Table of Content

25 December 2023, Volume 45 Issue 12 Previous Issue    Next Issue

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Review on Electro-Mechanical Brake Structure and Control Technology Lu Xiong,Congcong Li,Guirong Zhuo,Yulin Cheng,Le Qiao,Xinjian Wang 2023, 45 (12):  2187-2199.  doi: 10.19562/j.chinasae.qcgc.2023.12.001 Abstract ( 302 )   HTML ( 33 )   PDF (3849KB) ( 538 )   Save As a complete form of brake-by-wire system， electro-mechanical brake （EMB） system has many advantages such as simplified structure and rapid braking response. To give a comprehensive review on the development status of EMB structure， the development and industrialization of various structure schemes are summarized based on investigation of a large number of patents in this paper. And four basic structure schemes including the ball-screw-type， wedge-type， ball-ramp-type and cam-type scheme are analyzed and compared. For the clamping force control problem of the actuator with nonlinear characteristics and slow time-varying parameter perturbations， firstly a review on the actuator modeling methods is conducted in this paper. Then through the classification based on the presence or absence of pressure sensors， the research progress both home and abroad is reviewed from two aspects： clamping force control methods based on the feedback force value and control methods based on the estimated force value. Finally， future development trends of the actuator structure design， clamping force control and vehicle coordination redundancy control are prospected. Figures and Tables | References | Related Articles | Metrics

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Research on Sliding Mode Control Algorithm for Angle Tracking Prediction of Steer-by-wire System Lin He,Ziang Xu,Chunrong Huang,Chao Gong,Shuhua Li,Qin Shi 2023, 45 (12):  2200-2208.  doi: 10.19562/j.chinasae.qcgc.2023.12.002 Abstract ( 199 )   HTML ( 21 )   PDF (2712KB) ( 203 )   Save Steer-by-wire technology is one of the key technologies of intelligent chassis， in which high-precision steering angle following is the core control objective. Based on the hybrid control theory， a disturbance torque estimation-based predictive sliding mode control approach for steering angle following is designed. The sliding mode control is used as the core algorithm to adapt to the nonlinear characteristics of the steer-by-wire system dynamics. A PI observer is used to estimate the total disturbance torque and the adverse effect of the system is compensated. The model predictive control algorithm is used to optimize the sliding manifold parameters. The designed approach is tested and validated in a steering test vehicle equipped with an electric motor steer-by-wire system. The experimental results show that the designed predictive sliding mode controller can realize better angle trajectory following and the PI observer can estimate the lumped disturbance torque accurately. Figures and Tables | References | Related Articles | Metrics

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Simscape-Based APA-EPS System Modeling and Human-Machine Co-driving Steering Control Strategies Research Haobin Jiang,Peichun Yuan,Kun Yang,Bin Tang,Chenxu Li 2023, 45 (12):  2209-2221.  doi: 10.19562/j.chinasae.qcgc.2023.12.003 Abstract ( 88 )   HTML ( 5 )   PDF (6637KB) ( 108 )   Save Human-machine co-driving is a transitional stage before intelligent vehicles achieving a high degree of autonomous driving. As the driver and the driving assistance system are in the loop at the same time， there is a problem of assigning driving rights for human-machine co-driving. The steering system of an intelligent vehicle must execute the steering control commands of the driver and driving assistance system in real time and with agility. The human-machine co-driving steering control strategies based on the parallel-axis electric power steering system （APA-EPS） in a smart MPV is studied in this paper. Firstly， the APA-EPS system is modeled in Simscape and the accuracy of the model is verified by bench tests. Subsequently， the steering control strategies for human-driving and machine-driving modes are designed based on fuzzy PID control algorithm and fuzzy sliding mode control algorithm respectively. When the driver is driving the vehicle dominantly， the steering control weight is determined by the distance influence degree function and the angle influence degree function， when the driving assistance system is driving the vehicle dominantly， the driver’s takeover request is monitored by sliding time windows. The simulation results show that both the human-driving and machine-driving control strategies have good corner following effect， and in human-machine co-driving mode， the steering control rights can be switched in time， and the steering wheel angle response of the APA-EPS is rapid. Figures and Tables | References | Related Articles | Metrics

