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学者姓名：林歆悠
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Abstract ：
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 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 integration of ultracapacitors (UCs) into hybrid energy storage systems is a solution to mitigate battery degradation. Traditional strategies focus on fuel cell and battery power regulation while treating UC management as a passive element, resulting in suboptimal UC utilization. To optimize the energy utilization of UCs, this article proposes an active state control strategy within the hybrid system. Initially, leveraging the battery severity factor, the optimal power split strategy for HESS is proposed for a reference state-of-charge (SOC) of UC. Subsequently, a driving pattern severity factor is designed, and an online self-learning Markov predictor is employed to quantify the operational state of vehicle. To provide optimal reference SOC guidance to HESS in real time, a reinforcement learning algorithm featuring an experience replay mechanism is developed. Utilizing pretrained agents that integrate vehicle driving state abstraction parameters, the system generates the reference SOC of UC, enabling the optimal battery-UC power split in real time. Both numerical and semiphysical validations confirm the efficacy of the proposed strategy in enhancing the power output ratio of UC, optimizing energy storage space utilization, and reducing the battery severity factor, consequently improving overall battery lifespan.
Keyword ：
Deep reinforcement learning (RL) Deep reinforcement learning (RL) fuel cell hybrid electric vehicle (FCHEV) fuel cell hybrid electric vehicle (FCHEV) hybrid energy storage system (HESS) hybrid energy storage system (HESS)
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| GB/T 7714 | Lin, Xinyou , Huang, Hao , Xu, Xinhao . Battery Degradation Oriented Active Control Strategy by Using a Reinforcement Learning Algorithm in Hybrid Energy Storage System [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 . |
| MLA | Lin, Xinyou et al. "Battery Degradation Oriented Active Control Strategy by Using a Reinforcement Learning Algorithm in Hybrid Energy Storage System" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2024) . |
| APA | Lin, Xinyou , Huang, Hao , Xu, Xinhao . Battery Degradation Oriented Active Control Strategy by Using a Reinforcement Learning Algorithm in Hybrid Energy Storage System . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 . |
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Both the stochastic traffic information and state of charge (SOC) greatly impact the plug-in parallel hybrid electric vehicle performance. Uncertain cycles and driving styles affect the effectiveness of velocity prediction and further cause the instability of SOC estimate. These uncertain stochastic factors interfere with the solution of torque demand in different degrees in each control cycle. To address this issue, a stochastic model predictive control (SMPC) considering short-term forecast optimal SOC is proposed. Firstly, multiple linear regression of engine and battery is developed for energy management strategy (EMS), respectively. Then, the velocity prediction model is developed based Markov chain considering the driver styles, and reference SOC is optimized by dynamic programming with the forthcoming information. Finally, the SMPC-based EMS with the short-term optimal SOC is constituted. The verification results show Markov based on driver styles has better predictive performance than radial basis function neural networks and back propagation neural networks. The fuel economy of the proposed strategy improves by about 11.8% compared with normal model predictive control and is close to that of the globally optimal dynamic programming. The test results indicate that the SMPC with the short-term optimal SOC can promote EMS to improve the fuel economy. IEEE
Keyword ：
Adaptation models Adaptation models Batteries Batteries Energy management Energy management Energy management strategy Energy management strategy Engines Engines Multiple linear regression Multiple linear regression Plug-in hybrid electric vehicle Plug-in hybrid electric vehicle Predictive models Predictive models State of charge State of charge Stochastic model predictive control Stochastic model predictive control Torque Torque Velocity prediction Velocity prediction
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| GB/T 7714 | Lin, X. , Chen, X. , Chen, Z. et al. Stochastic Model Predictive Control Strategy with Short-term Forecast Optimal SOC for a Plug-in Hybrid Electric Vehicle [J]. | IEEE Transactions on Transportation Electrification , 2024 , 10 (4) : 1-1 . |
| MLA | Lin, X. et al. "Stochastic Model Predictive Control Strategy with Short-term Forecast Optimal SOC for a Plug-in Hybrid Electric Vehicle" . | IEEE Transactions on Transportation Electrification 10 . 4 (2024) : 1-1 . |
| APA | Lin, X. , Chen, X. , Chen, Z. , Xie, L. . Stochastic Model Predictive Control Strategy with Short-term Forecast Optimal SOC for a Plug-in Hybrid Electric Vehicle . | IEEE Transactions on Transportation Electrification , 2024 , 10 (4) , 1-1 . |
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为有效地提高插电式燃料电池汽车的经济性,实现燃料电池和动力电池的功率最优分配,考虑到行驶工况、电池荷电状态(State of charge,SOC)、等效因子与氢气消耗之间的密切联系,制定融合工况预测的里程自适应等效氢耗最小策略.通过基于误差反向传播的神经网络来实现未来短期车速的预测,分析未来车辆需求功率变化,同时借助全球定位系统规划一条通往目的地的路径,智能交通系统便可获取整个行程的交通流量信息,利用行驶里程和SOC实时动态修正等效消耗最小策略中的等效因子,实现能量管理策略的自适应性.基于MATLAB/Simulink软件,搭建整车仿真模型与传统的能量管理策略进行仿真对比验证.仿真结果表明,采用基于神经网络的工况预测算法能够较好地预测未来短期工况,其预测精度相较于马尔可夫方法提高 12.5%,所提出的能量管理策略在城市道路循环工况(UDDS)下的氢气消耗比电量消耗维持(CD/CS)策略下降55.6%.硬件在环试验表明,在市郊循环工况(EUDC)下的氢气消耗比CD/CS策略下降 26.8%,仿真验证结果表明了所提出的策略相比于CD/CS策略在氢气消耗方面的优越性能,并通过硬件在环实验验证了所提策略的有效性.
