Vehicle Acceleration Research Articles

A relationship between vehicle coastdown performance metrics and fuel efficiency

In contemporary times, societal and legal institutions are earnestly committed to mitigating carbon dioxide (CO2) emissions into the atmosphere, thereby averting global warming. The automotive industry mirrors this commitment, necessitating the development of vehicles characterized by heightened energy efficiency. Such vehicles should boast an extended range while minimizing fuel consumption and CO2 emissions. A pivotal aspect in vehicle development involves the early determination of fuel economy (FE), contingent upon the projected characteristics of the vehicle and its intended market positioning with regard to fuel economy. Over time, diverse methodologies for calculating FE have emerged. These include hybrid regression models incorporating key input variables such as instantaneous vehicle speed and acceleration measurements, theoretical approximation methods derived from the physical properties of engine-vehicle systems, correlations among traffic-related parameters, power-demand models, as well as artificial neural networks and genetic algorithms. These approaches leverage various vehicle input data, encompassing engine speed, torque, fuel flow, intake manifold mean temperature, make of the car, engine style, weight of the car, vehicle type, transmission system type, and other relevant factors. Regrettably, the literature lacks a straightforward and expeditious means of estimating FE for a group of analogous vehicles, a deficiency addressed by the present work. The proposed empirical physical model facilitates FE estimations for a vehicle using a parsimonious set of input information: engine displacement, performance (recovery speed times), and road load data. These inputs are subjected to mathematical parameter adjustments within the empirical model. To validate the efficacy of this approach, the empirical model is applied in a Brazilian case study. The results attest to the success of the method, demonstrating a margin of error within 6% when compared to the official Brazilian government public data for the tested vehicle, particularly in the contexts of city and highway cycles.

Risk-informed longitudinal control in autonomous vehicles: A safety potential field modeling approach

The advent of autonomous vehicles (AVs) has complicated traffic flow and raised safety concerns, there is a pressing need to enhance traffic efficiency alongside ensuring robust safety. This paper proposes a dynamic longitudinal control strategy for AVs, based on real-time accident risk assessments. Data from traffic flow, road characteristics, and weather conditions are analyzed using a random forest algorithm, producing a real-time accident risk identification model through a support vector machine. In addition, the Perceived Risk Field Model (PRFM), a car-following model calibrated using the NGSIM dataset, considers vehicle distance, velocity, relative velocity, and acceleration. These models underpin our proposed longitudinal control strategies, tailored to variable accident risk levels. Under the normal risk control strategy, the PRFM outperforms the Intelligent Driver Model (IDM) and the Cooperative Adaptive Cruise Control Model (CACC) in stability and driving comfort. For high accident risk scenarios, our approach shows improved collision avoidance and traffic oscillation mitigation. Simulations indicate that the high accident risk control strategy offers better safety and stability than the normal risk strategy. Furthermore, traffic flow diagrams suggest an increase in road capacity as AV penetration rates rise, pointing to the promising efficiency of our proposed solution. This research provides a robust framework for AV operation, ensuring optimal traffic safety and efficiency.

A multidirectional pendulum kinetic energy harvester system for low-power appliances in new energy buses

Wireless sensors play a crucial role in vehicle operations. The effective energy harvesting for self-powering these sensors has emerged as a critical area of interest in recent years. This paper presents a multidirectional pendulum kinetic energy harvester system (MP-KEHS) designed to power low-power sensors in vehicles. The MP-KEHS consists of four main components: an energy capture module, a motion transfer module, an energy conversion module, and an electric energy storage module. The energy capture module, comprising a mass ball and a turntable, captures omnidirectional inertial kinetic energy during vehicle acceleration, deceleration, and turning. In the motion transfer module, a one-way gear module converts the multidirectional rotations of the energy capture module into unidirectional rotations, while the planetary gear train enables speed coupling during simultaneous vehicle acceleration and turning. The energy conversion module utilizes a generator to transform rotational kinetic energy into electric energy. The electric energy storage module stores the electric energy in a supercapacitor after passing through a voltage regulator circuit, providing power to low-power devices. Using of multi-body dynamics software and Simulink, the swing angle of the mass ball was determined under four real driving cycles. Experimental results demonstrated that the average output power of the MP-KEHS reached 116.8 mW. Charging the capacitors of 220 μF, 470 μF, and 1000 μF from 0 V to 1 V takes 8 s, 24 s, and 93 s, respectively. Finally, the MP-KEHS was applied to a bus on the Liaocheng University campus, achieving an average output power of approximately 176 mW, which satisfied the power requirements of the sensors. These findings indicate that the MP-KEHS effectively harnesses the vehicle's otherwise wasted inertial energy, enabling wireless sensor self-powering.