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Human-Vehicle Shared Steering Control System for Dense Obstacle Avoidance Liang Yan,Xiaodong Wu,Chuan Hu 2023, 45 (12):  2222-2233.  doi: 10.19562/j.chinasae.qcgc.2023.12.004 Abstract ( 83 )   HTML ( 6 )   PDF (4645KB) ( 83 )   Save The innovation and development of vehicle chassis technology have injected new vitality into the design of intelligent driving systems. Combining the physical decoupling characteristics of steer-by-wire （SBW） systems， a human-machine shared steering control system for dense obstacle avoidance scenarios is proposed. Firstly， based on vehicle dynamics and the Monte Carlo tree search （MCTS） algorithm， a receding horizon steering field histogram （RH-SFH） method is utilized to achieve the real-time obstacle avoidance decision-making and planning. Secondly， a short-term driver behavior prediction model based on gated recurrent unit （GRU） network is established， which can directly output temporal signal of steering command. Then， expected time-to-collision is calculated referring to the vehicle steering characteristics， and the risk evaluation model is established accordingly. Finally， a dynamic allocation strategy for vehicle steering control rights is constructed， and hardware-in-the-loop experiments are conducted in the joint simulation environment based on MATLAB/Simulink. The results show that the shared steering control method can effectively reduce collision， improve the driving safety， and reduce the operational burden of drivers while ensuring the traffic efficiency compared with manual driving under multiple working conditions. Figures and Tables | References | Related Articles | Metrics

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Path Tracking Control Method with Steering Lag for Autonomous Vehicles Lü Ying,Xu Qi,Qiuzheng Liu,Xinyu Wang,Guoying Chen 2023, 45 (12):  2234-2241.  doi: 10.19562/j.chinasae.qcgc.2023.12.005 Abstract ( 119 )   HTML ( 7 )   PDF (3153KB) ( 188 )   Save The automatic driving path tracking system is an important supporting technology for realizing automatic driving above L2 level. The delayed response characteristic of the linear steering system reduces the robustness and control accuracy of the path-tracking controller， with even instability at high speed. For the above problems， an error tracking control architecture with feedforward plus finite time domain full state feedback is designed in this paper， on the basis of which the steering system delay is modeled as a first-order inertial link and integrated into the error tracking control architecture as a state variable augmentation. In this paper， the proposed tracking controllers are compared and validated using joint simulation tests， and the validation results show that the maximum lateral error of the proposed controllers is less than 0.3 m in the right-angle corner and high-speed lane change scenarios， and the steering wheel's rotational angle rms variance is reduced by 1.93% and 64.22%， respectively. The final results of the real-vehicle tests show that the controller proposed in this paper can effectively improve the lateral control accuracy in the high-speed lane-changing scenario， with the maximum lateral error less than 0.11 m. Figures and Tables | References | Related Articles | Metrics

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Research on Rear Wheel Angle Control Method of 4WS Vehicle Pei Zhang,Wenxin Sun,Jie Hu,Fuwu Yan,Qian Xu,Taowei Yan 2023, 45 (12):  2242-2250.  doi: 10.19562/j.chinasae.qcgc.2023.12.006 Abstract ( 165 )   HTML ( 11 )   PDF (2804KB) ( 159 )   Save In order to improve the maneuverability of four-wheel steering vehicles at low speeds， a dynamic model of two-degree-of-freedom four-wheel steering vehicle including lateral movement and sideways movement is established and the rear wheel angle control method of four-wheel steering vehicle is summarized and summarized. Then a joint simulation platform of CarSim and Matlab/Simulink is built to make the actual steering state of the vehicle model established in CarSim close to the preset ideal steering state of two degrees of freedom. Finally， the simulation comparative analysis of several control methods is carried out by setting three working conditions， including the front wheel angle step input. The results show that the control strategies mentioned in this paper can improve the maneuverability of four-wheel steering vehicles at low speeds， making the vehicles have higher maneuvering and motion ability， which lays a foundation for finding an optimal control strategy for the rear wheel angle of four-wheel steering vehicles. Figures and Tables | References | Related Articles | Metrics

