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学者姓名：谢丽萍
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Abstract ：
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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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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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 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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Abstract ：
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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Abstract ：
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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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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Abstract ：
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 Nondominated 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.
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, Xinyou , Huang, Hao , Xie, Liping 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, Xinyou 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, Xinyou , Huang, Hao , Xie, Liping , Zou, Songchun . 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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Automatic lane-changing is a complex and common task for autonomous vehicle control. In this study, a hierarchical decoupled path and velocity planning model for lane changing is proposed to enhance driving safety, comfort, and traffic efficiency. First, a parametric trajectory model is established based on the vehicle kinematic model, and the initial trajectory is solved quickly by the Sequential Quadratic Programming algorithm; in addition, the path optimization function is designed to optimize the trajectory curvature, and the distance-based velocity optimization method is used to improve the trajectory transverse, longitudinal acceleration, and jerk. To ensure the accuracy of path tracking, a comprehensive online trajectory optimization function is proposed according to tracking error fitting and vehicle reachability domain. The validation results demonstrate that the optimized path transverse velocity, acceleration, and jerk change curve are smoother, which meets the safety and comfort requirements of trajectory planning. Finally, the feasibility of the proposed trajectory planning is verified in a prototype vehicle real-world test.
Keyword ：
Autonomous vehicles Autonomous vehicles lane change lane change online trajectory planning online trajectory planning path re-optimization path re-optimization speed re-optimization speed re-optimization
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| GB/T 7714 | Lin, Xinyou , Wang, Tianfeng , Zeng, Songrong et al. Autonomous Vehicles Lane-Changing Trajectory Planning Based on Hierarchical Decoupling [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2024 . |
| MLA | Lin, Xinyou et al. "Autonomous Vehicles Lane-Changing Trajectory Planning Based on Hierarchical Decoupling" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2024) . |
| APA | Lin, Xinyou , Wang, Tianfeng , Zeng, Songrong , Chen, Zhiyong , Xie, Liping . Autonomous Vehicles Lane-Changing Trajectory Planning Based on Hierarchical Decoupling . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 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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