Electric Vehicle Motor Research Articles

Assessment of viscosity effects on high-speed coolant pump performance

The high-speed coolant pump facilitates thermal regulation in electric vehicle components, including batteries and motors, by circulating an ethylene glycol solution. This commonly used circulating fluid exhibits a notable negative correlation with temperature in terms of viscosity. Numerical simulations investigate the transient dynamics of a high-speed coolant pump operating at 6000 rpm, driving coolant flow at various temperatures. A high-speed coolant pump test rig is established, and the performance is evaluated under different temperature conditions. The numerical simulations at different temperatures align well with the experimental outcomes. Decreasing temperatures, from 100 to −20 °C, lead to reduced pump head and efficiency due to increased viscosity. Specifically, at a flow rate of 30 L/min, head decreases by 40.03% and efficiency by 44.19%. With escalating viscosity, the best efficiency point shifts toward lower flow rates. Notable impacts on both disk efficiency and hydraulic efficiency are observed due to viscosity fluctuations. It exerts minimal influence on volumetric efficiency at elevated flow rates but has a substantial impact on volumetric efficiency at lower flow rates. Increased fluid viscosity causes uneven pressure distribution within the pump, altering velocity profiles within the impeller. High-viscosity fluids tend to form large-scale vortex structures around the blades, reducing the thrust exerted by the blades on the fluid. Higher viscosity results in larger vortex structures around the blades, reducing thrust and increasing fluid frictional resistance. The study findings provide valuable insights for the advancement of high-efficiency, energy-saving, high-speed coolant pumps tailored for electric vehicles.

Pitch motion suppression of electric vehicle active suspensions based on multibody dynamics

Active suspension control strategies are proposed to suppress pitch motion and address the issue of motion sickness among electric vehicle passengers. In this work, we investigate the performance of the linear quadratic regulator (LQR), variable universe fuzzy control, adaptive-network-based fuzzy inference system (ANFIS), and fuzzy PID control under continuous acceleration and deceleration and bumpy road conditions. First, a 14-degrees-of-freedom (DOF) dynamics model of an electric vehicle is developed based on a semirecursive multibody dynamics method, which was originally proposed by Javier García de Jalón. The vehicle dynamics simulation is performed to accurately model the vehicle states and responses. Second, the LQR, variable universe fuzzy control, ANFIS, and Fuzzy PID control algorithms are tailored via vehicle states, including pitch, rate, and acceleration. Their performances are investigated and compared in detail. Finally, the robustness of the proposed control strategies is further discussed based on various speed bump parameters and initial velocities. The results indicate that the proposed control strategies are capable of suppressing the pitch motion of electric vehicles, enhancing the riding comfort under diverse operating conditions, thereby reducing the likelihood of motion sickness among passengers of electric vehicles.

CFD simulation analysis optimization and experimental verification of heat dissipation problem of electric vehicle motor controller

Abstract Pollution and energy crisis are two major challenges facing society today, and electric vehicles are regarded as an effective way to solve these problems. Solving the heat dissipation problem of the motor controller can effectively solve the problem of small motion range of electric vehicles, which is an effective attempt for new energy vehicles. Optimizing the heat dissipation structure of the power system can improve the efficiency of electric vehicles, save energy, and truly achieve optimal energy management. Therefore, the establishment of a reasonable and complete power system cooling system is of great significance for improving the overall performance of the vehicle. This article aimed to explore the heat dissipation problem of electric vehicle motor controllers. This article proposed that the motor can be divided into DC and AC based on the power supply. The DC motor has the characteristics of complex structure, cumbersome production process, easy to wear during use, and troublesome repairs in the later period. Through CFD simulation analysis, the experimental temperature of 1.5 kw natural convection is 62.35 °C, the simulation temperature is 64.05 °C; the experimental temperature of 1.1 kw natural convection is 50.45 °C, and the simulation temperature is 66.67 °C.

