Search Results (73)

Considerable progress has been made in predicting nominal NVH behavior in electric drivetrains, but the acoustic scatter observed across manufactured units remains insufficiently understood. In practice, nominally identical drive units may still exhibit noticeably different tonal behavior because small deviations in gears, shafts, bearings, fits, centering features, or assembly phase modify the excitation, transfer, and radiation mechanisms of the system. This review examines how manufacturing and assembly variability influences NVH performance in electric drive units and e-axles, with particular focus on the rotor–shaft–gear–bearing–housing system. Unlike broader EV NVH reviews, the present work focuses specifically on variability-induced acoustic scatter and its propagation along the drivetrain NVH generation and transmission path. To support transparency and consistency, the literature search and selection process followed a structured, PRISMA-inspired approach across Scopus, Web of Science, Google Scholar, and SAE Mobilus for the 2015–2026 period. From 387 identified records, 50 studies were retained after duplicate removal, screening, and full-text assessment. The selected literature was synthesized into eight thematic categories: imbalance; run-out and eccentricity; bearing clearance and preload; spline and pilot centering; thermal effects; phase indexing; transmission error and sidebands; and end-of-line NVH diagnostics. The reviewed literature shows that manufacturing- and assembly-induced deviations can significantly alter transmission error, sideband structure, shaft-order content, and final tonal response, even when individual components remain within nominal tolerance limits. Beyond synthesizing the evidence base, the review organizes existing modeling and diagnostic practices into a structured framework for variability-aware NVH assessment, based on explicit deviation parameterization, hierarchical model fidelity, intermediate excitation metrics, thermal-state awareness, and closer integration with production and measurement data. Overall, the findings support a shift from nominal NVH assessment toward robustness-oriented, production-representative interpretation and future prediction of acoustic scatter in electric drivetrains. Full article

Dual-motor electric drive axles (e-axles) can realize basic torque vectoring through motor-torque allocation. However, without an inter-wheel power-transfer path, they still face structural limitations under motor torque–speed envelopes and severe left–right adhesion asymmetry. To address this issue, this paper proposes a reconfigurable dual-motor e-axle based on fixed-carrier compound planetary gear trains and two cross-axle clutches. By switching between controlled-slip and lock-coupled states, the proposed topology creates a switchable inter-wheel power-transfer path. As a result, it enhances yaw-rate regulation capability under high-adhesion conditions and improves escape capability under severe adhesion asymmetry. A unified kinematic–static analytical framework is established to derive closed-form capability boundaries and compact structural indices for parameter matching. Vehicle-level co-simulation on a representative rear-wheel-drive platform is then carried out for validation. Under severe split-$\mu$ conditions, the peak high-adhesion wheel torque increases from 241.72 to 695.57 N·m, and the escape time decreases from 0.43 to 0.19 s. In a representative high-adhesion step-steer case, the mean yaw-rate tracking error is reduced from 6.75 to 0.20 deg/s, while the mean differential wheel torque reaches 1.83 times that of the baseline mode. The other high-adhesion cases show the same trend. These results verify the vehicle-dynamics significance and engineering feasibility of the proposed architecture. Full article

Fully leveraging the four-wheel independent drive characteristics of distributed-drive electric vehicles has become essential for enhancing their driving range. However, conventional regenerative braking strategies applied to such vehicles often fail to consider individual wheel slip ratios, which can easily lead to wheel lock and low energy recovery efficiency. To address these issues, this paper proposes a novel energy management method that integrates hybrid braking control with intelligent connected speed planning. A hierarchical control strategy for the hybrid braking system is first developed, explicitly accounting for the slip ratio of each wheel. The upper-level controller calculates the slip ratio for each wheel based on vehicle speed and wheel speed information and subsequently determines the braking torque distribution between the front and rear axles. The lower-level controller then allocates the motor braking torque and hydraulic braking torque to each wheel, subject to system constraints such as battery status and motor torque limits. Building on this framework, vehicle state and road information are incorporated as inputs to formulate a Markov decision process, which optimizes traffic efficiency, energy economy, and ride comfort as multiple objectives. The deep deterministic policy gradient (DDPG) algorithm is employed to achieve collaborative optimization of speed planning and energy management. Simulation results demonstrate that the proposed DDPG-based control strategy outperforms both rule-based control methods and classical dynamic programming algorithms in terms of comprehensive performance across traffic efficiency, energy consumption, and ride comfort. These findings validate its superiority in complex traffic conditions. Full article

