Acceleration Performance Research Articles

Relationship Between Isometric Mid-Thigh Pull Force, Sprint Acceleration Mechanics and Performance in National-Level Track and Field Athletes

This study aimed to examine the relationships between isometric mid-thigh pull maximal force (IMTPF), sprint mechanics, and performance. Fifteen national-level track and field athletes (sprinters and hurdlers) performed three maximal-effort isometric mid-thigh pulls on a force plate and two 30 m sprints. The IMTPF, the sprint mechanical variables (theoretical maximum horizontal force (F0), velocity (v0), and power (Pmax)), as well as the sprint performance data at 5 m distance intervals, were collected. Pearson’s product–moment correlation analysis revealed large linear associations between IMTPF and v0 (r = 0.65, R2 = 0.42, p = 0.009), as well as negative linear relationships between IMTPF and sprint times of 15 m (r = −0.53, R2 = 0.28, p = 0.043), 20 m (r = −0.55, R2 = 0.30, p = 0.033), 25 m (r = −0.57, R2 = 0.33, p = 0.025), and 30 m (r = −0.60, R2 = 0.36, p = 0.019). The F0, Pmax, and sprint times to 5 m and 10 m were not significantly correlated with the IMTPF (p < 0.05). The study results highlight that during the late acceleration phase (>15 m), the capacity to generate horizontal force at high running velocities is related to the ability to develop maximal force during isometric contractions.

Hydraulic Resistance Device for Force, Power, and Force–Velocity–Power Profile Assessment During Resisted Sprints with Different Overloads

This study aimed to evaluate the ability of a hydraulic resistance device (HRD) to assess the force and power output and force–velocity–power profile of short sprints, while examining the effects of hydraulic overload on these outcomes. Twenty-eight amateur athletes performed 20 m sprints under minimal (MiL), moderate (MoL), and high (HiL) overloads. Sprint velocity was measured with the HRD, while resistance force (Fr) was assessed from the pressure via the HRD and from the reaction force via the force plate (FP). Using velocity and Fr during the sprints, maximal velocity (vmax), average horizontal force (Favg), average power (Pavg), and FvP profile variables (F0, v0, and Pmax) were calculated. A two-way ANOVA analysed the effects of overload and calculation method. In addition, a correlation between the HRD and FP measurements was evaluated. For all variables, very high to excellent correlation between the HRD and FP was observed (r ≥ 0.96). However, the Favg, Pavg, F0, and Pmax calculated by the HRD were lower than the FP across all overloads (η2 ≥ 0.51; p < 0.001). Regardless of the method used, Favg, Pavg, and F0 were highest at HiL (η2 ≥ 0.38; p < 0.001), and v0 was highest at MiL (η2 = 0.35; p < 0.001), whereas overload had no significant effect on Pmax (η2 = 0.01; p = 0.770). The HRD is a feasible means for monitoring force and power output during hydraulic resisted sprints but should not be directly compared to other resistance devices. HiL produced the highest Favg, Pavg, and F0 and may be optimal for increasing power output and improving acceleration performance.

The Impact of Ankle Plantar-Flexor Muscle Strength on Sprint Acceleration in Floorball Players.

Speed is a crucial physical characteristic where each lower-limb ankle plantar-flexor (PF) muscle needs to generate significant force; however, there is limited evidence about the relationship between single-limb isometric, and reactive strength forms, and linear sprint. The aim of this study was to determine the impact of the maximal unilateral isometric strength of the ankle PF muscle and reactive strength on sprint acceleration performance. Thirty-two male floorball players (mean [SD] age 20.3 [3.0]y, height 181.5 [8.5]cm, body mass 77.4 [12.2]kg, and body mass index 22.3 [2.8]) from 2 clubs in the highest division in Latvia performed 20-m linear sprint, unilateral 10/5 repeated jump (reactive strength index [RSI]), ankle-dorsiflexion range of motion, and isometric seated plantar-flexion strength tests, where all data were used for correlational analysis. Unilateral RSI was associated with 20-m sprint time (r = -.52, P < .01) and average speed from 10 to 20m (r = .72, P < .01). Relative PF strength was associated with unilateral RSI (r = .35, P < .05) and 20-m sprint time (r = -.36, P < .05), whereas ankle-dorsiflexion range of motion was associated with unilateral RSI jump height (r = .47, P < .05). Floorball athletes with higher unilateral RSI and isometric PF muscle strength tend to accelerate faster, but subjects with better ankle-dorsiflexion range of motion jump higher in vertical repeated jumps. These findings suggest that both reactive and isometric strength are key indicators for sprint acceleration performance in floorball athletes, providing coaches with tools to monitor and improve performance.