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

Aluminium and praseodymium doped M-type hexaferrites for electric vehicle applications

The booming demand for electric vehicles, driven by the pursuit of enhanced efficiency, faces significant hurdles attributed to the costly integration of rare earth magnets like NdFeB. M-type strontium ferrite emerges as a promising alternative owing to its economic viability and exceptional magnetic characteristics, rendering it suitable for electric vehicle motor applications. This research focuses on the synthesis and characterization of SrFe12O19 and Sr1-xPrxFe10Al2O19 (where x = 0.00 to 0.20) hexaferrites, with praseodymium (Pr) substitution. Magnetic particles were synthesized utilizing the sol-gel auto-combustion technique, employing a calcination temperature of 1100 °C for a duration of 4 h. Structural and magnetic studies of aluminium-praseodymium (Al–Pr) doped samples are performed using X-ray diffraction (XRD), scanning electron microscopy (SEM), and vibrating-sample magnetometry (VSM). The structural analysis reveals a decrease in both particle size from 42 nm to 31.76 nm and lattice parameter with increasing doping concentration. Magnetic analysis yields crucial insights: Al3+ doping induces a substantial rise in coercivity from 5500 Oe to 10,500 Oe which is quite comparable to NdFeB, while Pr doping shows a subtle impact, enhancing remanence from 17 emu/g to 23.05 emu/g. These findings underscore the potential for optimizing magnetic properties through meticulous control of dopant concentrations in M-type hexaferrites, thereby positioning them as promising substitutes for rare earth magnets.

Review on cooling techniques and analysis methods of an electric vehicle motor

Review on cooling techniques and analysis methods of an electric vehicle motor

An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techniques extract fault signatures from motor measured data across time, frequency, or time-frequency domains. In contrast, AI-based methods automatically extract higher-order fault signatures from large volumes of preprocessed data, thereby enhancing the effectiveness of fault diagnosis. The strengths and limitations of each approach are thoroughly examined, providing valuable insights into the advancements in ITSF detection and diagnosis techniques for PMSMs in EV applications. The emphasis is placed on the application of signal processing methods and deep learning techniques in the diagnosis of ITSF in PMSMs in EV applications.

Epistemic justice and critical minerals–Towards a planetary just transition

Global transitions to low-carbon energy systems are heavily reliant on the large-scale extraction of critical minerals (e.g. cobalt, lithium, rare earth elements). These minerals are key components in green technological developments such as wind turbines, solar photovoltaics and electric vehicle motors. However, research has highlighted the multifaceted social and environmental costs of critical mineral extraction and processing across marginalised parts of the Global South, evidencing significant impacts on local people's health, livelihoods, human rights and land-based resources. This paper centres on epistemic justice in the critical mineral (CM) industry, an under-explored dimension of just energy transitions. Through the analysis, we identify two main forms of epistemic injustice in the CM industry: discriminatory epistemic injustice and distributive epistemic injustice. Building on Heffron's (2020) JUST framework on CM, we highlight the importance of epistemic injustice in the just transition to reveal the power dynamics, socio-environmental injustices, and exclusionary practices in the CM industry. Through bringing new insights on knowledge equity in the context of CM, we recommend creating participatory spaces for dialogue, decision-making, and knowledge sharing, which can facilitate more respectful and equitable interactions between all stakeholders, thereby contributing to more holistic understandings of a ‘planetary just transition’.

