The air-suction precision seed-metering device is prone to the instability of the seed adsorption state, which arises from blockage of the suction hole and leads to uneven seeding. This paper analyzed and determined key structural parameters of the seed-metering plate, then established an adsorption mechanics model of the seed during the migration process and designed the key structure of the air-suction seed-metering device with the aim of improving the uniformity of high-speed direct seeding of vegetables. Furthermore, we used the DEM-CFD coupling method to analyze the influence of the law of seeds on the change of the flow field with different hole types. Results showed that the turbulent kinetic energy (202.65 m²∙s⁻²) and the coupling force to the seeds (0.029 N) of the B-type hole are the largest, which is the best fluid domain structure for the suction hole of the seed-metering plate. Moreover, we used Adams to analyze the meshing process between the knock-out wheel and the seed-metering plate, affirming the rationality of the knock-out wheel design. Finally, in order to improve the working efficiency of the seed-metering device, we performed one-factor and response surface experiments of seeding performance using the air-suction seed-metering device designed with the optimized structure as the experimental object. Analysis of the influence of weights across each factor on the experimental performance evaluation indicators revealed an optimal combination of seeding performance parameters in the air-suction seed-metering device, namely a seed-throwing angle of 13°, a working speed of 14.5 km/h, and negative pressure of 3.1 kPa. Results from verification experiments revealed the corresponding experimental indicators, namely qualified, multiple, and missing indexes of 95.9, 1.2%, and 2.9%, respectively.

It is difficult to use conventional capacity detection methods to determine nondestructively and rapidly the capacity of lithium-ion (Li-ion) batteries used in electric vehicles.

Laser-induced graphene (LIG) has been widely used in flexible sensors due to its excellent mechanical properties and high conductivity. In this paper, a flexible pressure sensor prepared by bionic micro/nanostructure design and LIG mass fraction regulation is reported. First, prepared LIG and conductive carbon paste (CCP) solutions were mixed to obtain a conductive polymer. After the taro leaf structure was etched on the surface of the aluminum alloy plate by Nd:YAG laser processing, the conductive polymer was evenly coated on the template. Pressure sensors were packaged with a stencil transfer printing combined with an Ecoflex flexible substrate. Finally, the effects of different laser flux and the proportion of LIG in the composite on the sensitivity of the sensor are discussed. The results show that when the laser flux is 71.66 J·cm⁻² and the mass fraction of LIG is 5%, the sensor has the best response characteristics, with a response time and a recovery time of 86 ms and 101 ms, respectively, with a sensitivity of 1.2 kPa⁻¹ over a pressure range of 0–6 kPa, and stability of 650 cycle tests. The LIG/CCP sensor with a bionic structure demonstrates its potential in wearable devices.

The state of charge (SOC) is an important indicator for evaluating a battery management system (BMS), which is crucial for the reliability, performance, and life management of a battery. In this paper, the characteristics of a Li-ion battery are deeply studied to explore the charge/discharge curve under different environments. Meanwhile, a second-order RC equivalent circuit model is constructed. The function identification of the EMF and SOC is performed based on the least squares method. The model estimation error is verified by simulation to be less than 0.05 V. Based on the Suboptimal Multiple Fading Factor Extended Kalman Filter (SMFEKF) algorithm, the SOC under constant current and UDDS conditions are estimated. Matlab/simulink simulations illustrate that the estimated accuracy of the proposed algorithm is improved by 79.36% compared with the EKF algorithm. Finally, the validity of the algorithm is verified jointly with the BMS. The results show that the estimation error is within 4% in both constant current condition as well as UDDS conditions, and it can still be predicted quickly and accurately under the uncertainty in the initial value of the SOC.