![\"CleanWaveTM](\"https:\/\/www.eeweb.com\/wp-content\/uploads\/apr2121_figure1-CleanWaveTM-evaluation-system-.jpg?resize=563%2C317\" "\\"CleanWaveTM")
<\/a><\/figure>\n\n\n\nPower electronics needs AI<\/strong><\/h2>\n\n\n\nThe Pre-Switch AI solution allows users to migrate from costly, lossy, hard-switching implementations to efficient, soft-switching designs with a 10\u00d7 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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
Inverters for EVs<\/strong><\/h2>\n\n\n\nIn 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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
Furthermore, Renouard said that if you want to try to hard-switch FETs faster and keep the inverter\u2019s 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).<\/p>\n\n\n\n
Figure 2: Pre-Switch embeds AI into an FPGA, which precisely controls the timing of auxiliary resonant transistors S1 and S2.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
Furthermore, Renouard said that if you want to try to hard-switch FETs faster and keep the inverter\u2019s 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).<\/p>\n\n\n\n
Figure 2: Pre-Switch embeds AI into an FPGA, which precisely controls the timing of auxiliary resonant transistors S1 and S2.<\/p>\n\n\n\n