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学者姓名：谢丽萍
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Abstract ：
There is a bottleneck in the design of vehicle sound that the subjective perception of sound quality that combines multiple psychological factors fails to be accurately and objectively quantified.Therefore,EEG signals are introduced in this paper to investigate the evaluation and design method of vehicle acceleration sound with powerful sound quality.Firstly,the experiment of EEG acquisition and subjective evaluation under the stimulation of powerful vehicle sounds is conducted,respectively,then three physiological EEG features of PSD_p,PSD_y and DE are constructed to evaluate the vehicle sounds based on the correlation analysis algorithms.Subsequently,the Adaptive Genetic Algorithm(AGA)is proposed to optimize the Elman model,where an intelligent model(AGA-Elman)is constructed to objectively predicate the perception of subjects for the vehicle sounds with powerful sound quality.The results demonstrate that the error of the constructed AGA-Elman model is only 2.88％,which outperforms than the traditional BP and Elman model;Finally,two vehicle acceleration sounds(Design 1 and Design2)are designed based on the constructed AGA-Elman model from the perspective of order modulation and frequency modulation,which provide the acoustic theoretical guidance for the design of vehicle sound incorporating the EEG signals.
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| GB/T 7714 | Liping Xie , XinYou Lin , Wan Chen et al. An Innovation of Evaluation and Design of Vehicle Acceleration Sound Based on EEG Signals [J]. | 仿生工程学报（英文版） , 2024 , 21 (1) : 344-361 . |
| MLA | Liping Xie et al. "An Innovation of Evaluation and Design of Vehicle Acceleration Sound Based on EEG Signals" . | 仿生工程学报（英文版） 21 . 1 (2024) : 344-361 . |
| APA | Liping Xie , XinYou Lin , Wan Chen , Zhien Liu , Yawei Zhu . An Innovation of Evaluation and Design of Vehicle Acceleration Sound Based on EEG Signals . | 仿生工程学报（英文版） , 2024 , 21 (1) , 344-361 . |
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The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy performance of the fuel cell hybrid electric vehicles under various driving cycles, however, this method requires prior knowledge of the future driving cycles. To utilize the solutions of DP, a SOC-trajectory online learning generation algorithm based approximate global optimization energy management control strategy is proposed. Initially, the global optimality of DP is used to extract the optimal SOC gradients for diverse driving scenarios. Real-time generation of optimal gradient factors for SOC trajectories is facilitated through the training of a backpropagation neural network with DP solutions. Subsequently, the deterministic rules are designed to plan SOC under actual driving conditions, with a dynamically updated threshold by the trained agents. Finally, based on the above, the optimal calculation of energy allocation is performed by combining sequence quadratic programming. Numerical verification, inclusive of hardware-in-the-loop experiments, show the effectiveness of the proposed strategy. The results demonstrate that the proposed strategy improves fuel economy by 7.39% compared to ECMS. Additionally, it reduces the cost of fuel cell life loss by 32.09% and achieves over 90% optimization of global driving cost. © 2024
Keyword ：
Battery management systems Battery management systems Dynamic programming Dynamic programming E-learning E-learning Fuel cells Fuel cells Fuel economy Fuel economy Global optimization Global optimization Hybrid vehicles Hybrid vehicles Learning algorithms Learning algorithms Neural networks Neural networks Quadratic programming Quadratic programming Trajectories Trajectories
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| GB/T 7714 | Lin, Xinyou , Huang, Hao , Xu, Xinhao et al. Dynamic programming solutions extracted SOC-trajectory online learning generation algorithm based approximate global optimization control strategy for a fuel cell hybrid electric vehicle [J]. | Energy , 2024 , 295 . |
| MLA | Lin, Xinyou et al. "Dynamic programming solutions extracted SOC-trajectory online learning generation algorithm based approximate global optimization control strategy for a fuel cell hybrid electric vehicle" . | Energy 295 (2024) . |
| APA | Lin, Xinyou , Huang, Hao , Xu, Xinhao , Xie, Liping . Dynamic programming solutions extracted SOC-trajectory online learning generation algorithm based approximate global optimization control strategy for a fuel cell hybrid electric vehicle . | Energy , 2024 , 295 . |
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The active sound generation systems (ASGS) for electric vehicles (EVs) play an important role in improving sound perception and transmission in the car, and can meet the needs of different user groups for driving and riding experiences. The active sound synthesis algorithm is the core part of ASGS. This paper uses an efficient variable-range fast linear interpolation method to design a frequency-shifted and pitch-modified sound synthesis algorithm. By obtaining the operating parameters of EVs, such as vehicle speed, motor speed, pedal opening, etc., the original sound signal is interpolated to varying degrees to change the frequency of the sound signal, and then the amplitude of the sound signal is determined according to different driving states. This simulates an effect similar to the sound of a traditional car engine. Then, a dynamic superposition strategy is proposed based on the Hann window function. Through windowing and superposition processing of each sound signal segment generated by the algorithm, the coherence and real-time performance of the synthesized engine sound are improved, so that the ASGS can quickly and accurately reflect the driving status of EVs. Finally, through the analysis and verification of the sound quality of the synthesized sound through different parameter adjustments, an engine synthesized sound that satisfies the subjective evaluation of sound quality can be obtained. This paper proposes an effective active sound synthesis algorithm for EVs, which ensures that EVs produce more textured engine sound while emphasizing the timeliness of synthesized sound. It plays an important role in improving pedestrian safety perception and driving experience, and promotes the research and development of ASGS for EVs. © 2024 SAE International. All rights reserved.