Comparative Study on the Kinematic Performance of Front Wheel Drive (FWD) and Rear Wheel Drive (RWD) Vehicles

The automotive industry has continuously innovated drive systems. Among various types, Front Wheel Drive (FWD) and Rear Wheel Drive (RWD) systems are widely used. This research examines the influence of these systems on acceleration and deceleration performance through field tests conducted on public highways. Five speed variations—40, 60, 80, 100, and 110 km/h—were tested for acceleration and deceleration. Results indicate RWD vehicles outperform FWD vehicles in acceleration due to the rear location of the drive wheels, providing superior propulsion. Conversely, FWD vehicles demonstrate better deceleration performance, attributed to shorter braking distances and improved braking efficiency.

Performance effects of functional knee brace removal and prolonged use in healthy male athlete: Lower extremity power, acceleration, speed, and agility.

Knee braces were introduced to sports 30 years ago. However, knee brace use for non-contact anterior cruciate ligament injury prevention intervention remains contentious due to concerns about performance hindrances. Since knee brace use is a potential modifiable risk factor, we aimed to investigate the effect of discounting and continued functional knee brace (FKB) on lower extremity power-vertical jump (VJ), acceleration, speed, and agility performance. Prospective cohort crossover study. Twenty-seven healthy male athletes performed seven tests, over six days of 12 test sessions (S), during three test conditions (non-braced, braced, and removed brace or continued brace use). This study focuses on VJ, acceleration, speed, and agility performance during S12 when athletes were randomly selected to remove the FKB after 17.5 h or continue using the FKB for 21.0 h. After brace removal, nonsignificant performance levels improved in the VJ (2.7 %; 95 % CI 52.5-62.8; Cohen's effect size (ES) = trivial), acceleration (1.8 %; 95 % CI 0.500-0.562; ES = small), and agility (0.5 %; 95 % CI 9.25-10.13; ES = trivial), while a nonsignificant slower speed was recorded (0.5 %; 95 % CI 1.81-1.95; ES = trivial). Continued brace use led to a nonsignificant performance improvement in all tests; VJ (3.1 %; 95 % CI 53.5-60.2; ES = small), acceleration (1.5 %; 95 % CI 0.511-0.561; ES = trivial), speed (1.0 %; 95 % CI 1.83-1.95; ES = trivial), and agility (1.8 %; 95 % CI 9.26-10.04; ES = trivial). Removal of FKB led to improved performance in three performance tests, while continued brace use improved performance in all four tests.

Comparative performance analysis of mixed metal oxide sensors for dual-sensing leveraging machine learning

