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学者姓名：谢丽萍
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Lithium-ion battery capacitor (LIBC), which combines battery material and capacitor material in the cathode, has attracted attention for bridging the gap between high energy density and high power density in energy storage devices, but its application is hindered by polarization phenomena. To address this problem, an enhanced cathode conductive network is established by optimizing the conductive agent content and introducing conductive additives, which can improve the electronic and ionic conductivity. Cathode half-cell with enhanced conductive network, utilizing LiNi1/3Co1/3Mn1/3O2 and activated carbon as active materials, carbon black (CB) and vapor grown carbon fiber (VGCF) as conductive agents, indicates excellent capacity, rate capability, and cycle performance. And it shows low polarization with the voltage differences between the redox peaks of 91 mV at 0.1 mV s−1 in cyclic voltammetry experiments, nearly 26 % smaller than that for a half-cell with only 5 % CB as the conductive agent. Additionally, the complex polarization dynamics are revealed by distribution of relaxation times technique for extracting time scale information and a mathematical model based on the pseudo-two-dimensions theory. Consequently, a full-cell with a pre-lithiated soft carbon anode is assembled, displaying a great device performance of 300.3 Wh kg−1 and 15.7 kW kg−1. After 7500 cycles at 500 mA g−1, the capacity retention of the device can reach 81.1 % and the energy efficiency is 92.4 %. This study contributes to a better understanding of the polarization phenomenon of LIBCs. © 2025 Elsevier Ltd
Keyword ：
Analytical models Analytical models Approximation theory Approximation theory Computational chemistry Computational chemistry Dynamic models Dynamic models Ising model Ising model Lithium-ion batteries Lithium-ion batteries Mean field theory Mean field theory Monte Carlo methods Monte Carlo methods Numerical models Numerical models Optimization Optimization Semiconductor device models Semiconductor device models Time series analysis Time series analysis
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| GB/T 7714 | Guo, Zhang , Liu, Zhien , An, Yabin et al. Mitigating polarization effects in lithium-ion battery capacitors through conductive network enhancement [J]. | Journal of Energy Storage , 2025 , 127 . |
| MLA | Guo, Zhang et al. "Mitigating polarization effects in lithium-ion battery capacitors through conductive network enhancement" . | Journal of Energy Storage 127 (2025) . |
| APA | Guo, Zhang , Liu, Zhien , An, Yabin , Lu, Chihua , Li, Chen , Xu, Yanan et al. Mitigating polarization effects in lithium-ion battery capacitors through conductive network enhancement . | Journal of Energy Storage , 2025 , 127 . |
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电动车车内主动声音增强(active sound enhancement,ASE)系统在构造多元化声音特征和增加驾驶操纵感方面具有重要作用.本文面向电动车车内ASE技术,提出了一种可变权重的多模式切换声音合成算法,通过构建模式切换因子矩阵,将阶次合成、变调合成和粒子合成方法进行有机结合,形成深度声融合ASE系统,可实现以丰富主观听觉感知为目标的多模式车内声浪的实时合成,增加了 ASE系统的丰富度,使合成声音更具立体感和饱和感,提升了驾乘体验.然后使用C#语言开发了电动车车内声浪调制软件,集成了ASE系统控制和声浪调制功能,可快速实现对汽车声音的灵活调制.最后展示了声浪调制软件在某纯电SUV汽车声音调制中的应用,声音测试结合主观评价结果表明,该软件可以有效达成多模式声音合成目标,具有实际的工程应用价值.
Keyword ：
主动声音增强 主动声音增强 声音合成算法 声音合成算法 软件开发与应用 软件开发与应用
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| GB/T 7714 | 王涛 , 刘志恩 , 谢丽萍 et al. 电动车车内多模式声浪合成与应用研究 [J]. | 汽车工程 , 2025 , 47 (3) : 578-585,577 . |
| MLA | 王涛 et al. "电动车车内多模式声浪合成与应用研究" . | 汽车工程 47 . 3 (2025) : 578-585,577 . |
| APA | 王涛 , 刘志恩 , 谢丽萍 , 卢炽华 , 王颖 , 钱宇书 . 电动车车内多模式声浪合成与应用研究 . | 汽车工程 , 2025 , 47 (3) , 578-585,577 . |
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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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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 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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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 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-theloop 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.
Keyword ：
Back propagation neural network Back propagation neural network Dynamic programming Dynamic programming Energy management strategy Energy management strategy Fuel cell electric vehicle Fuel cell electric vehicle
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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 sound characteristic is a critical metric to manifest the brand differentiation of electric vehicle (EV), and the sound design with more diverse acoustic characteristics has become a hot issue in current research of EV technology. In this paper, an efficient technique of active sound design (ASD) is explored to develop the control system of active sound generation (ASG) with powerful sound quality for EV. Firstly, an optimization algorithm of sound synthesis based on multi-frequency superimposition is proposed to improve the harmonic interference phenomenon in the synthesized sounds. Subsequently, an adaptive sound control strategy is formulated, where an iterative accumulation method is proposed to calculate the "virtual engine speed" of EV, and a gain table is presented to divide the running state. Besides, an ASG system is developed based on the proposed ASD technique. The tests result demonstrate that there are 18 target order sounds are reproduced perfectly, and the powerful sound quality interior EV is enhanced while the original interior acoustic environment of EV is retained, which confirms the effectiveness of the proposed control technique of ASG system. The proposed ASD technique here accelerates the change from silence to sound quality in the electric vehicles, which has important theoretical significance and engineering value.
Keyword ：
active sound control strategy active sound control strategy active sound generation active sound generation Electric vehicle Electric vehicle sound synthesis algorithm sound synthesis algorithm
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| GB/T 7714 | Zhu, Yawei , Zhang, Yi , Xie, Liping et al. An efficient technique for developing the active sound control system in electric vehicle [J]. | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING , 2024 . |
| MLA | Zhu, Yawei et al. "An efficient technique for developing the active sound control system in electric vehicle" . | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING (2024) . |
| APA | Zhu, Yawei , Zhang, Yi , Xie, Liping , Liu, Zhien , Lu, Chihua . An efficient technique for developing the active sound control system in electric vehicle . | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING , 2024 . |
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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.
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, Xinyou , Zhou, Qiang , Tu, Jiayi 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, Xinyou 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, Xinyou , Zhou, Qiang , Tu, Jiayi , Xu, Xinhao , Xie, Liping . 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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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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