Keyword ：
Acoustic noise Acoustic noise Acoustic variables measurement Acoustic variables measurement Design Design Engines Engines Pedestrian safety Pedestrian safety Quality control Quality control Sound reproduction Sound reproduction Textures Textures Vehicle transmissions Vehicle transmissions
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| GB/T 7714 | Yu, Shangbo , Xie, Liping , Lu, Chihua et al. Research on Design of Electric Vehicle Sound Synthesis Based on Frequency Shift Algorithm [C] . 2024 . |
| MLA | Yu, Shangbo et al. "Research on Design of Electric Vehicle Sound Synthesis Based on Frequency Shift Algorithm" . (2024) . |
| APA | Yu, Shangbo , Xie, Liping , Lu, Chihua , Qian, Yushu , Liu, Zhien , Songze, Du . Research on Design of Electric Vehicle Sound Synthesis Based on Frequency Shift Algorithm . (2024) . |
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The integrated manufacturing of aerospace composite cryogenic tanks is crucial for enhancing payload efficiency, reducing costs, and leading the aerospace industry upgrade. Composite segmented tool, which balances internal support and mold surface, must not only meet the requirements of disassembly and demolding but also ensure sufficient stiffness without deformation under loads like winding tension and curing shrinkage during tank formation. This article addresses the challenge faced by composite tool with uniformly thick ply stacking schemes, where the weight increases significantly with the rocket body diameter, rendering functions such as disassembly and demolding unfeasible. A global-local optimization approach aimed at achieving variable-thickness ply stacking designs for composite tooling was proposed. Starting with a defined segmented tool design for the phi 3.35 m tank, models for calculating winding tension under complex service conditions and finite element models for curing shrinkage were established. Optimization of ply shapes, dimensions, and sequences using OptiStruct was conducted, which achieved a weight reduction of 34.48% while ensuring that deformations under loading met design standards. Subsequently, the engineering trials for the composite melon petal and wallboard corresponding to the phi 600 mm tank were conducted based on the optimized scheme. The maximum deformations for the two components were 0.43 mm and 0.15 mm, respectively, meeting the manufacturing requirements for engineering applications. The results provide a lightweight, high-stiffness, and detachable tool design scheme for achieving the integrated manufacturing of extra-large (phi 10 m) composite tanks.Highlights The external load was analyzed through theoretical and simulation approaches. The weight of composite tool was significantly reduced after optimization. The engineering prototypes of the segmented tools were achieved. Structure design and optimization for composite tool of aerospace cryogenic tank. image
Keyword ：
aerospace cryogenic tank aerospace cryogenic tank composite segmented tool composite segmented tool curing kinetics curing kinetics finite element simulation finite element simulation ply stacking optimization ply stacking optimization
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| GB/T 7714 | Guan, Chenglong , Chi, Tongming , Zhan, Lihua et al. Service load analysis and ply stacking optimization for composite tool of aerospace cryogenic tank [J]. | POLYMER COMPOSITES , 2024 , 45 (8) : 6845-6860 . |
| MLA | Guan, Chenglong et al. "Service load analysis and ply stacking optimization for composite tool of aerospace cryogenic tank" . | POLYMER COMPOSITES 45 . 8 (2024) : 6845-6860 . |
| APA | Guan, Chenglong , Chi, Tongming , Zhan, Lihua , Yao, Shunming , Chen, Junhao , Xie, Liping et al. Service load analysis and ply stacking optimization for composite tool of aerospace cryogenic tank . | POLYMER COMPOSITES , 2024 , 45 (8) , 6845-6860 . |
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Abstract ：
There is a bottleneck in the design of vehicle sound that the subjective perception of sound quality that combines multiple psychological factors fails to be accurately and objectively quantified. Therefore, EEG signals are introduced in this paper to investigate the evaluation and design method of vehicle acceleration sound with powerful sound quality. Firstly, the experiment of EEG acquisition and subjective evaluation under the stimulation of powerful vehicle sounds is conducted, respectively, then three physiological EEG features of PSD_beta, PSD_gamma and DE are constructed to evaluate the vehicle sounds based on the correlation analysis algorithms. Subsequently, the Adaptive Genetic Algorithm (AGA) is proposed to optimize the Elman model, where an intelligent model (AGA-Elman) is constructed to objectively predicate the perception of subjects for the vehicle sounds with powerful sound quality. The results demonstrate that the error of the constructed AGA-Elman model is only 2.88%, which outperforms than the traditional BP and Elman model; Finally, two vehicle acceleration sounds (Design1 and Design2) are designed based on the constructed AGA-Elman model from the perspective of order modulation and frequency modulation, which provide the acoustic theoretical guidance for the design of vehicle sound incorporating the EEG signals.
