With Update On Flywheel Tech, Volvo Eyes 2020 Rollout

As reported by Motor Authority: About a year ago Motor Authority brought you the first details on a flywheel-based hybrid system Volvo is developing that could reduce fuel consumption by up to 25 percent. 

Volvo has since partnered with Flybrid Automotive, part of transmission specialist Torotrak, to further develop the technology and eventually bring it to production.

The way the system works is that whenever a driver hits the brakes, such as during the approach to a red light, kinetic energy that would otherwise be lost as heat is transferred from the wheels to a Kinetic Energy Recovery System (KERS) mounted to the axle not driven by the engine. 

The kinetic energy spools up a flywheel inside the KERS, in essence storing the energy for as much as 20 minutes before it begins to disperse. When the driver hits the gas pedal, the stored energy is transferred back to the wheels via a specially designed transmission, and can either boost power or reduce load on the engine.

To maximize efficiency, the flywheel is made out of carbon fiber and weighs just over 13 pounds. It’s contained within a vacuum and spins at up to 60,000 rpm. The system is designed so that the engine is also switched off as soon as braking begins. 

The energy in the flywheel can then be used to accelerate the vehicle when it is time to move off again--even without the engine. Volvo says the KERS can deliver as much as 80 horsepower. As you may have guessed, the system would be most efficient during stop-start city driving.

With the pedal floored, drivers should experience boost for around 10 seconds. And since conventional brakes develop such a huge amount of energy, which is normally wasted, even gentle braking for eight seconds will fully recharge the KERS. That’s much quicker than what a conventional electric hybrid needs to charge up its batteries, and the flywheel-based system has the added benefit that it is cheaper to produce and maintain. It's also a lot lighter: the prototype KERS weighs only around 130 pounds.

A 254-horsepower S60 T5 prototype Volvo is using to test the system can accelerate from 0-62 mph in just 5.5 seconds, which is about 1.5 seconds quicker than the regular S60 T5. Better still, the KERS creates a part-time ‘through-the-road’ all-wheel-drive system to add extra traction and stability under acceleration since its attached to the axle not driven by the engine.

So when might we see in production? A Volvo engineer told Autocar that “some form of KERS” would be inevitable on production cars after 2020.

Survey: More Than 1 In 4 Car Crashes Involve Cellphone Use

As reported by CBS NY: Texting and driving is dangerous but a new survey finds talking on a cellphone while behind the wheel may be even worse.

As WCBS 880′s Paul Murnane reported from Stamford, the National Safety Council’s annual report found 26 percent of all crashes are tied to phone use, but noted just 5 percent involved texting.

Safety advocates are lobbying now for a total ban on driver phone use, pointing to studies that headsets do not reduce driver distraction.

Some motorists said they support the idea.

“Everybody’s on a telephone. If people do cut you off, you look and they’re talking on the telephone. I think they are a problem.” a driver told Murnane. “Hands-free or not.”

“People just get too involved in the conversation. Either pull over or wait,” said another man.

The survey found a 1 percent increase in cellphone-involved accidents compared with the previous year.

A spokesperson for the non-profit Governors Highway Safety Association told Marketwatch.com that it may be that drivers are more comfortable calling than texting in a moving vehicle.

The group believes the data on distracted crashes is underreported.

Taxis 2.0: Streamlining City Transport With Graph Theory

Big city taxi systems could be 40% more efficient with device
enabled taxi sharing.

As reported by the Medium: Everything today is about information and algorithms for processing it. Think of what Google’s PageRank algorithm did for web search, transforming an impenetrable jungle of web pages into an easy-to-use and hugely powerful information resource. That was then, in the late 1990s. Now think taxis.

In New York City, people take more than 100 million taxi trips every year, as individual parties hail cabs or book them by phone to suit their own needs. Taxis, as a result, criss-cross the city in a tangle of disorganized mayhem. Cabs run in parallel up and down Madison Avenue, often carrying isolated people along the same path. Those people could share a cab, yet lack a mechanism to achieve that coordination. But that mechanism might soon exist, and it could make taxi transport everywhere a lot more efficient.

