Powertrain Model Research Articles

Virtual Analysis and Optimization of Fuel Consumption for Diesel-Powered Buses

In this study, fuel consumption test cycles and commonly used SORT cycles for buses were discussed. An exemplary bus model was extensively modeled in terms of power systems. In ad-dition to main components such as the engine, transmission, torque converter, and axle, the drags of engine-powered components such as alternators, engine cooling fan, and air conditioning compressor were integrated into the powertrain model to ensure that the calculated consumption comes as close as possible to the real-life values. The tire model, which determines the quality standard in vehicle simulation, was also discussed in detail. During the modeling of these components, necessary parameters were obtained through analyses and tests conducted on the sample vehicle using the CAN bus and added sensors. After the completion of the model, real-life tests were conducted to validate the virtual analysis results. Once the model was validated, virtual studies continued to reduce the vehicle's consumption. Particularly during these studies, reducing consumption and enhancing performance through automatic transmission optimization were emphasized. When virtual models validated with SORT simulations are optimized with route-specific virtual analysis, public vehicles will minimize environmental impacts by reducing carbon emissions and at the same time perform more efficiently.

Effect of Honeycomb Geometrical Parameters on Equivalent Radiated Power and Frequency Response of Motor Casing and Gearbox Surface of a Powertrain

ABSTRACTThe powertrain, as a central source of noise and vibration, is crucial in determining the overall NVH (noise, vibration and harshness) performance of vehicles, necessitating the optimisation of its structural components for improved durability and passenger comfort. This paper investigates the influence of key geometrical parameters—cell thickness, skin thickness and cell length—on the complex frequency modes of honeycomb and square sandwich structures using the Altair OptiStruct solver 2022 and fast Fourier transform analyser. The driven and non‐driven ends of a motor casing and a gearbox, represented by honeycomb structures and ribs, were subjected to an evaluation of equivalent radiated power (ERP). The results show that the square structure performs better at higher skin thickness when resisting severe lateral stresses than the honeycomb while being less stiff at higher cell thickness. Notably, smaller cell length had a substantial impact on the modes of the honeycomb structure, whereas larger cell length had an impact on the square structure. Response surface methodology was used to optimise the modal frequencies of both square and honeycomb panels simultaneously. An ideal cell length of 5.88 mm, skin thickness of 1.303 mm and cell thickness of 0.381 mm were found for the honeycomb construction, yielding a composite desirability of 0.981. On the other hand, with a cell length of 5.045 mm, skin thickness of 1.484 mm and cell thickness of 0.331 mm, the square structure achieved a composite desirability of 0.989. It is interesting to note that the driven end casing of the motor casing made more noise with the addition of ribs than the non‐driven end and gearbox. But compared to the original powertrain model, adding a honeycomb structure resulted in a noise reduction of about 10%.

How Do We Calibrate a Battery Electric Vehicle Model Based on Controller Area Network Bus Data?

Transforming an up-to-date vehicle into a measurement system is a rewarding task due to the large number of different sensors in the onboard control and diagnostic systems. These procedures are not performed by a single control unit; it is necessary to share the signal values over a communication network, to which an external device can be connected to record the real traffic. The paper aims to use these recorded data for 1 DOF longitudinal vehicle and powertrain model validation. For repeatability, three city routes are selected: plain road, smaller road grade, and higher road grade in both directions. Therefore, the drivetrain system is tested in a high load range, even with long-term recuperation. The altitude changes are recorded with a DGPS system. By the recorded measurements, the vehicle and the drivetrain model can be calibrated, such as the air drag parameters, the rolling resistances, and the efficiencies of the drivetrain. The validation criteria are defined for speed tracking, and the relative tolerance of the cumulated energy should be below 10%. At the end of the day, a developed model is ready for energetic analysis or control strategy design. The energy balance of the applied cycles is also presented to prove that.