Keyword ：
Adaptive genetic algorithm Adaptive genetic algorithm Brain activity analysis Brain activity analysis EEG signal EEG signal Elman model Elman model Vehicle sound design Vehicle sound design
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| GB/T 7714 | Xie, Liping , Lin, Xinyou , Chen, Wan et al. An Innovation of Evaluation and Design of Vehicle Acceleration Sound Based on EEG Signals [J]. | JOURNAL OF BIONIC ENGINEERING , 2024 , 21 (1) : 344-361 . |
| MLA | Xie, Liping et al. "An Innovation of Evaluation and Design of Vehicle Acceleration Sound Based on EEG Signals" . | JOURNAL OF BIONIC ENGINEERING 21 . 1 (2024) : 344-361 . |
| APA | Xie, Liping , Lin, Xinyou , Chen, Wan , Liu, Zhien , Zhu, Yawei . An Innovation of Evaluation and Design of Vehicle Acceleration Sound Based on EEG Signals . | JOURNAL OF BIONIC ENGINEERING , 2024 , 21 (1) , 344-361 . |
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The power transients caused by switching from drive mode to brake mode in fuel cell hybrid electric vehicles (FCHEV) can result in significant degradation cost losses to the fuel cell. To address this issue, this study proposes a self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy. First, a real-time self-learning Markov predictor (SLMP) based on the traditional offline training Markov improvement is designed to predict the demand power and combined with the sequential quadratic programming (SQP) optimization algorithm to solve for the inner optimal demand power based on its global cost function minimization characteristic. On this basis, the fuel cell gradient drop power (FGDP) strategy is proposed to optimize the operating state of the vehicle powertrain under vehicle mode switching. This involves establishing a power gradient drop step based on considering the fuel cell hydrogen consumption cost and its lifetime degradation cost to further obtain the outer fuel cell demand power at the optimal step. And three execution modes are designed to trigger the FGDP strategy. Finally, by combining the above efforts, the SLMP-FGDP optimization control strategy is constructed. The numerical verification and hardware in loop experiments results show that the proposed improved SLMP can predict the vehicle demand power more accurately. Compared with the non-FGDP system, the SLMP-FGDP strategy can effectively near-eliminate the fuel cell power transient due to any braking scenario, thus effectively controlling the fuel cell lifetime degradation cost in a lower range and realizing a reduction of up to 52.21% of the fuel cell usage costs without significantly sacrificing the hydrogen fuel economy. © 2024 Elsevier Ltd
Keyword ：
Battery life degradation Battery life degradation Energy management strategy Energy management strategy Fuel cell hybrid electric vehicle Fuel cell hybrid electric vehicle Gradient drop power strategy Gradient drop power strategy Markov prediction Markov prediction
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| GB/T 7714 | Lin, X. , Zhou, Q. , Tu, J. et al. Self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy for the transient modes of fuel cell hybrid electric vehicles [J]. | Applied Energy , 2024 , 376 . |
| MLA | Lin, X. et al. "Self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy for the transient modes of fuel cell hybrid electric vehicles" . | Applied Energy 376 (2024) . |
| APA | Lin, X. , Zhou, Q. , Tu, J. , Xu, X. , Xie, L. . Self-learning Markov prediction algorithm based aging-oriented gradient drop power control strategy for the transient modes of fuel cell hybrid electric vehicles . | Applied Energy , 2024 , 376 . |