Policy analysis of electric vehicle infrastructure supporting sustainable energy use

The Presidential Regulation of the Republic of Indonesia 55 of 2019 concerning the Acceleration of the Battery-Based Electric Vehicle (EV) Program for transportation aims to provide direction, foundation, and legal certainty in the implementation of the acceleration of the battery-based electric motor vehicle program in Indonesia, one of which is the strategy of providing electric charging infrastructure. This policy had not fully significant impact on the provision of electric vehicle charging infrastructure, wherein 2021, the provision of EV charging facilities has reached approximately only 74 charging facilities, spread across 188 locations; this condition is still far from the target set by the government, expected about 168 charging facilities units in 2021-2031. The research evaluates existing policies as a reference for preparing policy recommendations to accelerate the provision of EV charging infrastructure. Research has shown that the adoption of electric vehicles is constrained, one of which is the need for more charging station availability of charging stations and their distribution. The government needs to provide incentives for domestic manufacturers of charging stations and battery swapping stations and instruct state-owned enterprises to accelerate the adoption of electric to provide charging station infrastructure.

Experimental Validation of a Driver Monitoring System

This paper presents an analysis of the risk of neck injury in vehicle occupants as a consequence of an impact. A review of the formulation of indexes that are used in the assessment and investigation of neck injury risk is discussed with the aim of providing a new, more appropriate index using suitable sensorized equipment. An experimental analysis is proposed with a new driver monitoring device using low-cost sensors. The system consists of wearable units for the head, neck, and torso where inertial measurement sensors (IMU) are installed to record data concerning the occupant’s head, neck, and torso accelerations while the vehicle moves. Two laser infrared distance sensors are also installed on the vehicle’s steering wheel to record the position data of the head and neck, as well as an additional IMU for vehicle acceleration values. To validate both the device and the new index, experiments are designed in which different sensorized volunteers reproduce an emergency braking maneuver with an instrumented vehicle at speeds of 10, 20, and 30 km/h before the beginning of any braking action. The neck is particularly sensitive to sudden changes in acceleration, so a sudden braking maneuver is enough to constitute a potential risk of cervical spine injury. During the experiments, large accelerations and displacements were recorded as the test speed increased. The largest accelerations were obtained in the experimental test at a speed of 30 km/h with values of 19.17, 9.57, 9.28, and 5.09 m/s2 in the head, torso, neck, and vehicle, respectively. In the same experiment, the largest displacement of the head was 0.33 m and that of the neck was 0.27 m. Experimental results have verified that the designed device can be effectively used to characterize the biomechanical response of the neck in car impacts. The new index is also able to quantify a neck injury risk by taking into account the dynamics of a vehicle and the kinematics of the occupant’s head, neck, and torso. The numerical value of the new index is inversely proportional to the acceleration experienced by the vehicle occupant, so that small values indicate risky conditions.

Assessment of identification performance for high emission heavy-duty diesel vehicles by means of remote sensing

To improve the accuracy of detecting high NO (nitric oxide) emissions from heavy-duty diesel vehicles (HDDV) by remote sensing (RS), the emissions of one HDDV complied with China V regulation and one HDDV complied with China VI regulation at constant speeds, with and without after-treatment devices, are tested by a portable emission measurement system (PEMS) and RS. The optimized measurement procedures for detecting high NO emissions from China V and China VI HDDVs by RS are summarized. The correlation of RS and PEMS data shows that the ratio of NO to CO2 (carbon dioxide) is a more appropriate RS measurement than NO concentration alone for identifying high emitters, although NO concentrations of 600 ppm and 100 ppm can be used as a basis for distinguishing between China V and China VI HDDVs, respectively. When the NO/CO2 ratio is >200 × 10−4 and 25 × 10−4, identifying China V and China VI HDDV high emitters, respectively, is possible. Additionally considering the vehicle speed can reduce the high emitter identification error rate, and excluding data where vehicle acceleration is less than −0.1 m/s2 can further improve identification accuracy. Four new high-emitter identification methods based on different combinations of measurements are shown to improve identification efficiency with only small increases in identification error. This study provides evidence to support the future development of high-precision RS methodologies for identifying high-emission vehicles.