Design of ABS sliding mode control system for in-wheel motor vehicle based on road identification

Aiming at the problem that the current Anti-lock Braking System (ABS) control algorithm can not make full use of the ground friction to complete the braking when emergency braking on complex roads, an ABS sliding mode control method based on road surface identification is proposed. Combined with the in-wheel motor of in-wheel motor electric vehicle, a coordinated control method of motor regenerative braking and mechanical friction braking is designed. Based on the neural network control method, the road friction coefficient is estimated to realize the identification of typical roads and dynamically obtain the optimal slip ratio of different roads. The ABS sliding mode controler is designed with the optimal slip ratio and the actual slip ratio as input, and the saturation function is used to replace the symbol function in the traditional sliding mode control to weaken the chattering problem, and then the ABS controller is designed. The research results show that compared with the traditional ABS sliding mode controller, the designed ABS neural network sliding mode controller has the following advantages: 1. Through the identification of tire-road friction coefficient by neural network, the real-time performance and road adaptability of ABS controller are improved; 2. When the braking simulation is carried out at a given speed, the braking time is reduced by about 1.03 % and the braking distance is shortened by about 1.01%; 3. The identification of tire-road friction coefficient is realized, which weakens the chattering problem of traditional sliding mode control and improves the stability and robustness of ABS controller.

A Study on the Challenges and Problems Faced by Electric Motor Vehicle in India

Contamination of the climate is at present a worldwide concern. Harmful outflow from gas powered motors is one of the essential air contaminations. To relieve the impacts of non- renewable energy source outflow and address ecological worries (ECs), electric vehicles (EVs) are being advanced forcefully everywhere. Different state run administrations are empowering individuals to change to EVs by boosting the progress. Past investigations demonstrate that the significant expense of the electric vehicle, non-accessibility of charging foundation, time and reach uneasiness go about as hindrances to customer reception. The Public authority of India has given a call for 'just Electric Vehicles' on Street by 2030.

Fuzzy logic controller-based Luo converter for light electric vehicles

<p>A brushless DC motor (BLDC) drive fed by a Luo converter is presented in this paper for the use in electric vehicle (EV) applications. The proposed Luo converter provides stable and ripple free output for EV. An approach for getting the stable output voltage is by using a fuzzy logic controller to control the Luo converter. It helps to generate the appropriate pulse with respect to the feedback voltage. This proposed system has the advantages like voltage increase, high-gain output with low ripples and high efficiency. The performance of this proposed drive is tested through hardware prototype at varying line voltage levels and results are demonstrated. A comparative analysis is presented to justify the effectiveness of the proposed Luo converter fed EV motor.</p>

Analysis of Multi-Objective Optimization Design of Interior Double Radial and Tangential Combined Magnetic Pole Permanent Magnet Drive Motor for Electric Vehicles

A new type of interior combined pole permanent magnet drive motor is proposed in this paper, which aims to improve the power and comfort of electric vehicles. In view of the complex magnetic circuit structure and rich harmonic magnetic field of the motor, the initial magnetic pole parameters of the rotor are determined by the equivalent magnetic circuit method. Then, aiming at the complex magnetic circuit and rich harmonic magnetic field, a multi-objective optimization method based on the Taguchi method and response surface method is proposed to reduce the cogging torque, high harmonic content in air gap magnetic flux density and increase the output torque. Based on the finite element analysis of the electromagnetic performance of the new type interior combined magnetic pole permanent magnet drive motor before and after optimization, it can be seen that the improved rotor structure can effectively reduce the torque ripple and increase the torque density. Finally, a prototype was developed and experiments were conducted, and experimental results verified the correctness of the proposed multi-objective optimization algorithm.

Selective neodymium oxide recovery from permanent magnet scraps using waste pickling solutions

Neodymium (Nd) magnets are in high demand for use in the manufacture of electric vehicle motors and medical equipment such as magnetic resonance imaging. In this study, we describe a new process for recycling Nd permanent magnets (PMs). PM scrap powder was leached into a waste pickling solution, evaporated under reduced pressure, concentrated, and converted into an oxide via heat treatment. Nd was selectively leached from the prepared oxide using hydrochloric acid. Iron was removed from the leached solution, and the Nd composite was then recovered using a urea-mediated Nd-recovery method. The oxidation temperature was determined using a thermogravimetric analysis. High-purity Nd-oxide powders were obtained by calcination at 900 °C. This economical process enables the recovery of PM powder by relying on the pickling process and acid-regeneration plants, which are both essential in steel manufacturing.

