System Identification And Analysis Of Track Vibration Energy Harvester

In recent years,rail transit has developed rapidly,and a large number of wireless sensing devices have been put into use to monitor rail safety,and the power consumption of these electronic devices has been reduced from milliwatts to microwatts.It is possible for collectors to power these electronic devices.In addition,the need for long-distance power supply in areas where power is scarce has greatly increased costs.Although batteries can temporarily solve the problem of power supply for these electronic devices,in the long run,these waste batteries may pollute the environment and require labor Maintenance,so domestic scholars set off a wave of research on vibration energy harvesters.The vibration energy harvester can pick up mechanical energy in the environment and convert the mechanical energy into electrical energy through a conversion mechanism to power low-power electronic devices.It is not affected by conditions such as weather and temperature,and has broad potential application prospects.At present,the related research on the piezoelectric or electromagnetic vibration energy harvesting of the electromechanical energy conversion mechanism has been relatively rich,but there is relatively little research in the field of electromagnetic vibration energy harvesting system identification,so this paper proposes electromagnetic vibration energy system identification method,and the non-parametric identification and parameter identification of the collector are performed using numerical solutions.The identification results are in good agreement with the theoretical results.Finally,experiments are used to verify the validity and accuracy of the identification method.a system identification method-voltage mapping method for a single degree of freedom electromagnetic vibration energy collector is proposed.The method can accurately identify the recovery force function with strong nonlinearity,electromagnetic electromechanical coupling function and equivalent inductance function under the condition of the recovery force function,the electromagnetic electromechanical coupling function and the equivalent inductance function of the unknown system.Using two typical nonlinear model examples to verify,one is an electromagnetic vibration energy collector system with non-linear elastic resilience(the electrical equation part is linear),and the other is a complex electromagnetic vibration energy acquisition system containing both nonlinear elastic resilience and nonlinear electrical equations.The time history response of the above two examples under the excitation of simple harmonic vibration is obtained by using Runge-Kutta method,and the nonlinear elastic resilience,damping resilience,electromagnetic force and inductance voltage contained in the system are successfully identified by using the identification method proposed above,and the corresponding nonlinear stiffness function,damping function and electromagnetic electromechanical coupling function and equivalent inductance function,the results show that the identification results are in good consistency with the accurate results,and verify the validity and accuracy of the method proposed in this paper.a parameter identification method for single-degree-of-freedom electromagnetic vibration energy harvester-improved particle swarm optimization(PSO)algorithm is proposed.Based on the particle swarm optimization algorithm,the performance of balancing global search and local search can be achieved by changing the inertia weight strategy,thus getting the global optimal solution accurately.Using the classical Duffing nonlinear system as an example of the parameter identification of the electromagnetic vibration energy harvester conducts simulating.The time history response of the above example under simple harmonic vibration excitation is calculated by using Runge-Kutta method.The electromagnetic coupling coefficient,equivalent inductance coefficient,stiffness coefficient and damping of the system are successfully identified by the parameter identification method proposed above.The coefficient shows that the identification result is in good agreement with the accurate result,which indicates that the improved particle swarm optimization algorithm can effectively identify the system parameters of the nonlinear electromagnetic vibration energy harvester;at the same time,compared with other intelligent algorithms,the algorithm has higher recognition accuracy.Finally,a cantilever-type electromagnetic vibration energy harvesting device is designed and produced.The permanent magnet and the coil move relative to each other under the effect of the set sinusoidal excitation signal to generate induced electromotive force.After the response signals are filtered,the system is identified.The identification results of electromagnetic coupling function,equivalent inductance function,stiffness function and damping function are obtained,and the effectiveness of the voltage mapping method is verified.