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AI-Based Inverter from Pre-Switch Enhances High Efficiency in EVs

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By Maurizio Di Paolo Emilio | Thursday, September 2,2021
Pre-Switch has published the highest-efficiency data for its 200-kW CleanWave200 inverter have been recently released by Pre-Switch. Interviewed by EETimes, Bruce Renouard, CEO of Pre-Switch, demonstrated that efficiency can reach 99.3% (space-vector–modulated) at a switching frequency of 100 kHz with a flat profile as the load varies, thus resulting in an increased electric vehicle (EV) range by up to 12%. “We have a huge amount of data published as of today showing how we can achieve 99.3% with an accuracy of 0.01%,” said Renouard.

Leveraging its artificial-intelligence–based DC/AC, AC/DC soft-switching technology, Pre-Switch demonstrated how this was achieved by using only three discrete, low-cost 35-mΩ SiC FETs per switch location.

“We are primarily focused on silicon carbide, with the goal to virtually eliminate almost 100% of switching losses,” said Renouard. “And as a result, [by] limiting the switching losses, we can reduce the amount of silicon carbide needed per system by approximately 50%.  The amount of SiC saved depends on the amount of switching losses the alternative system has, but it’s certainly a big chunk. And that’s a big cost saving.”

The CleanWave200 inverter (Figure 1) offers fast switching frequencies that create a near-pure sine wave that makes electric motors efficient. The increased switching frequencies also reduces the size and cost of the DC link capacitors, inproportion to the increased switching speed,  and has the added benefit of enabling low-weight low inductance motrors needed in aviation.

CleanWaveTM evaluation system (top view)

Power electronics needs AI

The Pre-Switch AI solution allows users to migrate from costly, lossy, hard-switching implementations to efficient, soft-switching designs with a 10× higher switching frequency that produces a near-pure sine-wave output. The AI technology analyzes its parameters in real time, making the necessary adjustments to the small resonant transistors, thus resulting in soft-switching even in difficult, changing environments. The Pre-Switch AI algorithm takes into account a range of parameters such as temperature, device degradation, changing input voltages, and abrupt current fluctuations.

Hard-switching simply forces the transistor to turn on and off by adding current or voltage to enable the modified states. Hard-switching is known to be very hardware-demanding on transistors, and it shortens their lifespan. The concept of soft-switching, on the other hand, uses an external circuit to avoid the overlapping of voltage and current waveforms when switching transistors.

Inverters for EVs

In the automotive sector, research into the efficiency of EVs focuses on battery performance  and the efficiency of the inverter and electric motor employed. Stringent automotive safety and quality standards are steering technological innovation to approaches that maximize the efficiency and autonomy of EVs while minimizing battery size and weight and reducing costs. AI is providing essential support in the push for EV autonomy and efficiency, including efforts to eliminate switching losses in order to ensure rapid transistor commutation.

Extending the range of an EV requires improving both motor and inverter efficiency know as drivetrain losses. Drivetrain losses dominate most EV losses up to about 50 mph, at which point wind resistance takes over. But drivetrain losses account for the largest share of all losses in EVs, so it is crucial to keep an eye on both the inverter and motor, with a trade-off between switching losses and higher motor efficiencies. Motor iron losses decrease as the switching frequency increases, but inverter losses increase.

Renouard pointed out that SiC helps the inverter at low power levels but that many EV inverters are still using SiC devices at lower switching frequencies -in the order of 10 kHz. However, increasing the switching frequency does not always solve the problem. Switching faster results in higher switching losses, which decreases the efficiency of the inverter.

Furthermore, Renouard said that if you want to try to hard-switch FETs faster and keep the inverter’s efficiecy high, you need to add more FETs to reduce conduction losses in an attempt to compensate for the higher switching losses. This results in increased cost, and often the high dV/dt assocaited with fast switching frequencies requires thicker motor insulation and ceramic bearings to make the motors more robust. Pre-Switch addresses this challenge by incorporating AI into an FPGA that is used to precisely control the timing of the auxiliary resonant transistors, shown as S1 and S2 in Figure 2. The result is the virtual elimination of all switching losses in the main SiC transistors (Q1 and Q2).

Figure 2: Pre-Switch embeds AI into an FPGA, which precisely controls the timing of auxiliary resonant transistors S1 and S2.

 

Figure 2: Pre-Switch embeds AI into an FPGA, which precisely controls the timing of auxiliary resonant transistors S1 and S2.

During each switching cycle, the timing of auxiliary resonant transistors S1 and S2 is adjusted to ensure that Q1 and Q2 have virtually zero switching losses. The algorithm calculates and minimizes dead time based on full knowledge of how and when each switch is transitioning. “Let’s look at Figure 3, which shows 20 switching cycles,” said Renouard. “At switch-on, the algorithm starts the learning process, and then at the fourth switching cycle, the first correction provided by the AI is made. In this case, a reduction in the resonant current of the inductor [shown in green] is observed. Moving on, the algorithm will adjust the inductor resonant current independently to ensure that it oscillates briefly above the load current [shown in blue]. All adjustments are fast enough to ensure accurate, smooth switching with any PWM input and can be used to create a perfect sine wave with a DC/AC inverter. The system also works perfectly in reverse.”

AI-Based Inverter from Pre-Switch Enhances High Efficiency in EVs – EEWeb

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