Heavy-duty Trucks Research Articles

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

The energy-saving effect of early-stage autonomous vehicles: A case study and recommendations in a metropolitan area

This paper presents the energy-saving potential of early-stage autonomous vehicles (AVs) by analyzing empirical AV driving records from a public road test. Vehicle-specific power-based microscopic energy consumption models are used to estimate the energy efficiency of the tested AVs, including light-duty vehicles and heavy-duty trucks. Overall, AVs do not necessarily save energy, largely depending on driving scenarios, such as traffic conditions and road types. On expressways, autonomous driving (AD) has an insignificant energy-saving effect (within 10 %) due to similar characteristics to human driving (HD). On urban roads with lower traffic speeds and more interventions from surrounding traffic, AD performs higher energy efficiency due to its capability to accelerate more smoothly by up to 60 %; however, frequent human disengagement generates additional energy consumption of 8%–40 %, especially on urban arterials. We further assessed the marginal effect of trip average speed and AD ratio on energy efficiency, illustrating varied relationships for different driving scenarios and power types. The study suggests a more moderate AD acceleration to elevate the energy-saving effect. Nevertheless, applying AD can hardly reduce the energy of early-stage AVs; instead, control methods to maintain an appropriate speed of the mixed traffic flow can help decrease the total energy consumption.

Application of Real-Life On-Road Driving Data for Simulating the Electrification of Long-Haul Transport Trucks

The worldwide commitment to the electrification of road transport will require a broad overhaul of equipment and infrastructure. Heavy-duty trucks account for over one-third of on-road energy use. Electrified roadways (e-Hwys) are an emerging technology where electric vehicles receive electricity while driving via dynamic wireless power transfer (DWPT), which is becoming highly efficient, and can bypass the battery to directly serve the motor. A modeling study was undertaken to compare long-haul trucks on e-Hwys with conventional battery technology requiring off-road recharging to assess the most favorable pathway to electrification. Detailed data taken from on-road driving trips from five diesel transport trucks were obtained for this study. This on-road data provided the simulations with both real-life duty cycles as well as performance targets for electric trucks, enabling an assessment and comparison of their performance on e-Hwys or with fast recharging. Battery-only trucks were found to have lifetimes down to 60% original battery capacity (60% SOH) of up to 9 years with 1600 kWh packs, and were similar to conventional diesel truck performance. On e-Hwys smaller pack sizes in the 500 to 900 kWh capacity range were sufficient for the driving duty, and showed lifetimes upwards of 20 years, comparing favorably to the battery calendar life limit of about 26 years. For a 535 kWh battery pack, an e-Hwy DWPT level of 250 kW was sufficient for a 36 tonne truck to complete all the daily driving as defined by the diesel reference trucks, and reach a battery pack end of life point of 60% SOH.

High-Precision Positioning, Perception and Safe Navigation for Automated Heavy-Duty Mining Trucks

High-Precision Positioning, Perception and Safe Navigation for Automated Heavy-Duty Mining Trucks

Preview-based terrain adaptive active suspension control strategy for heavy-duty trucks

This paper proposes a preview-based active suspension control method for heavy-duty trucks, where the vehicle front wheel terrain preview information is employed to improve the performance of the rear, focusing on enhancing the handling stability and vehicle smoothness. To begin with, the vehicle front wheel preview information is introduced to the half-vehicle model, and a state quantity is employed to calculate the time lag between front and rear for predicting the control input of the rear wheel. Secondly, based on the developed model, an [Formula: see text] controller is designed with the half-vehicle model based on the current stochastic linear optimal control combined with the preview controller, which provides more stable effect than the prior controllers by merging perceived ground information. Furthermore, an established seven-degree-of-freedom vehicle suspension model is utilized for the preview-based controller to govern the kinetic behavior of the heavy-duty truck, allowing for a more thorough analysis of operation smoothness and vehicle stability. At last, the vehicle comparison simulations are carried out, which indicates that the preview-based [Formula: see text] controller designed by inspecting the terrain preview information can straighten out the smoothness and safety of the heavy-duty truck more effectively in contrast with the LQG controller.