Accurate estimation of axle torque is essential for performance evaluation and energy management of electric tractors. However, direct torque measurement and access to motor controller data are often limited in commercial platforms. This study proposes a machine learning-based framework for predicting axle torque in a commercial electric tractor using field-measured sensor signals. The framework incorporates a horizon-aware architecture to capture the temporal dependencies of dynamic load fluctuations. Field experiments were conducted during plow tillage operation under multiple gear–speed combinations. Several machine learning models (multiple linear regression, multilayer perceptron, and CatBoost) were evaluated for axle torque prediction. The results showed that rear axle torque exhibited a stronger relationship with traction demand under two-wheel-drive operation, resulting in higher prediction accuracy than front axle torque. Among the evaluated models, CatBoost achieved the best overall performance, with an $R^2$ of 0.83 and an RMSE of 189.35 Nm for the rear axle prediction. The proposed framework enables real-time axle torque estimation using commonly available sensor signals and provides a practical alternative to direct torque measurement for onboard load monitoring and energy management in electric tractor systems. Full article

The rapid electrification of road vehicles has fundamentally reshaped the priorities of noise, vibration, and harshness (NVH) engineering. In the absence of combustion-related broadband masking, tonal and order-related phenomena originating from the electric machine, inverter switching, and high-speed reduction gearing have become clearly perceptible and, in many cases, acoustically dominant. Consequently, drivetrain noise in electric vehicles can no longer be assessed at component level alone; it must be understood as a coupled system response shaped by excitation mechanisms, structural dynamics, transfer paths, radiation efficiency, and ultimately human perception. This review adopts a source-to-perception perspective and consolidates the principal physical mechanisms governing vibro-acoustic behavior in integrated electric drive units. Electromagnetic force harmonics and torque ripple are discussed alongside transmission-error-driven gear mesh excitation, while bearing and shaft nonlinearities are examined in the context of high-speed operation. In addition, ancillary thermoacoustic and aerodynamic contributions are considered, reflecting the increasingly integrated packaging of modern e-axle architectures. On this mechanism-oriented basis, dominant excitation types are linked to frequency-appropriate modeling strategies, spanning electromagnetic force extraction, multibody drivetrain simulation, structural finite element analysis, transfer path analysis, and acoustic radiation prediction. Particular attention is given to workflow integration across domains. Finally, the paper identifies research challenges that predominantly arise at system level, including multi-source interaction effects, installation-dependent transfer-path variability, emergent resonances in assembled structures, manufacturing-induced tonal artifacts, and the still limited correlation between predicted vibration fields and perceived sound quality. Full article

To address the core challenges of low energy utilization efficiency and limited range in front-wheel-drive electric vehicles (FWD EVs), this study proposes a dynamic series braking energy recovery strategy featuring adaptive braking force distribution and multi-factor correction. A comprehensive simulation model integrating five core modules—Cycle, Driver, Controller, Vehicle, and Display—was developed using Matlab/Simulink, combining the dynamic series recovery strategy with traditional parallel recovery strategies. Model reliability was validated through chassis dynamometer test data (maximum error ≤ 3.2%), followed by simulation comparisons under CLTC conditions. Results demonstrate that compared to parallel strategies, the dynamic series approach increases range by 25.8% (from 318 km to 400 km). Key innovations include real-time adaptive front axle braking coefficients based on braking intensity and a correction mechanism integrating vehicle speed and state of charge (SOC), achieving a balance between recovery efficiency, braking stability, and battery protection. This study provides actionable design guidance for FWD EV powertrain optimization while establishing a validated regenerative braking simulation framework. Full article

