The demand for battery-powered products, ranging from consumer goods to electric vehicles, keeps increasing. As a result, batteries are manufactured and shipped globally, and the safe and reliable transport of batteries from production sites to suppliers and consumers, as well as for disposal, must be guaranteed at all times. This is especially true of lithium batteries, which have been identified as dangerous goods when they are transported. This paper reviews the international and key national (U.S., Europe, China, South Korea, and Japan) air, road, rail, and sea transportation requirements for lithium batteries. This review is needed because transportation regulations are not consistent across countries and national regulations are not consistent with international regulations. Comparisons are thus provided to enable proper and cost-effective transportation; to aid in the testing, packaging, marking, labelling, and documentation required for safe and reliable lithium cell/battery transport; and to help in developing national and internal policies.

Nowadays, the temperature gradient is considered as one of the most important parameters which impact the performance of the solid oxide fuel cell (SOFC). In this paper, a control strategy based on an input–output feedback linearization technology is derived for controlling the maximum temperature gradient within the anode fuel flow channel at the desired value. For the controller design, the temperature dynamic model is proposed and simplified to a control-oriented multi-input and multioutput nonlinear dynamic model. Then, this paper presents an input–output feedback linearization controller to realize the control objective by adjusting the cathode input air flow. Finally, the simulation results are given to demonstrate the accuracy of the proposed model in reflecting the temperature dynamic characteristics. Moreover, the compound controller is added for simulation as a comparison, which shows that the proposed controller is equipped with better effectiveness and efficiency in the presence of external disturbances.

During the actual operation of the solid oxide fuel cell (SOFC), degradation is one of the most difficult technical problems to overcome. Predicting the degradation trend and estimating the remaining useful life (RUL) can effectively diagnose the potential failure and prolong the useful life of the fuel cell. To study the degradation trend of the SOFC under constant load conditions, a SOFC degradation model based on the ohmic area specific resistance (ASR) is presented first in this paper. Based on this model, a particle filter (PF) algorithm is proposed to predict the long-term degradation trend of the SOFC. The prediction performance of the PF is compared with that of the Kalman filter, which shows that the proposed algorithm is equipped with better accuracy and superiority. Furthermore, the RUL of the SOFC is estimated by using the obtained degradation prediction data. The results show that the model-based RUL estimation method has high accuracy, while the excellence of the PF algorithm for degradation trend prediction and RUL estimation is proven.

Too high or too low water content in the proton exchange membrane (PEM) will affect the output performance of the proton exchange membrane fuel cell (PEMFC) and shorten its service life. In this paper, the mathematical mechanisms of cathode mass flow, anode mass flow, water content in the PEM and stack voltage of the PEMFC are deeply studied. Furthermore, the dynamic output characteristics of the PEMFC under the conditions of flooding and drying membrane are reported, and the influence of water content in PEM on output performance of the PEMFC is analyzed. To effectively diagnose membrane drying and flooding faults, prolong their lifespan and thus to improve operation performance, this paper proposes the state assessment of water content in the PEM based on BP neural network optimized by genetic algorithm (GA). Simulation results show that compared with LS-SVM, GA-BP neural network has higher estimation accuracy, which lays a foundation for the fault diagnosis, life extension and control scheme design of the PEMFC.

The business process modeling tool selection problem has a significant impact on the overall performance of enterprise business process modeling, which will directly affect the development of enterprise information systems. Apart from that, the process to select the business process modeling tool from all alternatives is a Multi-Criteria Decision Making (MCDM) problem. This paper develops a methodology based on the hybrid fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to help companies select the optimal business process modeling tool, where the business process modeling process is more efficient, economic and safe. The proposed method has the following state-of-the-art contributions and features: (1) the latest application of the MCDM methodology to the field of BPM tool selection, (2) addressing the direct and indirect impact between criteria in the selection of BPM tools, and (3) considering the hybrid fuzzy (uncertainty) decision-making issue in the BPM tool selection process. Meanwhile, the mathematical formula in TOPSIS can be regarded as a formula for solving a symmetric problem. The hybrid fuzzy DEMATEL method is used to obtain the weight for the criteria to be considered in the BPM tool selection process, and the TOPSIS method is used to obtain the final business process modeling tool.

For predicting the voltage and temperature dynamics synchronously and designing a controller, a control-oriented dynamic modeling study of the solid oxide fuel cell (SOFC) derived from physical conservation laws is reported, which considers both the electrochemical and thermal aspects of the SOFC. Here, the least squares support vector regression (LSSVR) is employed to model the nonlinear dynamic characteristics of the SOFC. In addition, a genetic algorithm (GA), through comparing a simulated annealing algorithm (SAA) with a 5-fold cross-validation (5FCV) method, is preferably chosen to optimize the LSSVR’s parameters. The validity of the proposed LSSVR with GA (GA-LSSVR) model is verified by comparing the results with those obtained from the physical model. Simulation studies further indicate that the GA-LSSVR model has a higher modeling accuracy than the LSSVR with SAA (SAA-LSSVR) and the LSSVR with 5FCV (5FCV-LSSVR) models in predicting the voltage and temperature transient behaviors of the SOFC. Furthermore, the convergence speed of the GA-LSSVR model is relatively fast. The availability of this GA-LSSVR identification model can aid in evaluating the dynamic performance of the SOFC under different conditions and can be used for designing valid multivariable control schemes. 为了同时预测固体氧化物燃料电池(SOFC)的电压、 温度动态特性和设计控制器, 建立SOFC的控制相关动态辨识模型。 为了建立SOFC 更精确的最小二乘支持向量回归机(LSSVR)动态模型, 采用遗传算法(GA)优化LSSVR 的参数。 所建GA-LSSVR 模型可同时预测SOFC 的电压和温度动态特性。 1. 分析SOFC 的电化学和能量平衡子模型。 2. 利用所选择的最优LSSVR 参数, 建立了SOFC的GA-LSSVR 动态辨识模型。 通过仿真分析和比较, 验证了所建模型的有效性(图3 和4)。 3. 利用所建模型的预测结果, 与模拟退火算法优化最小二乘支持向量回归机(SAA-LSSVR)和5 折交叉验证最小二乘支持向量回归机(5FCV-LSSVR)模型的预测结果进行了比较, 表明所建立的GA-LSSVR 模型具有较高的预测精度(表3 和4)。 通过比较SAA-LSSVR 和5FCV-LSSVR 模型的预测结果, 发现所建GA-LSSVR 模型具有较好的预测性能和精度。 基于所建立的GA-LSSVR 模型可进行有效的多变量控制器设计。