This paper presents the synthesis of mixed metal oxide (BaTiO3: ZnO) (B: Z) sensors with various molar ratios using a low-temperature hydrothermal method for dual sensing applications (gas and acceleration). The sensor developed with an equal molar ratio of 1B:1Z, showcases superior performance compared to unmixed and alternative mixed metal oxide sensors. This equilibrium in ratios optimally enhances synergistic effects between elements B and Z, resulting in improved sensing properties. Furthermore, it contributes to structural stability, enhancing performance in gas and acceleration sensing. A decreased band gap of 2.82 eV and a rapid turn-on voltage of 0.18 V were achieved. The acceleration performance of 1B:1Z sensor exhibits a maximum voltage of 2.62 V at a 10 Hz resonant frequency and an output voltage of 2.52 V at 1 g acceleration, achieving an improved sensitivity of 3.889 V g-1. In addition, the proposed gas shows a notable sensor response of ∼63.45% (CO) and 58.29% (CH4) at 10 ppm with a quick response time of 1.19 s (CO) and 8.69 s (CH4) and recovery time of 2.09 s (CO) and 8.69 s (CH4). Challenges in selectivity are addressed using machine learning, employing various classification algorithms. Linear discriminant analysis achieves superior accuracy in differentiating between CO and CH4,reaching 96.6% for CO and 74.6% for CH4at 10 ppm. Understanding these concentration-dependent trends can guide the optimal use of the sensors in different current applications.

Diagnostic Performance of the Maximal Systolic Acceleration for Detecting a Significant Stenosis in the Aortoiliac and Popliteal Pathway: A Retrospective Cohort Study.

Identifying peripheral arterial disease (PAD) remains challenging with currently used bedside tests. The maximal systolic acceleration (ACCmax) is a promising noninvasive parameter measured by duplex ultrasonography and reflects the arterial perfusion proximal to its measurement point. The principal aim of this study was to analyze the diagnostic accuracy of the ACCmax for detecting significant stenosis in different arterial segments, which could be useful in clinical decision-making. A retrospective cohort study was conducted in a tertiary referral hospital. Patients aged 18 years and older who underwent ACCmax measurement(s) alongside computed tomography angiography (CTA) of the abdominal aorta and lower extremities were qualified for inclusion. A significant stenosis was defined as a lumen reduction of more than 50% on CTA. Diagnostic accuracy of the ACCmax was investigated for the aortoiliac and popliteal arterial pathways. A total of 196 patients (373 limbs) were included in the study. Diagnostic performance of the ACCmax (cut-off value of 7.70 m/s2) to detect a significant stenosis in the aortoiliac pathway showed a sensitivity of 89%, specificity of 97%, positive likelihood ratio of 29.23 and negative likelihood ratio of 0.12 (area under the curve [AUC] 0.941). For the popliteal pathway (cut-off value of 6.30 m/s2), these results were 90%, 95%, 17.14 and 0.12, respectively, with an AUC of 0.958. The ACCmax showed a promising diagnostic accuracy for detecting a significant stenosis in the aortoiliac and popliteal pathway. The maximal systolic acceleration (ACCmax) is a promising non-invasive parameter measured by duplex ultrasonography to diagnose peripheral arterial disease (PAD) and reflects the arterial perfusion proximal to its measurement point. This study focused on its diagnostic accuracy to detect a significant stenosis in the aortoiliac and popliteal pathway, which revealed to be promising with excellent sensitivity and specificity. These findings suggest that ACCmax measurements could play a key role in developing a new diagnostic approach for PAD.

Understanding the Potential of FPGA-based Spatial Acceleration for Large Language Model Inference

Recent advancements in large language models (LLMs) boasting billions of parameters have generated a significant demand for efficient deployment in inference workloads. While hardware accelerators for Transformer-based models have been extensively studied, the majority of existing approaches rely on temporal architectures that reuse hardware units for different network layers and operators. However, these methods often encounter challenges in achieving low latency due to considerable memory access overhead. This article investigates the feasibility and potential of model-specific spatial acceleration for LLM inference on field-programmable gate arrays (FPGAs). Our approach involves the specialization of distinct hardware units for specific operators or layers, facilitating direct communication between them through a dataflow architecture while minimizing off-chip memory accesses. We introduce a comprehensive analytical model for estimating the performance of a spatial LLM accelerator, taking into account the on-chip compute and memory resources available on an FPGA. This model can be extended to multi-FPGA settings for distributed inference. Through our analysis, we can identify the most effective parallelization and buffering schemes for the accelerator and, crucially, determine the scenarios in which FPGA-based spatial acceleration can outperform its GPU-based counterpart. To enable more productive implementations of an LLM model on FPGAs, we further provide a library of high-level synthesis (HLS) kernels that are composable and reusable. This library will be made available as open-source. To validate the effectiveness of both our analytical model and HLS library, we have implemented Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT2) on an AMD Xilinx Alveo U280 FPGA device. Experimental results demonstrate our approach can achieve up to 13.4× speedup when compared to previous FPGA-based accelerators for the BERT model. For GPT generative inference, we attain a 2.2× speedup compared to Design for Excellence, an FPGA overlay, in the prefill stage, while achieving a 1.9× speedup and a 5.7× improvement in energy efficiency compared to the NVIDIA A100 GPU in the decode stage.