Multi-axle vehicles are commonly used for heavy-duty special operations, which easily leads to high driving torque demands when adopting distributed electric drive configurations. This study achieves the objective of reducing the driving torque of each in-wheel motor while controlling the stability of multi-axle vehicles. Taking a five-axle distributed drive test vehicle as the research object, a hierarchical control strategy integrating active all-wheel steering and direct yaw moment control is proposed. The upper layer is implemented based on model predictive control, with fuzzy control introduced to dynamically adjust control weights; the lower layer accomplishes the allocation of targets calculated by the upper layer through minimizing the objective function of tire load ratio. A linear parameter varying (LPV) tire model is introduced into the vehicle model to improve the calculation accuracy of tire lateral forces, and a neural network method is employed to solve the real-time performance issue of the model predictive control (MPC) controller. The proposed strategy is verified through a combination of simulation and real vehicle tests. High-speed condition simulations demonstrate that the AWS/DYC strategy significantly outperforms the ARS/DYC approach: compared to the active rear-wheel steering strategy, while the sideslip angle is reduced by 90.98%, the peak driving torque is reduced by 30.78%. Notably, tire slip angle analysis reveals that AWS/DYC maintains relatively uniform slip angle distribution across axles with a maximum of 4.7°, entirely within the linear working region, optimally balancing tire performance utilization with lateral stability while preserving safety margin, whereas ARS/DYC causes slip angles to exceed 11.9° at the rear axle, entering saturation. Low-speed real vehicle tests further confirm the engineering applicability of the strategy. The proposed method is of significant importance for the application of distributed drive configurations in the field of special vehicles. Full article

Accurate determination of a vehicle’s centre of gravity (CoG) is fundamental to driving dynamics, safety, and engineering design. However, existing static CoG estimation methods often neglect tyre deflection and detailed wheel geometry, which can introduce significant errors, particularly in electric vehicles, where the low and concentrated mass of the battery pack increases the sensitivity of vertical CoG calculations. This study presents a refined static axle-load-based method for electric vehicles, in which the influence of tyre deformation and lifting height on the accuracy of the vertical centre of gravity coordinate is explicitly considered and quantitatively justified. To minimise human error and accelerate the evaluation process, a custom-developed Python (Python 3.13.2.) software tool automates all calculations, provides an intuitive graphical interface, and generates visual representations of the resulting CoG position. The methodology was validated on a Volkswagen e-Golf, demonstrating that the proposed approach provides reliable and repeatable results. Due to its accuracy, reduced measurement complexity, and minimal equipment requirements, the method is suitable for design, educational, and diagnostic applications. Moreover, it enables faster and more precise preparation of vehicle dynamics tests, such as rollover assessments, by ensuring that sensor placement does not interfere with vehicle behaviour. Full article

This study proposes a dynamic braking force distribution strategy based on a PID algorithm for Formula Student electric racing cars, addressing the limitations of fixed-ratio distribution methods in adapting to dynamic braking conditions. The strategy utilizes a PID controller targeting the desired slip ratio to dynamically adjust the braking force distribution coefficient (β) between the front and rear axles. The proposed method was validated through co-simulation using CarSim and Simulink, as well as real vehicle testing. Simulation results show a 7.7% reduction in braking distance under emergency braking at 100 km/h with the PID control strategy, while real vehicle testing confirmed a braking distance of 30 m, with a 5.6% deviation from the simulation. Additionally, both yaw and roll angles were significantly reduced, improving vehicle stability during braking. Experimental data confirmed that the system dynamically maintains an optimal pressure difference of approximately 1.6 MPa between the front and rear axles, effectively preventing rear wheel lock-up and ensuring stable braking performance. The research demonstrates that this PID-based brake-by-wire distribution strategy significantly enhances both braking efficiency and driving stability, providing valuable insights for the development of high-performance electric vehicles. Full article