An Inertial Energy Harvester Based on Noncontact Magnetic Force for Self‐Powered Applications in New Energy Buses

The advent of energy‐efficient, next‐generation driverless buses places substantial reliance on an array of wireless sensors. In response to this challenge, converting and utilizing these buses' local inertial kinetic energy emerges as a practical strategy for achieving self‐powered capabilities for low‐power appliances. Herein, an inertial energy harvester based on noncontact magnetic force for self‐powered applications in new energy buses is introduced. The harvester system comprises energy capture, mechanical amplification, conversion, and storage modules. The energy capture module proficiently captures and converts the otherwise wasted inertial kinetic energy into magnetic force. The magnified magnetic force produced by the mechanical amplification module efficiently transforms into electrical power within the energy conversion module. Afterward, the alternating current is regulated and stored within the supercapacitor by the energy storage module, achieving self‐powered capabilities for low‐power vehicle appliances. At the same time, combined with the bench test, the maximum output power of the harvester under different vehicle acceleration scenarios is obtained. Taking the campus bus of Liaocheng University as the application object, the average output power of the harvester in the actual operation of the campus is 23.71 mW, which can effectively promote the realization of wireless sensor self‐powered technology in new energy vehicles.

Identification of Dynamic Vibration Parameters of Partial Interaction Composite Beam Bridges Using Moving Vehicle

The vibration response of a partial composite beam bridge under the influence of moving vehicular loads was investigated. Due to the coupling effect between the vehicle and the bridge, the vibration information of the vehicle encompassed the vibration information of the bridge. Consequently, the dynamic response of the vehicle could be utilized to extract the dynamic information of the composite beam. A moving mass-spring-damping system and composite beam elements considering interfacial slips were used for the interaction vibration of a vehicle-composite bridge. A finite element program for the interaction vibration analysis of the vehicle-composite beam bridge was developed. The program was used to extract the vibration information of the composite beam bridge by analyzing the vehicle displacement, velocity, and acceleration in the interaction vibration of the beam and the vehicle. Taking the Hangzhou Jiubao Bridge as the engineering background, the influences of structural parameters such as shear stiffness of connections, prestress magnitude, as well as vehicle parameters, including vehicle stiffness, damping, and mass, on frequency identification were analyzed. Furthermore, the influences of road roughness, disturbance force generated by vehicle random vibrations, and interference signals generated by signal transmission on frequency identification of the bridge were investigated.

Exhaust Emissions from Euro 6 Vehicles in WLTC and RDE—Part 2: Verification by Experimental Measurement

The subject of assessing exhaust emissions in real driving conditions has been relevant for a long time. Its introduction into approval tests focused attention on the comparative possibilities of tests performed on a chassis dynamometer and in road conditions. The article is a continuation of research on the possibilities of estimating emissions in the Real Driving Emission test based on emission data from Worldwide harmonized Light Vehicles Test Cycles. The first part discussed the possibility of comparing dynamic parameters in these tests, and the second part discussed the possibility of estimating road exhaust emissions. The work was done in two stages: the first stage involved the use of distance-specific emissions in individual parts of the WLTC test, and the second stage involved the use of exhaust emission rates as datasets divided into intervals defined by vehicle speed and acceleration. Comparative tests were performed for conventional vehicles (gasoline, diesel) and hybrid vehicles. A chassis dynamometer was used to carry out WLTC tests and PEMS equipment was used for the RDE tests. The exhaust gas components that had to be measured in road tests, namely: carbon monoxide, carbon dioxide, nitrogen oxides, and the number of particulate matter, were analyzed. Based on the data collected, parameters such as road emissions and the exhaust emissions rate were determined for each phase of the dynamometer test as well as the road test. Because of this, it was possible to compare the distance-specific exhaust emissions of each vehicle in the two emission tests. The comparison resulted in establishing that it is possible to estimate distance-specific exhaust emissions of conventional and hybrid vehicles in road test conditions, using only the results obtained in the approval test (for selected test phases). The research concluded that it is possible to estimate selected RDE test parameters based on the results obtained in the WLTC test for the tested vehicles.