That’s the message of some fascinating new research by a group of network theorists (the paper is unpublished, currently under review at a journal). It’s fairly mathematical, and relies on some technical results from graph theory (as did Google’s PageRank algorithm), but the basic insight from which it starts is quite simple: a good fraction of the trips that taxis take in a city overlap, at least partially, and so present opportunities for people to share cabs. Using real data from NYC, they’ve shown that a simple algorithm can calculate which rides could easily be shared by two parties without causing either much delay. In principle, the algorithm could be exploited by smart phones to help people organize themselves — it could make NYC taxi system 40% more efficient, reducing miles traveled, pollution, and costs.

7 days of taxi traffic history.

A little more detail: Imagine that you label every taxi ride by its origin and destination, plus departure and arrival time. Represent each such ride by a point on some very big page. For NYC in 2011, there were some 150 million rides starting or ending in Manhattan, so imagine a page with 150 million points on it, each labelled by the above data. These points, in effect, show you all the taxi rides that took place to get people in NYC to the places they wanted to go. 

What Santi and colleagues do is to ask whether some of these rides might have been “shareable,” in the sense that they actually traveled along parallel routes (or between the same points, along different routes) at close to the same time. If so, then people given the right knowledge could have shared a portion of the trip.

This huge collection of points becomes a mathematical graph once you begin linking together the points for any pair of rides that are “shareable.” By studying the properties of this graph, the researchers show that if people were willing to be delayed by up to ten minutes on their journeys, then there are roughly 100 billion pairs of trips that were shareable. If people are more choosy — unwilling to accept more than a five minutes delay — then fewer rides become shareable, but still enough to reduce the total miles driven by taxis by 40%. That 40% might be slightly optimistic, they note, given the constraints on any network system attempting to process this information in real time (and thereby having less than perfect knowledge of the whole set of taxi journeys).

The point is that people make their decisions about taxis in an information vacuum, knowing only what they require themselves, and nothing about all the other taxi needs of others around them. Information vacuum isn't good. How many times have you needed a taxi and waited as ten of them zipped by, all traveling in your direction, with each one carrying just one or two passengers? For a few minute delay, many of those people might have been wiling to save some money by sharing part of the ride. To make this happen requires information and algorithms to sift through it, plus a means for sharing this information with everyone who wants it.

All of which may be a reality soon. For more detail on this work, see the website of the HubCab project.

Galileo Conjures Up Improbable GPS Cutoff Scenario

As reported by Space News: The European Commission’s argument that its Galileo satellite positioning, navigation and timing program is a hedge against the day when the U.S. government arbitrarily shuts off GPS — for whatever reason — has been a driving political motivation for Galileo since the project’s beginning in the mid-1990s.

So has the idea that GPS, which is funded mainly by the U.S. Defense Department, should be seen as inherently unreliable for non-military users compared to compared to Galileo, which is 100 percent financed by civil authorities.

U.S. government officials — military and civil — have gone hoarse over the years explaining that GPS has been formally declared a dual-use system overseen by a civil-military board. The infrastructure, often described as a global utility, generates thousands of jobs and billions in annual commercial revenue and underpins the global financial system in addition to being the default positioning and navigation service for the NATO alliance.

A scenario in which GPS would be simply shut down — outside of limited-area jamming during a war — is inconceivable, they say. Despite these assurances, and perhaps because of Galileo’s unstable financial history, the commission continues to wave the GPS-shutoff-threat shibboleth.

Here is an example of it from the commission’s “Why we need Galileo” brochure:
“How secure is your security?



“From the beginning, the American GPS system has been aimed at providing a key strategic advantage to the U.S. and allied military troops on the battlefield. Today, the free GPS signal is also used around the world by security forces such as the police.

“Stieg Hansen is a retired military officer from Malmo now representing a large producer of security systems. Today he is speaking to a group of people at an important trade show. Behind him, a bold sign reads, ‘GPS for Security.’ His audience includes a number of stern men and women, and one person who looks like a journalist.

“‘The Stryker, as we like to call it in the field, is the hand-held GPS receiver for domestic security.’ Brandishing a notebook-sized electronic device, he continues: ‘This little baby has all the hardware you will ever need to locate, mobilize and coordinate your security team, wherever they may be.’
“Someone in the audience calls out: ‘What if GPS gets cut off?’

“Hansen hesitates, does not look at the person asking the question, then continues: ‘Most European governments have placed restrictions on the sale and use of this little baby, due to the powerful electronics inside. Very robust, very difficult to jam.’

“‘What if the little baby can’t get GPS?’

