Hydrogen fuel cell city bus is a type of new energy public transportation. In this paper, in order to evaluate the safety performance of a newly developed hydrogen fuel cell city bus body frame designed by the collaborating enterprise, finite element analysis is conducted to investigate its structural mechanics and dynamic characteristics under four typical operating conditions, including horizontal bending, ultimate torsion, emergency cornering, and emergency braking. Based on the simulation results, although the body frame of the bus meets the stiffness design requirements and avoids body resonance, it exhibits maximum stresses of 328.9 MPa and 348.6 MPa under emergency cornering and ultimate torsion conditions, respectively, exceeding the material yield strength and failing to satisfy the strength design requirements. Therefore, the size optimization method is employed to optimize the thickness of the body frame components. After optimization, the maximum stresses are reduced to 262.7 MPa and 300.6 MPa, respectively, representing a reduction of up to 20.13%. The optimization significantly improves performance and meets the strength design requirements. Furthermore, the body frame is lightened by 106 kg, achieving the goal of weight reduction.

As the issue of energy scarcity becomes increasingly critical, the adoption of electric construction machinery emerges as a pivotal strategy to address the energy crisis. During the travel and operation of electric construction machinery, the machinery-specific battery packs are subjected to long-term mechanical shocks and random vibration loads, leading to resonance and structural damage failure. To address the multi-objective optimization design issues of machinery-specific battery packs for electric construction machinery under the action of random vibration and impact loads and to enhance the fatigue life and reduce the mass of the battery pack, this paper conducts optimization design research on a newly developed battery pack for an electric excavator. Firstly, a finite element model of the battery pack is established to conduct simulation analyses on its impact resistance characteristics and fatigue life. Secondly, through a comprehensive contribution analysis method, key components are identified, with the thickness dimensions of the battery pack parts selected as design parameters. Finally, using maximum stress under mechanical shock conditions and first-order constraint mode as constraint conditions, mass minimization and fatigue life maximization are set as optimization objectives. The Box–Behnken experimental design is employed alongside a Kriging approximation model; subsequently, the NSGA-II algorithm is utilized for multi-objective optimization. The optimization results show that, while meeting the basic static and dynamic performance requirements, the mass of the optimized battery pack outer frame is reduced by 56.8 kg, a decrease of 5.75%. Concurrently, the optimized battery pack’s fatigue life has increased by 1,234,800 cycles, which is an enhancement factor of 1.65 compared to pre-optimization levels. These findings provide significant reference points for optimizing structural performance and achieving lightweight designs in electric excavator battery packs.

Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles’ power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks.

The hybrid electric dump truck is equipped with multiple power sources, and each powertrain component is controlled by an energy management strategy (EMS) to split the demanded power. This study proposes an EMS based on deep reinforcement learning (DRL) algorithm to extend the battery life and reduced total usage cost for the vehicle, namely the twin delayed deep deterministic policy gradient (TD3) based EMS. Firstly, the vehicle model is constructed and the optimization objective function, including battery aging cost and fuel consumption cost, is designed. Secondly, the TD3-based EMS is used for continuous action control of ICE power based on vehicle state, and the action mask is applied to filter out invalid actions. Thirdly, the simulations of the EMSs are trained under the CHTC-D driving cycle and C-WTVC driving cycle. The results show that the action mask improves the convergence efficiency of the strategies, and the proposed TD3-based EMS outperforms the deep deterministic policy gradient (DDPG) based EMS. Meanwhile, the battery life is extended by 36.17% under CHTC-D and 35.49% under C-WTVC, and the total usage cost is reduced by 4.30% and 2.49% when the EMS considers battery aging. In summary, the proposed TD3-based EMS can extend the battery life and reduce usage cost, and provides a method to solve the optimization problem for the EMS of hybrid power systems.

Remaining useful life (RUL) prediction of rolling bearings is a crucial issue in intelligent predictive maintenance, thereby ensuring equipment safety and reducing maintenance costs. To address the challenge that traditional deep learning models struggle to simultaneously capture local temporal features and global degradation trends when processing degradation health indicators (HI), this paper proposes a hybrid RUL prediction model based on extended Long Short-Term Memory (xLSTM) and Transformer. The model employs an encoder–decoder architecture, integrating the Multi-Head Attention mechanism with the xLSTM module. This design simultaneously enhances the modeling capability of short-term dynamic features and effectively captures long-term degradation patterns. Validation was conducted on the XJTU-SY and PHM2012 datasets. The proposed model outperformed the comparative models across evaluation metrics such as Root Mean Square Error (RMSE), Coefficient of Determination (R²) and the Score, achieving a significant improvement in prediction accuracy and multi-dataset generalization capability. The proposed network provides a more accurate and generalizable solution for bearing health assessment and remaining useful life prediction and demonstrates significant potential for intelligent health management of industrial equipment.

Localized defects are common in gear transmission systems and can sometimes cause serious production problems or even catastrophic accidents. To reveal the failure mechanisms and study the localized defects in gear transmission systems, a 24-degree-of-freedom (DOF) dynamic coupling model is proposed considering shafts, bearings, and gears. The dynamic characteristics of the established model when defects appear on the raceways of bearings and surfaces of gears are analyzed. It can be found in the results that the response of the established model produces periodic shocks when localized defects appear on bearings or gears through numerical analysis. Sidebands generated by fault frequencies can be detected from the frequency spectrum. Especially, bearing-localized defects on the inner race and gear surface are similar in modulation form envelope analysis, and the increase in rotating frequency leads to difficulties in distinguishing defects on bearings and gears. The established coupling dynamic model was validated through experimentation and offers a theoretical basis for the fault diagnosis of gear transmission systems.