Energy


A Study of EV BMS Cyber Security Based on Neural Network SOC Prediction

arXiv.org Machine Learning

Recent changes to greenhouse gas emission policies are catalyzing the electric vehicle (EV) market making it readily accessible to consumers. While there are challenges that arise with dense deployment of EVs, one of the major future concerns is cyber security threat. In this paper, cyber security threats in the form of tampering with EV battery's State of Charge (SOC) was explored. A Back Propagation (BP) Neural Network (NN) was trained and tested based on experimental data to estimate SOC of battery under normal operation and cyber-attack scenarios. NeuralWare software was used to run scenarios. Different statistic metrics of the predicted values were compared against the actual values of the specific battery tested to measure the stability and accuracy of the proposed BP network under different operating conditions. The results showed that BP NN was able to capture and detect the false entries due to a cyber-attack on its network.


3 industries saving billions with cognitive machine learning

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Natural human flaws can have severe impacts on business with lasting damage – 82% of operational asset failures are attributed to human performance. Indeed, a recent study by ARC Advisory Group found that the global process industry loses up to $20 billion a year due to unscheduled downtime – or $12,500 hourly, on average. However, machine learning is helping eliminate these costly flaws and is helping transform the manufacturing industry. This technology, along with others like big data analytics, are able to predict if and when something will break – cancelling the possibility of costly downtime. See also: Anticipating downtime will be business' next competitive advantage Seth Page is a cognitive computing veteran and industrial IoT pioneer based in Washington DC, and is CEO and co-founder of DataRPM, a Progress company.


5 Innovative Uses for Machine Learning

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Though its time horizon can't be predicted, artificial intelligence (AI) promises to foundationally influence modern society, for better or worse. A sub-genre of AI -- machine learning -- has garnered particular attention from the pundits for its potential impact on the world's most important industries.


CES 2018 will have an extra focus on smart cities and the impact of IoT

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The Consumer Technology Association (CTA) and financial firm Deloitte have made a major investment at this year's Consumer Electronics Show to highlight smart cities technology and encourage attendees to explore the solutions presented by this emerging sector.


Intelligent IoT

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With a wave of investment, a raft of new products, and a rising tide of enterprise deployments, artificial intelligence is making a splash in the Internet of Things (IoT). Companies crafting an IoT strategy, evaluating a potential new IoT project, or seeking to get more value from an existing IoT deployment may want to explore a role for AI. Artificial intelligence is playing a growing role in IoT applications and deployments,12 a shift apparent in the behavior of companies operating in this area. Venture capital investments in IoT start-ups that are using AI are up sharply. Companies have acquired dozens of firms working at the intersection of AI and IoT in the last two years.


Drones and Robots Are Taking Over Industrial Inspection

MIT Technology Review

Avitas Systems, a GE subsidiary based in Boston, is now using drones and robots to automate the inspection of infrastructure such as pipelines, power lines, and transportation systems. The company is using off-the-shelf machine-learning technology from Nvidia (50 Smartest Companies 2017) to guide the checkups, and to automatically identify anomalies in the data collected. The effort shows how low-cost drones and robotic systems--combined with rapid advances in machine learning--are making it possible to automate whole sectors of low-skill work. While there is plenty of worry about the automation of jobs in manufacturing and offices, routine security and safety inspections may be one of the first big areas to be undermined by advances in AI. Drones have been used on some industrial sites for a while (see "New Boss on Construction Sites Is a Drone"), and various companies, such as Kespry, Flyability, and CyPhy, offer aerial systems for monitoring mines, inspecting wind turbines, and assessing building insurance claims.


Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins

arXiv.org Artificial Intelligence

Vehicle climate control systems aim to keep passengers thermally comfortable. However, current systems control temperature rather than thermal comfort and tend to be energy hungry, which is of particular concern when considering electric vehicles. This paper poses energy-efficient vehicle comfort control as a Markov Decision Process, which is then solved numerically using Sarsa({\lambda}) and an empirically validated, single-zone, 1D thermal model of the cabin. The resulting controller was tested in simulation using 200 randomly selected scenarios and found to exceed the performance of bang-bang, proportional, simple fuzzy logic, and commercial controllers with 23%, 43%, 40%, 56% increase, respectively. Compared to the next best performing controller, energy consumption is reduced by 13% while the proportion of time spent thermally comfortable is increased by 23%. These results indicate that this is a viable approach that promises to translate into substantial comfort and energy improvements in the car.


Charged EVs Online energy management for PHEVs claims 30% improvement in fuel efficiency

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Engineers at the University of California, Riverside (UCR) have developed a new online energy management system (EMS) that they say can improve PHEV fuel efficiency by more than 30%. In "Development and Evaluation of an Evolutionary Algorithm-Based Online Energy Management System for Plug-In Hybrid Electric Vehicles," published in IEEE Transactions on Intelligent Transportation Systems, Xuewei Qi and colleagues explain that improving the efficiency of current PHEVs is limited by shortfalls in their energy management systems (EMS), which control the power split between engine and battery. In general, existing EMS for PHEVs are either rule-based systems, which use a set of predefined rules, or optimization-based, which can adapt according to driving conditions and driver behavior. However, notes the UCR team, most systems have only limited adaptability to real-time information. "In reality, drivers may switch routes, traffic can be unpredictable, and road conditions may change, meaning that the EMS must source that information in real-time," Qi said.


GE Uses AI to Charge Electric Cars Without Running Up the Bill

AITopics Original Links

Optimistic government officials and automakers want to put millions of electric cars on American roads in the next decade. There are a lot of issues to be solved to make that happen (limited range, insufficient infrastructure, high costs, to name the big three), but if we ever get there, we'll be faced with a new problem: How to ensure the country's aging electrical grid can handle the added strain of charging millions of cars every day. We've got a while before that becomes a real issue, but it's something electric companies and their suppliers are already thinking about. It's a complicated problem, which is why General Electric, teaming up with Con Edison and researchers Columbia University's Center for Computational Learning, has picked out one element of the puzzle to address first: How to run EV chargers in New York City buildings without also running up a ginormous bill. It turns out that in NYC, very large buildings, which often include parking garages and EV chargers, are billed on both their total energy usage and their peak usage, the maximum amount of power used in a 15-minute window during a month.


Drive.ai puts a deep learning spin on self-driving technology

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You can add one more name to the constantly expanding list of companies that want a slice of that autonomous driving pie, as a new company named Drive.ai The new company, which also announced that it has added former General Motors Vice Chairman and Board Member Steve Girsky to its Board of Directors, is looking to put its stamp on the self-driving space with its own deep learning algorithms. These full stack deep learning algorithms, Drive.ai CEO Sameep Tandon says that the team at Drive.ai has been working on these deep learning applications since the company was founded in 2015. For now, the company says it will offer a retrofitted system that can be used in existing vehicle fleets.