“‘This little bab…,’ Hansen stops short before finishing the word. ‘This device’s primary mission is to provide positioning support, velocity, navigation and timing to all land-based security operations, including police forces in pursuit of criminals or transporting dangerous prisoners, border guards in anti-smuggling operations and…’

“‘He’s not answering the question,’ someone murmurs. Other members of the audience are now looking at each other. One person says to his neighbor, ‘That’s right. What if GPS stops working?’ Hansen takes a step backwards.”

The pamphlet ends by saying: “The stories presented in this brochure are fictitious. Any resemblance to real events or persons is purely coincidental.”

Using Adaptive Notch Filters To Reduce The Effects Of GPS Jamming

As reported by Inside GNSS: GNSS jammers are small portable devices able to broadcast powerful disruptive signals in the GNSS bands. A jammer can overpower the much weaker GNSS signals and disrupt GNSS-based services in a geographical area with a radius of several kilometers. Despite the fact that the use of such devices is illegal in most countries, jammers can be easily purchased on the Internet and their rapid diffusion is becoming a serious threat to satellite navigation.

Several studies have analyzed the characteristics of the signals emitted by GNSS jammers. From the analyses, it emerges that jamming signals are usually characterized by linear frequency modulations: the instantaneous frequency of the signal sweeps a range of several megahertz in a few microseconds, affecting the entire GNSS band targeted by the device.

The fast variations of their instantaneous frequency make the design of mitigation techniques particularly challenging. Mitigation algorithms must track fast frequency variations and filter out the jamming signals without introducing significant distortions on the useful GNSS components. The design problem becomes even more challenging if only limited computational resources are available.

We have analyzed the ability of an adaptive notch filter to track fast frequency variations and mitigate a jamming signal. In this article, we begin by briefly describing the structure of the selected adaptive notch filter along with the adaptive criterion used to adjust the frequency of the filter notch.

When the adaptation parameters are properly selected, the notch filter can track the jamming signals and significantly extend the ability of a GNSS receiver to operate in the presence of jamming. Moreover, the frequency of the filter notch is an estimate of the instantaneous frequency of the jamming signal. Such information can be used to determine specific features of the jamming signal, which, in turn, can be used for jammer location using a time difference of arrival (TDOA) approach.

The capabilities of the notch filter are experimentally analyzed through   a series of experiments performed in a large anechoic chamber. The experiments employ a hardware simulator to broadcast GPS and Galileo signals and a real jammer to disrupt GNSS operations. The GNSS and interfering signals were recorded using an RF signal analyzer and analyzed in post-processing. We processed the collected samples using the selected adaptive notch filter and a custom GNSS software receiver developed in-house.

The use of mitigation techniques, such as notch filtering, significantly improves the performance of GNSS receivers, even in the presence of strong and fast-varying jamming signals. The presence of a pilot tone in the Galileo E1 signal enables pure phase-locked loop (PLL) tracking and makes the processing of Galileo signals more robust to jamming.

Adaptive Notch Filter
Several interference mitigation techniques have been described in the technical literature and are generally based on the interference cancellation principle. These techniques attempt to estimate the interference signal, which is subsequently removed from the input samples. For example, transform domain excision techniques at first project the input signal onto a domain where the inference signal assumes a sparse representation. (See the articles by J. Young et alia and M. Paonni et alia, referenced in the Additional Resources section near the end of this article.) The interference signal is then estimated from the most powerful coefficients of the transformed domain representation. The interfering signal is removed in the transformed domain, and the original signal representation is restored.

When the interfering signal is narrow band, discrete Fourier transform (DFT)-based frequency excision algorithms, described in the article by J. Young and J. Lehnert, are particularly effective. Transform domain excision techniques are, however, computationally demanding, and other mitigation approaches have been explored. For example, notch filters are particularly effective for removing continuous wave interference (CWI). M. Paonni et alia, cited in Additional Resources, considered the use of a digital notch filter for removing CWI, the center frequency of which was estimated using the fast Fourier transform (FFT) algorithm. Despite the efficiency of the FFT algorithm, this approach can result in a significant computational burden and alternative solutions should be considered.

The article by M. Jones described a finite impulse response (FIR) notch filter for removing unwanted CW components and highlighted the limitations of this type of filter. Thus, we adopted an infinite impulse response (IIR) structure and experimentally demonstrated its suitability for interference removal. In particular we considered the adaptive notch filter described in the article by D. Borio et alia listed in Additional Resources and investigated its suitability for mitigating the impact of a jamming signal.

