The practical driving cycle is of great significance in studying the control strategy of vehicles, and effective clustering of micro-trips is the key to obtaining the typical driving cycle. A novel and efficient method for constructing typical driving cycles is presented in this paper. First, by combining the preying behavior and random behavior of the artificial fish swarm algorithm (AFSA) with particle swarm optimization (PSO), a modified particle swarm optimization (MPSO) is proposed. By comparing the means and standard deviations of the optima, MPSO is verified as much more accurate and stable than PSO, AFSA, select particle swarm optimization (SPSO) and cross particle swarm optimization (CPSO) in the optimization calculation of four typical multi-modal benchmark functions. Second, by applying MPSO to optimize the k-means algorithm, the k-MPSO clustering algorithm is obtained. In the case of clustering the Iris standard data set, the average error rates of the k-means algorithm and k-MPSO clustering algorithm are 11.6% and 7.8%, respectively, which means that the k-MPSO clustering algorithm has a stronger searching ability. Finally, with the ECAN Tools software, real-world driving data that include thousands of microtrips in Jinan are collected, and 19 representative characteristic parameters are selected to fully describe the driving conditions. After principal component analysis (PCA), the k-MPSO clustering algorithm method is applied to cluster the micro-trips into three classes and construct the typical driving cycle in Jinan.

The proton exchange membrane fuel cell (PEMFC) is a promising energy conversion device due to its high reliability, fast response speed, and low pollutant emission. However, the reality of its commercial application requires further cost reduction and efficiency improvement. The material distribution in the channel and the performance of PEMFC can be improved by setting the boss inside the flow channels. In this paper, the performance of PEMFC with the boss in flow channels in a parallel flow field was analyzed by simulation. The influence of different boss arrangements and heights on gas pressure drop, distribution uniformity, gas component distribution, temperature distribution, and output performance of the fuel cell were analyzed in detail. The bosses would increase the pressure drop and the distribution uniformity of reactive gases in flow channels significantly. The cross-arrangement of bosses is better than the cross-arrangement of bosses and juxtaposition according to increasing performance and pressure drop. The cross-arrangement with a boss height of 0.4 mm is suitable for the parallel flow field in this paper. The improved scheme of flow channel design is proposed to provide a reference for fuel cells for subsequent research.

Fuel economy and system durability are critical yet interdependent performance metrics for fuel cell hybrid vehicles (FCHVs). This paper devises an integrated framework for optimizing component sizing and energy management in a fuel cell/battery hybrid passenger vehicle. A unified cost function is proposed, combining fuel economy and system durability through a weighting coefficient, based on a comprehensive model of the hydrogen consumption and degradation characteristics of fuel cells and batteries. Utilizing the dynamic programming (DP) algorithm, the total cost is optimized to derive the optimal weighting factors and component sizing, effectively addressing the multi-objective optimization problem and balancing efficiency and durability. Furthermore, the impact of power prices on the optimal parameters is carefully examined. The simulation results indicate that a battery capacity of 44 Ah and a fuel cell maximum power of 80 kW represent the optimal sizing configuration. A weighting factor of 0.5 achieves the minimum equivalent total cost by effectively balancing fuel economy and system durability for the light-duty fuel cell passenger vehicle. Additionally, the battery price affects the weighting factor, indicating that future reductions in power source costs will shift focus away from system durability to fuel economy in FCHV optimization. These findings provide recommendations for FCHV manufacturers to advance the application of fuel cells in passenger vehicles.

To further improve the thermal efficiency of diesel engines, a waste heat recovery system model utilizing organic Rankine cycle (ORC) is constructed and verified through system bench test and heat exchanger bench test. To recover waste heat from diesel engine exhaust, ethanol, cyclopentane, cyclohexane, R1233zd (E), and R245fa were selected for comparison. The quality of heat source, the quality of evaporator, the system output, and the system complicity were taken as variables for comparison. Analysis shows that for ORC systems without recuperator, ethanol system has the best system output of the five in a wide operation temper range, with the highest exergy efficiency of 24.1%, yet the exergy efficiency increase after the application of recuperator, 9.0%, is limited. For low temperature exhaust, cyclopentane system has the best performance with or without recuperator, and the cyclopentane system with recuperator has the best performance in terms of exergy efficiency, 27.6%, though complex heat exchangers are also required for high power output. The system output of the R1233zd system is better than the R245fa system, yet the advantage of low evaporate temperature can be better utilized for low quality waste heat recovery.

Fuel cell hybrid electric vehicles are currently being considered as ideal means to solve the energy crisis and global warming in today’s society. In this context, this paper proposes a method to solve the problem related to the dependence of the so-called optimal equivalent factor (determined in the framework of the equivalent consumption minimum strategy-ECMS) on the working conditions. The simulation results show that under typical conditions (some representative cities being considered), the proposed strategy can maintain the power balance; for different initial battery’s states of charge (SOC), after the SOC stabilizes, the fuel consumption is 5.25 L/100 km.