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2 Bulletin of the Polish Academy of Sciences. Technical Sciences

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Identification and classification of turn short-circuit and demagnetization failures in PMSM using LSTM and GRU methods

Lale Timur, Yüksek Gökhan

Bulletin of the Polish Academy of Sciences. Technical Sciences

2025

Vol. 73, nr 1

art. no. e151958

In an extremely broad range of industrial applications, especially in electric vehicles, permanent magnet synchronous motors (PMSMs) play a vital role. Any failure in PMSMs may cause possible safety hazards, a drop in productivity, and expensive downtime. Therefore, their reliable operation is essential. Accurate failure identification and classification allow for addressing problems before they escalate, which helps ensure the seamless operation of PMSMs and reduces the likelihood of equipment failure. Therefore, in this paper, novel failure identification methods based on gated recurrent unit (GRU) and long short-term memory (LSTM) from recurrent neural network (RNN) methods are proposed for early identification of stator inter-turn short circuit failure (ISCF) and demagnetization failure (DF) occurring in PMSMs under multiple operating conditions. The proposed methods use three-phase current signals recorded from the experimental study under multiple operating conditions of the motor as input data. In the proposed methods, both feature extraction and classification are executed within a unified framework. The experimental outcomes obtained demonstrate that the proposed methods can identify a total of six unique motor conditions, including three ISCF variations and two DF variations, with high accuracy. The LSTM and GRU approaches predicted the identification of failures with 98.23% and 98.72% accuracy, respectively. Compared to existing methods, the success of the proposed approaches is satisfactory. In addition, LSTM and GRU-based failure identification methods are also compared in detail for accuracy, precision, sensitivity, specificity, and training time in this study.

Improved thermal management of lithium-based batteries employing genetic algorithm optimization

Yüksek Gökhan, Lale Timur

Bulletin of the Polish Academy of Sciences. Technical Sciences

2024

Vol. 72, nr 6

art. no. e151378

Lithium-based battery systems (LBS) are used in various applications, from the smallest electronic devices to power generation plants. LBS energy storage technology, which can offer high power and high energy density simultaneously, can respond to continuous energy needs and meet sudden power demands. The lifetime of LBSs, which are seen as a high-cost storage technology, depends on many parameters such as usage habits, temperature and charge rate. Since LBSs store energy electrochemically, they are seriously affected by temperature. High-temperature environments increase the thermal stress exerted on LBS and cause its chemical structure to deteriorate much faster. In addition, the fast charging feature of LBSs, which is generally presented as an advantage, increases the internal temperature of the cell and negatively affects the battery life. The proposed energy management approach ensures that the ambient temperature affects the charging speed of the battery and that the charging speed is adaptively updated continuously. So, the two parameters that harm battery health absorb each other, and the battery has a longer life. A new differential approach has been created for the proposed energy management system. The total amount of energy that can be withdrawn from LBS is increased by 14.18% as compared to the LBS controlled with the standard energy management system using the genetic algorithm optimized parameters. Thus the LBS replacement period is extended, providing both cost benefits and environmentally friendly management by LBSs turning into chemical waste distinctly later.