This technique has been selected for its reduced computational requirements and for its good performance in the presence of CWI. Note that the notch filter under consideration has been extensively tested in the presence of CWI; however, its performance in the presence of frequency-modulated signals has not been assessed. Also, note that removing a jamming signal poses several challenges that derive from the swept nature of this type of interference. (For details, see the paper by R. H. Mitch et alia.)


Jamming signals are usually frequency modulated with a fast-varying center frequency. The time-frequency evolution of the signal transmitted by an in-car GPS jammer is provided as an example in Figure 1. The instantaneous center frequency of the jamming signal sweeps a frequency range of more than 10 megahertz in less than 10 microseconds. The adaptation criterion selected for estimating the center frequency of the jamming signal has to be sufficiently fast to track these frequency variations.

The notch filter considered in this work is characterized by the following transfer function (illustrated on the opening page of this article) 

Equation 1 (for equations see inset photo, above right)
where k_α is the pole contraction factor and z₀[n] is the filter zero. k_α controls the width of the notch introduced by the filter, whereas z₀[n] determines the notch center frequency. Note that z₀[n] is progressively adapted using a stochastic gradient approach described in the textbook by S. Haykin with the goal of minimizing the energy at the output of the filter. A thorough description of the adaptation algorithm can be found in the article by D. Borio et alia.

The notch filter is able to place a deep null in correspondence with the instantaneous frequency of narrow band interference and, if the zero adaptation parameters are properly chosen, to track the interference frequency variations. The energy of the filter output is minimized when the filter zero is placed in correspondence with the jammer instantaneous frequency 

Equation 2
where Φ(nT_s) is the jammer instantaneous frequency and f_s = 1/T_s is the sampling frequency.

This implies that z₀[n] can be used to estimate the instantaneous frequency of the interfering signal. The magnitude of z₀[n] also strongly depends on the amplitude of the interfering signal. Indeed, |z₀[n]| approaches one as the amplitude of the jamming signal increases. Thus, |z₀[n]| can be used to detect the presence of interference, and the notch filter activates only if |z₀[n]| passes a predefined threshold, T_z. A value of T_z= 0.75 was empirically selected for the tests described in the following section.

Experimental Setup and Testing
To test the capability of the adaptive notch filter to mitigate against a typical in-car jammer, we conducted several experiments in a large anechoic chamber at the Joint Research Centre (JRC) of the European Commission.


Figure 2 provides a view of the JRC anechoic chamber where the jamming tests were conducted. The anechoic chamber offers a completely controlled environment in which all sources of interference besides the jammer under test can be eliminated.

The experimental setup is similar to that employed to test the impact of LightSquared signals on GPS receivers (For details, see the article by P. Boulton et alia listed in Additional Resources). We used a simulator to provide a controlled GPS and Galileo constellation, with a static receiver operating under nominal open-sky conditions. The GNSS signals were broadcast from a right hand circular polarization (RHCP) antenna mounted on a movable sled on the ceiling of the chamber. A survey grade GNSS antenna was mounted inside the chamber, and the sled was positioned at a distance of approximately 10 meters from this antenna. The GNSS receiving antenna was connected via a splitter to a spectrum analyzer, an RF signal analyzer, and a commercial high sensitivity GPS receiver. Table 1 (see inset photo, above right) lists the RF signal analyzer parameters.

To provide the source of jamming signals a commercially available (though illegal) in-car jammer was connected to a programmable power supply. We removed the jammer’s antenna and connected the antenna port, via a programmable attenuator with up to 81 decibels of attenuation, to a calibrated standard gain horn antenna. This gain horn was positioned at approximately two meters from the GNSS receiving antenna.

The goal of this configuration was to permit variation of the total jammer power received at the antenna.
Unfortunately, the jammer itself is very poorly shielded; so, a significant amount of the interfering power seen by the receiver was found to come directly from the body of the jammer, rather than through the antenna.
To minimize this effect, we exercised great care to shield the jammer as much as possible from the GNSS antenna. We placed the jammer body in an aluminum box, which was subsequently surrounded by RF absorbent material. The jammer body and the receiving GNSS antenna were separated by approximately 15 meters, thereby ensuring approximately 60 decibels of free space path loss.

The experiment was controlled via a PXI controller, which generated synchronous triggers for the RF data collection and simulator signal generation, controlled the power supplied to the jammer, and updated the attenuation settings according to a desired profile. All events (trigger generation, jammer power on/off, attenuation setting) were time stamped using an on-board timing module. The commercial receiver was configured to log raw GPS measurements including carrier-to-noise (C/N₀) values.