Design and Performance Analysis of an Outer-Rotor PMaSynRM

Permanent magnet synchronous motors (PMSMs) are consciously used as traction motors in electric vehicles (EVs) and hybrid electric vehicles (HEVs). The rotor position (inner-rotor, outer-rotor) and topology of PMSMs significantly impact their torque profile, efficiency, and demagnetization characteristics. This article focuses on designing an outer-rotor permanent-magnet-assisted synchronous reluctance motor (PMaSynRM) under traction motor requirements and investigating its electromagnetic performance using the finite element method (FEM). This study's primary challenge is achieving optimal machine performance, considering high maximum torque and the risk of demagnetization at low levels. The design, derived from analytical calculations, was subjected to Finite Element Method (FEM) analyses. These analyses investigated motor performance in terms of efficiency, the ability to generate torque at different drive currents, and the risk of demagnetization. As a result of the study, the proposed PMaSynRM design was obtained that provides an adequate demagnetization performance even under 300% loading, has a maximum torque exceeding 35 Nm, and achieves an efficiency of 90% at rated conditions.

A Comprehensive Analysis of the Reliability of Electric Vehicle Motor Systems: Exploring the Intricacies of Performance and Durability

Electric vehicles (EVs) have garnered significant attention in recent years due to their distinctive zero carbon emission capability. While their environmental advantages are widely acknowledged, concerns about the reliability of key components, particularly the drive motors and associated controllers, continue to linger. Previous studies have made strides in evaluating the reliability of individual drive motors in EVs. However, given the integrated nature of drive motors and motor controllers in EVs, a comprehensive assessment that considers both components as a unified system is imperative for more precise reliability forecasting. Furthermore, the multifaceted nature of drive motors and motor controllers, encompassing various components with diverse structures, types, and characteristics, can significantly influence the overall reliability of the motor system. Unfortunately, these nuances have often been overlooked in prior research, creating a knowledge gap that needs to be addressed. To address this gap, our study offers a holistic investigation into the reliability of the entire motor system in pure electric vans, which includes both the drive motors and motor controllers. We initiate our research by employing theoretical models to predict the failure rates of individual subassemblies and components within the drive motor and motor controller. Building upon these predictions, we conduct an in-depth assessment of the overall motor system reliability. Our findings reveal the vulnerabilities associated with specific subassemblies and components within the motor system, offering critical insights that challenge some of the prevailing assumptions in existing reliability research. These insights are instrumental in guiding future advancements in reliability design and maintenance practices tailored for pure electric vans. The adoption of EVs continues to grow, driven by their environmental benefits and expanding market share, it becomes increasingly crucial to address concerns related to the reliability of essential components like drive motors and control systems. While previous research has largely focused on component-level reliability, our study adopts a more comprehensive approach, evaluating the entire motor system. Through a combination of theoretical modeling and empirical analysis, we aim to provide a robust framework for predicting failure rates and enhancing the overall reliability of EV motor systems. By uncovering vulnerabilities and potential areas for improvement, this research offers valuable insights to advance the design, development, and maintenance of reliable electric vehicles.

A metaheuristic algorithm for model predictive control of the oil-cooled motor in hybrid electric vehicles

The current energy management methods for hybrid vehicles are primarily focused on matching the power flow between the engine and the motor. In order to further reduce overall energy consumption and extend the vehicle's lifespan, this paper utilizes model predictive control (MPC) in the energy management of hybrid vehicles to control the energy consumption and temperature of the oil-cooled motor, with a focus on studying the algorithm for solving the optimization problem in MPC. The main work includes: Establishing a simplified model for the oil-cooled motor and training the working condition prediction model based on the worldwide harmonized light vehicles test cycle (WLTC) conditions utilizing long short-term memory (LSTM) recurrent neural network technology. Furthermore, proposing a novel metaheuristic algorithm based on the temporal nature of the problem, multiple hierarchical clustering algorithm (MHCA). In comparison with typical metaheuristic algorithms, the MPC optimized using MHCA exhibits significantly reduced computation time, with an average time of 0.1967s, indicating that using MHCA can theoretically achieve real-time control and eliminate lag in actual control processes. Furthermore, compared to other optimization algorithms, the MHCA algorithm demonstrates the lowest correlation between computation time and operational conditions, highlighting its high robustness in ensuring the safety of practical control.

