首页|Findings in Artificial Intelligence Reported from Zhejiang University (Artificia l-intelligence-based Hybrid Extended Phase Shift Modulation for the Dual Active Bridge Converter With Full Zvs Range and Optimal Efficiency)
Findings in Artificial Intelligence Reported from Zhejiang University (Artificia l-intelligence-based Hybrid Extended Phase Shift Modulation for the Dual Active Bridge Converter With Full Zvs Range and Optimal Efficiency)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Artificial Intelligence are presented in a new report. According to news originating from Hangzhou, Peop le's Republic of China, by NewsRx correspondents, research stated, "The dual act ive bridge (DAB) converter is the key enabler in many popular applications, such as wireless charging, electric vehicle, and renewable energy. ZVS range and eff iciency are two significant performance indicators for the DAB converter." Our news journalists obtained a quote from the research from Zhejiang University, "To obtain the desired ZVS and efficiency performance, modulation should be ca refully designed. Hybrid modulation (HM) considers several single modulation str ategies to achieve good comprehensive performance. Conventionally, to design an HM, a harmonic approach or piecewise approach is used, but they suffer from a ti me-consuming model-building process and inaccuracy. Therefore, an artificial-int elligence-based hybrid extended phase shift (HEPS) modulation is proposed. Gener ally, the HEPS modulation is developed in an automated fashion, which alleviates the cumbersome model-building process while keeping high model accuracy. In HEP S modulation, two EPS strategies are considered to realize optimal efficiency wi th full ZVS operation over entire operating ranges. Specifically, to build data- driven models of ZVS and efficiency performance, extreme gradient boosting (XGBo ost), which is a state-of-the-art ensemble learning algorithm, is adopted. After ward, particle swarm optimization with state-based adaptive velocity limit (PSO- SAVL) is utilized to select the best EPS strategy and optimize modulation parame ters."
HangzhouPeople's Republic of ChinaAs iaArtificial IntelligenceEmerging TechnologiesMachine LearningZhejiang U niversity
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