A robust online energy management strategy for fuel cell/battery hybrid electric vehicles
یک استراتژی مدیریت انرژی آنلاین قوی برای خودروهای برقی هیبریدی سلول / باتری-2020
Traditional optimization-based energy management strategies (EMSs) do not consider the uncertainty of driving cycle induced by the change of traffic conditions, this paper proposes a robust online EMS (ROEMS) for fuel cell hybrid electric vehicles (FCHEV) to handle the uncertain driving cycles. The energy consumption model of the FCHEV is built by considering the power loss of fuel cell, battery, electric motor, and brake. An offline linear programming-based method is proposed to produce the benchmark solution. The ROEMS instantaneously minimizes the equivalent power of fuel cell and battery, where an equivalent efficiency of battery is defined as the efficiency of hydrogen energy transforming to battery energy. To control the state of charge of battery, two control coefficients are introduced to adjust the power of battery in objective function. Another penalty coefficient is used to amend the power of fuel cell, which reduces the load change of fuel cell so as to slow the degradation of fuel cell. The simulation results indicate that ROEMS has good performance in both fuel economy and load change control of fuel cell. The most important advantage of ROEMS is its robustness and adaptivity, because it almost produces the optimal solution without changing the control parameters when driving cycles are changed.
Keywords: Fuel cell | Hybrid electric vehicles | Online energy management strategy | Robustness | Uncertaint
A solution to reconstruct cross-cut shredded text documents based on constrained seed K-means algorithm and ant colony algorithm
یک راه حل برای بازسازی اسناد متنی خرد شده برش خورده بر اساس الگوریتم بذر محدود K-means و الگوریتم کلونی مورچه ها-2019
The reconstruction of cross-cut shredded text documents (RCCSTD) is an important problem in forensics and is a real, complex and notable issue for information security and judicial investigations. It can be considered a special kind of greedy square jigsaw puzzle and has attracted the attention of many re- searchers. Clustering fragments into several rows is a crucial and difficult step in RCCSTD. However, exist- ing approaches achieve low clustering accuracy. This paper therefore proposes a new clustering algorithm based on horizontal projection and a constrained seed K-means algorithm to improve the clustering ac- curacy. The constrained seed K-means algorithm draws upon expert knowledge and has the following characteristics: 1) the first fragment in each row is easy to distinguish and the unidimensional signals that are extracted from the first fragment can be used as the initial clustering center; 2) two or more prior fragments cannot be clustered together. To improve the splicing accuracy in the rows, a penalty coefficient is added to a traditional cost function. Experiments were carried out on 10 text documents. The accuracy of the clustering algorithm was 99.1% and the overall splicing accuracy was 91.0%, according to our measurements. The algorithm was compared with two other approaches and was found to offer significantly improved performance in terms of clustering accuracy. Our approach obtained the best re- sults of RCCSTD problem based on our experiment results. Moreover, a more complex and real problem –reconstruction of cross-cut shredded dual text documents (RCCSDTD) problem –was tried to solve. The satisfactory results for RCCSDTD problems in some cases were obtained, to authors’ best knowledge, our method is the first feasible approach for RCCSDTD problem. On the other hand, the developed system is fundamentally an expert system that is being specifically applied to solve RCCSTD problems.
Keywords: Reconstruction of cross-cut shredded | documents (RCCSTD) | Constrained seed K-means algorithm | Horizontal projection | Penalty coefficient | Ant colony algorithm