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学者姓名：林歆悠
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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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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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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 (06) : 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 . 06 (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 (06) , 982-992 . |
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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 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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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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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 : 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 (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 , 1-1 . |
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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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Considering the trade-off on the improvement of fuel economy performance and mode transition comfort for multimode hybrid electric vehicles (MMHEV), a bi-objective trade-off control strategy is designed based on the equivalent consumption minimization strategy (ECMS) with Pareto method. Aiming at the problem of optimal economy of multimode working area of vehicles, the equivalent fuel consumption cost and torque distribution MAP in different modes are determined based on ECMS. In order to solve the problem of torque instability caused by engine response lag and demand torque fluctuation, the motor torque optimization coefficient (MTOC) is introduced as the control variable by taking advantage of the motor's fast and accurate response to torque, and the genetic algorithm is used to optimize the MTOC, and a smoother torque distribution is obtained. Because there is a coupling relationship between the economy and ride comfort of the system, the trade-off optimization is carried out using the NSGA-II algorithm based on Pareto principle. The simulation verification conducted in nonslope and slope conditions, as well as hardware-in-the-loop experiments, demonstrates the effectiveness of the proposed control strategy in managing the trade-off between economy and ride comfort for the MMHEV. This article focuses on the trade-off between fuel economy and driving comfort of multimode hybrid electric vehicle (MMHEV). Based on equivalent consumption minimization strategy-Pareto joint algorithm, a bi-objective trade-off control strategy is designed. The results show that the driving comfort can be greatly improved with a small sacrifice of economy.image (c) 2023 WILEY-VCH GmbH
Keyword ：
bi-objective optimization bi-objective optimization equivalent consumption minimization strategy equivalent consumption minimization strategy genetic algorithm genetic algorithm motor torque optimization coefficient motor torque optimization coefficient multimode hybrid electric vehicle multimode hybrid electric vehicle Pareto Pareto
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| GB/T 7714 | Yang, Xuelan , Lin, Xinyou , Huang, Qiang et al. Bi-objective Trade-Off Optimization Control Strategy Based on the Equivalent Consumption Minimization Strategy-Pareto Algorithm for a Multimode Hybrid Electric Vehicle [J]. | ENERGY TECHNOLOGY , 2023 , 12 (1) . |
| MLA | Yang, Xuelan et al. "Bi-objective Trade-Off Optimization Control Strategy Based on the Equivalent Consumption Minimization Strategy-Pareto Algorithm for a Multimode Hybrid Electric Vehicle" . | ENERGY TECHNOLOGY 12 . 1 (2023) . |
| APA | Yang, Xuelan , Lin, Xinyou , Huang, Qiang , Zheng, Qingxiang , Wu, Hao . Bi-objective Trade-Off Optimization Control Strategy Based on the Equivalent Consumption Minimization Strategy-Pareto Algorithm for a Multimode Hybrid Electric Vehicle . | ENERGY TECHNOLOGY , 2023 , 12 (1) . |
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