Simulink Model for LMV and Conceptualization of 2 Speed E-Axle

Heavy-duty trucks are the primary contributor to the global emissions of greenhouse gases. Because of this, electric haulage has seen significant growth in popularity. Electric heavy-duty trucks aren't widely adopted because of their limited range, payload capacity, and high prices. Electric axle systems, sometimes known as "E-Axles," are capable of electrifying heavy-duty vehicles effectively. The motor, power electronics, and gearbox are all combined into one unit in an E-axle. E-axles that are devoid of motors and gearboxes have the potential to boost performance while also reducing weight and fuel consumption. Heavy-duty automobiles are similar to them. E-axles have the potential to increase the fuel efficiency, flexibility, and emissions of heavy-load trucks. The e-axles are being inspected. E-axles for heavy-duty trucks bring both issues and opportunities concerning finances, infrastructure, and costs. Examine the performance standards as well as the technology behind batteries. This paper provides an introduction to MATLAB Simulink, which focuses on engineering and science. Simulink facilitates the design, simulation, and evaluation of systems. This article demonstrates the fundamental principles, features, and benefits of Simulink through the application of system modeling, control design, and dynamic simulation. It presents Simulink methodologies, libraries, and blocks, as well as interactive simulations.
 Keywords: MATLAB, Simulink, EV Control systems, EV simulation, EV powertrain modeling, EV test and validation Simulink, WLTP drive cycle

An Assessment of Accurate Range Estimator for Electric Vehicle Applications

Abstract: The world’s primary modes of transportation are facing two major problems: rising oil costs and increasing carbon emissions. As a result, electric vehicles (EVs) are gaining popularity as they are independent of oil and do not produce greenhouse gases. However, despite their benefits, several operational issues still need to be addressed for EV adoption to become widespread. One of the main obstacles for the acceptance of EV’s is the driver’s fear of being stranded by a depleted battery. In other words, accurate estimation of the remaining SOC is required to provide positive edge towards adoption of EV. In this work, an EV is tested for a drive line cycle of 10 Km for a trip, and key parameters such as elevation, speed, body dynamics, vehicle dimensions, on track speed have been collected. A model of EV power train is developed in MATLAB Simulink from the data logged and a static app is being developed which estimates the vehicle dynamic forces, power consumption, energy utilized for the trip, State of Charge (SOC) and remaining mileage of the vehicle.

Efficiency Analysis of Ipoh Driving Cycle Using Fuel Powered and Electric Vehicle Powertrain Model in Simulink

Efficiency Analysis of Ipoh Driving Cycle Using Fuel Powered and Electric Vehicle Powertrain Model in Simulink

Modeling and performance analysis of a lithium‐ion battery pack with an electric vehicle power‐train for different drive cycles and highway conditions

AbstractLithium‐ion cell chemistries are favored in the automotive sector, as they enable electric vehicles (EVs) to compete with traditional gasoline‐powered vehicles in terms of performance, range, and cost. The life and performance of these packs depend upon the Battery management system which monitors and controls the pack. The modeling, simulation, and analysis of a lithium‐ion battery pack that closely resembles an actual automobile battery are the focus of this paper. Real cells that are commercially available and specifically developed for automotive applications are modeled in the simulation environment. The cell characterization and formation of pack topology with heat exchange are addressed. A commercial electric car's power train model and EV dynamics are used in the simulation environment to test the modeled battery pack over various drive cycles. The performance analysis is upscaled by considering different aspects like resistive forces while driving, ambient temperature, initial conditions while the vehicle starts, etc. The battery manufacturer can verify the battery's performance under different load conditions, with a high degree of similarity to real‐world scenarios. The simulations are conducted on MATLAB/Simulink 2021b software.

Effect of Soil Properties and Powertrain Configuration on the Energy Consumption of Wheeled Electric Agricultural Robots

Agricultural emissions can be significantly reduced with smart farming, which includes moving away from large conventional tractors to fleets of compact wheeled electric robots. This paper presents a novel simulation modeling approach for an ATV-sized wheeled electric agricultural robot pulling an implement on deformable terrain. The 2D model features a semiempirical tire–soil interaction model as well as a powertrain model. Rear-wheel drive (RWD), front-wheel drive (FWD), and all-wheel drive (AWD) versions were developed. Simulations were carried out on two different soils to examine the energy consumption and tractive performance of the powertrain options. The results showed that energy consumption varies the least with AWD. However, RWD could provide lower energy consumption than AWD with light workloads due to lower curb weight. However, with the heaviest workload, AWD had 7.5% lower energy consumption than RWD. FWD was also found to be capable of lower energy consumption than AWD on light workloads, but it was unsuited for heavy workloads due to traction limitations. Overall, the results demonstrated the importance of taking the terrain characteristics and workload into account when designing electric agricultural robots. The developed modeling approach can prove useful for designing such machines and their fleet management.

