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Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problem of insufficient range caused by the excessive weight of the pure electric bus, a multi-objective genetic algorithm (GA) and radial basis function (RBF) model are combined in this paper to realize the lightweighting of steel and aluminum hybrid body of the pure electric bus. First, the upper and lower frames of the pure electric bus body are initially designed with aluminum alloy and steel materials respectively to meet the lightweight requirements. Second, a finite element (FE) model of the bus body is established, and the validity of the model is validated through physical tests. Then, the sensitivity analysis is performed to identify the relative importance of individual design parameters over the entire domain. The Hamosilei sampling method is selected for the design of the experiment (DOE) because users can specify the number of experiments and ensure that the set of random numbers is a good representative of real variability, and the RBF model is adopted to approximate the responses of objectives and constraints. Finally, the multi-objective optimization (MOO) method based on GA with RBF model is used to solve the optimization problem of the lightweight steel-aluminum hybrid bus body. The results show that compared with the traditional fully steel body, the use of the aluminum alloy lower-frame structure can reduce body mass by 38.4%, and the proposed optimization method can further reduce the mass of the steel-aluminum body to 4.28% without affecting the structural stiffness and strength performance of the body.

In order to solve the problem of insufficient range caused by the excessive weight of the pure electric bus, a multi-objective genetic algorithm (GA) and radial basis function (RBF) model are combined in this paper to realize the lightweighting of steel and aluminum hybrid body of the pure electric bus. First, the upper and lower frames of the pure electric bus body are initially designed with aluminum alloy and steel materials respectively to meet the lightweight requirements. Second, a finite element (FE) model of the bus body is established, and the validity of the model is validated through physical tests. Then, the sensitivity analysis is performed to identify the relative importance of individual design parameters over the entire domain. The Hamosilei sampling method is selected for the design of the experiment (DOE) because users can specify the number of experiments and ensure that the set of random numbers is a good representative of real variability, and the RBF model is adopted to approximate the responses of objectives and constraints. Finally, the multi-objective optimization (MOO) method based on GA with RBF model is used to solve the optimization problem of the lightweight steel-aluminum hybrid bus body. The results show that compared with the traditional fully steel body, the use of the aluminum alloy lower-frame structure can reduce body mass by 38.4%, and the proposed optimization method can further reduce the mass of the steel-aluminum body to 4.28% without affecting the structural stiffness and strength performance of the body. [ABSTRACT FROM AUTHOR]

Accurate and reasonable matching design is a current and difficult point in electric vehicle research. This paper presents a parameter optimization method for the power system of a medium-sized bus based on the combination of the orthogonal test and the secondary development of ADVISOR software. According to vehicle theoretical knowledge and the requirements of the vehicle power performance index, the parameters of the vehicle power system were matched and designed. With the help of the secondary development of MATLAB/Simulink and ADVISOR software, the modeling of the key parts of the vehicle was carried out. Considering the influence of the number of battery packs, motor power model, wheel rolling resistance coefficient, and wind resistance coefficient on the design of the power system, an L9 (34)-type orthogonal table was selected to design the orthogonal test. The dynamic performance and driving range of the whole vehicle were simulated using different design schemes, and the accuracy of the simulation results was verified by comparing and analyzing the simulation images. The results demonstrated that in the environment where the wind resistance coefficient was 0.6 and the wheel rolling resistance coefficient was 0.009, with 240 sets of lithium batteries (battery energy, 264 kW h; battery capacity, 100 Ah) as the power source, the pure electric medium-sized bus equipped with the PM165 permanent magnet motor (rated power, 60 kW; rated torque, 825 N m) could obtain the best power performance and economic performance. The research content of this paper provides a certain reference for the design of shuttle buses for Nantong's bus system, effectively reduces the testing costs of the vehicle development process, and provides a new idea for the power system design of pure electric buses. [ABSTRACT FROM AUTHOR]