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Research on Fault-Tolerant Control of Multi-Actuator for Distributed Drive Electric Vehicles Qin Li,Jianming Tang,Boyuan Zhang,Yong Chen,Yong Wang 2023, 45 (12):  2251-2259.  doi: 10.19562/j.chinasae.qcgc.2023.12.007 Abstract ( 104 )   HTML ( 4 )   PDF (2234KB) ( 135 )   Save Distributed drive electric vehicles with independently controllable torque for each wheel hub motor represent a typical over-actuated system. By optimizing the distribution of driving torque among the wheels， fault-tolerant control can be achieved. Taking distributed drive electric vehicles as the research object and focusing on the trajectory-tracking problem in the presence of simultaneous failure of the steer-by-wire system and multiple actuators of the wheel hub motors， a fault-tolerant control method based on differential steering and torque allocation is proposed in this paper. The method employs a hierarchical architecture. In the upper-level controller， the desired front-wheel steering angle is obtained using a model predictive control method. When failure occurs in the steering system actuators， a sliding mode control method is utilized to calculate the differential steering torque. In the lower-level controller， the torque optimization allocation strategy for failed drive motors is determined based on quadratic programming algorithms， taking into account of fault diagnosis information. Finally， simulation experiments are conducted to evaluate the effectiveness of the proposed fault-tolerant control method under both single actuator failure and multiple actuators failure scenarios. The results validate the effectiveness of the proposed approach. Figures and Tables | References | Related Articles | Metrics

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Parameter-Free H∞ Control of Vehicle Active Suspension Based on Q-learning Gang Wang,Kunpeng Li,Hui Jing,Suqi Liu 2023, 45 (12):  2260-2271.  doi: 10.19562/j.chinasae.qcgc.2023.12.008 Abstract ( 97 )   HTML ( 3 )   PDF (5458KB) ( 87 )   Save Active suspension plays a crucial role in the all-by-wire chassis of intelligent vehicles， enabling full-vector control of the vehicle chassis in conjunction with various driven-by-wire execution systems， significantly improving driving safety. However， the traditional control method involves calibrating numerous vehicle model parameters， which reduces the efficiency of control development. On this basis， a parameter-free H∞ control method for the vehicle's active suspension is studied in this paper. Firstly， an approximate dynamic programming model based on the behavior dependence of the active suspension is established， transforming the H∞ control problem into a zero-sum game process involving pavement disturbance and control behavior. Secondly， an adaptive evaluation method is used to set the action network and the critical network and online Q-learning is used to solve the system's Game algebraic Riccati equation， which provides an optimal control solution without requiring model parameters. The stability analysis demonstrates that the proposed method can converge to the Nash equilibrium point of the system. Finally， a hardware-in-the-loop system is built to verify the effectiveness of the proposed method， and the active control simulation is carried out for the bump pavement and random pavement with different road grades. The results show that the control method based on Q-learning has the optimal control effect， which can improve vehicle ride comfort and handling stability in the low frequency range. Figures and Tables | References | Related Articles | Metrics