Starting driving style recognition of electric city bus based on deep learning and CAN data

Drivers with aggressive driving style driving electric city buses with rapid response and high acceleration performance characteristics are more prone to have traffic accidents in the starting stage. It is of great importance to accurately identify the drivers with aggressive driving style for preventing traffic accidents of city buses. In this article, a starting driving style recognition method of electric city bus is firstly proposed based on deep learning with in-vehicle Controller Area Network (CAN) bus data. The proposed model can automatically extract the deep spatiotemporal features of multi-channel time series data and achieve end-to-end data processing with higher accuracy and generalization ability. The sample data set of driving style is established by pre-processing the collected in-vehicle CAN bus data including the status of driving and vehicle motion, the data pre-processing method includes data cleaning, normalization and sample segmentation. Data set is labelled with subjective evaluation method. The starting driving style recognition method based on Convolutional Neural Network (CNN) model is constructed. Multiple sets of convolutional layers and pooling layers are used to automatically extract the spatiotemporal characteristics of starting driving style hidden in the data such as velocity and pedal position etc. The fully connected neural network and incentive function Softmax are applied to establish the relationship mapping between driving data characteristics and the starting driving styles, which are categorized as cautious, normal and aggressive. The results show that the proposed model can accurately recognize the starting driving style of electric city bus drivers with an accuracy of 98.3%. In addition, the impact of different model structures on model performance such as accuracy and F1 scores was discussed, and the performance of the proposed model was also compared with Support Vector Machine (SVM) and random forest model. The method can be used to accurately identify drivers with aggressive starting driving style and provide references for driver’s safety education, so as to prevent accidents at the starting stage of electric city bus and reduce crash accidents.

Ideal comfort ellipses and comfort dynamics model for mitigating motion sickness in battery electric vehicle

Due to the rapid response and high torque output of the electric motor, drivers and passengers in battery electric vehicles are more prone to dizziness. This is particularly challenging for mitigating motion sickness, as acceleration performance conflicts with ride comfort. In this article, we develop ideal comfort ellipses based on various driving cycles, which show a comfort zone for driving. A comfort dynamics model is formulated using acceleration, which is then applied to design a model predictive controller. In addition, the motion sickness dose value is used as an indicator to evaluate the effectiveness of the control strategy. The proposed approach is validated on a battery electric vehicle under the Economic Commission for Europe driving cycle, Worldwide harmonised Light-duty vehicles Test Cycle and linear driving condition. The results indicate that, compared to scenarios without the control strategy, this approach reduces the motion sickness dose value by 20.34%, 34.67%, and 24.11%, respectively, enhancing ride comfort and mitigating motion sickness.