In order to meet the safety requirements of automobile braking, to improve the braking energy recovery rate of pure electric buses and increase their driving range, the maximum regenerative braking force that the motor can provide is used to determine the front and rear wheel friction braking force distribution curve. A parallel regenerative braking control strategy, A, is proposed to make full use of the motor performance. Aiming at the problems of low power density and short cycle life with a single power battery, a composite energy system composed of power batteries and supercapacitors is designed, and an alternative energy control strategy, D, using real-time wavelet transform control is proposed. The required power is decomposed into high-frequency components and low-frequency components by using the wavelet transform control, in which the high-frequency power is borne by the supercapacitor to avoid impact on the power battery. The simulation model was created using MATLAB/Simulink software, and the simulation was carried out under combined cycle conditions to verify the effectiveness of the control strategy. The simulation results showed that compared with the original vehicle control strategy, adopting the A regenerative braking control strategy can reduce the battery SOC drop by 5.15%, increase the relative increase by 47.9%, and improve the braking energy recovery rate. Compared with the traditional logic threshold regenerative braking control strategy, AC, the AD control strategy can effectively reduce the impact of the peak current and high-frequency demands of the power battery on the battery. The maximum output current of the battery was reduced by 39.5%. The decrease in battery SOC decreased by 0.69%, and the relative increase increased by 12.43%. The driving range and braking performance of the vehicle have thus been effectively improved. [ABSTRACT FROM AUTHOR]

Accurate and reasonable matching design is a current and difficult point in electric vehicle research. This paper presents a parameter optimization method for the power system of a medium-sized bus based on the combination of the orthogonal test and the secondary development of ADVISOR software. According to vehicle theoretical knowledge and the requirements of the vehicle power performance index, the parameters of the vehicle power system were matched and designed. With the help of the secondary development of MATLAB/Simulink and ADVISOR software, the modeling of the key parts of the vehicle was carried out. Considering the influence of the number of battery packs, motor power model, wheel rolling resistance coefficient, and wind resistance coefficient on the design of the power system, an L9 (34)-type orthogonal table was selected to design the orthogonal test. The dynamic performance and driving range of the whole vehicle were simulated using different design schemes, and the accuracy of the simulation results was verified by comparing and analyzing the simulation images. The results demonstrated that in the environment where the wind resistance coefficient was 0.6 and the wheel rolling resistance coefficient was 0.009, with 240 sets of lithium batteries (battery energy, 264 kW h; battery capacity, 100 Ah) as the power source, the pure electric medium-sized bus equipped with the PM165 permanent magnet motor (rated power, 60 kW; rated torque, 825 N m) could obtain the best power performance and economic performance. The research content of this paper provides a certain reference for the design of shuttle buses for Nantong’s bus system, effectively reduces the testing costs of the vehicle development process, and provides a new idea for the power system design of pure electric buses.

In order to meet the safety requirements of automobile braking, to improve the braking energy recovery rate of pure electric buses and increase their driving range, the maximum regenerative braking force that the motor can provide is used to determine the front and rear wheel friction braking force distribution curve. A parallel regenerative braking control strategy, A, is proposed to make full use of the motor performance. Aiming at the problems of low power density and short cycle life with a single power battery, a composite energy system composed of power batteries and supercapacitors is designed, and an alternative energy control strategy, D, using real-time wavelet transform control is proposed. The required power is decomposed into high-frequency components and low-frequency components by using the wavelet transform control, in which the high-frequency power is borne by the supercapacitor to avoid impact on the power battery. The simulation model was created using MATLAB/Simulink software, and the simulation was carried out under combined cycle conditions to verify the effectiveness of the control strategy. The simulation results showed that compared with the original vehicle control strategy, adopting the A regenerative braking control strategy can reduce the battery SOC drop by 5.15%, increase the relative increase by 47.9%, and improve the braking energy recovery rate. Compared with the traditional logic threshold regenerative braking control strategy, AC, the AD control strategy can effectively reduce the impact of the peak current and high-frequency demands of the power battery on the battery. The maximum output current of the battery was reduced by 39.5%. The decrease in battery SOC decreased by 0.69%, and the relative increase increased by 12.43%. The driving range and braking performance of the vehicle have thus been effectively improved.