish whether stacking machine learning models actually provide robustness to otherwise weak-to-noise models. The electrochemical data were tested with 12 standalone models and stacking model. This includes XGB, LGBM, RF, GB, ADA, NN, ELAS, LASS, RIDGE, SVM, KNN, DT, and the stacking model. It is found that linear models handle noise well with the average error of (slope) to 1.75 F g-1 up to error per 100% percent noise added; but it suffers from prediction accuracy due to having an average of 60.19 F g-1 estimated at minimal error at 0% noise added. Tree-based models fail in terms of noise handling (average slope is 55.24 F g-1 at 100% percent noise), but it can provide higher prediction accuracy (lowest error of 23.9 F g-1) than that of linear. To address the controversial between prediction accuracy and error handling, the stacking model was constructed, which is not only show high accuracy (intercept of 25.03 F g-1), but it also exhibits good noise handling (slope of 43.58 F g-1), making stacking models a relatively low risk and viable choice for beginner and experienced machine learning research in electrochemistry. Even though neural networks (NN) are gaining popularity in the electrochemistry field. However, this study presents that NN is not suitable for electrochemical data, and improper tuning resulting in a model that is susceptible to noise. Thus, STACK models should provide better benefits in that even with untuned base models, they can achieve an accurate and noise-tolerant model. Overall, this work provides insight into machine learning model selection for electrochemical data, which should aid the understanding of data science in chemistry context.

Electric city bus gains popularity in recent years for its low greenhouse gas emission, low noise level, etc. Different from a passenger car, the weight of a city bus varies significantly with different amounts of onboard passengers. After analyzing the importance of battery aging and passenger load effects on an optimal energy management strategy, this study introduces the passenger load prediction into the hybrid-electric city buses energy management problem, which is not well studied in the existing literature. The average model, Decision Tree, Gradient Boost Decision Tree, and Neural Networks models are compared in the passenger load prediction. The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability. Given the predicted passenger load, a dynamic programming algorithm determines the optimal power demand for supercapacitor and battery by optimizing the battery aging and energy usage leveraging cloud techniques. Then, rule extraction is conducted on dynamic programming results, and the rule is real-time loaded to the vehicle onboard controller to handle prediction errors and uncertainties. The proposed cloud-based Dynamic Programming and rule extraction framework with the passenger load prediction show 4% and 11% lower bus operating costs in off-peak and peak hours, respectively. The operating cost by the proposed framework is less than 1% of the dynamic programming with the true passenger load information.

Estimating the state of charge (SOC) of compound energy storage devices in the hybrid energy storage system (HESS) of electric vehicles (EVs) is vital in improving the performance of the EV. The complex and variable charging and discharging current of EVs makes an accurate SOC estimation a challenge. This paper proposes a novel deep learning-based SOC estimation method for lithium-ion battery-supercapacitor HESS EV based on the nonlinear autoregressive with exogenous inputs neural network (NARXNN). The NARXNN is utilized to capture and overcome the complex nonlinear behaviors of lithium-ion batteries and supercapacitors in EVs. The results show that the proposed method improved the SOC estimation accuracy by 91.5% on average with error values below 0.1% and reduced consumption time by 11.4%. Hence validating both the effectiveness and robustness of the proposed method.

Researchers at the Yonsei University of Seoul have developed a new type of robotic contact lens that can be recharged wirelessly and which could bring a wide variety of futuristic uses for contact lenses one step closer to reality. The new devices are built around a circular translucent antenna and super capacitor system that can receive continual power without needing to be plugged in to an external power source. These experimental new contact lenses will also be able to draw electricity without raising the temperature of the lens, eliminating a potential long-term cause of harm to wearers and the device itself. According to a report from Yonhap News Agency, because the lenses are completely self-enclosed they can be maintained with standard contact solutions without any risk of degradation. The team used soft contact lens material instead of rigid material to ensure the tools could be used in as wide a variety of circumstances as possible.

The Papago GoSafe S810 camera duo has more "safety" features than you can shake a stick at, including one I'd never even considered--stop sign recognition. It recognizes stop signs and pops the digital equivalent up on its display. Kind of fun, but as I'm wont to say: If you need this stuff, call a cab or wait for self-driving vehicles. Admonishment aside, the $170 S810 is more than just fancy features. It takes very, very good day and night video, and the rear camera, unlike some we've seen recently, actually captures enough detail to be useful.

You've probably got at least one on your person right now. They're built to fit into smartwatches and smaller things, and that small size hampers how well they can sense changes. Engineers in Florida have now come up with a new take on the accelerometer that is as much as 1 million times as sensitive as a typical smartphone accelerometer, and it maintains that sensitivity up to a car-crash-scale 100 gs. That combination of high sensitivity and large dynamic range in a cube that's just 3 millimeters on a side should make the new accelerometer particularly useful in things that move quickly in three-dimensions, such as military drones, microrobots, and self-guided projectiles, according its inventors. Ordinary MEMS accelerometers are made up of a moveable plate and a stationary plate, oriented perpendicular to each dimension measured.

If you're sick of high fuel prices, Carnegie Mellon is running a car conversion project that takes gasoline-powered Hondas and makes them all electric. The automaker showed off an electric Fit last year that can travel 100 miles per charge, but if you want a greener Civic, the CMU Robotics Institute's ChargeCar Electric Vehicle Conversion Project might be for you. Researchers such as Illah Nourbakhsh of the institute's Create Lab work with local mechanics in converting Civics. At an open house near CMU on March 25, they will unveil a 2002 Civic EX four-door sedan that's been electrified. The Civic's conventional powertrain has been replaced with a 35-horsepower electric motor and 33 lithium-iron-phosphate batteries. According to a CMU release, the car can drive more than 40 miles in mixed urban and highway driving, and has a top speed of more than 70 miles per hour (CMU would not specify an exact number).

With increasing numbers of electric and hybrid vehicles on the road, transportation presents a unique opportunity to leverage data-driven intelligence to realize large scale impact in energy use and emissions. Energy management in these vehicles is highly sensitive to upcoming power load on the vehicle, which is not considered in conventional reactive policies calculated at design time. Advancements in cheap sensing and computation have enabled on-board upcoming load predictions which can be used to optimize energy management. In this work, we propose and evaluate a novel, real-time optimization strategy that leverages predictions from prior data in a simulated hybrid battery-supercapacitor energy management task. We demonstrate a complete adaptive system that improves over the lifetime of the vehicle as more data is collected and prediction accuracy improves. Using thousands of miles of real-world data collected from both petrol and electric vehicles, we evaluate the performance of our optimization strategy with respect to our cost function. The system achieves performance within 10% of the optimal upper bound calculated using a priori knowledge of the upcoming loads. This performance implies improved battery thermal stability, efficiency, and longevity. Our strategy can be applied to optimize energy use in gas-electric hybrids, battery cooling in electric vehicles, and many other load-sensitive tasks in transportation.