Exploring the REEs Energy Footprint: Interlocking AI/ML with an Empirical Approach for Analysis of Energy Consumption in REEs Production

Rare earth elements (REEs including Sc, Y) are critical minerals for developing sustainable energy sources. The gradual transition adopted in developed and developing countries to meet energy targets has propelled the need for REEs in addition to critical metals (CMs). The rise in demand which has propelled REEs into the spotlight is driven by the crucial role these REEs play in technologies that aim to reduce our carbon footprint in the atmosphere. Regarding decarbonized technologies in the energy sector, REEs are widely applied for use in NdFeB permanent magnets, which are crucial parts of wind turbines and motors of electric vehicles. The underlying motive behind exploring the energy and carbon footprint caused by REEs production is to provide a more complete context and rationale for REEs usage that is more holistic. Incorporating artificial intelligence (AI)/machine learning (ML) models with empirical approaches aids in flowsheet validation, and thus, it presents a vivid holistic picture. The energy needed for REEs production is linked with the source of REEs. The availability of REEs varies widely across the globe. REEs are either produced from ores with associated gangue or impurities. In contrast, in other scenarios, REEs can be produced from the waste of other mineral deposits or discarded REEs-based products. These variations in the source of feed materials, and the associated grade and mineral associations, vary the process flowsheet for each type of production. Thus, the ability to figure out energy outcomes from various scenarios, and a knowledge of energy requirements for the production and commercialization of multiple opportunities, is needed. However, this type of information concerning REEs production is not readily available as a standardized value for a particular material, according to its source and processing method. The related approach for deciding the energy and carbon footprint for different processing approaches and sources relies on the following three sub-processes: mining, beneficiation, and refining. Some sources require incorporating all three, whereas others need two or one, depending on resource availability. The available resources in the literature tend to focus on the life cycle assessment of REEs, using various sources, and they focus little on the energy footprint. For example, a few researchers have focused on the cumulative energy needed for REE production without making assessments of viability. Thus, this article aims to discuss the energy needs for each process, rather than on a specific flowsheet, to define process viability more effectively regarding energy need, availability, and the related carbon footprint.

An Exhaustive Examination of the Motor System’s Reliability in Electric Vehicles

Due to their unique zero carbon emission feature, electric vehicles (EVs) have garnered increasing interest in recent years. However, concerns persist regarding their reliability, particularly concerning critical components. Efforts have been made to assess the reliability of drive motors in EVs. Yet, given the integration of drive motors and motor controllers as a single system in EVs, a combined assessment is essential for a more accurate reliability prediction. Additionally, considering the multiple components within both drive motors and motor controllers, their structure, type, and characteristics may influence the overall reliability of the motor system, a factor overlooked in previous research. To bridge this knowledge gap, this study investigates the reliability of the entire motor system in pure electric vans, encompassing both drive motors and motor controllers. The research begins with predicting the theoretical failure rates of subassemblies and components within the drive motor and motor controller. Subsequently, based on these predictions, the reliability of the entire motor system is assessed. The findings highlight vulnerable subassemblies and components within the motor system, shedding light on potential shortcomings in existing reliability research. These new insights are deemed crucial for informing future reliability design and maintenance practices for pure electric vans. This study undertakes an exhaustive investigation into the reliability of the motor system in EVs. As EVs gain prominence for their environmental benefits and increasing market share, concerns regarding the reliability of critical components, such as the drive motor and associated control systems, become paramount. While prior research has addressed component-level reliability, this study takes a holistic approach by assessing the entire motor system. Through theoretical modeling and empirical analysis, the study predicts failure rates of subassemblies and components within the drive motor and control systems, culminating in an evaluation of the overall motor system reliability. By uncovering vulnerabilities and potential areas for improvement, this research offers valuable insights to advance the design, development, and maintenance of reliable electric vehicles.