Physics-guided diagnosis framework for bridge health monitoring using raw vehicle accelerations

Damage detection of bridges using vibrations from a passing vehicle has received a lot of interest recently. Though non-modal parameter-based methods (e.g., data-driven approaches) have shown promising results in this context, their advancement towards a comprehensive and rigorous monitoring system is hampered by their overreliance on machine learning techniques. On this background, this paper proposes a novel automatic physics-guided diagnosis framework for bridge health monitoring utilizing only raw vehicle accelerations. First, numerical studies are conducted to investigate the relationship between vehicle time-domain signals and bridge damage, based on which a new damage index is proposed. At the same time, it also explores the identification of damage locations and proposes a location index. Second, a damage diagnosis framework, which consists of a data processing method and a physics-guided model, is designed to overcome deficiencies from a drive-by measurement and to automate the damage detection process. The proposed framework was validated using datasets acquired from laboratory experiments employing a scale vehicle model and a steel beam. The results affirmed the method's efficacy in damage indication, quantification, and localization. Moreover, the superiority of the proposed damage index and the rationale for the proposed physics-guided approach were also demonstrated through comparisons with machine learning-based methods.

Heterogeneous Traffic Flow Signal Control and CAV Trajectory Optimization Based on Pre-Signal Lights and Dedicated CAV Lanes

This paper proposes a control system to address the efficiency and pollutant emissions of heterogeneous traffic flow composed of human-operated vehicles (HVs) and connected and automated vehicles (CAVs). Based on the comprehensive collection of information on the flow of heterogeneous traffic, the control system uses a two-layer optimization model for signal duration calculation and CAV trajectory planning. The upper model optimizes the phase duration in real time based on the actual total number and type of vehicles entering the control adjustment zone, while the lower model optimizes CAV lane-changing strategies and vehicle acceleration optimization curves based on the phase duration optimized by the upper model. The target function accounts for reducing fuel usage, carbon emission lane-changing costs, and vehicle travel delays. Based on the Webster optimal cycle formula, an improved cuckoo algorithm with strong search performance is created to solve the model. The numerical data confirmed the benefits of the suggested signal control and CAV trajectory optimization method based on pre-signal lights and dedicated CAV lanes for heterogeneous traffic flow. Intersection capacity was significantly enhanced, CAV average fuel consumption, carbon emission and lane-changing frequency were significantly reduced, and traffic flow speed and delay were significantly improved.

A vehicle-bridge interaction model considering contact patch size and vehicle self-generated excitation – A theoretical study

This paper presents an improved theoretical damped single-axle vehicle-bridge dynamic interaction model to consider the effect of the contact patch size and motor-induced vehicle excitation. The contact patch issue is critical as it determines the minimum time step for simulation and maximum identifiable frequency, while the inclusion of the motor-induced vehicle excitation benefits the design of autonomous self-driven rather than towed vehicles. Estimations of the contact patch size for both the pneumatic tire and solid wheel scenarios are discussed. The contact patch responses, which degenerate into contact point responses when the contact patch size is assumed to be infinitely small, were derived for the first time both from the vehicle and bridge responses to confirm their equivalence. The minimum time step, which determines maximum identifiable frequency but is arbitrarily chosen in literature, is proposed to be determined by the vehicle speed and contact patch length. The procedures to extract multiple bridge triad information including natural frequencies, mode shapes, and damping ratios from the vehicle responses are also presented. Based on extensive parametric analyses, the sinusoidal vehicle excitation becomes more prominent as its amplitude and/or frequency increase and may overshadow the analysis of bridge frequencies of interest. The vehicle acceleration leads to a more accurate extraction of bridge mode shapes and damping ratios than the vehicle displacement since the displacement is dominated by the fundamental mode of bridge vibration. The damping ratio extraction shows an average error of 0.28% from the instantaneous amplitude of the vehicle acceleration signal.

Approximate Global Energy Management Based on Macro–Micro Mixed Traffic Model for Plug-in Hybrid Electric Vehicles

A multi-scale physical process management system is presented in this paper, taking the plug-in hybrid electric vehicle system as the physical interface connecting the macro traffic system to the micro energy conversion process, with the ultimate goal of global energy management in the full temporal–spatial domain for autonomous plug-in hybrid electric vehicles. This novel method adopts a macro traffic flow model at a large time scale, in which only the initial conditions and the traffic information of key road sections are required, and a car following model at the micro scale. Furthermore, local replanning of energy management is carried out by adjusting the power threshold and the efficiency weight through the type of reinforcement learning that is closest to human learning, once a short term speed disturbance is induced by unknown disturbances in the macro traffic flow. Due to the nonlinear relationship between speed fluctuation and power fluctuation, it is necessary to map the vehicle speed and acceleration characteristics to the power characteristics, instead of directly utilizing the traffic model characterized by the speed and acceleration characteristics. The results show that novel multi-scale physical management can achieve a smaller deviation from the global optimal solution and enhanced robustness of global energy management. Additionally, close coupling between the dynamic characteristics of vehicle components and speed fluctuation ensures correct tracking of the optimized target value.

