Designing and modeling of a new MSMA vibration energy transducer

The conversion of mechanical vibrational energy into electrical energy to power wireless electronic devices using the smart material magnetic shape memory alloy (MSMA) has garnered substantial attention. This paper presents a vibration energy transducer founded on the inverse effect of MSMA, elucidating the principle of power generation. The variations in martensite and magnetic domain characteristics within MSMA were analyzed, and a constitutive model was established for the MSMA vibration energy transducer, integrating the thermodynamic theory of the Gibbs free energy function. Although this model performs well in predicting experimental outcomes, it falls short in capturing all features of the experimental data. To comprehensively encompass these features, the PSO-XGBoost machine learning approach was introduced to train the experimental data by incorporating factors such as stress, magnetic field, and induced voltage. An experimental prototype of the MSMA vibration energy transducer is fabricated, and the predictions of both models are compared with the collected experimental data, validating the accuracy of the model and indicating the enhanced effectiveness of machine learning methods in prediction. This research not only validates the correctness of the models but also emphasizes the potential for more precise predictions using machine learning methods, thereby establishing a robust foundation for the thorough study and broader application of MSMA vibration energy transducers.