State-of-health (SOH) is crucial to the maintenance of various kinds of energy storage systems, including power batteries. Relevant research articles are mostly based on battery external information, such as current, voltage, and temperature, which are susceptible to fluctuation and ultimately affects the SOH estimation accuracy. In this article, to solve these problems, a fast impedance calculation-based battery SOH estimation method for lithium-ion battery is proposed from the perspective of electrochemical impedance spectroscopy (EIS). The relationship between EIS and state of charge and that between EIS and degraded capacity is first studied by experimental tests. Some impedance features called health factors effectively indicating battery aging states are selected. Second, an improved fast Fourier transform (FFT) utilizing the conversion relationship between the real and complex signals is proposed to realize online fast EIS acquisition. Compared with ordinary FFT, such treatments reduce computational complexity. Then, the SOH evaluation model is built by the extreme learning machine with regularization mechanism, further reducing the computational burden. The relationship between the health factors and aging capacity of batteries is established. Finally, an experimental bench is established. The results indicate that the estimated SOH can be obtained within 35 s for a four-cell series-connected battery pack and the estimation errors are less than 2%.

This paper proposes an improved disturbance observer to realize accurate contact force estimation using the joint torque sensor. The joint torque sensor separates the dynamics of the link side from the motor side of the robot manipulator. Therefore, only computing the partial dynamics on the link side can realize external force estimation. This can considerably reduce the modeling workload and error terms that may affect the estimation results. Furthermore, this paper presents that the observed residual value during free motion can be considered as the friction dynamics, which is approximated by the neural network (NN) due to its inherent capacity in approximating nonlinear functions. After that, the estimation accuracy of the observer is considerably improved. Compared to other local NN approximation method, we analyzes the properties of the friction force in detail to select appropriate excitation trajectory for accurate global approximation results using only limited training data. We have presented that the suitable excitation trajectory and the use of global basis function are the sufficient conditions for global friction approximation. The proof of this theorem is also given. The Kalman filter is also utilized to reduce the noise of the estimation results in real-time. The experimental results also demonstrate the efficacy of the proposed method, which accurately estimates the contact force for flexible joint robots.

The state of health (SOH) is essential for the safety and reliability of lithium-ion batteries in different applications. Moreover, the inconsistency of battery cells in battery packs is inevitable. To determine the SOH difference is even more challenging without the calculation of each cell's SOH. To solve the problem, this paper proposes a novel relative state of health (rSOH) estimation method based on wavelet analysis, and the SOH differences for lithium-ion battery cells can be obtained. No battery model is needed in this method. The more accessible battery voltage of each battery cell is sufficient to obtain the SOH differences, without SOH estimation for each cell. With the wavelet analysis, more detailed information of the battery cells is obtained. The rSOH is then introduced as the index of the SOH difference, and the estimation procedure is further analyzed. Finally, experimental validations are conducted by different aged battery cells. The different state of charge conditions and different driving cycles are applied to show the robustness of the proposed method. The experimental results show that the rSOH estimation error is limited within 5%, equivalently with less than 1% SOH estimation error, thus proving the effectiveness of the proposed rSOH estimation method.

Silicon carbide (SiC) ceramics have been widely used for microelectronics, aerospace, and other industrial fields due to their excellent chemical stability and thermal tolerance. However, hard machinability and low machining precision of SiC ceramics are the key limitations for their further applications. To address this issue, a novel method of underwater femtosecond laser machining was introduced in this study to obtain high precision and smooth surface of the microgrooves of SiC ceramics. The removal profiles were characterized in terms of width, depth, and surface morphology, which exhibited high dependence on the femtosecond laser processing parameters. The instability during the underwater processing affected by laser-induced gas bubbles and material deposition, however, limits the high surface accuracy of microgrooves and processing efficiency. The process condition transformation from a bubble-disturbed circumstance to a disturbance-free model was carefully investigated through a high speed camera for the femtosecond laser processing of SiC ceramics in water. The experiment results indicated that degree of disturbed effect was heavily dependent on size, distribution, and motion of laser-induced gas bubble. Furthermore, some typical evolution mechanisms of gas bubble and their influence on the removal profiles of microgrooves were discussed in detail. Bubble evolution has been proven to be mainly responsible for the behavior of laser propagation (focus model, total reflection, etc.), which notably affects microstructural characteristic of the microgrooves.

Accurate and reliable state-of-health (SOH) estimation is an important topic in battery management. Single data-driven model based SOH estimation suffers significant discrepancy problems over different cases. Moreover, existing ensemble based SOH estimation methods suffer serious problems, such as insufficient diversity of base models, complicated weight calculation, and severe overfitting. To address these problems, a stacking-based ensemble learning method for SOH estimation is proposed in this article. A second-level learner is used to integrate three heterogeneous base models without any weight calculation step. Fused datasets are generated by cross validation, maximizing the model generalization. Comprehensive validations are performed on batteries with two different cathode materials using two training strategies. The results show that the proposed ensemble method outperforms not only all base models (29% better than the optimal base model), but also the average method (more than 32%) and the state-of-the-art ensemble method (more than 44%).