The experimental procedure involved two trials, each lasting approximately 40 minutes. In the first trial, the simulator and data collection equipment were both enabled, but the jammer remained powered off. In the second trial, the same scenario was generated in the simulator, the data collection equipment was enabled and, after a period of three minutes, the jammer was powered on.

We initially set the attenuation to its maximum value of 81 decibels. We subsequently reduced this in two-decibel decrements to a minimum value of 45 decibels. We maintained each level for a period of 60 seconds. Finally, we again increased the attenuation in two-decibel increments to its maximum value. Figure 3 presents this attenuation profile.

We performed a calibration procedure whereby the total received jammer power at the output of the active GNSS receiving antenna was measured using a calibrated spectrum analyzer while the attenuation level was varied. Further, the total noise power was measured in the same 12-megahertz bandwidth with the jammer switched off. This permitted the computation of the received jammer-to-noise density power ratio (J/N₀) as a function of the attenuator setting.

Figure 3 also shows the calibrated J/N₀ at the output of the active GNSS antenna as a function of time. The analysis provided in the next section is conducted as a function of the J/N₀.

Sample Results
This section provides sample results obtained using the adaptive notch filter described earlier. In particular, the loss in C/N₀ experienced by the GPS and Galileo software receivers used for analysis is experimentally determined as a function of the J/N₀.

The adaptive notch filter is used to reduce the C/N₀ loss. Figure 4 shows the loss in C/N₀ experienced in the presence of the jammer as a function of J/N₀. The first curve arises from software receiver processing of the GPS signals, the second plot from software receiver processing of the Galileo signals, and the third from the commercial high sensitivity receiver that processed only the GPS signals.

Note the small difference between the GPS and Galileo results. This is to be expected due to the wideband nature of the jammer. In fact, for both GPS and Galileo processing the jammer is effectively averaged over many chirp periods, thereby giving it the appearance of a broadband (white) noise source. The one difference between the GPS and Galileo signals is that the tracking threshold of the Galileo signals is approximately six decibels lower than that for the GPS signals. This is due to the use of a pure PLL processing strategy using only the E1C (pilot) component of the Galileo signal.

The other interesting point to note from Figure 4 is that the commercial receiver exhibits better resilience against the jammer. This is most likely due to a narrower front-end bandwidth in the commercial receiver, although this cannot be confirmed because the receiver manufacturer does not provide this information.

From the time-frequency evolution of the jamming signal used for the experiment and shown in Figure 1, it emerges that the bandwidth of the jamming component is approximately 10 megahertz. If the commercial receiver had a smaller bandwidth, then it would effectively filter out some of the jammer power, thereby improving its performance with respect to the software receiver results.

Figure 4 provides an indication of the performance degradation caused by a jamming signal when no mitigation technique is employed. The notch filter is expected to improve the receiver performance. The improvement depends on the filter parameters and their ability to track the jammer’s rapid frequency variation.

Two configurations of the adaptive notch filter were tested: k_α = 0.8 and k_α = 0.9. The first case has a smaller contraction factor and, hence, a wider notch than the latter.

The adaptive step size of the stochastic gradient algorithm was tuned for the jammer under consideration. (The adaptation of the filter zero must be fast to track the frequency variations of the jammer’s chirp signal.) In each case the magnitude of the zero of the notch filter was used as a detector for interference. We chose a threshold of 0.75 so that when the amplitude of the zero was greater than this threshold, the notch filter was enabled and the receiver processed this filtered data. Otherwise the receiver processed the raw data collected from the antenna.

Figure 5 and Figure 6 illustrate the results of the filtering for the two cases. In these plots, the upper portion shows the time evolution of the frequency content of the raw data, with the frequency estimate of the notch filter superimposed as a dashed red line. The lower plots show the time evolution of the frequency content of the filtered data. From these lower plots the wider notch appears to do a better job of removing the jammer signal. On the other hand, this will also result in a greater reduction of the useful signal power.

The effect of the notch filter on the reception of GNSS signals in terms of the C/N₀ degradation is illustrated in Figure 7 and Figure 8 for Galileo and GPS signals, respectively. Again, the difference between the impact on GPS and Galileo signals is slight, due to the wideband nature of the interferer. On the other hand, the benefit of the notch filter is clear in both figures. The sidebar, “Track the Jamming Signal,” (at the end of this article) provides access to data and tools with which readers can test different configurations of the notch filters themselves.