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A Characteristics-Based Dynamic Model of the Full-Floating Cab for the Commercial Vehicle Xin Guan,Li Li,Chunguang Duan,Jun Zhan 2023, 45 (12):  2272-2279.  doi: 10.19562/j.chinasae.qcgc.2023.12.009 Abstract ( 77 )   HTML ( 5 )   PDF (3048KB) ( 114 )   Save The driver perceives the vehicle’s motion response while sitting in the cab， and the cab and its suspension mechanism are essential factors affecting the driving sensation of the driver. For real-time simulation of the cab movement， a characteristics-based modelling technical route is adopted in this paper， in which the cab’s movement is decoupled into low-frequency in-plane Motion and high-frequency out-of-plane Ride， comprehensively considering the load-carrying properties， RC/PC properties and K&C properties of the cab suspension mechanism， and a full-floating cab model for the commercial vehicle is established. Finally， the cab model is embedded into the 89DOF truck model to simulate a tractor， and the model’s fidelity is verified by comparing it with the field test results. Figures and Tables | References | Related Articles | Metrics

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Research on Visible Light and Infrared Post-Fusion Detection Based on TC-YOLOv7 Algorithm Linhui Li,Xinliang Zhang,Yifan Fu,Jing Lian,Jiaxu Ma 2023, 45 (12):  2280-2290.  doi: 10.19562/j.chinasae.qcgc.2023.12.010 Abstract ( 81 )   HTML ( 1 )   PDF (4913KB) ( 72 )   Save For the problem that it is difficult to achieve fast and accurate detection of visual targets in complex scenes of autonomous driving， a TC-YOLOv7 detection algorithm based on attention mechanism is proposed， which is applied to visible light， infrared and post-fusion scenarios. Firstly， the YOLOv7 benchmark detection model is improved based on the CBAM and Transformer attention mechanism modules， and the performance of visible light and infrared detection is verified by multi-scene datasets. Secondly， the detection methods of three different non-maximum suppression post-fusion methods including SS-PostFusion， DS-PostFusion， and DD-PostFusion are constructed， with the performance verified. Finally， the method combining TC-YOLOv7 and DD-PostFusion is compared with the single-sensor detection results. The results show that the TC-YOLOv7 method has more than 3% accuracy improvement compared with the benchmark method YOLOv7 mAP@.5 in daytime， night， haze， rain， snow visible light and infrared scenes. In the comprehensive scene test set， the TC-YOLOv7 post-fusion method improves the detection accuracy by 4.5% compared with visual light detection， by 11.1% compared with infrared detection and by 0.6% compared with the YOLOv7 post-fusion method. Furthermore， the TC-YOLOv7 post-fusion method inference speed is 39 fps， meeting the real-time requirements of autonomous driving scenarios. Figures and Tables | References | Related Articles | Metrics

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Unsupervised Monocular Infrared Image Depth Estimation Based on Local Plane Guidance Layer Qin Shi,Yafang Chen,Teng Cheng,Qiang Zhang,Wenchong Wang,Benyi Shi 2023, 45 (12):  2291-2298.  doi: 10.19562/j.chinasae.qcgc.2023.12.011 Abstract ( 53 )   HTML ( 0 )   PDF (2066KB) ( 103 )   Save At present， the unsupervised monocular infrared image depth estimation method is difficult to deal with low texture and low contrast areas， resulting in poor estimation effect， so an unsupervised monocular infrared image depth estimation algorithm based on local plane guide layer is proposed in this paper. The algorithm consists of continuous video frame input， multi-scale feature extraction， ASPP and local planar guidance layer， computational loss， joint training， and output image module. Firstly， by using multiple small-resolution grayscale blocks and multi-scale feature fusion， the problems of blurring edges and occluding objects in infrared images are solved. Secondly， by using the local plane guidance layer to introduce a plane constraint on the depth image， the noise and discontinuity in the depth image are reduced， and the problem of lack of clear processing of low texture areas of the traditional algorithm is solved. The experimental results show that the proposed depth estimation algorithm effectively improves the accuracy of monocular depth estimation and reduces the error， and the Abs Rel， Sq Rel， RMS， RMS（log） on the Iray dataset is 0.262， 3.621， 9.473 and 0.332， respectively， and the accuracy reaches 60.5%， 85.2% and 94.5% when the threshold indicators are less than 1.25， 1.252 and 1.253. Figures and Tables | References | Related Articles | Metrics

