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Electric vehicles and the Future

Electric vehicles and the Future 

The popularity of electric vehicles has shot up over the last few years with a great shift in the public’s attitude towards electric vehicles and a constantly improving public recharging system. Electric cars and trucks are powered by electricity and are cleaner and cheaper to drive than conventional vehicles.  With a rapid increase in the use of personal vehicles around the world, the demand for fuel is also increasing. Transportation accounts for about one-fifth of global energy use, and passenger vehicles account for about ten percent of energy-related carbon dioxide emissions. In such a scenario, electric vehicles hold significant potential for increasing energy security, cutting emissions and improving local air quality.

Electric vehicles is a broad term that refers to a wide range of cars and other vehicles. However electric vehicles are of following types:

  1. Hybrid-electric vehicles are powered by an internal combustion engine system with an electric propulsion system to reduce fuel consumption and tailpipe emissions. These advanced vehicles cut fuel use and costs while maintaining performance, protecting public health and the environment.
  2. Plug-in hybrids are hybrids with high capacity batteries that can be charged by plugging them into an electrical outlet or charging station for short-range travel on battery power alone.
  3. Battery electric vehicle run entirely on battery power, utilizing chemical energy stored in rechargeable battery packs thereby producing zero tailpipe emissions. They are recharged from an electrical outlet.
  4. Fuel cell vehicles uses a fuel cell instead of an electrical battery. Fuel cell electric vehicles are powered by hydrogen. Unlike conventional vehicles which run on gasoline or diesel, fuel cell vehicles combine hydrogen and oxygen to produce electricity, which runs a motor.

Electric vehicles have several advantages over conventional vehicles like electric vehicles are energy efficient and environment friendly providing performance benefits and reduced energy dependence. However the market of electric vehicles is still developing, and there are many battery related challenges, particularly with technology integration, optimization, and scale-up.

Recent Patents filed in the electric vehicle market:

WO/2017/213838

Title: ALL ELECTRIC VEHICLE WITHOUT PLUG-IN REQUIREMENT

Assignee: WELSCHOFF, Heinz (1820 NE 56th Court, Fort Lauderdale, FL, FL, US)

Publication Date: 14 Dec 2017

Abstract:

An electric powered vehicle includes a battery pack capable of storing electric energy, A fuel engine operated with a clean fuel. A generator or alternator 126 having communication with the engine, and supply electric energy to the electric driving motor A starter of the fuel engine activates when a sensed charge of the battery pack falls to or below the DMF value. Included is a second 128 and third generator or alternator 130 in communication with the fuel engine during periods when no electrical communication exists between the EDM and the battery pack. The second and third generators maintain an electrical output to the battery pack until the battery packs are fully charged. Included is a rear generator or alternator 152 in communication with a rear drive shaft assembly, or differential, including a level orientation sensor and a rotational velocity sensor communication between an output of such communication enabled upon any downhill motion of the vehicle above a predetermined operational velocity determined by the velocity sensor. A second rear generator 156 is controlled by an accelerator pedal, rpm sensor, electric clutch and is activated when no pressure is applied by a driver upon the accelerator pedal, permitting charging of the battery pack by the second rear generator 156 only upon a condition of zero acceleration. A third rear generator 300 is activated by the brake pedal and brake pedal switch does charge the battery packs 102/104.

WO/2017/211793

Title: ELECTRIC POWERTRAIN, TRANSMISSION, AND VEHICLE

Assignee: PUNCH POWERTRAIN N.V. (Industriezone Schurhovenveld 4125, 3800 Sint-Truiden, 3800, BE)

Publication Date: 14 Dec 2017

Abstract:

The present disclosure relates to a powertrain (K) for driving a vehicle (C). The powertrain (K) comprises two powertrain modules (Y,Y’) comprising each a rotating machine (M,M’) and a transmission (T,T’). The transmissions (T,T’) are arranged to align their respective output shafts (S2,S2′) towards a common wheel axis (A1) for driving a pair of opposite wheels of the vehicle (C). The first rotating axis (A3), the second rotating axis (A3) and a central axis (A0) of the powertrain K are parallel and offset with respect to the each other and the first rotating axis (A3) and the second rotating axis (A3′) are mirror-symmetrically offset on opposite sides of the central axis (A0). In this way a compact and versatile design is achieved.

WO/2017/213850

Title: RESPONSE AMPLITUDE MODIFICATION FOR HYBRID ELECTRIC VEHICLE MISFIRE DETECTIONS

Assignee: FCA US LLC (1000 Chrysler Drive, Auburn Hills, Michigan, 48326, US)

Publication Date: 14 Nov 2017

Abstract:

Misfire detection techniques for a hybrid electric vehicle (HEV) including an internal combustion engine and an electric motor involve utilizing a crankshaft speed sensor configured to generate a crankshaft speed signal indicative of a rotational speed of a crankshaft of the engine that is coupled to the electric motor via a flywheel. The techniques also utilize a controller configured to control the electric motor to provide a vibrational response to dampen disturbances to the crankshaft, receive the crankshaft speed signal, selectively modify the crankshaft speed signal to obtain a modified crankshaft speed signal, and detect a misfire of the engine based on the modified crankshaft speed signal and a set of thresholds including at least one of a negative misfire threshold and a positive vibrational response threshold.