Interestingly, it appears that two limiting curves exist, one for the case of no filtering and one for the case where a notch filter is applied. The variation in the contraction factor (over the range considered) has little effect on the C/N₀ effectively measured by the GPS and Galileo software receivers.

The separation between the two curves is approximately five decibels, i.e., the receiver that applies the notch filter experiences approximately five decibels less C/N₀ loss than an unprotected receiver for the same J/N₀. Of course, we must remember that this result applies for the data collection system considered in this test, which consists of a 14-bit analog-to-digital converter (ADC) with no automatic gain control (AGC). In commercially available receivers with a limited number of bits for signal quantization the non-linear losses due to the combination of these two front-end components will likely lead to additional losses.

Conclusion
We have proposed an IIR adaptive notch filter as an easy means to implement mitigation technique for chirp signals typical of the type of commercially available jammers that have become ever more present in recent years. A simple stochastic gradient adaptation algorithm was implemented, with an associated simple interference detection scheme. Our analysis showed that, for a receiver with sufficient dynamic range, the proposed technique leads to an improvement of approximately five decibels in terms of effective C/N₀.
We tested the proposed scheme on data collected from a low-cost commercial jammer in a large anechoic chamber. We used a software receiver to process both GPS and Galileo signals. The broadband nature of the chirp signal means that its effect on GNSS signal processing is similar to an increase in the thermal noise floor. Hence, the impact is very similar on both GPS and Galileo receivers. On the other hand, the chirp signal is instantaneously narrowband, a feature that is exploited by the use of a notch filter with a highly dynamic response to variations in the frequency of the interferer.

Acknowledgment
This study is mainly based on the paper “GNSS Jammers: Effects and Countermeasures” presented by the authors at the Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing, (NAVITEC), December 2012.

Additional Resources
[1] Borio, D., Camoriano, L., and Lo Presti, L., “Two-pole and Multi-pole Notch Filters: A Computationally Effective Solution for GNSS Interference Detection and Mitigation,” IEEE Systems Journal, Vol. 2, No. 1, pp. 38–47, March 2008
[2] Boulton, P., Borsato, R., and Judge, K., “GPS Interference Testing, Lab, Live, and LightSquared,” Inside GNSS, pp. 32-45, July/August 2011
[3] Haykin, S., Adaptive Filter Theory, 4th ed., Prentice Hall, September 2001
[4] Jones, M., “The Civilian Battlefield, Protecting GNSS Receivers from Interference and Jamming,” Inside GNSS, pp. 40-49, March/April 2011
[5] Mitch, R. H., Dougherty, R. C., Psiaki, M. L., Powell, S. P., O’Hanlon, B. W., Bhatti, J. A., and Humphreys, T. E., “Signal Characteristics of Civil GPS Jammers,” Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011), Portland, OR, pp. 1907–1919, September 2011
[6] Paonni, M., Jang, J., Eissfeller, B., Wallner, S., Avila-Rodriguez, J. A., Samson, J., and Amarillo- Fernandez, F., “Wavelets and Notch Filtering, Innovative Techniques for Mitigating RF Interference,” Inside GNSS, pp. 54 – 62, January/February 2011
[7] Young, J. and Lehnert, J., “Analysis of DFTbased Frequency Excision Algorithms for Direct Sequence Spread-Spectrum Communications,” IEEE Transactions on Communications, Vol. 46, No. 8, pp. 1076 –1087, August 1998 

Could Future Gasoline Engines Emit Less CO2 Than Electric Cars?

As reported by Green Car Reports: Is it possible to make a gasoline engine so efficient that it would emit less carbon dioxide per mile than is created by generating electricity to run an electric car over that same mile?

Small Japanese carmaker Mazda says yes.

In an interview published last week with the British magazine Autocar, Mazda claimed that its next generation of SkyActiv engines will be so fuel-efficient that they'll be cleaner to run than electriccars.

That's possible. But as always, the devil is in the details.

Specifically, total emissions of carbon dioxide (CO2) in each case depend on both the test cycles used to determine the cars' emissions and the cleanliness of the electric generating plants used to make the electricity.