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Parallel Parking Trajectory Planning Based on Double-Layer Solution Strategy Hongchang Zhang,Peng Ning,Jie Yang,Jianwei Song,Lin Hao,Juan Zeng 2023, 45 (12):  2299-2309.  doi: 10.19562/j.chinasae.qcgc.2023.12.012 Abstract ( 67 )   HTML ( 7 )   PDF (3656KB) ( 107 )   Save Trajectory planning plays a key role in shortening parking time and reducing tracking difficulty， as it connects the upper-level perception and the lower-level control in parking systems. However， it is challenging to balance trajectory quality， generalization ability， and computational efficiency in parallel parking trajectory planning. To address this issue， the parallel parking trajectory planning based on double-layer solution strategy （DLSS） is proposed. The strategy includes two layers： in the first layer， the parallel parking path is divided into two segments connected by anchor points. The anchor point is identified through a backward path planning approach. The paths from the endpoint to the anchor point and from the anchor point to the starting point are planned separately. Then， a "time-optimal" profile is added to the path， and the state and control variables at specific time points are obtained in reverse order. In the second layer， the simultaneous orthogonal configuration method is used to transform the continuous state and control variables in the parallel parking optimal control into discrete variables in the trajectory nonlinear programming. The state and control variables obtained in the first layer are used as the initial values for the nonlinear programming to obtain numerical solutions. Five parallel parking scene models are established and simulated， and the results show that the optimal trajectory that meets the constraints requirements can be planned for different parking starting postures and parking space sizes， which improves the generation quality and generalization ability of trajectories， and has satisfactory computational efficiency. Figures and Tables | References | Related Articles | Metrics

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An End-to-End Lane Change Method for Autonomous Driving Based on GCN and CIL Lü Yanzhi,Chao Wei,Yuanhao He 2023, 45 (12):  2310-2317.  doi: 10.19562/j.chinasae.qcgc.2023.12.013 Abstract ( 62 )   HTML ( 4 )   PDF (2673KB) ( 71 )   Save For the lane change of autonomous driving， to solve the problems of unstable output and difficulty to extract dynamic interactive scene feature in conventional end-to-end method， an end-to-end learning method for autonomous lane change based on graph convolutional network （GCN） and conditional imitation learning （CIL） is proposed in this paper. Firstly， the dynamic interactive information of driving scenarios is aggregated in the form of graph-structured data. Secondly， the driving behavior instructions that the ego vehicle should take are output through GCN， which is then combined with CIL. The driving instructions output by GCN are taken as high-level commands for guiding CIL， and are finally mapped to underlying control actions of the vehicle with other perception data to complete autonomous lane change without collision. Experimental verification is carried out on CARLA simulation platform. The experimental results prove that the performance of this method is better than that of conventional end-to-end method， and it has better success rate and generalization performance. Figures and Tables | References | Related Articles | Metrics

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Research on Active Obstacle Avoidance Control of Off-axis Trailing Vehicle on Unstructured Road Dandan Hu,Pengfei Yin,Guochen Niu,Jinju Zhao 2023, 45 (12):  2318-2329.  doi: 10.19562/j.chinasae.qcgc.2023.12.014 Abstract ( 56 )   HTML ( 2 )   PDF (3915KB) ( 92 )   Save To achieve active obstacle avoidance of off axis-towed vehicles on unstructured road， an active obstacle avoidance controller based on model predictive control （MPC） without the support of global path and path tracker is proposed. Firstly， a kinematic model of the towing system for coupling fuller trailer based on rigid-body kinematics and incomplete restraints is established， and its motion characteristics are analyzed and verified. Secondly， according to the kinematic model of the trailer system， the prediction model of the off-axle trailer vehicle is established. Finally， a penalty function for turning collision avoidance without global path support is proposed， and an objective function is designed for the safety and stability of the trailer system， which is optimized by a nonlinear solver， with the optimal output discrete control sequence applied to the trailer system. Simulation and real vehicle experiments show that the active obstacle avoidance controller of the off-axis towing system can make the towing vehicle avoid obstacles safely on the premise of meeting the shear collision constraint. Besides， lateral error and heading error of obstacle avoidance path and shortest non-obstacle avoidance path of full trailer can be reduced. Figures and Tables | References | Related Articles | Metrics