Stable nullspace adaptive parameter identification of 6 degree-of-freedom plant and actuator models for underactuated vehicles: Theory and experimental evaluation

Model-based approaches to navigation, control, and fault detection that utilize precise nonlinear models of vehicle plant dynamics will enable more accurate control and navigation, assured autonomy, and more complex missions for such vehicles. This paper reports novel theoretical and experimental results addressing the problem of parameter estimation of plant and actuator models for underactuated underwater vehicles operating in 6 degrees-of-freedom (DOF) whose dynamics are modeled by finite-dimensional Newton-Euler equations. This paper reports the first theoretical approach and experimental validation to identify simultaneously plant-model parameters (parameters such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster models) in 6-DOF. Most previously reported studies on parameter identification assume that the control-actuator parameters are known a priori. Moreover, this paper reports the first proof of convergence of the parameter estimates to the true set of parameters for this class of vehicles under a persistence of excitation condition. The reported adaptive identification (AID) algorithm does not require instrumentation of 6-DOF vehicle acceleration, which is required by conventional approaches to parameter estimation such as least squares. Additionally, the reported AID algorithm is applicable under any arbitrary open-loop or closed-loop control law. We report simulation and experimental results for identifying the plant-model and control-actuator parameters for an L3 OceanServer Iver3 autonomous underwater vehicle. We believe this general approach to AID could be extended to apply to other classes of machines and other classes of marine, land, aerial, and space vehicles.

Automotive Lightweighting through novel Composite Materials

Abstract: The weight saving demand for motorcars has come more important, since the increase in the environmental issues. For boosting the energy frugality, maintaining safety and performance of ultramodern motorcars; featherlight compound accoutrements are essential. For acceleration of a lighter object, lower energy is needed as compare to acceleration of heavier one; so the featherlight compound accoutrements offer the great eventuality for adding vehicles effectiveness. Using lighter accoutrements in bus manufacturing improves vehicle project, acceleration, and continuity. Featherlight accoutrements are essential for boosting the energy frugality of ultramodern buses while maintaining their safety and performance. Because it takes lower energy to accelerate a lighter auto than a heavier one, featherlight accoutrements offer more implicit for both automakers and bus buyers. Composite accoutrements, similar as aluminium, magnesium and sword composites, have been proven to have better energy immersion than solid essence similar as sword alone, and therefore give further protection upon impact. Not only are featherlight vehicles energy effective, but they also enhance performance. drop in energy consumption gain by reduction in vehicle weight. By replacing sword & cast iron conventional factors with featherlight compound accoutrements similar as Mg & Al essence matrix compound, carbon & glass fiber corroborated polymer mixes can directly reduce the weight of the corridor of an automotive i.e. machine block and lattice and results in reduction of energy consumption by the vehicle. It can reduce the exhaust emigration and enhanced the energy frugality. By the use of light mixes in automotive can also carry fresh advanced emigration control system, safety bias, and integrated electronic system without increase in the overall weight of vehicles.

Anti-slip regulation method for electric vehicles with four in-wheel motors based on the identification of slip ratio

To improve the stability and acceleration of electric vehicles with four in-wheel motors under various road conditions, an anti-slip regulation method considering the identification of slip ratios is proposed. In this paper, the model of the whole vehicle is built, and the road identifier based on the tire model established by Burckhardt is designed. Then, an anti-slip controller that utilizes the variable universe fuzzy proportional integral derivative (PID) algorithm is established to adjust the driving torques of the four in-wheel motors according to the road conditions. The simulation results show that the designed control strategy can quickly and accurately identify the optimal slip ratio under each typical road condition and make the tire slip ratio approach the optimal slip ratio in a short time, so as to effectively improve the driving stability and dynamic performance of the vehicle.