In the U.S., the "wells-to-wheels" emissions from running a plug-in electric car 1 mile on even the dirtiest grids in the nation (North Dakota and West Virginia, which burn coal to produce more than 90 percent of their power) equate to those from the best non-hybrid gasoline cars: 35 miles per gallon or more.

The U.S. average for MPG equivalency is far higher, however, and it's roughly three times as high--near 100 mpg--for California, the state expected to buy as many plug-in cars as the next five states combined.

In Europe, however, 35 mpg is a perfectly realistic real-world fuel efficiency for small diesel cars (generally compacts and below). And their official ratings are often higher still.

European test cycles for measuring vehicle emissions (which translate directly to fuel efficiency) are gentler than the adjusted numbers used in the U.S. by the EPA to provide gas-mileage ratings.

On the generation side, some European countries use coal to produce a large proportion of their national electricity. (Some also buy their natural gas from Russia, a supplier that may appear more problematic today than in years past.)

So if Mazda can increase the fuel economy of its next-generation SkyActiv engines by 30 percent in real-world use, as it claims, it's possible that its engines might reach levels approaching 50 mpg or more--without adding pricey hybrid systems.

And those levels would likely be better than the wells-to-wheels carbon profile of an electric car running in a coal-heavy country--Poland, for example.

Mazda will raise its current compression ratio of 14:1 to as much as 18:1 and add elements of homogeneous charge-compression ignition (HCCI) to its new engines.

The HCCI concept uses compression itself to ignite the gas-air mixture--as in a diesel--rather than a spark plug, improving thermal efficiency by as much as 30 percent, though so far only under light loads.

Mazda's next round of SkyActiv engines won't emerge until near the end of the decade, "before 2020." Even its current-generation diesel models still haven't been launched in the U.S.

With rising proportions of renewable sources like wind and solar, and perhaps more natural gas, some European grids will then be cleaner than they are today--making the comparison tougher for Mazda.

But the company's assertion is at least plausible. We'll wait for actual vehicles fitted with the new and even more efficient engines to emerge, and see how they compare to the lastest grid numbers then.

Electric-car advocates may be tempted to pooh-pooh any vehicle with any tailpipe emissions. Or they may point out that electric-car owners in the U.S. appear to have solar panels on their homes at a much higher rate than the country at large--meaning much of their recharging is done with virtually zero carbon emitted.

But every effort to reduce the carbon emissions per mile of the trillions of miles we drive globally every year is a step in the right direction.

Will Mazda lead the march along that path? We look forward to learning more about its next SkyActiv engines.

Boom In Sale Of Telematics Solutions Predicted

Vehicle tracking using OBDII port devices
is becoming more popular due to easy installation
and the ability to track personal vehicles and/or
rental/leased vehicles.

As reported by OutLaw.com:  Sales of telematics devices are expected to increase by more than 1000% over the next five years, according to a new study.

Technology market analysts ABI Research said that 117.8 million 'onboard diagnostics (OBD) aftermarket telematics solutions' are expected to be subscribed to in 2019, up from 9.5m this year.

It said the demand for solutions that plug in to OBD port built into vehicles is strongest in European and North American markets at the moment but that smartphone applications will provide increasing competition within the telematics technology market.

Smartphone HTML5 applications are being
used more commonly to provide detailed
information not only of a vehicle's location
but to gather and process sensor information.

"Beyond the 2019 forecast horizon, the window of opportunity for OBD-dongles will gradually close as open factory-installed OEM (original equipment manufacturer) telematics becomes more widespread," ABI Research said in a statement.

"OBD solutions will also face competition from aftermarket telematics solutions based on smartphones connecting directly to the vehicle OBD port via Bluetooth or Wi-Fi adapters. Even standalone smartphone applications are starting to be explored for applications such as UBI (usage-based insurance) and driver behavior monitoring of truck drivers leveraging the built-in GPS, accelerometer, and connectivity," it said.

'Telematics' is a term most commonly associated with the motor insurance industry; though in general it refers to any system that collects remote or mobile sensor data. Insurance companies are increasingly recording information via devices in cars that allows them to set insurance premiums that reflect the driving style of motorists. Using recorded data, companies are able to pinpoint specific driver or vehicle risks rather than using more generalized area statistics.

Earlier this year competition law expert Natasha Pearman of Pinsent Masons, the law firm behind Out-Law.com, warned insurance companies to be careful to ensure that arrangements governing how telematics data is gathered, managed and accessed do not fall foul of competition and privacy laws.