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The conventional automatic battery equalizer is characterized by its low cost in batter management system. However, its effectiveness is limited by the absence of signal feedback, leading to inadequate self‐regulation and protection of the topology. In this paper, an equalizer with constant magnetizing current is proposed. By adjusting the duty cycle of the MOSFETs, the magnetizing current can be flexibly controlled, which greatly improves the equalization rate and safety. The topology has multiple modes such as cell to cell (C2C), cell to string (C2S), and string to string (S2S). A creative signal sampling method is designed to obtain the cell voltage and magnetizing current with few sensors. And a novel balancing strategy is proposed, which can achieve wonderful accuracy of equalization at any initial voltage distribution. [ABSTRACT FROM AUTHOR]

This paper investigates the stability problem of delayed neural networks (DNNs) with time-varying delay. In the stability analysis of DNNs, the stability conditions are usually in form of second-order polynomial functions with respect to the time delay, which are nonlinear matrices inequalities and difficult to deal with. In order to obtain the stability criteria in terms of linear matrices inequalities (LMIs), some quadratic function negative-definiteness lemmas (QFNDLs) are proposed and widely employed. However, the research about QFNDL is still facing two challenges: large conservativeness and difficulty of optimisation. To overcome the challenges, this paper proposes a parameter-dependent quadratic function negative-definiteness lemma (PDQFNDL) to find the negative-definiteness condition. Compared with existing QFNDLs, the proposed lemma has two advantages: (i) Two moving points are introduced and the whole search interval is divided into two parts by an adjustable parameter, which contribute to the less conservativeness of stability criteria. (ii) The search interval of the adjustable parameter is smaller than the existing QFNDLs and the time of optimisation process is decreased. The proposed lemma is utilised to derive the less conservative stability criteria for DNNs based on a suitable Lyapunov–Krasovskii functional (LKF). Finally, the effectiveness and superiority of proposed stability criteria are demonstrated by several numerical examples. [ABSTRACT FROM AUTHOR]

For the load-dependent nonlinear properties of the nitrogen oxide (NOx) decomposition process in thermal power plants, a local-linearization modeling approach based on a kind of global Nonlinear AutoRegressive Moving Average with eXogeneous input (NARMAX) model, named the exponential ARMAX (ExpARMAX) model, is presented. The ExpARMAX model has exponential-type coefficients that depend on the load of power plants and are estimated offline. In order to take advantage of existing conventional controllers and to reduce the cost of the industrial identification experiment, we propose a model structure that makes it possible for the ExpARMAX model to be identified using commercial operation data. On the basis of the model, a long-range predictive control strategy, without resorting to parameter estimation online, is investigated. The influence of some intermediate variables treated as process disturbances is studied, and the scheme using a set of multi-step-ahead predictors of the intermediate variables to improve control performance is also presented. A simulation study shows that the ExpARMAX model can give satisfactory modeling accuracy for the NOx decomposition (de-NOx) process in a large operating range, and the control algorithm proposed significantly improves the control performance. Index Terms--Local linearization, modeling, nitrogen oxide decomposition (de-NOx) process, Nonlinear AutoRegressive Moving Average eXogenous (NARMAX) models, nonlinear system, predictive control, process control.