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Abstract ：
The stochasticity of vehicle velocity poses a significant challenge to enhancing fuel cell energy management strategy (EMS). Under these circumstances, a self-learning Markov algorithm-based EMS with stochastic velocity prediction capability is proposed. First, building upon the traditional offline-trained Markov model, a real-time self-learning Markov predictor (SLMP) is proposed, which collects historical data during the vehicle's driving process and continuously updates the state transition matrix on a rolling basis. It provides excellent prediction performance under stochastic driving cycles. and the impact of different prediction time-steps is analyzed. Subsequently, by employing sequential quadratic programming for optimal power allocation, the Stochastic Velocity-Prediction Conscious EMS for fuel cell hybrid electrical vehicle based on SLMP is constructed. Finally, the predictors and EMSs based on back-propagation neural network and offline-trained Markov are selected for performance comparison. The validation results indicate that the performance of SLMP improves as driving mileage accumulates. Meanwhile, the proposed Stochastic Velocity-Prediction Conscious EMS significantly improves economic performance in different driving cycles. Hardware-in-the-Loop experiments further validate the superior fuel cell efficiency and robustness of the proposed EMS. The key contribution lies in the real-time adaptability of the SLMP, which ensures improved prediction accuracy and economic performance as driving mileage accumulates.
Keyword ：
Energy management Energy management Fuel cell hybrid electric vehicle Fuel cell hybrid electric vehicle Markov chain Markov chain Predictive control strategy Predictive control strategy Self-learning Self-learning Velocity prediction Velocity prediction
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| GB/T 7714 | Lin, Xinyou , Ren, Yukun , Xu, Xinhao . Stochastic velocity-prediction conscious energy management strategy based self-learning Markov algorithm for a fuel cell hybrid electric vehicle [J]. | ENERGY , 2025 , 320 . |
| MLA | Lin, Xinyou 等. "Stochastic velocity-prediction conscious energy management strategy based self-learning Markov algorithm for a fuel cell hybrid electric vehicle" . | ENERGY 320 (2025) . |
| APA | Lin, Xinyou , Ren, Yukun , Xu, Xinhao . Stochastic velocity-prediction conscious energy management strategy based self-learning Markov algorithm for a fuel cell hybrid electric vehicle . | ENERGY , 2025 , 320 . |
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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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The eco-driving strategy is of great significance in driving cost for plug-in hybrid electric vehicles in driving trips, especially at signalized intersections. To address the issue of further energy saving, this study proposes an ecological approach and departure-driving strategy by using syncretic learning with trapezoidal collocation algorithm. First, a syncretic learning-based speed predictor is built by merging back propagation neural networks and radial basis function neural networks. Second, the syncretic learning-based speed predictor and trapezoidal collocation algorithm are combined to optimize the speed trajectory. Third, the torque between the engine and the motor is distributed by the dynamic programming algorithm. Then, model predictive control optimizes torque output in the control time domain. Finally, the driving interval optimization method is designed to avoid mixed-integer programming problems and redundant constraints, which make vehicles cross intersections without stopping. The numerical verification results show that the trapezoidal collocation algorithm with syncretic learning has more advantages than other methods in speed trajectory planning. Compared with the original trajectory, the driving time through the intersection is reduced and the total driving cost is lowered by 19.82%. Validation results confirm the effectiveness of the proposed strategy in energy consumption management at signalized intersections. This study proposes an ecological approach and departure-driving strategy by using syncretic learning with trapezoidal collocation algorithm. The speed predictor is combined with the trapezoidal collocation algorithm to obtain the speed trajectory with higher accuracy. The driving interval optimization method is proposed to make vehicles cross the intersections without stopping.image (c) 2024 WILEY-VCH GmbH
Keyword ：
plug-in hybrid electric vehicles plug-in hybrid electric vehicles speed prediction speed prediction speed trajectory planning speed trajectory planning torque distribution torque distribution trapezoidal collocation algorithm trapezoidal collocation algorithm
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| GB/T 7714 | Lin, Xinyou , Chen, Xiankang , Chen, Zhiyong et al. Ecological Approach and Departure-Driving Strategy Optimized by Using Syncretic Learning with Trapezoidal Collocation Algorithm for the Plug-In Hybrid Electric Vehicles [J]. | ENERGY TECHNOLOGY , 2024 , 12 (4) . |
| MLA | Lin, Xinyou et al. "Ecological Approach and Departure-Driving Strategy Optimized by Using Syncretic Learning with Trapezoidal Collocation Algorithm for the Plug-In Hybrid Electric Vehicles" . | ENERGY TECHNOLOGY 12 . 4 (2024) . |
| APA | Lin, Xinyou , Chen, Xiankang , Chen, Zhiyong , Wu, Jiayun . Ecological Approach and Departure-Driving Strategy Optimized by Using Syncretic Learning with Trapezoidal Collocation Algorithm for the Plug-In Hybrid Electric Vehicles . | ENERGY TECHNOLOGY , 2024 , 12 (4) . |
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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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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 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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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 , 72 (5) : 4922-4932 . |
| 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 72 . 5 (2024) : 4922-4932 . |
| 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 , 72 (5) , 4922-4932 . |
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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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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 at different degrees in each control cycle. To address this issue, a stochastic model predictive control (SMPC) considering short-term forecast optimal SOC is proposed. First, multiple linear regression of engine and battery is developed for energy management strategy (EMS). Then, the velocity prediction model is developed based on Markov chain considering the driver styles, and reference SOC is optimized by dynamic programming (DP) with the forthcoming information. Finally, the SMPC-based EMS with the short-term optimal SOC is constituted. The verification results show that Markov based on driver styles has better predictive performance than radial basis function neural networks and backpropagation 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 DP. The test results indicate that the SMPC with the short-term optimal SOC can promote EMS to improve fuel economy.
Keyword ：
Adaptation models Adaptation models Batteries Batteries Energy management Energy management Energy management strategy (EMS) Energy management strategy (EMS) Engines Engines multiple linear regression multiple linear regression plug-in hybrid electric vehicle (PHEV) plug-in hybrid electric vehicle (PHEV) Predictive models Predictive models State of charge State of charge stochastic model predictive control (SMPC) stochastic model predictive control (SMPC) Torque Torque velocity prediction velocity prediction
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| GB/T 7714 | Lin, Xinyou , Chen, Xiankang , Chen, Zhiyong 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) : 8685-8697 . |
| MLA | Lin, Xinyou 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) : 8685-8697 . |
| APA | Lin, Xinyou , Chen, Xiankang , Chen, Zhiyong , Xie, Liping . 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) , 8685-8697 . |
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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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