Energy Storage


Bosch's Battery in the Cloud aims to reduce battery cell aging with AI

#artificialintelligence

AI running in the cloud might be the solution to electric vehicles' battery woes, if Bosch is on the right track. The Stuttgart, Germany-based company this morning announced a new service -- Battery in the Cloud -- designed to supplement vehicles' battery management systems by implementing protections to reduce cell aging. It's able to cut down on wear and tear by as much as 20%, the company claims, through continuous analysis of battery status, optimization of recharging processes, and delivery of energy conservation tips to drivers via in-car displays. The first customer is Beijing-based mobility giant DiDi Chuxing, which as of 2018 had 550 million users and tens of millions of drivers on its platform. Bosch says DiDi will equip a pilot vehicle fleet with its battery services in the city of Xiamen.


Scientists create an AI from a sheet of glass

#artificialintelligence

It turns out that you don't need a computer to create an artificial intelligence. In fact, you don't even need electricity. In an extraordinary bit of left-field research, scientists from the University of Wisconsin–Madison have found a way to create artificially intelligent glass that can recognize images without any need for sensors, circuits, or even a power source -- and it could one day save your phone's battery life. "We're always thinking about how we provide vision for machines in the future, and imagining application specific, mission-driven technologies," researcher Zongfu Yu said in a press release. In a proof-of-concept study published on Monday in the journal Photonics Research, the researchers describe how they made a sheet of "smart" glass that could identify handwritten digits.


Researchers make a robotic fish with a battery for blood

#artificialintelligence

Lots of experimental robots involve a little bit of cheating. Rather than containing all the necessary electronics and energy sources, they have tethers and wires that provide power and control without weighing the robot down or taking up too much internal space. This is especially true for soft-bodied robots, which typically pump air or fluids to drive their motion. Having to incorporate a power source, pumps, and a reservoir of gas or liquid would significantly increase the weight and complexity of the robot. A team from Cornell University has now demonstrated a clever twist that cuts down on the weight and density of all of this by figuring out how to get one of the materials to perform two functions.


Researchers make a robotic fish with a battery for blood

#artificialintelligence

Lots of experimental robots involve a little bit of cheating. Rather than containing all the necessary electronics and energy sources, they have tethers and wires that provide power and control without weighing the robot down or taking up too much internal space. This is especially true for soft-bodied robots, which typically pump air or fluids to drive their motion. Having to incorporate a power source, pumps, and a reservoir of gas or liquid would significantly increase the weight and complexity of the robot. A team from Cornell University has now demonstrated a clever twist that cuts down on the weight and density of all of this by figuring out how to get one of the materials to perform two functions.



DataLearner: A Data Mining and Knowledge Discovery Tool for Android Smartphones and Tablets

arXiv.org Machine Learning

Smartphones have become the ultimate 'personal' computer, yet despite this, general-purpose data-mining and knowledge discovery tools for mobile devices are surprisingly rare. DataLearner is a new data-mining application designed specifically for Android devices that imports the Weka data-mining engine and augments it with algorithms developed by Charles Sturt University. Moreover, DataLearner can be expanded with additional algorithms. Combined, DataLearner delivers 40 classification, clustering and association rule mining algorithms for model training and evaluation without need for cloud computing resources or network connectivity. It provides the same classification accuracy as PCs and laptops, while doing so with acceptable processing speed and consuming negligible battery life. With its ability to provide easy-to-use data-mining on a phone-size screen, DataLearner is a new portable, self-contained data-mining tool for remote, personalised and learning applications alike. DataLearner features four elements - this paper, the app available on Google Play, the GPL3-licensed source code on GitHub and a short video on YouTube.


Unsupervised Temporal Clustering to Monitor the Performance of Alternative Fueling Infrastructure

arXiv.org Machine Learning

Zero Emission Vehicles (ZEV) play an important role in the decarbonization of the transportation sector. For a wider adoption of ZEVs, providing a reliable infrastructure is critical. We present a machine learning approach that uses unsupervised temporal clustering algorithm along with survey analysis to determine infrastructure performance and reliability of alternative fuels. We illustrate this approach for the hydrogen fueling stations in California, but this can be generalized for other regions and fuels.


Toward Runtime-Throttleable Neural Networks

arXiv.org Machine Learning

As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded controllers. These platforms are often resource-constrained, especially in energy storage and power, but state-of-the-art NN architectures are designed with little regard for resource use. Existing techniques for reducing the resource footprint of NN models produce static models that occupy a single point in the trade-space between performance and resource use. This paper presents an approach to creating runtime-throttleable NNs that can adaptively balance performance and resource use in response to a control signal. Throttleable networks allow intelligent resource management, for example by allocating fewer resources in "easy" conditions or when battery power is low. We describe a generic formulation of throttling via block-level gating, apply it to create throttleable versions of several standard CNN architectures, and demonstrate that our approach allows smooth performance throttling over a wide range of operating points in image classification and object detection tasks, with only a small loss in peak accuracy.


Autonomous Management of Energy-Harvesting IoT Nodes Using Deep Reinforcement Learning

arXiv.org Artificial Intelligence

Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes by solving the problem of autonomous management in non-stationary, resource-constrained settings. We show that the state-of-the-art policy-gradient approaches to RL are appropriate for the IoT domain and that they outperform previous approaches. Due to the ability to model continuous observation and action spaces, as well as improved function approximation capability, the new approaches are able to solve harder problems, permitting reward functions that are better aligned with the actual application goals. We show such a reward function and use policy-gradient approaches to learn capable policies, leading to behavior more appropriate for IoT nodes with less manual design effort, increasing the level of autonomy in IoT.


US Navy tests underwater robots that recharge by eating fish faeces

New Scientist

Underwater robots could get their batteries recharged by munching the sea floor. A device created by the US Navy extracts electrical energy from layers of fish faeces and other organic matter to provide an endless source of power. All underwater devices have a fundamental limitation – battery life. They are incredibly useful for exploring and monitoring the depths, but once their power reserves start to run low there's no choice but to bring them to the surface or abandon them.