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Take-over Performance Prediction Under Different Cognitive Loads of Non-driving Tasks in Highly Automated Driving Yanli Ma,Jun Lu,Jieyu Zhu,Xiaoxue Han 2023, 45 (12):  2330-2337.  doi: 10.19562/j.chinasae.qcgc.2023.12.015 Abstract ( 68 )   HTML ( 2 )   PDF (2989KB) ( 96 )   Save In highly automated driving， accurate prediction of takeover performance is of great significance to improve the safety of automated driving takeover. Based on the design of driving take-over scenarios under different cognitive load non driving-related tasks （NDRT）， the significance of takeover performance indicators and EEG indicators under different cognitive load NDRT of automated driving is analyzed. Using eSense value and brainwave data of the driver as input， a prediction model of take-over performance based on random forest is constructed to analyze the prediction effect of the model within time windows of 3， 5， 7 and 9 s， and the validity of the model is verified. The results show that there are significant differences in take-over time， maximum lateral acceleration， minimum TTC and driver’s eSense under different loads of NDRT. It is found that the random forest has the best prediction performance within 9 s time window， with the accuracy of 0.94. The prediction accuracy and micro-AUC area of random forest are higher than the results of support vector machine， naive Bayes and logistic regression. The proposed method can effectively predict the take-over performance and provide a theoretical basis for the interaction design between the driver and the autonomous vehicle. Figures and Tables | References | Related Articles | Metrics

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CAN Bus Load Rate Optimization Based on Improved Continuous Hopfield Neural Network Zhicheng He,Leihao Du,Enlin Zhou,Gaofeng Qin,Jin Huang 2023, 45 (12):  2338-2347.  doi: 10.19562/j.chinasae.qcgc.2023.12.016 Abstract ( 54 )   HTML ( 1 )   PDF (3835KB) ( 63 )   Save The Busload rate of CAN bus is of vital importance to the security and delay bounds of Vehicle-Bus. Whereas， the traditional continuous Hopfield neural network （Continuous Hopfield Neural Network， CHNN） has the defects of poor penalty parameter robustness and the resulting solution that easy to be trapped in local optimality when solving such problems， based on the Metropolis thought in the simulated annealing algorithm， an improved continuous Hopfield neural network algorithm （SA-CHNN） applied to the CAN bus load rate optimization problem is proposed in this paper. Micro-electric vehicle’s ninety-nine communication signals are selected and tested as experimental data. The results show that the SA-CHNN algorithm successfully solves the problems of the traditional CHNN algorithm in solving the CAN bus load rate optimization， which has obvious advantages. Finally， based on the Simulink-Speedgoat CAN bus experimental platform， the real-time load rate simulation with the optimal signal distribution messages is conducted and the result reveals the accuracy of the SA-CHNN algorithm. Figures and Tables | References | Related Articles | Metrics