Analysis of longitudinal-vertical coupling vibration of four hub motors driven electric vehicle under unsteady condition

The influence of hub motor unbalanced magnetic force (UMF) on the vibration of electric vehicle under steady state conditions has been known, but under unsteady conditions, the hub motor UMF will change with the vehicle operation condition, and there would exist complex coupling vibration for the hub motors driven electric vehicle. Here, the longitudinal-vertical coupling dynamics of the four hub motors driven electric vehicle under unsteady condition is studied. Integrating the motor electromagnetic excitation and electric vehicle dynamics, a longitudinal-vertical coupling dynamics model of the four hub motors driven electric vehicle is established. Based on the variable switching frequency field-oriented control model, analytical model of the UMFs acting on the motor stator and rotor parts under unsteady condition are developed. For model validation, a four hub motors driven electric vehicle has been tested, the accuracy of the longitudinal-vertical coupling dynamics model established in this paper was verified. Then, longitudinal-vertical coupling vibration characteristics of the four hub motors driven electric vehicle under road excitation and coupling excitation are analyzed. The results show that the longitudinal and vertical movement of the four hub motors driven electric vehicle is coupled by hub motor. In addition, under unsteady condition, the motor UMFs will cause vertical vibration of the electric vehicle body and hub motor stator, the vibration shows order characteristics including low order harmonic hfc and inverter switching frequency sideband harmonic k1 fs±k2 fc ( fc = pn/60, fs is inverter switching frequency, k1 and k2 are positive integers, p is the number of pole pairs and n is motor speed.). The motor electromagnetic torque will cause longitudinal vibration of the electric vehicle body, the vibration shows order characteristics including harmonics fs±3 fc and 2 fs. The main harmonic of vehicle body pitch angle acceleration is 2 fs.

Factors influencing traumatic brain injuries in maxillofacial fractures: A 12-year retrospective analysis of 2841 patients.

Results of studies investigating the association between traumatic brain injury (TBI) and maxillofacial fractures (MFs) have varied considerably. The present study aimed to evaluate the correlation between TBIs and MFs, as well as the impact of age, sex, trauma mechanism, and season on TBIs. This 12-year retrospective study of 2841 patients used univariate and multivariate logistic regression to assess the association between MFs and other factors impacting TBIs. Among 2841 patients, 1978 TBIs occurred in 829 (29.2%), with intracranial injuries (n = 828) is the most common. Of 829 patients with TBIs, 688 were male and 141 were female, corresponding to a male-to-female ratio of 4.9:1.0. The most common age group was 40-49 years (24.6%). Vehicles (including motor vehicles and electric vehicles) accidents were the primary causes of injuries. Multivariate regression analyses revealed an increased risk for TBIs among males (odds ratio [OR] 0.632, p < 0.001). Patients >40 years of age were at higher risk for TBIs, especially those ≥70 years (OR 3.966, p = 0.001). Vehicle accidents were a high-risk factor for TBIs (OR 6.894, p < 0.001), and winter was the most prevalent season for such injuries (OR 1.559, p = 0.002). Risk for TBI increased by 136.4% in combined midfacial and mandibular fractures (p = 0.016) and by 101.6% in multiple midfacial fractures (p = 0.045). TBIs were less common in single mandibular fractures, notably in single-angle fractures, with a risk of only 0.204-fold. TBIs in MFs were significantly correlated with sex, age, aetiology, season and fracture location. Maxillofacial surgeons and emergency physicians must be aware of the possible association between TBIs and MFs to assess and manage this complicated relationship in a timely manner.