Dephasing of ion beams as magnetic vortex acceleration regime transitions into a bubble-like field structure

The interaction of an ultra-intense laser pulse with a near critical density target results in the formation of a plasma channel, a strong azimuthal magnetic field and moving vortices. An application of this is the generation of energetic and collimated ion beams via magnetic vortex acceleration. The optimized regime of magnetic vortex acceleration is becoming experimentally accessible with new high intensity laser beamlines coming online and advances made in near critical density target fabrication. The robustness of the acceleration mechanism with realistic experimental conditions is examined with three-dimensional simulations. Of particular interest is the acceleration performance with different laser temporal contrast conditions, in some cases leading to pre-expanded target profiles prior to the arrival of the main pulse. Preplasma effects on the structure of the accelerating fields are explored, including a detailed analysis of the ion beam properties and the efficiency of the process. Improved scaling laws for the magnetic vortex acceleration mechanism, including the laser focal spot size effects, are presented.

Beam dynamics studies on a specific radio-frequency quadrupole injector for superconducting linacs

Radio-frequency quadrupole (RFQ) linear accelerators are widely used at the front end of large-scale hadron accelerator facilities owing to their extraordinary performance of progressive bunching, high-efficiency acceleration and low emittance growth. Meanwhile, it is worth noting that RFQ linacs gradually become the only normal conducting accelerators in modern continuous-wave high-power superconducting hadron linacs. As a result, high-quality beams at the exit of RFQ linacs with respect to more strict beam dynamics requirements are demanded, where minimizing the emittance growth is one of the most critical design targets. Not only a small rms emittance is required for an easy rms match to the following superconducting cavities but also a small total emittance is needed to reduce the beam loss in the superconducting cavities. In this paper, the design parameters of some typical RFQ injectors for superconducting linacs worldwide were compared, and the beam dynamics design of a 162.5 MHz, 2.1 MeV, continuous-wave RFQ linac was performed. Guided by the design approach called MEGLET (Minimizing Emittance Growth via Low Emittance Transfer), the beam dynamics design of the example RFQ injector was completed with high transmission efficiency, low emittance growth, and large error tolerance. The design result could be a reference for similar RFQ linacs, and the beam dynamics design approach is useful and applicable for other linacs intended to improve the figures of merit.

Village-level hydropower for developing economies: Accelerating access to electricity using low-cost hydrokinetic technology for remote community: 1. Laboratory flume experiment

ABSTRACT This article presents the feasibility of a low-cost hydrokinetic turbine technology developed and experimentally tested in the laboratory for enhancing electricity access in isolated off-grid communities in the rural areas. The research and development involved using e-waste and locally available materials to develop a modular energy conversion system using rivers’ kinetic flow. A decommissioned boat motor with a 0.24 m diameter rotor is operated as a turbine. Four augmentation structures were developed and tested in a flume to evaluate their performance for flow acceleration and application for energy conversion. The four varied configurations produced distinctive performances in an overflow test condition at an approach velocity of 0.25 m/s such that: nozzle 1(8.111 ± 0.107 V DC, 7.116 ± 0.098 V AC), Nozzle 2 (10.038 ± 0.103 V DC, 8.804 ± 0.123 V AC), nozzle 3 (8.523 ± 0.009 V DC, 7.543 ± 0.008 V AC) and Diffuser_type 1 (8.053 ± 0.082 V DC, 7.147 ± 0.144 V AC). Only nozzle 1 was tested in dammed condition and produced 13.248 ± 0.123 V DC and 11.395 ± 0.008 V AC. Nozzle 2 produced a promising performance serving as a reference in comparison to the other three configurations under similar flow conditions.

Application of data partitioned Kriging algorithm with GPU acceleration in real-time and refined reconstruction of three-dimensional radiation fields