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Prediction of Remaining Useful Life of Real-World Vehicle Lithium-Ion Power Battery Based on Aseq2seq-PF Fengchong Lan,Wei Pan,Jiqing Chen 2023, 45 (12):  2348-2356.  doi: 10.19562/j.chinasae.qcgc.2023.12.017 Abstract ( 61 )   HTML ( 2 )   PDF (3448KB) ( 109 )   Save Remaining useful life （RUL） prediction of lithium-ion power battery can estimate the future state of batteries， which can guide battery maintenance and reduce the risk of failure. The battery cycle conditions are not controlled in real-world vehicle conditions， and RUL prediction under dynamic operating conditions still suffers from difficulties in processing messy data， poor accuracy of prediction results and inability to take aging uncertainty into account. For this case， the Attention Mechanism Sequence to Sequence-Particle Filter （Aseq2seq-PF） hybrid model is proposed， where the common State of Charge （SOC） charging interval is selected to obtain the normalized battery capacity and a fusion prediction strategy of Iteration and Direct is adopted， with the Aseq2seq model as the Iteration part to achieve accurate prediction of capacity sequences， the PF model as the Direct part to achieve uncertainty prediction of capacity fluctuations， and RUL is predicted by extrapolating the trend of battery capacity degradation. Verified by the real-world vehicle power battery data， the public SOC charging interval effectively obtains a clear trend of capacity degradation. The hybrid model improves the long-term prediction accuracy of the capacity degradation with good robustness， with reduction of average absolute error by more than 56% compared with existing models， and outputs confidence intervals to meet the needs of different application to achieve aging uncertainty description. Figures and Tables | References | Related Articles | Metrics

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Driving Range Prediction of Fuel Cell Vehicles Based on Energy Consumption Weighting Strategy Junzhao Jiang,Wenhao Yang,Bin Peng,Ting Guo,Yekai Xu,Guozhuo Wang 2023, 45 (12):  2357-2365.  doi: 10.19562/j.chinasae.qcgc.2023.12.018 Abstract ( 90 )   HTML ( 6 )   PDF (2654KB) ( 122 )   Save Energy consumption and driving range of fuel cell vehicle are the key indexes to evaluate its performance. Taking electric hybrid fuel cell vehicle as an example， a data-driven method is used to design and build a multi-model collaborative energy consumption prediction algorithm for fuel cell vehicle， taking into account of real-time energy consumption predicted by integrated learning model and fragment energy consumption calculated by fuzzy C-means clustering conditions， so as to get the corrected energy consumption value. Based on this， a driving range prediction algorithm weighted by historical and real-time energy consumption is constructed to solve the problem of large deviation in driving range prediction caused by changes in extreme operating conditions within segments， to achieve effective driving range prediction for fuel cell vehicles. Finally， the indoor drum experiment and open road experiment of fuel cell vehicle are carried out， and the predicted results are in good agreement with the experimental results， which verifies the accuracy of the algorithm. Figures and Tables | References | Related Articles | Metrics

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Life Cycle Assessment Research and Uncertainty Analysis of Hydrogen Fuel Cell Heavy-Duty Commercial Vehicles Shuo Zhang,Xu Cai,Chunmei Zhang,Libo Lan,Xuqi Zhang,Yisong Chen 2023, 45 (12):  2366-2386.  doi: 10.19562/j.chinasae.qcgc.2023.12.019 Abstract ( 100 )   HTML ( 9 )   PDF (6786KB) ( 230 )   Save The hydrogen fuel cell heavy-duty commercial vehicle （FCHCV） is one of the ideal solutions to address energy security and greenhouse gas emissions. However， the full life cycle energy consumption and emissions of FCHCVs under key parameter scenario simulations are still unclear. This study evaluates a domestic FCHCV based on the life cycle assessment method， with a focus on analyzing the life cycle energy consumption and emissions results of FCHCV under four hydrogen production paths， which are coal gasification， methane reforming， mixed power electrolysis， and photovoltaic electrolysis. Uncertainty analyses are conducted on the fuel cell stack degradation， hydrogen consumption per hundred kilometers， and vehicle lifespan. The results show that the FCHCV using photovoltaic electrolysis has the lowest life cycle energy consumption and carbon emissions. Improving fuel economy and vehicle lifespan， and delaying fuel cell stack degradation can effectively improve the environmental impact of FCHCV during the life cycle. Figures and Tables | References | Related Articles | Metrics