The popularity of electric vehicles or EVs as an environmentally friendly substitute for traditional vehicles with gasoline-powered internal combustion engines has increased.

According to a SciTechDaily report, this has resulted in major study initiatives directed toward developing high-efficiency EV batteries.

Nonetheless, the substantial effectiveness of EVs is caused by inaccurate estimations of the battery charge.

The present battery output is gauged to examine the charge state of an electric vehicle battery. This is used to compute an estimate of the vehicles' remaining vehicles.

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Electric Vehicle Battery Charging
(Photo: Christopher Furlong/Getty Images)
A battery electric vehicle (BEV) charges on a level 2 home charge point on April 21, 2022, in Manchester, England.


Driving Range Extended by 10 Percent

Usually, the battery currents in electric vehicles can reach hundreds of amperes. Nevertheless, commercial sensors that can identify such currents cannot gauge tiny changes in the current at milliampere levels.

This results in an uncertainty of approximately 10 percent in the battery charge estimation. Meaning, that the driving range of EVs could be extended by 10 percent. In turn, this would lessen insufficient usage of battery.

Fortunately, a research team has devised a way to solve the problem. In their study, the team reported a diamond quantum sensor-based detection method that can approximate the battery charge with one-percent accuracy while gauging high currents typical of electric vehicles.

The research team from Japan was headed by Professor Mutsuko Hatano from the Tokyo Institute of Technology or Tokyo Tech, and their research was published in the Scientific Reports journal.

Quantum Sensor-Based Detection

Essentially, a diamond quantum sensor-based detection approach can more precisely approximate battery charge, resulting in an extended range for EVs.

In this research, the team reported a diamond quantum sensor-based detection technique that can approximate one percent preciseness while gauging high currents typical of electric vehicles.

Professor Hatano explained that they developed diamond sensors sensitive to milliampere currents and close enough to be implemented in automobiles.

Furthermore, he added, they gauged currents in a wide range and detected milliampere-level currents in noisy surroundings.

Diamond Quantum Sensors

In their work, the study investigators developed a model sensor using a pair of diamond quantum sensors that were positioned on either side of the busbar, an electrical junction for incoming and outgoing currents, in the vehicle.

Then, they used a "differential detection" strategy to remove the common noise detected by both sensors and keep only the actual signal.

This, in turn, allowed them to detect a small current from 10 mA in the middle of background environmental noise, as specified in a Bioengineer.org report.

Next, the researchers used a mixed analog-digital control of the frequencies produced by two microwave generators to trace the magnetic resonance frequencies of the quantum sensor over a one-gigahertz bandwidth.

Lighter Battery Weight

The researchers tested this model for Worldwide Harmonized Light Vehicle Test Cycle or WLTC driving, a standard test performed for energy consumption in electric vehicles.

The sensors precisely traced the charge or discharge current from -50 and 130 A and presented the battery charge estimation preciseness within one percent.

As implications of the findings, Professor Hatano remarked that increasing the usage efficiency of batteries by 10 percent would lessen battery weight by 10 percent, which will decrease by 3.5 percent running energy and five percent production energy of 10 million new electric vehicles in 2030 WW.

In turn, this corresponds to a 0.2 percent reduction in carbon dioxide emissions in the 2030 WW transportation field. The team said they hope this breakthrough leads one step closer to a carbon-neutral society.

Related information about range extenders for EVs is shown on IDTechEx's YouTube video below:

 

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