Accurately assessing personal radiation doses in real radiation environments like nuclear power plants requires precise and real-time reconstruction of three-dimensional radiation fields. The Kriging algorithm, known for its accuracy in spatial interpolation, provides a promising approach for this task. However, its computational demands can be significant, especially in real-time scenarios. To address this, we enhance the Kriging algorithm with GPU acceleration and data partitioning strategies, enabling efficient and accurate reconstruction of three-dimensional nuclear radiation fields. Using Fluka software for Monte Carlo simulations, we generated a virtual radiation field of dimensions 5 m × 5 m × 5 m for a single-source, unshielded scenario, and a field of dimensions 20 m × 6 m × 8 m for a multi-source, shielded scenario. Using the simulated data, we compared the prediction accuracy of the improved algorithm with the conventional Kriging algorithm and further explored factors influencing the acceleration ratio of the improved algorithm. The results indicate that the GPU-accelerated and data-partitioned Kriging algorithm achieves nearly identical accuracy compared to the traditional method. In the single-source, unshielded scenario, with more than 343 known (measurement) points and predicting 95×95×95= 857,375 points, the prediction accuracy remains above 92.25%. In the multi-source, shielded scenario, with more than 8000 known (measurement) points and predicting 95×95×95= 857,375 points, the prediction accuracy remains above 91.17%. The acceleration performance of the improved algorithm is consistent across both scenarios, with the acceleration ratio increasing as the number of known and predicted points grows, reaching approximately 20 for smaller datasets and up to 93 for larger datasets. Additionally, the acceleration effect of the improved algorithm varies with data partition size, initially increasing and then decreasing as the partition size increases. When the number of known points is 512 and the number of predicted points is 884,736, the optimal partition size lies between 80,000 and 90,000, resulting in a prediction time of only 0.24 s.

Effects of maximum thickness position on hydrodynamic performance for fish-like swimmers

When designing the internals of robotic fish, variations in the internal arrangements of power and control systems cause differences in external morphological structures, particularly the positions of maximum thickness. These differences considerably affect swimming performance. This study examines the impact of the topological structure of self-propelled fish-like swimmers on hydrodynamic performance using fluid-structure interaction techniques. Fish-like swimmers with maximum thickness closest to the head exhibit optimal swimming performance, characterized by modest energy consumption for fast-response acceleration during the starting phase and higher swimming velocity for high-speed travel during steady swimming. As the maximum thickness moves toward the middle, acceleration performance significantly weakens and swimming speed decreases, although maximum energy consumption is relatively reduced. This study will provide a notable reference for the morphological design of underwater robotic fish.

Optical ionization effects in kHz laser wakefield acceleration with few-cycle pulses

We present significant advances in laser wakefield acceleration (LWFA) operating at a 1 kHz repetition rate, employing a sub-TW, few-femtosecond laser and a continuously flowing hydrogen gas target. We conducted a comprehensive study assessing how the nature of the gas within the target influences accelerator performance. This work confirms and elucidates the superior performance of hydrogen in LWFA driven by few-cycle, low-energy laser pulses. Our system generates quasimonoenergetic electron bunches with energies up to 10 MeV, bunch charges of 2 pC, and angular divergences of 15 mrad. Notably, our scheme relying on differential pumping enables continuous operation at kHz repetition rates, contrasting with previous systems that operated in burst mode to achieve similar beam properties. Particle-in-cell simulations explain hydrogen's superior performances: the ionization effects in nitrogen and helium distort the laser pulse, negatively impacting the accelerator performance. These effects are strongly mitigated in hydrogen plasma, thereby enhancing beam quality. This analysis represents a significant step forward in optimizing and understanding kHz LWFA. It underscores the critical role of hydrogen and the imperative need to develop hydrogen-compatible target systems capable of managing high repetition rates, as exemplified by our differential pumping system. These advances lay the groundwork for further developments in high-repetition-rate laser plasma accelerator technology. Published by the American Physical Society 2024

Distributed optimal control design with the feed-forward compensator for high-speed train

The distributed optimal design of high-speed train movement is systematically investigated in this article. A distributed optimal control law is proposed, addressing the train consist of cars coupled by spring buffers, and is affected by aerodynamic drag and rolling resistance. A new distributed controller is proposed to decouple the train model by fully removing the in-train force, which greatly simplifies the complexity of calculation. Then the pending problem is redescribed to the control of cars with different mass. Grounded on the Lyapunov stability theory and optimal control theory, distributed optimal control law is proposed in line with guaranteed cost function, which enables faster updates of the real-time status of each car and adaptive vehicle mass. It ensures consistency in the tracking process of each car of the train, and further reduces the in-train force among cars. To eliminate the speed overshoot which results from the influence of acceleration change during train operation, we weigh in with the feed-forward compensator to assure the train’s good acceleration performance. Ultimately, numerical simulations results are obtained to demonstrate convincingly the significance of our proposed control law.