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Energy management strategies play an essential role in improving fuel economy and extending battery lifetime for fuel cell hybrid electric vehicles. However, the traditional energy management strategy ignores the lifetime of the battery for good fuel economy. To overcome this drawback, a battery longevity-conscious energy manage-ment predictive control strategy is proposed based on the deep reinforcement learning algorithm predictive equivalent consumption minimization strategy (DRL-PECMS) in this study. To begin with, the back-propagation neural network is devised for predicting demand power, and the predictive equivalent consumption minimum strategy (PECMS) is proposed to improve the hydrogen consumption. Then, in order to improve the battery durability performance, the deep reinforcement learning algorithm is utilized to optimize the battery power and improve battery lifetime. Finally, numerical verification and hard-ware in the loop experiments are conducted to validate hydrogen consumption and battery durability performance of the proposed strategy. The validation results show that, compared with CD/CS and SQP(Sequential Quadratic Programming), the PECMS combined can achieve better fuel economy with the fuel consumption reduction by 55.6 % and 5.27 %, which effectively improves the fuel economy. The DRL-PECMS can reduce the effective Ah-throughput by 3.1 % compared with the PECMS. The numerous validations and comparisons demonstrate that the proposed strategy effectively accom-plishes the trade-off optimization between energy consumption and battery durability performance.
Keyword ：
Battery longevity -conscious strategy Battery longevity -conscious strategy Energy management strategy Energy management strategy Fuel cell electric vehicle Fuel cell electric vehicle Velocity prediction Velocity prediction
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| GB/T 7714 | Ren, Xiaoxia , Ye, Jinze , Xie, Liping et al. Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle [J]. | ENERGY , 2024 , 286 . |
| MLA | Ren, Xiaoxia et al. "Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle" . | ENERGY 286 (2024) . |
| APA | Ren, Xiaoxia , Ye, Jinze , Xie, Liping , Lin, Xinyou . Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle . | ENERGY , 2024 , 286 . |
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The evaluation of automobile sound quality is an important research topic in the interior sound design of passenger car, and the accurate and effective evaluation methods are required for the determination of the acoustic targets in automobile development. However, there are some deficiencies in the existing evaluation studies of automobile sound quality. (1) Most of subjective evaluations only considered the auditory perception, which is easy to be achieved but does not fully reflect the impacts of sound on participants; (2) similarly, most of the existing subjective evaluations only considered the inherent properties of sounds, such as physical and psychoacoustic parameters, which make it difficult to reflect the complex relationship between the sound and the subjective perception of the evaluators; (3) the construction of evaluation models only from physical and psychoacoustic perspectives does not provide a comprehensive analysis of the real subjective emotions of the participants. Therefore, to alleviate the above flaws, the auditory and visual perceptions are combined to explore the inference of scene video on the evaluation of sound quality, and the EEG signal is introduced as a physiological acoustic index to evaluate the sound quality; simultaneously, an Elman neural network model is constructed to predict the powerful sound quality combined with the proposed indexes of physical acoustics, psychoacoustics, and physiological acoustics. The results show that evaluation results of sound quality combined with scene videos better reflect the subjective perceptions of participants. The proposed objective evaluation indexes of physical, psychoacoustic, and physiological acoustic contribute to mapping the subjective results of the powerful sound quality, and the constructed Elman model outperforms the traditional back propagation (BP) and support vector machine (SVM) models. The analysis method proposed in this paper can be better applied in the field of automotive sound design, providing a clear guideline for the evaluation and optimization of automotive sound quality in the future.