Keyword ：
反向传播神经网络 反向传播神经网络 工况预测 工况预测 燃料电池汽车 燃料电池汽车 等效消耗最小策略 等效消耗最小策略 能量管理策略 能量管理策略
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| GB/T 7714 | 林歆悠 , 叶锦泽 , 王召瑞 . 融合工况预测的燃料电池汽车里程自适应等效氢耗最小控制策略 [J]. | 工程科学学报 , 2024 , 46 (2) : 376-384 . |
| MLA | 林歆悠 et al. "融合工况预测的燃料电池汽车里程自适应等效氢耗最小控制策略" . | 工程科学学报 46 . 2 (2024) : 376-384 . |
| APA | 林歆悠 , 叶锦泽 , 王召瑞 . 融合工况预测的燃料电池汽车里程自适应等效氢耗最小控制策略 . | 工程科学学报 , 2024 , 46 (2) , 376-384 . |
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The road gradient and trip range are of great significance in fuel consumption and emissions of a range-extended electric vehicle (REEV). However, the traditional energy management strategy failed to consider the road gradient. To address this issue, a multi-objective optimization adaptive control strategy is proposed to improve the fuel consumption and emissions of the REEVs. Firstly, a multi-objective optimization adaptive control strategy is developed based the equivalent consumption minimization strategy integrated with adaptive equivalent factor (EF). The EF is updating according to the road slope by using a proportional-integral controller. To investigate the impacts of the road gradient on emissions, the numerical models between road gradient and emissions are established. Furthermore, an optimal torque distribution strategy is proposed according to the weights of fuel and emissions, which realizes the tracking of the SOC trajectory and improves the fuel economy and emission performance of the vehicle. Finally, various strategies are carried out to verify the superiority of the proposed strategy by numerical validations. Compared with the control strategy considered fuel consumption only, the validation results show that the engine CO, HC, and NOx are reduced by 9.47, 2.33, and 4.10%, respectively, while compromising fuel economy by 3.3%.
Keyword ：
Emissions Emissions Energy management Energy management Equivalent consumption minimization strategy Equivalent consumption minimization strategy Fuel economy Fuel economy Multi-objective optimization Multi-objective optimization Range-extended electric vehicle Range-extended electric vehicle
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| GB/T 7714 | Lin, Xinyou , Chen, Zhiyong , Zhang, Jiajin et al. A Multi-objective Optimization Control Strategy of a Range-Extended Electric Vehicle for the Trip Range and Road Gradient Adaption [J]. | INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY , 2024 . |
| MLA | Lin, Xinyou et al. "A Multi-objective Optimization Control Strategy of a Range-Extended Electric Vehicle for the Trip Range and Road Gradient Adaption" . | INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY (2024) . |
| APA | Lin, Xinyou , Chen, Zhiyong , Zhang, Jiajin , Wu, Chaoyu . A Multi-objective Optimization Control Strategy of a Range-Extended Electric Vehicle for the Trip Range and Road Gradient Adaption . | INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY , 2024 . |
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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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The environment pollution and petroleum problems, which are increasingly becoming serious, have caused the vehicle industry to transition into a low-carbon and energy-saving industry. During processes, plug-in fuel-cell electric vehicles (PFCEVs) play an important role due to their advantages of rapid fueling, high energy density and efficiency, low operating temperature, and zero onboard emissions. PFCEVs use high-capacity rechargeable batteries to avoid working in low-efficiency areas. However, a robust energy management strategy that can achieve reliable energy distribution by regulating the output power of the fuel cell and battery within the hybrid powertrain merits further investigation. Considering the close relationship between the driving cycle, state of charge (SOC), equivalent factor, and hydrogen consumption, a trip distance adaptive equivalent consumption minimum strategy integrating driving cycle prediction is proposed. A backpropagation-based neural network is used to predict short-term vehicle velocity and analyze future changes in vehicle demand power. Planning a path to the destination with the help of the global positioning system, the intelligent transportation system can also obtain traffic flow information for the entire trip. The equivalent factor is dynamically corrected in real time using the driving distance and SOC to realize the adaptability of the energy management strategy. Finally, the velocity prediction sequence is combined with the objective function. The sequential quadratic programming algorithm is used to optimize the equivalent hydrogen consumption of the objective function and to obtain the distributed power of the fuel cell and battery. The vehicle simulation model is built and compared with a traditional energy management strategy based on MATLAB/Simulink software. The simulation results show that the driving cycle prediction algorithm based on the backpropagation-based neural network predicts future short-term conditions better, with a 12.5% higher accuracy than the Markov method. The proposed energy management strategy allows the fuel cell to operate in high-efficiency areas. The hydrogen consumption is 55.6% less than that of the CD/CS strategy under the UDDS cycle. The hardware in the loop experiment verifies a hydrogen consumption that is 26.8% less than that of the CD/CS strategy under the EUDC cycle. The numerical validation results demonstrate the superior performance of the proposed strategy in terms of hydrogen consumption over the CD/CS strategy. The effectiveness of the proposed strategy is validated by hardware during the loop experiment. © 2024 Science Press. All rights reserved.