US 20170355272

Title: IN-VEHICLE STRUCTURE OF ELECTRIC-POWER CONVERTER

Assignee: TOYOTA JIDOSHA KABUSHIKI KAISHA (Toyota-shi, JP)

Publication Date: 14 Dec 2017

Abstract:

In an in-vehicle structure described in the present specification, an electric-power converter is fixed onto a transaxle and positioned in front of a cowl top. The electric-power converter includes a capacitor configured to restrain a high-frequency fluctuation in a voltage of electric power supplied from a battery, and a discharge circuit configured to discharge the capacitor. A connector (a signal connector) to which a wiring harness for communication of a discharge instruction signal to operate the discharge circuit at a time of a collision is connected is provided on a side face of the electric-power converter, the side face of the electric-power converter being facing in a vehicle width direction.

US 9843234

Title: Electric machine for a vehicle, in particular for a utility vehicle, and method for protection against ingress of water

Assignee: MAN Truck & Bus AG (Munich, DE)

Publication Date: 12 Dec 2017

Abstract:

An electric machine for a vehicle, in particular for a utility vehicle, includes a rotor mounted rotatably on a shaft and a stator surrounding the rotor, at least one device of the electric machine located in at least one pressure chamber which is subjected at least intermittently to an overpressure which is elevated with respect to the ambient pressure. The over pressure in at least one pressure chamber protects against ingress of water

US 9843187

Title: System and method for aggregating electric vehicle loads for demand response events

Assignee: Honda Motor Co., Ltd. (Tokyo, JP)

Publication Date: 12 Dec 2017

Abstract:

A computer-implemented method for aggregating electric vehicle loads for demand response events includes receiving a demand response (DR) event request from a utility system indicative of a DR event for an area. The DR event request includes at least one event parameter for participation in the DR event. The method includes determining a first original equipment manufacturer (OEM) DR event load for the area based on the DR event request and charging data received from electric vehicles associated with a first OEM. Upon determining the first original OEM DR event load does not meet the at least one event parameter, the method includes aggregating charging data from electric vehicles associated with a second OEM with the first OEM DR event load to determine an aggregated DR load for the area.

US 9840164

Title: Electric vehicle

Assignee: SUZUKI MOTOR CORPORATION (Hamamatsu-Shi, Shizuoka-Ken, JP)

Publication Date: 12 Dec 2017

Abstract:

An electric vehicle capable of efficiently and reliably cooling a power converter disposed inside an exterior. An electric vehicle includes a frame extending in a longitudinal direction, a power converter being long in the longitudinal direction along the frame, and an exterior extending in the longitudinal direction to cover the frame and the power converter, the exterior defining a cooling air path between the power converter to allow cooling air to flow through the cooling air path along the longitudinal direction. The power converter extends in the longitudinal direction in the cooling air path, and includes a plurality of heat radiation fins protruding toward an inner surface of the exterior, and the exterior includes an air induction port provided at a front end of the cooling air path to allow travelling wind to flow into the cooling air path.

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Deploy Artificial Intelligence & Machine learning

Deploy Artificial Intelligence & Machine learning in Medical Diagnosis

India is experiencing 22-25 % growth in medical tourism and the industry is expected to double its size from present (April 2017) US$ 3 billion to US$ 6 billion by 2018. Medical tourist arrivals in India increased more than 50 per cent to 200,000 in 2016 from 130,000 in 2015. Affordable medicines and good doctors is very lucrative to patients across the globe. Medical tourism is on rise and Indian healthcare industry operates in both private and public sectors.

Healthcare industry in India has come a long way in terms of advancement. The ultimate AIM of research and development in medical diagnostics is to be able to identify different diseases and diagnose them correctly.

Current Drawbacks

Despite ever-improving diagnostic technology, crucial time and valuable resources are lost everyday due to “Misdiagnosis” resulting in unnecessary tests, delayed treatment and present a threat to the health and life of the patients.

Artificial Intelligence and Machine learning are NEW AGE Technologies

 Machine Learning (ML) and Artificial Intelligence (AI) are transformative technologies in most areas of our lives.

Artificial Intelligence and Machine learning offers tremendous opportunities for the healthcare industry. The use of machine learning in identifying and diagnosing, diseases has actually been one of the biggest breakthroughs in the medical industry.