Advanced Torque Control of Interior Permanent Magnet Motors for Electrical Hypercars

Nowadays, electric vehicles have gained significant attention as a promising solution to the environmental concerns associated with traditional combustion engine vehicles. With the increasing demand for high-performance hypercars, the need for advanced torque control strategies has become paramount. Field-Oriented Control using Current Vector Control represents a consolidated solution to implement torque control. However, this kind of control must take into account the DC link voltage variation and the variation of motor parameters depending on the magnets’ temperature while providing the maximum torque production for specific inverter current and voltage limitations. Multidimensional lookup tables are needed to provide a robust torque control from zero speed up to maximum speed under deep flux-weakening operation. Therefore, this article aims to explore the application of FOC 4D control in electrical hypercars and its impact on enhancing their overall performance and control stability. The article will delve into the principles underlying FOC 4D control and its advantages, challenges, and potential solutions to optimize the operation of electric hypercars. An electric powertrain model has been developed in the Simulink environment with the Simscape tool using a S-function block for the implementation of digital control in C-code. High-power electric motor electromagnetic parameters, derived from a Finite Element Method magnetic model, have been used in the simulation. The 4D LUTs have been computed from the motor flux maps and implemented in C-code in the S-function. The choice of FOC 4D control has been validated in the main load points of a hypercar application and compared to the conventional FOC. The final part of the research underlines the benefits of the FOC 4D on reliability, critical in motorsport applications.

A dynamic programming approach for energy management in hybrid electric vehicles under uncertain driving conditions

ABSTRACT This paper addresses the limited adaptability and the computational burden of energy management systems (EMSs) for hybrid electric vehicles (HEVs) implemented via dynamic programming (DP)-based approaches. First, a deterministic dynamic programming (DDP) framework is presented to solve HEV EMS problems subject to a specific driving cycle. To address this limitation, an improved DDP approach, integrating the actual travelled position of the vehicle into the control law, is proposed. This way, a given DDP-based EMS can be applied to all the driving cycles, yet still measured on the same road. Stochastic dynamic programming (SDP)-based EMSs are also developed and prove to be more adaptive to driving scenarios completely different from the ones used for their computation. Real-world driving cycles are employed in all the presented cases, while a reduced HEV powertrain model is used to alleviate the typical DP computational burden.

Study on methanol-hydrogen engine in extended-range electric vehicles

The demand for clean and efficient new vehicle models is steadily increasing. Methanol-hydrogen engines use methanol as the main fuel and can recover the heat from engine exhaust to dissociate part of methanol into hydrogen-rich syngas. The syngas is recirculated back into the engine cylinder to improve combustion. Therefore, methanol-hydrogen engines have a higher energy utilization efficiency. This paper designed a methanol-hydrogen engine system based on a Miller cycle methanol engine. Bench tests showed that combusting methanol and dissociated methanol gas together could accelerate the fuel burn rate and improve the engine’s fuel economy. At a fixed engine speed of 2500 r/min, the BSFC of the methanol-hydrogen engine was reduced up to 5.55% compared to the original methanol engine. Further, a methanol-hydrogen extended-range electric vehicle powertrain model was built in Simulink. Simulation results indicated that, under the WTLC cycle, the methanol-hydrogen engine achieved a 22.60% reduction in fuel consumption per 100 km and saved 58.89% in fuel costs compared to the conventional gasoline engine. The methanol-hydrogen engine presented potential application in the extended-range electric vehicle.

Model predictive control energy management strategy integrating long short-term memory and dynamic programming for fuel cell vehicles

This paper proposes a novel energy management strategy satisfying real-time and highly efficient energy management strategies for fuel cell hybrid electric vehicles (FCHEVs). The strategy is based on model predictive control (MPC), which integrates long short-term memory (LSTM) and dynamic programming (DP). A high-precision powertrain model of the investigated FCHEV is established for subsequent simulations. After training under several typical working conditions, LSTM is designed to forecast future power demands of the entire vehicle. Using the prediction results, the DP algorithm calculates the control scheme based on model predictive control. Considering the economy and durability of power sources, the results of four different control strategies are compared: thermostat, power following, traditional DP, and MPC. The MPC proposed in this paper reduces the total usage cost per 100 km on the test set by 9%, 33.5%, and −4.6%.