As the core component of new energy vehicles, the performance of the steering axle will directly affect the overall maneuverability, stability, and safety of vehicle driving. The structural performance indexes of the steering axle of the pure electric vehicle are analyzed by the finite element method, and a reasonable improvement plan is given according to its shortcomings. Firstly, the 3D model of the steering axle is established by SolidWorks (SOLIDWORKS 2023), and the details are simplified appropriately and then imported into the ANSYS (ANSYS2020R2 software) platform for static force analysis and modal analysis. Then, the stress distribution, deformation, and the first six orders of intrinsic frequency values of the steering axle are calculated and analyzed by using four working conditions, such as regular driving, emergency braking, lateral slip, and uneven road excitation, and it is concluded that the maximum stress of the original structure under each working condition is less than the requirement of the ultimate stress value. However, from the results, the maximum stress value is concentrated in the emergency braking condition and appears in the intermediate beam corner and the steering knuckle journal, which is also the most dangerous condition. In the modal analysis, it is concluded that the intrinsic frequency of this symmetry structure is much larger than the excitation frequency, and it can produce better dynamic effects under the working conditions, and the dynamic performance is better. Based on this, combined with the results of the static analysis of the proposed new increase in the thickness of the intermediate beam to improve the structural strength of the improvement measures, for this symmetry structure, through the re-simulation of the effect of the most critical conditions (emergency braking), the maximum deformation of the steering axle has been greatly reduced. In addition, the overall stiffness of the symmetry structure has been greatly improved, while the maximum stress is still less than the value of the permissible stress range, and the modal characteristics of the structure has not been affected. The finite element analysis software can effectively evaluate the performance and improve the optimization of the steering axle, which has certain theoretical significance and engineering reference value. Full article

Vibration-based predictive maintenance is an essential element of reliability engineering for modern automotive powertrains including internal combustion engines, hybrids, and battery-electric platforms. This review synthesizes advances in sensing, signal processing, and artificial intelligence that convert raw vibration into diagnostics and prognostics. It characterizes vibration signatures unique to engines, transmissions, e-axles, and power electronics, emphasizing order analysis, demodulation, and time–frequency methods that extract weak, non-stationary fault content under real driving conditions. It surveys data acquisition, piezoelectric and MEMS accelerometry, edge-resident preprocessing, and fleet telemetry, and details feature engineering pipelines with classical machine learning and deep architectures for fault detection and remaining useful life prediction. In contrast to earlier reviews focused mainly on stationary industrial systems, this review unifies vibration analysis across combustion, hybrid, and electric vehicles and connects physics-based preprocessing to scalable edge and cloud implementations. Case studies show that this integrated perspective enables practical deployment, where physics-guided preprocessing with lightweight models supports robust on-vehicle inference, while cloud-based learning provides cross-fleet generalization and model governance. Open challenges include disentangling overlapping sources in compact e-axles, coping with domain and concept drift from duty cycles, software updates, and aging, addressing data scarcity through augmentation, transfer, and few-shot learning, integrating digital twins and multimodal fusion of vibration, current, thermal, and acoustic data, and deploying scalable cloud and edge AI with transparent governance. By emphasizing inverter-aware analysis, drift management, and benchmark standardization, this review uniquely positions vibration-based predictive maintenance as a foundation for next-generation vehicle reliability. Full article