Intelligence is the ability to learn or the ability to think and reason and Artificial intelligence refers to programming computers and machines to exhibit seemingly intelligent behaviour based on software algorithms.

Today Machines are capable of analysing and interpreting medical scans with super-human performance are within reach. Deep learning, in particular, has emerged as a promising tool and is able to detect brain damage automatically.

WHAT is the Challenge?

How do we know when the machine gets it wrong?

Can we predict failure, and can we make the machine robust to changes in the clinical data?

Human brain is the greatest gift of god. NO machine and neural network can mimic the spiking of neurons just like human brain. The CURIOSITY of human mind to answer questions of HOW, WHAT, WHY and WHEN which is able to take the human mind to even radiate electromagnetic signals differentiates us from machines.

Computers can analyse massive amounts of data at a rapid speed and with higher accuracy as compared to humans. With the help of Computers and the algorithms they run scientists, physicians and medical practitioners or professionals can get better insight into patient’s health and condition and thus can make better decisions regarding the treatment.

Healthcare has become a key industry for investment in the field of Artificial Intelligence and Machine Learning based on its potential to improve health care system and save lives and money.

Some healthcare and technology innovators are collaborating and trying to change our current reality by experimenting with artificial intelligence (AI) and machine learning. Not only have major players such as IBM and Microsoft jumped into their own AI healthcare projects, but several start-ups and smaller organizations have begun their own efforts to create tools to aid healthcare.

And, the savings would be tremendous. One report from McKinsey estimates big data could save medicine and pharma up to $100B annually as a result of improved efficiencies in clinical trials and research, better insight for decision-making and new tools that will help insurers, regulators, physicians and consumers make better decisions.

Machine learning algorithms improve the more data they are exposed to.

If there is one thing the healthcare systems has in abundance, it’s data. Due to different storage systems, ownership and privacy concerns, and no established process that allows people to easily share data with each other, there is a major amount of analysis that’s not currently being done that could glean tremendous results for patients, doctors and healthcare organizations.

Artificial Intelligence (AI) aids in disease identification and diagnosis Much of the AI work done thus far in healthcare is focused on disease identification and diagnosis. From Sophia Genetics that is using AI to evaluate DNA to diagnose illnesses to smartphone apps that can determine a concussion and monitor other concerns such as newborn jaundice, lung function of those suffering from chronic respiratory diseases, blood pressure, hemoglobin levels and even evaluate coughs, disease and health monitoring is at the forefront of the machine learning efforts. Since heart disease is a primary killer of human beings around the world, it’s no surprise that effort and focus from many AI innovators is on heart disease diagnosis and prevention.

The current process to determine an individual’s risk factor for a heart attack is to look at the American College of Cardiology/American Heart Association’s (ACC/AHA) list of risk factors that include age, blood pressure and more. However, this is really a simplistic approach and doesn’t take into account medications someone might be on, the health of the patient’s other biological systems and other factors that could increase odds of a heart ailment. Several research teams, including those at Carnegie Mellon University and a study from Stephen Weng and his associates at University of Nottingham in the United Kingdom, are working toward enhancing machine learning so algorithms will be able to predict (better than humans) who is at risk and when they might be at risk for a heart attack. Preliminary results of the AI algorithms were significantly better at predicting heart attacks than the ACC/AHA guidelines. From liver disease to cancer and even psychosis and Schizophrenia, AI algorithms are changing the game in terms of disease diagnosis. Machines are now learning how to read CT scans and other imaging diagnostic tests to identify abnormalities. Although some predict the end of radiologists as we know them, others see AI acting as a radiologist’s assistant.

How Can we at TCIS, INDIA assist YOU with YOUR Artificial Intelligence & Machine learning Research?

Analysis of medical technologies is essential to development in modern medicine. With the increasing amount of patient data also known as BIG DATA, new challenges and opportunities arise for different phases of the clinical routine, such as diagnosis, treatment and monitoring.

WE at TCIS, INDIA focus on the patent and non-patent literature (NPL) analysis of technologies related to Artificial Intelligence & Machine learning Research. We use state of the art patent research techniques which is very helpful to scientists across the globe.

Technologies we have worked on in recent past:

  • Automatic delineation and measurement techniques
  • Monitoring disease progression
  • Computer-aided diagnosis,
  • Personalized medicine and
  • Efficient data management and big data analysis

Our mission at TCIS, INDIA is to advance the state of the prior art studies and freedom to operate analysis. I have personally researched more than 1000++ technologies over a span of 12++ years. Our team of patent geeks are enthusiastic about performing patent research.

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April 26, 2017, George was a client of Prity Khastgir IPR’S

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July 19, 2017, Sulabh was a client of Prity Khastgir

‘Ridiculously efficient’ is the phrase that comes to mind when I think about Prity Khastgir. It’s rare that you come across standout talent like her. We hired her for patent filing and her execution is flawless.

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