Search Results (46)

To improve the transient performance of speed tracking control while ensuring stability and considering actuator constraints in heavy-haul train systems, this paper proposes a novel cruise control method based on a nonparallel distributed compensation (non-PDC) fuzzy-based composite nonlinear feedback (CNF) technique. First, a low-dimensional nonlinear multi-particle error dynamics model is established based on the fencing concept, simplifying the model significantly. To facilitate controller design, a Takagi–Sugeno (T-S) fuzzy model is derived from the nonlinear model. Subsequently, sufficient conditions for the non-PDC fuzzy-based CNF controller are provided in terms of linear matrix inequalities (LMIs), with the controller design addressing asymmetric constraints on control inputs due to differing maximums of traction and braking forces. Simulations based on MATLAB/Simulink are conducted under different maneuvers to validate the effectiveness and superiority of the proposed method. The simulation results demonstrate a notable enhancement in transient performance (over 22.3% improvement in settling time) and steady-state cruise control performance for heavy-haul trains using the proposed strategy. Full article

The energy crisis and environmental pollution have driven the rapid development of new energy vehicles (NEVs). As a core technology for integrating electrification and intelligence in NEVs, the brake-by-wire (BBW) system has become a research hotspot due to its excellent braking energy recovery efficiency and precise active safety control performance. This paper provides a comprehensive review of the research progress in BBW technology for NEVs and provides a forward-looking perspective on its future development. First, the types and structures of the BBW system are introduced, and the development history and representative products are systematically reviewed. Next, this paper focuses on key technologies, such as the design and modeling methods of the BBW system, braking force optimization and distribution strategies, precise actuator control, multi-system coordination, driver operation perception, intelligent decision-making, personalized control, and fault diagnosis and fault-tolerant control. Finally, the main challenges faced in the research of BBW technology for NEVs are analyzed, and future development directions are proposed, providing insights for the optimization designs and industrial application of the BBW system in the future. Full article

A fault-tolerant collaborative control strategy for four-wheel-drive electric vehicles is proposed to address hidden safety issues caused by one or more in-wheel motor faults; the basic design scheme is that the control system is divided into two layers of motion tracking and torque distribution, and three systems, including driving, braking, and front-wheel steering are controlled collaboratively for four-wheel torque distribution. In the layer of motion tracking, a vehicle model with two-degree-of-freedom is employed to predict the control reference values of the longitudinal force and additional yaw moment required; four types of sensors, such as wheel speed, acceleration, gyroscope, and steering wheel angle, are used to calculate the actual values. At the torque distribution layer, SSOD and MSCD distribution schemes are designed to cope with two operating conditions, namely sufficient and insufficient output capacity after local hub motor failure, respectively, focusing on the objective function, constraints, and control variables of the MSCD control strategy. Finally, two operating environments, a straight-line track, and a DLC track, are set up to verify the effectiveness of the proposed control method. The results indicate that, compared with traditional methods, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 12.9% and 5.88%, respectively, in the straight-line track environment. In the DLC track environment, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 6% and 4.5%, respectively. The proposed fault-tolerant controller ensures that the four-wheel-drive electric vehicle meets the requirements of handling stability and safety under one or more hub motor failure conditions. Full article

In order to solve the problem that electric forklifts are prone to rollover when turning, a coordinated control strategy for anti-rollover of electric forklifts is proposed. A forklift dynamics simulation model with integrated centroid position is constructed, the stability of the forklift is judged by the phase plane area division method, the upper controller, including the active steering controller, and the differential brake controller are designed, the control weight coefficient of the active steering controller and the differential brake controller in different control domains is determined through the coordination controller, so as to obtain the required additional rear wheel rotation angle and additional yaw torque, and the braking force distribution controller exerts braking force to the wheel according to the additional yaw torque. A simulation model is built to verify the effectiveness of this control strategy, and the simulation results show that the control strategy can greatly reduce the risk of rollover when the forklift is cornering and further improve the stability of the forklift. Full article