This research is focused on driving stability issues, which can be caused by specifics of electric vehicle (EV) powertrains. Specific driving conditions, such as intensive road curvature and low grip, require precise control from the driver and very accurate and not delayed vehicle stabilization from its active safety systems. These systems, typically anti-lock braking systems (ABS) and electronic stability programs (ESP), perform their tasks sufficiently well, but new vehicle architectures are forcing a reassessment of their reliability, sometimes requiring additional safety subsystems. In the context of EV architecture and its propulsion systems, a possible lack of stability is anticipated when operating intensive regenerative braking in EVs with a rear–wheel–drive transmission. Experimental research conducted on two popular electric vehicles confirmed this hypothesis, as additional oversteering occurs even when ESP systems have intervened. Based on the experiment, a theoretical simulation model of an EV with regenerative braking on the rear axle was created and validated in MATLAB/Simulink (R2024a). The simulations showed how relevant this issue is and how limited stability systems are; therefore, new strategies were proposed and theoretically tested to ensure car safety. These dedicated regenerative braking control subsystems enable optimal use of regenerative braking and ensure more reliable stability in slippery corners. Full article

In the V-cycle of distributed electric wheel loaders (DEWLs), transport accounts for about 70% of the cycle, making energy saving urgent, while shovel-stage slip limits traction stability. This paper proposes a two-module control framework: (i) a preferred-motor transport strategy that reduces parasitic losses and concentrates operation in high-efficiency regions; and (ii) a load-ratio-based front–rear torque distribution for shoveling that allocates tractive effort according to instantaneous axle vertical loads so that each axle’s torque respects its available adhesion. For observability, we deploy a pre-calibrated lookup-table (LUT) mapping from bucket cylinder pressure to the front-axle load ratio, derived offline from a back-propagation neural network (BP-NN) fit. Tests on a newly developed DEWL show that, compared with dual-motor fixed-ratio control, transport-stage mechanical and electrical power drop by 18–37%, and drive-system efficiency rises by 6–13%. During shoveling, the strategy reduces the peak inter-axle slip from 22–35% to 13–15% and lowers the mean slip to 2.6–5.9%, suppressing sawtooth-like wheel-speed oscillations without sacrificing peak capacity. The method reduces parasitic energy flow, improves traction utilization, and is readily deployable. Full article

This study investigates premature fatigue failures in three EA1N steel axles from permanent magnet direct-drive locomotives during wheel-seat bending tests. Complete fracture occurred in one axle at 3 million cycles, and in the other two axles, cracks appeared and were observed through magnetic particle detection at 3.5 million and 1.6 million cycles, respectively. A comprehensive failure analysis was conducted through metallurgical examination, fractography, mechanical testing, residual stress measurement, and finite element analysis. The fractographic results revealed fractures consistently initiated at the wheel-seat to axle-body transition arc, exhibiting characteristic ratchet marks and beach patterns. The premature fracture mechanism was identified as a high-stress fatigue fracture. The residual stress measurements showed detrimental tensile stresses at the surface. Coupled with the operating stress, the stress on the axle exceeds fatigue strength, which accelerates the initiation and propagation of fatigue cracks. Based on these observations, the failure mechanism was identified, and preventive methods were proposed to reduce the risk of recurrence of the in-service axles. Full article

Aiming at the problems of energy utilization efficiency and braking stability in electric vehicles, a high-efficiency and energy-saving control strategy that takes both driving and braking into account is proposed with the distributed hub motor-driven vehicle as the research object. Under regular driving and braking conditions, the front and rear axle torque distribution coefficients are optimized by an adaptive particle swarm algorithm based on simulated annealing and a multi-objective co-optimization strategy based on variable weight coefficients, respectively. During emergency braking, the anti-lock braking strategy (ABS) based on sliding mode control realizes the independent distribution of torque among four wheels. The joint simulation verification based on MATLAB R2023a/Simulink-Carsim 2020.0 shows that under World Light Vehicle Test Cycle (WLTC) conditions, the optimization strategy reduces the driving energy consumption by 3.20% and 2.00%, respectively, compared with the average allocation and the traditional strategy. The braking recovery energy increases by 4.07% compared with the fixed proportion allocation, improving the energy utilization rate of the entire vehicle. The wheel slip rate can be quickly stabilized near the optimal value during emergency braking under different adhesion coefficients, which ensures the braking stability of the vehicle. The effectiveness of the strategy is verified. Full article