Optimal Electrical Vehicle Charging Planning and Routing Using Real-Time Trained Energy Prediction With Physics-Based Powertrain Model

Optimal Electrical Vehicle Charging Planning and Routing Using Real-Time Trained Energy Prediction With Physics-Based Powertrain Model

Flight-Validated Electric Powertrain Efficiency Models for Small UASs

Minimizing electric losses is critical to the success of battery-powered small unmanned aerial systems (SUASs) that weigh less than 25 kgf (55 lb). Losses increase energy and battery weight requirements which hinder the vehicle’s range and endurance. However, engineers do not have appropriate models to estimate the losses of a motor, motor controller, or battery. The aerospace literature often assumes an ideal electrical efficiency or describes modeling approaches that are more suitable for controls engineers. The electrical literature describes detailed design tools that target the motor designer. We developed SUAS powertrain models targeted for vehicle designers and systems engineers. The analytical models predict each component’s losses using high-level specifications readily published in SUAS component datasheets. We validated the models against parametric experimental studies involving novel powertrain flight data from a specially instrumented quadcopter. Given propeller torque and speed, our integrated models predicted a quadcopter’s battery voltage within 5% of experimental data for a 5+ min mission despite motor and controller efficiency errors up to 10%. The models can reduce development costs and timelines for different stakeholders. Users can evaluate notional or existing powertrain configurations over entire missions without testing any physical hardware.

A transfer-based reinforcement learning collaborative energy management strategy for extended-range electric buses with cabin temperature comfort consideration

Electric vehicles (EVs) have received extensive attention as an environmentally friendly and sustainable mode of transportation. To address “range anxiety” issues, extended-range electric vehicles (EREVs) have gradually gained popularity as a solution. However, current research on energy management strategies (EMS) for EVs often overlooks the energy consumption of the air conditioning (AC) system, resulting in suboptimal energy allocation. Therefore, this study focuses on the extended-range electric bus (EREbus), an extended-range electric bus, and incorporates the AC system into its EMS, enabling coordinated optimization with the powertrain system. First, the study embeds a control-oriented cabin thermal management model based on the powertrain model. Next, representations transfer-based reinforcement learning (RTRL) transfers the learned policy representations from the AC-off state to the EMS in the AC-on state. Furthermore, the study analyzes the impact of different representation transfers on powertrain performance and thermal comfort. The results demonstrate that the proposed EMS can improve the convergence rate and stability of training. Compared to direct learning methods, RTRL exhibits clear advantages in reducing operating costs and improving cabin thermal comfort, achieving reductions of 8.3%–12.6 % and 5.2%–27.0 % in operating costs for driving modes with different battery levels. Moreover, setting the transfer layer appropriately promotes the utilization of the global optimal potential of RTRL. This research provides support for energy management and holds the potential for promoting the development of EVs.

Physics-informed data-driven modeling approach for commuting-oriented hybrid powertrain optimization

The physics-informed data-driven approach is instructive in powertrain modeling with high-fidelity, which can be adopted for powertrain optimization to further improve vehicle performances. In this paper, a plug-in hybrid electric vehicle (PHEV) equipped with a novel powertrain is considered as the research object, and a series of vehicle bench experiments are carried out for operative data acquisition. With the collected data, the physics-informed data-driven models for engine fuel consumption and battery state of health (SOH) loss are proposed. The accuracy and superiority of the proposed model are demonstrated compared with other approaches. In order to realize the commuting-oriented powertrain optimization, the driving cycle representing the commuting scenario is reconstructed based on the collected traffic data in Beijing. The synthesized driving cycle generated by the proposed method can describe the practical application scenario in Beijing within the allowable error range. In addition, the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to further optimize the parameters for the hybrid powertrain according to vehicle performances. The multi-objective optimization is completed with the engine fuel consumption and battery state of health (SOH) loss as targets. The results indicate that the optimized configuration parameters can reduce fuel consumption by 4.85% and battery capacity loss by 3.7% compared with the original vehicle.

Model Predictive Control for Automatic Transmission Upshift Inertia Phase

This article deals with model predictive control (MPC) design for automatic transmission (AT) upshift inertia phase, which aims to optimally coordinate the actions of oncoming (ONC) and off-going (OFG) clutches and engine and to facilitate calibration. The designed MPC strategy accounts for clutch actuation dynamics and constraints, while setting the tradeoff between three key and conflicting shift quality criteria: comfort; duration; and efficiency. The shift comfort and duration are ensured by minimizing output shaft torque and ONC clutch slip speed tracking errors, and the shift efficiency is reflected in clutch energy loss minimization on a prediction horizon. This allows for the calibration of the MPC performance through setting the inertia phase duration, the output shaft torque reference, cost function weighting coefficients, and constraints, rather than optimizing the shift control profiles themselves. The MPC problem is formulated as a constrained quadratic programming problem and efficiently solved online by an interior-point solver. The proposed MPC strategy is applicable to other transmissions with multiple actuators, such as parallel hybrid transmissions. The MPC system is examined through nonlinear powertrain model simulations for one to three shift and its performance is compared with an offline, multiobjective optimization-based control strategy. The MPC design flexibility and ease of calibration are demonstrated for different shift comfort and duration targets, as well as cost function tuning, and robustness with respect to clutch actuation parameter uncertainties is examined.

