Integrating the electric motor with a multi-speed transmission is an effective way to improve the efficiency and performance of battery electric vehicles (BEVs). This paper innovatively proposes a design method for matching a single-motor and dual-speed dual-clutch transmission (2-Speed Wet DCT) powertrain system and constructs a variable speed efficiency model (VSEM) and constant speed efficiency model (CSEM) for the inverter, motor, and transmission. Research shows that the design parameters of the motor and transmission significantly affect the optimal powertrain system. This study uses an enhanced NSGA-II multi-objective genetic algorithm to optimize the driving performance of energy efficiency and powertrain cost under two different acceleration times (10 s and 12 s), with the key parameters of the motor and transmission as optimization variables and dynamic indicators as constraints, and compares VSEM and CSEM. The optimization results indicate that VSEM have better energy-saving effects than CSEM, with the energy consumption reduced by 3.7% and 3.3% under the two driving performances, respectively. The Pareto frontier further confirms that, for multi-speed transmission systems in electric vehicles, matching a high-power, high-torque motor with a smaller transmission ratio powertrain can achieve higher energy efficiency and thus longer driving range. Additionally, this study quantifies the correlation between energy efficiency and powertrain cost using grey relational analysis (GRA), with a result of 0.77431.

In this paper, based on the coupling calculation of Simulink software and GT-Power software, an Optimizer model method was proposed for a 16V265H diesel engine to study the effects of different ratios of biodiesel (B0, B10, and B20) on the performance of a 16V265H diesel engine at 1000 rpm and 75% load. Firstly, the four parameters of diesel engine power, BSFC, soot emission, and NO_x emission were taken as the result variables of the optimization model. Taking the intake and exhaust timing of the diesel engine as the independent variable of the optimization model, the maximum power, minimum BSFC, and minimum diesel engine emission were studied and analyzed. Finally, the performance parameters were comprehensively analyzed to determine the best intake and exhaust valve timing. Moreover, based on the model optimization, the diesel engine’s BSFC and power performance were compared, and the optimal intake timing scheme for the diesel engine with different biodiesel ratios at 75% operating conditions was obtained. The results showed that the maximum power, optimum BSFC, and minimum emissions of the 16V265H diesel engine with different ratios of biodiesel and different intake valve timing angles were also different. Under 75% load conditions, the BSFC reduction rate was up to 6.32%, and the power increase rate was up to 5.87%. In addition, by optimizing the model with B10 biodiesel and the intake valve timing close to 202°CA and the exhaust valve timing close to 98°CA, the diesel engine had the lowest NO_x emission; with B10 biodiesel and the intake timing at 180°CA, the diesel engine had the lowest BSFC; and with B10 biodiesel and the intake valve timing close to 179.5°CA, the diesel engine had the maximum power. In conclusion, the diesel engine is best with B10 biodiesel. This research method can provide a reference for implementing variable intake system technology for the 16V265H diesel engine.

The optimization of power parameters is the key to the design of pure electric vehicles. Reasonable matching of the relationship between various parameters can effectively reduce energy consumption and achieve energy sustainability. In this paper, several vehicle performance indexes such as maximum vehicle speed, acceleration time and power consumption per 100 km were used as optimization target vectors, and transmission ratio was used as optimization variable to establish the optimization problem of parameter matching. Then, the feasible domain of the transmission ratio was obtained by taking the lowest performance index of the vehicle as the constraint condition. In the feasible domain, the multi-objective genetic algorithm is used to solve the optimization problem. The Pareto optimal solution set is obtained for fixed ratio transmission and two-gear transmission, which is used as an alternative solution set. The final parameter-matching scheme is determined by comparing the alternative scheme set of different motors comprehensively. The results show that the competition relationship between multiple optimizable indexes can be described effectively by solving the Pareto front. Specifically, the Pareto optimal solution set for the motor A + fixed transmission scheme is 1.33~1.85; the Pareto optimal solution set for the motor A + 2 transmission scheme is [1.72, 0.98]~[2.99, 1.57], and the Pareto optimal solution set for the motor B + 2 transmission scheme is [2.99, 1.40]~[2.99, 1.57]. The motor A + fixed transmission scheme does not require A clutch and does not require designing a shift algorithm. Therefore, after comprehensive consideration, the motor A + fixed transmission ratio transmission scheme is set as the final scheme.

Many electric vehicles connected to the grid will lead to problems such as poor stability of power grid generation. The key to solving these problems is to propose an efficient, stable, and economical valley-filling charging scheme for electric vehicles and grid users in the vehicle network system. Firstly, the convex optimization theory is used to make the grid achieve the optimization effect of valley filling. On this basis, the electricity price scheme with a time-varying coefficient as the variable is proposed to meet the single objective optimization of EV charging cost optimization, and its degree of influence on the grid valley-filling effect is analyzed. Secondly, based on the competitive relationship between EV charging cost and battery life, the P2D model is simplified and analyzed, and the attenuation law of battery capacity is quantitatively described. The multi-objective optimization problem is established to express in a Pareto matrix. Finally, the compatibility between the multi-objective optimization and grid valley charging is analyzed. The simulation results show that: (1) The convexity electricity price scheme can satisfy the requirements of various retention rates to achieve the valley-filling effect; (2) The filling effect is satisfied with the electricity price scheme that minimizes the charging cost, and the key factors affecting the filling effect are analyzed; (3) The multi-objective optimization scheme with charging cost and battery life is compatible with the valley-filling effect.

The structural fracture of the compressor blade is the main cause of fatigue failure. The novelty of this paper is the creative application of bent swept-back modeling to the blade of the turbocharger impeller. This paper is based on a compressor impeller satisfying the k-ε turbulence model. A simulation model was established in ANSYS software, the fluid–structure interaction was calculated in the three models before and after improvement, and the results were compared and analyzed. The optimized blade could improve the blade structure, reduce stress and deformation, and improve the pressurization ratio. In this paper, the optimization scheme of different parameters was discussed in line with the optimal solution. Based on the combination of fuzzy and grey correlation theory, it was concluded that the correlation between pressure and total deformation was higher than that of equivalent stress, and these two values reached 0.8596 and 0.8001, respectively. The results showed that the pressure and total deformation were significantly related to the flow rate. It provides a feasible scheme for further improvement of the supercharger compressor.

In this paper, the simulation model was established based on GT-Power software, and a scheme using the Miller cycle based on biodiesel was proposed. Taking diesel engine 16V265H as the research object, the accuracy of the simulation model was verified by experiments. Combined with the comparison of physical and chemical characteristics of biodiesel and the experimental analysis of biodiesel under three different combinations, it was concluded that low ratio biodiesel was the best choice to meet the power, economy, and emission performance of diesel. Through the simulation scheme of the two Miller cycles for pure diesel (B0) and biodiesel (B10) under different load conditions at 1000 rpm, the NO_x emission performance of pure diesel in a Miller cycle was significantly improved. On this basis, the comprehensive performance of the two Miller cycles was compared with biodiesel. The results showed that both the Miller cycles could reduce NO_x emission. Combined with other key performances of a diesel engine, the best scheme to improve the performance of the diesel engine was to burn B10 biodiesel and overlap angle the Miller cycle of the variable valve at 30 °CA. The scheme has guiding significance for the application of the 16V265H diesel engine.