This paper describes an integrated control strategy that utilizes semi-active suspension and differential braking to enhance lateral stability while maintaining roll performance. The integrated control architecture adopts a hierarchical structure consisting of an estimator, a supervisor, a controller, and an allocator. In the estimation layer, an algorithm is proposed to robustly estimate the side slip angle and roll angle in various situations. The control mode is established by the supervision layer based on the state of the vehicle. The maneuverability mode tracks the driver’s intentions, and the lateral stability mode ensures the vehicle’s stability. Reference values such as yaw rate and roll angle are determined by the control mode. In the controller layer, the yaw and roll moments are generated using sliding mode control to achieve the target yaw rate and roll angle. Brake torque and suspension damping force are distributed to each wheel in the allocator layer. In particular, a damping distribution method based on the roll region index is proposed. The proposed method is compared with conventional methods, such as full stiff damping and yaw-assisted damping, through simulation and real-world evaluation. The tests demonstrate that the proposed approach enhances lateral and roll stability, particularly regarding maximum side slip and roll angle values. The roll-region-index-based distribution method reduces the maximum roll angle by about 17.4% and the maximum side slip angle by about 8.7% compared to each conventional method. Compared to conventional methods, the proposed method showed more stable driving performance by ensuring stability in both directions in extreme lane change situations. Full article

The normalized operation of 30,000-ton heavy-haul trains is of significant importance for enhancing the transportation capacity of heavy-haul railways. However, with the increase in train formation size, traditional braking strategies result in excessive longitudinal impulse when combined pneumatic and electric braking is applied on long, steep gradients. This presents a serious challenge to the braking safety of the train. To this end, this paper establishes a longitudinal dynamic model of a 30,000-ton heavy-haul train based on vehicle system dynamics theory, and validates the model’s effectiveness through line test data. On this basis, the influence of two braking parameters, namely, the distribution of the magnitude of the electric braking force and the matching time of pneumatic braking and electric braking, on the longitudinal dynamic behavior of heavy-haul trains is studied. Thereby, an optimized combined pneumatic and electric braking strategy is formulated to reduce the longitudinal impulse of the trains. The results show that setting reasonable braking parameters can effectively reduce the longitudinal impulse, with the braking matching time having a significant impact on the longitudinal impulse. Specifically, when using a strategy where the electric braking forces of three locomotives are set to 90 kN, 300 kN, and 300 kN, with a 30 s delay in applying the electric braking force, a better optimization effect is achieved. The two proposed braking strategies reduce the maximum longitudinal forces by 20.27% and 47.83%, respectively, compared to conventional approaches. The research results provide effective methods and theoretical guidance for optimizing the braking strategy and ensuring the operational safety of 30,000-ton heavy-haul trains. Full article

Regenerative braking energy recovery is of critical importance for electric vehicles due to their range limitations. To further enhance regenerative braking energy recovery, a dual-fuzzy regenerative braking control strategy based on braking intention recognition is proposed. Firstly, the distribution strategy for braking force is devised by considering classical curves like ideal braking force allocation and ECE regulations; secondly, taking the brake pedal opening and its opening change rate as inputs, the braking intention recognition fuzzy controller is designed for outputting braking strength. Based on the recognized braking strength, and considering the battery charging state and the speed of the vehicle as inputs, a regenerative braking duty ratio fuzzy controller is developed for regenerative braking force regulation to improve energy recovery. Furthermore, a control experiment is established to evaluate and compare the four models and their respective nine braking modes, aiming to define the dual fuzzy logic controller model. Ultimately, simulation validation is conducted using Matlab/Simulink R2019b and CRUISE 2019. The results show that the strategy in this paper has higher energy savings compared to the single fuzzy control and parallel control methods, with energy recovery improved by 26.26 kJ and 96.13 kJ under a single New European Driving Cycle (NEDC), respectively. Full article

Truck skidding crashes on horizontal curves pose a significant road safety concern, with improper braking being the primary cause. A data- and model-integrated driven method is proposed to investigate the mechanism and recommend the maximum safe braking deceleration rates without skidding (abbreviated as MSBDRs) for trucks on horizontal curves. Firstly, a comprehensive road–vehicle interaction model was developed, considering dynamic changes in brake force distribution, vertical tire load, and longitudinal and side friction during braking. Secondly, leveraging the “HighD” data set and employing cluster analysis principles, parameter data were extracted using Python and Matlab. Finally, through parameterizing model inputs, the transient dynamic response of trucks was examined, the potential of truck skidding was predicted, and the MSBDRs were recommended. The results indicate the following. (1) There is little concern of truck skidding during car-following braking maneuvers; however, there is a high potential of truck skidding during emergency braking maneuvers. (2) The MSBDR is 4.5 m/s² on a limit-minimum-radius horizontal curve; however, when combined with steep slopes, an overspeed exceeding 20%, and extremely wet road conditions, respectively, the MSBDRs decrease to 4 m/s², 3 m/s², and 2 m/s². These results provide a theoretical foundation for braking strategies in autonomous vehicles. Full article