Keyword ：
Battery management systems Battery management systems Charging (batteries) Charging (batteries) Electric power distribution Electric power distribution Forecasting Forecasting Fuel cells Fuel cells Gasoline Gasoline Intelligent systems Intelligent systems MATLAB MATLAB Neural networks Neural networks Petroleum industry Petroleum industry Plug-in electric vehicles Plug-in electric vehicles Plug-in hybrid vehicles Plug-in hybrid vehicles Secondary batteries Secondary batteries
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| GB/T 7714 | Lin, Xinyou , Ye, Jinze , Wang, Zhaorui . Trip distance adaptive equivalent hydrogen consumption minimization strategy for fuel-cell electric vehicles integrating driving cycle prediction [J]. | Chinese Journal of Engineering , 2024 , 46 (2) : 376-384 . |
| MLA | Lin, Xinyou et al. "Trip distance adaptive equivalent hydrogen consumption minimization strategy for fuel-cell electric vehicles integrating driving cycle prediction" . | Chinese Journal of Engineering 46 . 2 (2024) : 376-384 . |
| APA | Lin, Xinyou , Ye, Jinze , Wang, Zhaorui . Trip distance adaptive equivalent hydrogen consumption minimization strategy for fuel-cell electric vehicles integrating driving cycle prediction . | Chinese Journal of Engineering , 2024 , 46 (2) , 376-384 . |
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The battery degradation and equivalent hydrogen consumption are of great significance in vehicle performance improvement for fuel cell hybrid electric vehicle. However, few energy management strategies proposed by previous researchers can optimize the objectives at the same time. To overcome this drawback, a battery degradation -aware energy management strategy with a driving pattern severity factor feedback correction algorithm is developed to achieve the optimal trade-off between power battery degradation and vehicle economy improvement. The proposed strategy adjusts the equivalent factor according trip distance and correct the battery power to directly address the battery degradation and vehicle economy problem. First, a cost function using weighting factor to trade off the battery degradation and vehicle economy is formulated. The severity factor based on the battery aging model with effective ampere -hour throughput is used to measure the degree of battery degradation. Then, the genetic algorithm back propagation neural network for driving pattern recognition is developed. The trip distance adaptive equivalent consumption minimization strategy is introduced to correct the equivalent factor according to the driving pattern information and the battery degradation feedback correction strategy of the severity factor integrated with driving patterns recognition is constructed. Finally, a comparison analysis study and hardware -in -the -loop experiment are conducted to validate the effectiveness of the proposed strategy. Experimental results under Urban Dynamometer Driving Schedule and Extra Urban Driving Cycle combined with the equivalent consumption minimum strategy indicate that the battery degradation feedback correction algorithm can significantly reduce the battery degradation degree while sacrificing hydrogen consumption optimization to a small extent.
Keyword ：
Driving pattern recognition Driving pattern recognition Feedback correction algorithm Feedback correction algorithm Fuel cell hybrid electric vehicles (FCHEV) Fuel cell hybrid electric vehicles (FCHEV) Power battery degradation Power battery degradation Severity factor Severity factor
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| GB/T 7714 | Lin, Xinyou , Xi, Longliang , Wang, Zhaorui . Battery degradation-aware energy management strategy with driving pattern severity factor feedback correction algorithm [J]. | JOURNAL OF CLEANER PRODUCTION , 2024 , 450 . |
| MLA | Lin, Xinyou et al. "Battery degradation-aware energy management strategy with driving pattern severity factor feedback correction algorithm" . | JOURNAL OF CLEANER PRODUCTION 450 (2024) . |
| APA | Lin, Xinyou , Xi, Longliang , Wang, Zhaorui . Battery degradation-aware energy management strategy with driving pattern severity factor feedback correction algorithm . | JOURNAL OF CLEANER PRODUCTION , 2024 , 450 . |
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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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