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This paper presents an improved scheme for the state-of-health (SOH) estimation, applicable to battery management system (BMS) of electric vehicles. The original scheme requires the prior information for the estimation, which is the prior SOH identified from the last charging. This limit implies that if a battery or its BMS is replaced, the prior SOH stored within the BMS no longer matches with the actual SOH, resulting in a critical error. To avoid this potential but critical pitfall, we newly devise an improved SOH estimation scheme. The original scheme is revised by adopting the fixed-point iteration method into its parameter estimation. By removing dependencies on the prior information, the revised scheme can function regardless of such replacements. The revised scheme is experimentally validated and demonstrated that even without the prior information, it can satisfy the requirement of the SOH estimation (within 3%) thanks to its improved design. [ABSTRACT FROM AUTHOR]

Plant-wide oscillation detection is an important task in the maintenance of large-scale industrial control systems, owing to the fact that in an interactive multi-loop environment oscillation generated in one loop may propagate to the different parts of the plant. In such a scenario, it is required that different loops oscillating due to a common cause and hence similar frequency may be grouped together. In this paper an adaptive method for plant-wide oscillation detection based on multivariate empirical mode decomposition (MEMD) along with a grouping algorithm is proposed. The method can identify multiple oscillation groups among different variables as well as variables with random noise only. The proposed method is also applicable to both non-linear and non-stationary time series where the techniques based on the conventional Fourier analysis are prone to errors. Within each group that oscillate due to a common cause, the method can also indicate the location of the probable root cause of oscillations. The efficacy of the proposed method is established with the help of both simulation and industrial case studies. [ABSTRACT FROM AUTHOR]

Abstract: Most of the advanced integrated energy systems need a heat recovery steam cycle (HRSC), either fired or unfired, that recovers the waste heat from gas turbines and process units in order to generate electric power and supply mechanical power to compressors, heat to endothermic processes, and steam to external users. The key feature of such HRSCs is the integration between the heat recovery steam generator (HRSG) and the external heat exchangers. This paper presents a rigorous mathematical programming model, a linear approximation, and a two-stage algorithm for optimizing the design of integrated HRSGs and HRSCs, simultaneously considering the HRSG together with the heat recovery steam network and the intensive steam cycle variables. A detailed application of the methodology is described for an integrated gasification combined cycle plant with CO2 capture and results for other interesting plants are reported. A significant efficiency gain is obtained with respect to usual practice designs. [Copyright &y& Elsevier]

Abstract: This paper focuses on chemical process design and layout optimization based on a theoretical approach that takes into account the dynamic feature of price/cost fluctuations. As originally supposed, conceptual and systematic process design consider fixed prices and costs as input data in their models. These models do not account for well-known price and cost fluctuations such as electric energy and crude oil prices. The conventional modeling approach to conceptual design can find a sub-optimal solution because of neglecting the dynamic changes of economic terms within a given time horizon. This manuscript modifies the perspective, and considers the fluctuations of prices and costs within the conceptual design activity, in order to maximize a so-called economic potential. The term fluctuation refers to the deterministic variations of prices and costs while it does not quantify the stochastic oscillations of the market. These oscillations are bound to the mood of investors and require a forecasting approach, which is typical of forms of investment such as the exchange-traded derivatives. A straightforward case study, based on an energy intensive chemical process, shows the benefits and the opportunities of this approach. The mathematical model is based on the implementation of dynamic superstructures, which call for a MINLP formulation. Finally, the manuscript presents and discusses some numerical results and the economic benefits coming from the dynamic approach to conceptual design. [Copyright &y& Elsevier]