Fuel cell vehicles have been widely used in the commercial vehicle field due to their advantages of high efficiency, non-pollution and long range. In order to further improve the fuel economy of fuel cell commercial vehicles under complex working conditions, this paper proposes an adaptive rule-based energy management strategy for fuel cell commercial vehicles. First, the nine typical working conditions of commercial vehicles are classified into three categories of low speed, medium speed and high speed by principal component analysis and the K-means algorithm. Then, the crawfish optimization algorithm is used to optimize the back propagation neural network recognizer to improve the recognition accuracy and optimize the rule-based energy management strategy under the three working conditions to obtain the optimal threshold. Finally, under WTVC and combined conditions, the optimized recognizer is used to identify the conditions in real time and call the optimal rule threshold, and the sliding average filter is used to filter the fuel cell output power in real time, which finally realizes the adaptive control. The simulation results show that compared with the conventional rule-based energy management strategy, the number of fuel cell start–stops is reduced. The equivalent hydrogen consumption is reduced by 7.04% and 4.76%, respectively.

Traditional model predictive torque control (MPTC) predicts the torque and flux values for the next time step and selects the voltage vector that minimizes the cost function as the optimal vector to apply to the inverter. This control approach is straightforward and allows for multi-objective control, but it has some issues in terms of the dynamic steady-state performance and parameter robustness. Therefore, this paper proposes a weightless model predictive control method based on an extended state observer (ESO). By designing an improved ESO to observe and compensate for motor parameter disturbances in real time, and employing a novel 2-D switching table and voltage vector sector selection diagram, the method evaluates three out of eight voltage vectors based on the torque and stator flux error signals. This reduces the computational load while increasing the number of candidate voltage vectors. Finally, a cost function without weighting factors is designed to lower the computational complexity. The simulation results show that the proposed new control method effectively reduces the torque and flux ripple and improves the current waveform compared to traditional MPTC.

Lithium-ion batteries (LIBs) are currently widely used in the electric vehicle sector and have become one of the core components of new energy vehicles. To ensure that the maximum temperature (T_max) and maximum temperature difference (∆T_max) remain within acceptable limits after high-rate discharge, this study proposes a novel air-cooled battery thermal management system (BTMS). This BTMS features innovative design elements in its novel battery case walls and contour-following fin structure. Through physical testing of 21,700 LIB discharges and comparative numerical simulations, the accuracy of the simulation model is ensured. Orthogonal experimental analysis is conducted at four distinct levels for each of the four structural factors involved. The final results demonstrate that the novel battery pack wall and contour-shaped fin structure proposed in this paper significantly enhance the heat dissipation capability of air-cooled BTMS. The proposed Model 9 configuration exhibits optimal thermal performance metrics. The T_max after 3C rate discharge reaches 39.4 °C, with a ∆T_max of 7.4 °C. This study demonstrates significant application potential in the structural implementation of air-cooled BTMSs.

Fuel cell commercial vehicles are widely used in commercial transport for their high efficiency and long range. However, in mixed operating scenarios, their energy economy and fuel cell operational stability cannot be fully balanced. Traditional strategies lack adaptability in mixed operating scenarios. Therefore, based on the equivalent factor regulation formula of the Adaptive Equivalent Hydrogen Consumption Minimization Strategy (A-ECMS) and the improved Sparrow Search Algorithm-Long Short-Term Memory (SSA-LSTM) hybrid model, short-term speed prediction and three-stage speed interval division are embedded into the equivalent factor regulation logic. A dynamic equivalent factor regulation strategy integrating SOC deviation is constructed, and an improved Predictive Equivalent Hydrogen Consumption Minimization Strategy (P-ECMS) is finally derived. The SSA-LSTM algorithm is optimized via constrained hyperparameter tuning for short-term speed prediction. A time-decay weighting mechanism enhances recent speed data weight, with weighted results as inputs to boost accuracy. Moving Average Residual Correction (MARC) is used to verify the speed prediction model accuracy and correct residuals. Multi-scenario tests show that the SSA-LSTM model outperforms the Gated Recurrent Unit (GRU) model in prediction accuracy and generalization ability, providing reliable data support for segmented regulation. With battery SOC deviation and the SSA-LSTM-predicted speed trend as core inputs, combined with three-stage speed interval division, A-ECMS’s equivalent factor regulation formula is improved. The model adopts a segmented dynamic regulation logic to integrate dual factors into equivalent factor adjustment, and it reasonably adjusts the energy output ratio of fuel cells and power batteries according to speed intervals and operating condition changes. In scenarios with significant speed fluctuations and frequent operating condition transitions, power shocks are mitigated by the power battery’s peak-shaving and valley-filling function. Simulation results for C-WTVC and NREL2VAIL show that, compared with traditional A-ECMS, the improved P-ECMS has notable energy benefits, with equivalent hydrogen consumption reduced by 3.41% and 5.48%, respectively. The fuel cell’s state is significantly improved, with its high-efficiency share reaching 63%. The output power curve is smoother, start–stop losses are reduced, and the fuel cell’s service life is extended, balancing the energy economy and component durability.

To address the significant vibration and noise problems caused by the zero-order radial electromagnetic force (REF) in integer-slot permanent magnet synchronous motors (PMSMs), while simultaneously improving the motor’s overall electromagnetic performance, this paper proposes a hierarchical iterative optimization strategy integrating Taguchi methods and genetic algorithms. The optimization objectives include minimizing the zero-order REF amplitude, cogging torque, and torque ripple, while maximizing the average torque, with efficiency and back electromotive force total harmonic distortion (back-EMF THD) treated as constraints. First, an 8-pole 48-slot double-layer embedded PMSM model is constructed. An innovative parameter selection strategy, combining theoretical analysis with finite-element analysis, is employed to investigate the spatial order and frequency characteristics of the electromagnetic force. Subsequently, a sensitivity analysis is performed to stratify parameters: highly sensitive parameters undergo first-round optimization via the Taguchi method, followed by second-round optimization using a multi-objective genetic algorithm. The results demonstrate significant reductions in both the zero-order REF amplitude and cogging torque. Specifically, the motor’s peak vibration acceleration is reduced by 32.96%, and the peak sound pressure level (SPL) drops by 9.036 dB. Vibration acceleration and sound pressure across all frequency bands are significantly reduced to varying extents, validating the effectiveness of the proposed optimization approach.