Effect of vehicle external acceleration signal light on pedestrian-vehicle interaction

The number of casualties resulting from collisions between pedestrians and motor vehicles continues to rise. A significant factor is the misunderstanding of vehicle behavior intentions by pedestrians. This is especially true with the continuous development of vehicle automation technology, which has reduced direct interaction between drivers and the outside world. Therefore, accurate communication of vehicle behavior intentions is becoming increasingly important. The purpose of this study is to investigate the impact of external vehicle acceleration signal light on the interaction experience between pedestrians and vehicles. The differences between the use and nonuse of acceleration signal light are compared through controlled test track experiments in real scenarios and in videos.The results show that acceleration signal light help pedestrians understand vehicle behavior intentions more quickly and make safer crossing decisions as well as improving their perception of safety when crossing the street and their trust in vehicle behavior.

An Adaptive Shift Schedule Design Method for Multi-Gear AMT Electric Vehicles Based on Dynamic Programming and Fuzzy Logical Control

This paper proposes an adaptive shift schedule design framework based on dynamic programming (DP) algorithm and fuzzy logical control to promote the shift schedule’s adaptability whilst improving the comprehensive performance of the multi-gear automated manual transmission (AMT) electric vehicles in real-time application. First, the DP algorithm is employed to extract an offline optimal gear-shift schedule based on a set of driving conditions, including 11 groups of typical driving cycles. Second, a fuzzy logical controller is formulated considering the variation in the vehicle load and acceleration, where a velocity increment is exported online to adjust the gear-shift velocity of the predesigned DP-based schedule to develop a Fuzzy-DP shift schedule. In addition, multi-objective particle swarm optimization (MOPSO) is utilized to construct a comprehensive shift schedule by simultaneously considering the dynamic and economic performance of the vehicle. Then, the dynamic and economic shift schedules are deployed as the benchmark to examine the performance of the proposed shift schedule. Finally, the effectiveness of the Fuzzy-DP shift schedule is evaluated by comparison with others under various combined driving cycles (including vehicle load and velocity). The comparisons demonstrate the remarkable promotion in the adaptability of the Fuzzy-DP shift schedule in terms of acceleration time, energy-saving potential, and shift frequency. The most significant improvements in the dynamic, economic, and shift frequency can reach 8.86%, 10.12%, and 8.56%, respectively, in contrast to the MOPSO-based shift schedule.

Personalized Driving Styles in Safety-Critical Scenarios for Autonomous Vehicles: An Approach Using Driver-in-the-Loop Simulations

This paper explores the use of driver-in-the-loop simulations to detect personalized driving styles in autonomous vehicles. The driving simulator used in this study is modular and adaptable, allowing for the testing and validation of control and data-collecting systems, as well as the incorporation and proof of car models. The selected scenario is a double lane change maneuver to overtake a stationary obstacle at a relatively high speed. The user’s behavior was recorded, and lateral accelerations during the maneuver were used as criteria to compare the user-driven vehicle and the autonomous one. The tuning parameters of the lateral and longitudinal controllers were modified to obtain different lateral accelerations of the autonomous vehicle. A neural network was developed to find the combination of the two controllers’ tuning parameters to match the driver’s lateral accelerations in the same double lane change overtaking action. The results are promising, and this study suggests that driver-in-the-loop simulations can help increase autonomous vehicles’ safety while preserving individual driving styles. This could result in creating more individualized and secure autonomous driving systems that consider the preferences and behavior of the driver.

A compact hydro-mechanical transmission solution to wheel loader propulsion system and its control strategy

Given the need for high efficiency to wheel loader powertrain, hydro-mechanical transmission is a promising solution to wheel loader propulsion system since it enables efficient engine operations owing to its continuously variable transmission function. The current hydro-mechanical transmission consists of planetary gear set and two hydrostatic units, making it bulky and complex in controls. This study proposes a simple and compact hydro-mechanical transmission solution to wheel loader powertrain. It improves vehicle acceleration performance by providing hydraulic torque assistance at low vehicle speed. The steady-state characteristics of the proposed hydro-mechanical transmission are analyzed, and the mathematical models of proposed wheel loader powertrain are constructed. An optimization strategy is developed as a reference for the design of an engine-speed based control strategy. The simulation results of the key components of proposed powertrain are analyzed. Furthermore, the engine-speed based control strategy is evaluated by the optimization strategy in different forms of the loading cycles. The results demonstrate the effectiveness of proposed wheel loader powertrain and its control strategy.