Active torsional vibration suppression of hybrid electric vehicle drive system based on optimal harmonic current instruction calculation method

Torsional vibrations in the drive system of a hybrid electric vehicle powered by an engine are generated by cyclic torque fluctuations. These fluctuations stem from the combustion torque and the reciprocating inertial torque of the cylinder in the reciprocating piston engine. To address this issue, a real-time engine transient torque model that considers the thermodynamic characteristics of each component is established. A method was designed to identify the torsional vibration of a powertrain based on the engine torque, electric motor torque, and powertrain model. Building on this foundation, a method was proposed to calculate the optimal harmonic current instruction of the electric motor to mitigate torsional vibration in the Hybrid electric vehicles (HEV) powertrain. Furthermore, a proportional integral-resonance (PIR) controller was designed for the injection of an electric motor reference harmonic current. Finally, a method was proposed for suppressing the torsional vibration in HEV drive systems that incorporate active harmonic current injection. The simulation and test results indicate that the proposed method can effectively diminish the amplitudes of the drive system's 2nd, 4th, 6th, and 8th harmonic torque, ensuring stable operation of the powertrain.

Eco-Coasting Controller Using Road Grade Preview: Evaluation and Online Implementation Based on Mixed Integer Model Predictive Control

Coasting is a common method used in eco-driving to reduce fuel consumption by utilizing kinetic energy. However, in order to avoid excessive computation induced by integer coasting maneuvers, the powertrain model used in eco-driving controllers that rely on look-ahead road information has been oversimplified. This oversimplification assumes that the engine goes to idle when coasting, which significantly limits the fuel-saving potential. To address this issue, we propose an eco-coasting strategy that calculates the optimal timing and duration of coasting maneuvers using road information preview. Different from the engine-idling method, two control-oriented coasting methods, fuel cut-off method and engine start/stop method are formulated for the model-based optimal control. To evaluate and choose the best coasting mechanism for eco-coasting strategy, dynamic programming (DP) is performed to provide the globally optimal performance (i.e., benchmark results) for evaluating the engine-idling method, fuel cut-off method, and engine start/stop method. Based on the offline simulation results, the engine start/stop method consistently outperforms the fuel cut-off method in terms of both fuel consumption and travel time. This is attributed to the engine start/stop method eliminating the engine drag torque during deceleration, despite the additional energy cost required for engine restart being taken into account in the modeling, thus providing a fair evaluation. Then, the online performance of the eco-coasting strategy with engine start/stop mechanism is evaluated using Mixed Integer Model Predictive Control (MIMPC). We propose a tailored mixed-integer programming algorithm to facilitate online implementation. Simulation results show that the proposed eco-coasting strategy achieves near-optimal performance compared to DP and outperforms the rule-based method.

Selection/control concurrent optimization of BLDC motors for industrial robots.

This paper aims to concurrently select and control off-the-shelf BLDC motors of industrial robots by using a synergistic model-based approach. The BLDC motors are considered with trapezoidal back-emf, where the three-phase (a,b,c) dynamics of motors are modeled in a mechatronic powertrain model of the robot for the selection and control problem, defining it as a multi-objective dynamic optimization problem with static and dynamic constraints. Since the mechanical and electrical actuators' parameters modify the robot's performance, the selection process considers the actuators' parameters, their control input, operational limits, and the mechanical output to the transmission of the robot joints. Then, three objective functions are to be minimized, the motor's energy consumption, the tracking error, and the total weight of installed motors on the robot mechanism. The control parameterization approach via a cascade controller with PI controllers for actuators' voltage and a PID controller for actuators' torque is used to solve the multi-objective dynamic optimization problem. Based on simulations of the closed-loop system, a Pareto front is obtained to examine trade-offs among the objective functions before implementing any actuators in the existing robotic system. The proposed method is tested on an experimental platform to verify its effectiveness. The performance of an industrial robot with the actuators originally installed is compared with the results obtained by the synergic approach. The results of this comparison show that 10.85% of electrical power can be saved, and the trajectory tracking error improved up to 57.41% using the proposed methodology.