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学者姓名：林歆悠
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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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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 等. "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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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 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_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 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 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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针对具有不确定性的自动驾驶电动车辆的运动控制问题,提出了一种基于参数预测的径向基函数(RBF)神经网络自适应协调控制方案.首先,考虑系统参数的不确定性及外部干扰的影响,利用预瞄方法建立可表征车辆循迹跟车行为的动力学模型;其次,采用RBF神经网络补偿器对系统不确定性进行自适应补偿,设计车辆横纵向运动的广义协调控制律;之后,考虑前车车速及道路曲率影响,以车辆在循迹跟车控制过程中的能耗及平均冲击度最小为优化目标,利用粒子群优化(PSO)算法对协调控制律中的增益参数K进行滚动优化,并最终得到一系列优化后的样本数据;在此基础上,设计、训练一个反向传播(BP)神经网络,实现对广义协调控制律中增益参数K的实时预测,以保证车辆的经济性及乘坐舒适性.仿真结果证实了所提控制方案的有效性.
Keyword ：
不确定性 不确定性 参数预测 参数预测 径向基函数神经网络 径向基函数神经网络 粒子群优化算法 粒子群优化算法 自动驾驶电动车辆 自动驾驶电动车辆
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| GB/T 7714 | 陈志勇 , 李攀 , 叶明旭 et al. 自动驾驶电动车辆基于参数预测的径向基函数神经网络自适应控制 [J]. | 中国机械工程 , 2024 , 35 (6) : 982-992 . |
| MLA | 陈志勇 et al. "自动驾驶电动车辆基于参数预测的径向基函数神经网络自适应控制" . | 中国机械工程 35 . 6 (2024) : 982-992 . |
| APA | 陈志勇 , 李攀 , 叶明旭 , 林歆悠 . 自动驾驶电动车辆基于参数预测的径向基函数神经网络自适应控制 . | 中国机械工程 , 2024 , 35 (6) , 982-992 . |
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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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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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