Regenerative braking technology is essential for reducing energy consumption in electric vehicles (EVs). This study introduces a method for optimizing the distribution of deceleration forces in front-wheel-drive electric vehicles that complies with the distribution range outlined by ECE-R13 braking regulations and aligns with an ideal braking distribution curve. In addition, using a fuzzy control strategy to manage the complex variables of the regenerative braking process, a robust and adaptable system is developed on the Simulink platform. Tested across various driving cycles are NEDC (New European Driving Cycle), WLTC (World Light Duty Vehicle Test Cycle), FTP-72 (Federal Test Procedure 72), and FTP-75 (Federal Test Procedure 75). The method significantly improves energy efficiency: 13% for WLTC, 16% for NEDC, and 30% for both FTP-72 and FTP-75. The simulation results were compared to regenerative braking control techniques A and B, showing that the proposed control method achieves a higher brake energy recovery rate. This leads to a considerable improvement in the vehicle’s energy recovery efficiency. These findings confirm the efficacy of the proposed regenerative brake control system, highlighting its potential to significantly enhance the energy efficiency of electric vehicles. Full article

With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro–mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles. Full article

As a transitional vehicle between fuel and electric vehicles, hybrid vehicles achieve energy savings and emission reductions without range anxiety. Regenerative braking has a direct impact on the fuel consumption of the whole vehicle; however, the current regenerative braking strategy for commercial vehicles is not yet perfect and has a poor adaptability in terms of working conditions and whole-vehicle load changes. Therefore, this paper proposes a regenerative braking strategy based on the identification of working conditions, by considering the influence of the vehicle load state and driving conditions on braking. Firstly, historical driving data of commercial vehicles were obtained from GPS data, driving conditions were classified using principal component analysis (PCA) and K-means, and a working condition recogniser was constructed using a back propagation neural network (BPNN) optimised with the Coati optimisation algorithm (COA). The recognition accuracy of the COA-BPNN was 7.6% better than that of the BPNN. Secondly, front and rear axle braking force distribution strategies are proposed, according to the braking intensity magnitude and load state under empty-, half-, and full-load conditions. Finally, a genetic algorithm (GA) was used to find the optimal control parameters for each category of working conditions, and the COA-BPNN condition recogniser identified the current category of working conditions needed to retrieve the corresponding optimal control parameters in the offline parameter library. The simulation results under C-WTVC and synthetic conditions show that the energy recovery rate of the proposed strategy in this paper reached up to 69.65%, which is at most 206.3% higher than that of the fixed-ratio strategy and at most 37.4% higher than that of the fuzzy control strategy. Full article

Electric wheel-drive multi-axle heavy-duty vehicles have the characteristics of strong maneuverability and good passability, thereby they are widely used in heavy equipment transportation. However, current research on the composite braking of multi-axle heavy-duty vehicles is rare, which is not conducive to improving braking performance and braking energy utilization efficiency. This work proposes a multi-mode composite braking control strategy for the five-axle distributed electric wheel-drive heavy-duty vehicle. Firstly, given the differences in braking dynamics between two-axle vehicles and multi-axle vehicles, the brake dynamics characteristics of multi-axle vehicles are analyzed, and the vehicle dynamics model of multi-axle vehicles is constructed. Next, a multi-mode composite braking control strategy including a fully electric braking state and hybrid electro–hydraulic braking state is proposed in order to improve the braking energy recovery and braking stability. Finally, a hardware-in-the-loop simulation system is established, and the single-braking conditions and China heavy-duty commercial vehicle test cycle-heavy truck (abbreviated as CHTC-HT) are conducted to verify the performance of the braking control strategy. The results indicate that the recaptured braking energy and braking stability are significantly increased by applying the control strategy proposed in this work. Full article