Keyword ：
Automotive sound quality Automotive sound quality Evaluation models Evaluation models Physiological acoustics Physiological acoustics Scene video Scene video Subjective and objective evaluation Subjective and objective evaluation
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| GB/T 7714 | Xie, Liping , Liu, Zhien , Sun, Yi et al. Investigating the Influence of Scene Video on EEG-Based Evaluation of Interior Sound in Passenger Cars [J]. | COGNITIVE COMPUTATION , 2024 , 16 (5) : 2297-2314 . |
| MLA | Xie, Liping et al. "Investigating the Influence of Scene Video on EEG-Based Evaluation of Interior Sound in Passenger Cars" . | COGNITIVE COMPUTATION 16 . 5 (2024) : 2297-2314 . |
| APA | Xie, Liping , Liu, Zhien , Sun, Yi , Zhu, Yawei . Investigating the Influence of Scene Video on EEG-Based Evaluation of Interior Sound in Passenger Cars . | COGNITIVE COMPUTATION , 2024 , 16 (5) , 2297-2314 . |
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Road gradients not only affect the actual performance of control strategies but also impact battery life due to the drastic changes in power demands. To balance battery degradation with fuel economy using gradient information, this study proposes a gradient-aware trade-off control strategy. Initially, a vehicle dynamics model and a battery degradation model are established. Based on the characteristics of known road information and remaining driving distance, state of charge planning of the battery is conducted. Subsequently, the Non-dominated Sorting Genetic Algorithm-II is applied for bi-objective optimization, yielding a set of Pareto solutions that represent different levels of energy consumption and battery degradation. Thereafter, by introducing a real-time battery degradation severity factor, an optimized bias coefficient is obtained, which adjusts in accordance with the gradient changes. Through the optimization of the bias line, the optimal bias solution set under different working conditions is determined, achieving the optimal control for power system. The fuel economy of the proposed strategy is improved by 6.8% relative to the mileage adaptive Equivalent Consumption Minimization Strategy, and the battery degradation inhibition is improved by 9.3%. After real-world conditions validation, the proposed strategy demonstrates good performance in both economic efficiency and battery life. © 2024 Hydrogen Energy Publications LLC
Keyword ：
Energy management strategy Energy management strategy Fuel cell electric vehicle Fuel cell electric vehicle Gradient-aware dynamic optimization Gradient-aware dynamic optimization NSGA-II algorithm NSGA-II algorithm
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| GB/T 7714 | Lin, X. , Huang, H. , Xie, L. et al. Gradient-aware trade-off control strategy dynamic optimization for a fuel cell hybrid electric vehicle [J]. | International Journal of Hydrogen Energy , 2024 , 81 : 1107-1120 . |
| MLA | Lin, X. et al. "Gradient-aware trade-off control strategy dynamic optimization for a fuel cell hybrid electric vehicle" . | International Journal of Hydrogen Energy 81 (2024) : 1107-1120 . |
| APA | Lin, X. , Huang, H. , Xie, L. , Zou, S. . Gradient-aware trade-off control strategy dynamic optimization for a fuel cell hybrid electric vehicle . | International Journal of Hydrogen Energy , 2024 , 81 , 1107-1120 . |
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The transition of large-scale cryogenic propellant tanks from metal to composite materials is the main trend in the global aerospace industry. Aiming to address the challenges of achieving the manufacturing of integrated and cost-effective manufacturing of aerospace cryogenic composite tanks that cannot be realized through the conventional autoclave process, and those of existing out-of-autoclave processes that are unable to effectively suppress defects under low-pressure conditions, a vibration pretreatment was innovatively introduced into the microwave curing process of composite materials in this study. Based on a systematic analysis of the inhibitory mechanisms of vibration pretreatment on void formation and the uniform heating mechanisms of microwaves in composite materials, the experimental results showed that the compound curing process enabled the production of components with complex structural features under low-pressure conditions while achieving equivalent surface precision and comprehensive properties, including porosity, interlaminar shear strength, and cryogenic permeation resistance, as those obtained through the standard 0.6 MPa autoclave process. This holds great promise for the application of out-of-autoclave processes in the manufacturing of large-scale aerospace cryogenic composite tanks.
Keyword ：
cryogenic composite tank cryogenic composite tank deformation deviation deformation deviation melon petal melon petal microwave curing microwave curing permeation rate permeation rate vibration pretreatment vibration pretreatment
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| GB/T 7714 | Guan, Chenglong , Chi, Tongming , Zhan, Lihua et al. Analysis of Deformation and Properties of Composite Melon Petals via Vibration Pretreatment-Microwave Compound Curing [J]. | POLYMERS , 2023 , 15 (23) . |
| MLA | Guan, Chenglong et al. "Analysis of Deformation and Properties of Composite Melon Petals via Vibration Pretreatment-Microwave Compound Curing" . | POLYMERS 15 . 23 (2023) . |
| APA | Guan, Chenglong , Chi, Tongming , Zhan, Lihua , Chen, Junhao , Wang, Bing , Xie, Liping et al. Analysis of Deformation and Properties of Composite Melon Petals via Vibration Pretreatment-Microwave Compound Curing . | POLYMERS , 2023 , 15 (23) . |
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