Accurately predicting the remaining useful life (RUL) of lithium-ion batteries enables their timely replacement and ensures the proper operation of equipment. This study presents a novel hybrid approach for predicting nonlinear and nonsmooth battery capacity sequences. To develop this approach, first, the original battery capacity sequence was adaptively decomposed through Northern Goshawk Optimization (NGO)-Variational Mode Decomposition (VMD). NGO-VMD could efficiently extract useful information at different scales and could considerably reduce the complexity of the battery capacity sequence. Second, the decomposed sequences were grouped into high- and low-frequency components on the basis of the over-zero rate. A convolutional neural network-bidirectional long short-term memory model was then constructed to predict the low-frequency components, and a temporal convolutional network-attention mechanism-deep neural network model was developed to predict the high-frequency components. In addition, a tensor-based transfer learning approach was employed to predict the low-frequency components of capacity sequences from same-type batteries. The RUL prediction errors of the proposed approach did not exceed 2 cycles, which was fewer than those of other comparable approaches. Accordingly, the proposed approach has favorable generalizability and robustness.

Artificial neural networks (ANNs), which have excellent self-learning performance, have been applied to various applications, such as target detection and industrial control. In this paper, a reference-model-based ANN controller with integral-proportional-derivative (I-PD) compensation has been proposed for temperature control systems. To improve the ANN self-learning efficiency, a reference model is introduced for providing the teaching signal for the ANN. System simulations were carried out in the MATLAB/SIMULINK environment and experiments were carried out on a digital-signal-processor (DSP)-based experimental platform. The simulation and experimental results were compared with those for a conventional I-PD control system. The effectiveness of the proposed method was verified.

With the rapid development of industrial technology, the multi-point (multi-input multi-output) heating processing systems with integrated temperature control have been increasingly needed to achieve high-quality and high-performance processing. In this paper, in response to the demand for proper transient response and to provide more accurate temperature controls, a novel pole-zero cancelation method is proposed for multi-input multi-output (MIMO) temperature control in heating process systems. In the proposed method, the temperature differences and transient characteristics of all points can be improved by compensating dead time difference and coupling effect together by matrix compensation and pole-zero cancelation with the feedforward reference model. Both simulations and experiments were carried out. The results were compared to the well-tuned conventional PI control system and PI plus decoupling compensation system to evaluate the control efficiency of the proposed method.

Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller. The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified.

The DC micro-grid system, as a new generation of shipboard DC micro-grid system, has the advantages of integrating renewable energy and enhancing the stability and reliability of the power system. For the energy distribution problem of energy storage battery charging and discharging in shipboard DC micro-grid, P-V voltage droop control and SOC-I current droop control methods are proposed. The SOC-I control method incorporated in the battery charging and discharging process can effectively balance the SOC and output power of the energy storage battery. For the on/off-grid process of shipboard power converter and onshore power, the control method of voltage and current control mode switching is proposed, which realizes the fast response of output voltage in off-grid mode and the controllable transfer of power interacting with the grid in grid-connected mode. Secondly, a pre-synchronized control based on amplitude and frequency modulation achieves a flexible on-grid/off-grid connection of the power converter. A method based on the parallel connection of multiple reduced-order generalized integrator (ROGI) is introduced to achieve suppression of output voltage unbalance as well as $5th$ and $7th$ harmonics. Finally, the efficiency and correctness of the proposed control method has been successfully evaluated an level 750KVA shipboard micro-grid system.

In recent years, thermal processing systems with integrated temperature control have been increasingly needed to achieve high quality and high performance. In this paper, responding to the growing demands for proper transient response and to provide more accurate temperature controls, a novel slow-mode-based control (SMBC) method is proposed for multi-point temperature control systems. In the proposed method, the temperature differences and the transient response of all points can be controlled and improved by making the output of the fast modes follow that of the slow mode. Both simulations and experiments were carried out, and the results were compared to conventional control methods in order to verify the effectiveness of the proposed method.