The thermal decay of the brake has a great impact on the long downhill braking stability of pure electric commercial vehicles. Based on the road slope and using the fuzzy control method, the motor regenerative braking force and friction braking force distribution strategies were designed to reduce the friction braking force, improve the braking stability and recover the braking energy. By establishing road driving conditions with different slopes, numerical analysis methods are used to verify the proposed control strategy. The results show that the vehicle maintains a constant speed downhill at 30 km/h under the condition of 6% constant slope driving, and the braking energy recovery rate reaches 50.93% under 60% initial battery SOC, 50.89% under 70% initial battery SOC, and 50.81% under 80% initial battery SOC. The speed of the vehicle fluctuates slightly under the driving condition of an 18 km long variable slope distance, but the power torque of the electric mechanism can still be maintained at a constant speed of 30 km/h by adjusting the electric mechanism, and the braking energy recovery rate reaches 49.96%. During the downhill driving at a constant speed, the friction braking force does not participate in braking, and the recuperation rate of braking is determined by the slope and the magnitude of braking deceleration. Full article

Road slope is an essential parameter in the study of vehicle driving processes. In future traffic development, constructing road segments with slopes is indispensable. Furthermore, road slope is a fundamental parameter for realizing energy recovery during braking. Hence, research on road slope estimation is extremely crucial. This article proposes a combination of adaptive filtering and strong tracking filter factors for road slope estimation, followed by establishing case settings for verification. It was found that the proposed slope estimation algorithm has a high degree of accuracy in estimating the slope angle, with a mean absolute error (MAE) and a root mean square error (RMSE) of 0.0254 and 0.0359, respectively, at fixed slopes, and a MAE and a RMSE of 0.2799 and 0.3710, respectively, at varying slopes. By combining the slope angle with a braking force distribution optimization algorithm, an optimized braking distribution coefficient is obtained. In the Cruise2019 software, slope angles of 0° and 5° are set and combined with the braking force distribution strategy built in Matlab2021/Simulink for verification under China Heavy-duty Commercial Vehicle Test Cycle (CHTC-HT) and Worldwide Transient Vehicle Cycle (C-WTVC) conditions. The recovered energy increased by 7.24% and 4.99%, respectively, under CHTC-HT conditions, and by 6.42% and 1.73%, respectively, under C-WTVC. Full article

With increasing global attention to climate change and environmental sustainability, the sustainable development of the automotive industry has become an important issue. This study focuses on the regenerative braking issues in pure electric vehicles. Specifically, it intends to elucidate the influence of the braking force distribution of the front and rear axles on access to energy recovery efficiency. Combining the I curve of a pure electric vehicle and the boundary line of the Economic Commission of Europe (ECE) regulations, the braking force distribution relationship between the front and rear axles is formulated to satisfy braking stability. The maximum regenerative braking force of the motor is determined based on the motor torque characteristics and battery charging power, and the regenerative braking torque is optimized by combining the constraints of the braking strength, battery state of charge (SOC), and vehicle speed. Six road working conditions are built, including the New European Driving Cycle (NEDC), the World Light-Duty Vehicle Test Cycle (WLTC), Federal Test Procedure 72 (FTP-72), Federal Test Procedure 75 (FTP-75), the China Light-Duty Vehicle Test Cycle—Passenger (CLTC-P), and the New York City Cycle (NYCC). The efficiency of the regenerative braking strategy is validated by using the Simulink/MATLAB simulation. The simulation results show that the proposed dynamic logic threshold control strategy can significantly improve the energy recovery effect of electric vehicles, and the energy recovery efficiency can be improved by at least 25% compared to the situation without regenerative braking. Specifically, under the aforementioned road working conditions, the braking energy recovery efficiency levels are 27.69%, 42.18%, 49.54%, 47.60%, 49.28%, and 51.06%, respectively. Moreover, the energy recovery efficiency obtained by the current dynamic logic threshold is also compared with other published results. The regenerative braking control method proposed in this article makes the braking control of electric vehicles more precise, effectively reducing energy consumption and improving the driving range of electric vehicles. Full article