Dynamic analysis of two-stage steering reducer for electric vehicle based on ROMAX

Abstract. With the rapid development of electric vehicles in the modern market, the efficiency and performance requirements of electric vehicle transmission systems are getting higher and higher. As an important part of the electric vehicle reducer, the steering reducer can make full use of the advantages of the two-stage deceleration transmission, achieve more efficient power transmission, and improve the acceleration performance and driving performance of electric vehicles. This paper aims to design a two-stage steering reducer for an electric vehicle, which adopts planetary gear transmission type. Firstly, the working principle of the electric vehicle steering reducer is analyzed, and the overall design scheme of the reducer is drawn up. Secondly, the transmission system parameters of the reducer are designed and calculated, including transmission ratio distribution, tooth number ratio calculation, etc. Secondly, the transmission structure of the reducer is designed, including input shaft, output shaft, first planetary gear train, second planetary gear train, reducer shell, bearing, etc., and the reducer model is drawn by three-dimensional software Solidworks. Finally, the strength and deformation check analysis of the two-stage reducer is carried out by using the finite element software ROMAX to evaluate the performance of the designed reducer under this working condition. The full text design has a certain reference, and can be applied to other electric vehicle reducer design to provide theoretical guidance.

An Improved Adaptive Sliding Mode Control Approach for Anti-Slip Regulation of Electric Vehicles Based on Optimal Slip Ratio

To optimize the acceleration performance of independently driven electric vehicles with four in-wheel motors, this paper proposes an anti-slip regulation (ASR) strategy based on dynamic road surface observer for more efficient tracking of the optimal slip ratio and enhanced vehicle acceleration. The method uses the Unscented Kalman Filter (UKF) observer to estimate vehicle speed and calculate the actual slip ratio, while a fuzzy controller based on the Burckhardt tire model identifies road surfaces. The road’s peak adhesion coefficient and optimal slip ratio curve are fitted using a Back Propagation Neural Network (BPNN) optimized by Particle Swarm Optimization (PSO). The control strategy further refines torque management through an adaptive sliding mode control (ASMC) that integrates adaptive laws and a super-twisting sliding mode approach to track the optimal slip ratio. Joint simulations with MATLAB/Simulink and Carsim on low-adhesion, joint, and split road surfaces demonstrate that the strategy quickly and accurately identifies the optimal slip ratio across various road surfaces. This enables the tire slip ratio to approach the optimal value in minimal time, significantly improving vehicle dynamic performance. Compared to conventional sliding mode controllers, the optimized ASMC reduces chattering and improves control precision.

Driving Control Strategy and Specification Optimization for All-Wheel-Drive Electric Vehicle System with a Two-Speed Transmission

Equipping electric vehicles with a two-speed gearbox allows for achieving high torque and maximum speed through appropriate gear ratio adjustments. Additionally, tuning motor operating points to efficient zones, considering energy efficiency, significantly enhances the vehicle’s overall performance. This paper presents an AWD system configuration method, integrating a two-speed transmission to improve energy efficiency and driving performance through front and rear motor torque distribution and powertrain specification optimization. Based on vehicle simulations conducted using MATLAB/Simulink, a strategy for torque distribution between the front/rear axles was established using fuzzy logic, considering energy efficiency and driving stability. Furthermore, a multi-objective optimization was performed using a surrogate model trained through MATLAB parallel simulations. When the optimization results were applied to various vehicle specifications, it was observed that energy efficiency was improved, and acceleration performance was increased compared to a baseline vehicle without optimization.