Analysis and design of multi-mode power split hybrid transmission scheme considering mode connection

For heavy-duty truck, multi-mode power split hybrid transmission scheme (MPSHTS) has good adaptability. However, the moment of inertia at both ends of the clutch is large. If there is a large speed difference in the clutch engagement process, it will increase clutch wear, engine stall and other problems. This paper proposed a new design method of multi-mode power split hybrid powertrain, which was different from the proof by exhaustion aiming at mode switching without speed difference. It included: the conditions for achieving mode connection without speed difference was obtained based on the lever method for dynamic characteristic analysis. Evaluation index for power split mechanisms that comprehensively considered power source and transmission system was established. Combining the mode connection conditions and the optimized basic configuration, the characteristic of no speed difference could be ensured by adjusting the power coupling mechanism. A multi-mode scheme based on optimized configuration and work mode requirements could be obtained. Finally, parameter matching and simulation verification on the designed scheme was carried out. It suggested that the designed system improved fuel economy by 4.14% compared with the original scheme.

A hybrid method combining Lévy process and neural network for predicting remaining useful life of rotating machinery

The accurate prediction of remaining useful life (RUL) for rotating machinery with gears and bearings at its core plays a crucial role in ensuring equipment's safe operation and preventing catastrophic accidents. Therefore, this paper focuses on the RUL issue of rotating machinery, proposing a novel RUL prediction framework. Initially, leveraging multi-domain feature extraction and self-organizing map (SOM) networks, this paper constructs the dynamic entropy weighted minimum quantization error (DEWMQE) as the initial health indicator (HI). Subsequently, employing windowed inertia smoothing and scale correction mechanisms, this paper filters anomalies in HI, ensuring trend smoothing and scale consistency. Considering the degradation process and characteristics of rotating machinery systems, this study utilizes linear multi-fractional Lévy stable motion (LMLSM) to build a degradation model. Building upon this, to effectively utilize historical data and overcome the shortcomings of random process theory requiring predefined degradation paths, this study further integrates bidirectional gated recurrent units (BGRU) and similarity transfer learning methods to devise the BGTLLM hybrid model, enabling adaptive fitting of the degradation trend. Finally, the Monte Carlo (MC) method is employed to evaluate the uncertainty in RUL prediction. The effectiveness and accuracy of the proposed hybrid prediction model in RUL forecasting are validated using heavy-duty truck axle data and the PHM2012 dataset.

The impact of heavy truck electrification on greenhouse gas emissions in Ontario, Canada

In this work, the new energy demand and greenhouse gas emissions that would be associated with heavy truck electrification in Ontario is evaluated. A new equation is derived to calculate the pollution-producing electricity generation based on the breakdown of the Ontario generation mosaic. Using this as a basis, for 4 scenarios of 5 %, 25 %, 50 % and 75 % of heavy-duty truck electrification, the Marginal Emission Factor (MEF) is calculated. This evaluation suggests that Ontario's peak demand in 2040 for the least and the most heavy-duty truck electrification scenarios can be up to 27.5 GW and 30.4 GW, respectively, compared to a baseline of 26.8 GW when assuming no heavy truck electrification condition. It is also forecasted that if the extra demand for electric trucks is supplied by clean renewable resources, the GHG emission reduction for 2040 can be as high as 0.9 MT, 4.3 MT, 8.6 MT and 12.9 MT GHG, respectively.

THEORETICAL FRAMEWORKS OF ECOPFM PREDICTIVE MAINTENANCE (ECOPFM) PREDICTIVE MAINTENANCE SYSTEM

The Frameworks of EcoPFM Predictive Maintenance (PM) System presents a novel approach to maintenance optimization within eco-friendly power facilities, addressing the critical need for sustainable, efficient asset management. This paper introduces an integrated framework leveraging advanced predictive analytics, machine learning algorithms, and Internet of Things (IoT) technology to enable proactive maintenance interventions based on real-time data insights. Focusing on the context of the United States it highlights the significance of implementing such a system in the realm of eco-friendly energy infrastructure. The automotive and heavy-duty truck industries in the United States grapple with the challenge of optimizing maintenance strategies to ensure vehicle reliability, safety, and environmental sustainability. Traditional maintenance approaches, primarily reactive or scheduled maintenance, fall short in addressing the complexities of modern vehicle operations. The U.S. Department of Transportation reports that heavy-duty trucks transport approximately 70% of the nation's freight by weight, underscoring the sector's critical role in the economy. However, inefficiencies in maintenance strategies contribute to significant economic and operational setbacks. According to the American Transportation Research Institute, unscheduled truck maintenance and repairs are leading operational costs for fleets, with an average expense of 16.7 cents per mile in 2020, highlighting the financial strain of current maintenance practices. In the United States, the demand for eco-friendly power solutions is rapidly increasing, driven by a growing awareness of environmental sustainability and the imperative to reduce carbon emissions. As the nation transitions towards renewable energy sources and eco-friendly power facilities, the effective management of these assets becomes paramount to ensuring reliability, performance, and longevity. The EcoPFM PM System integrates diverse data sets sourced from eco-friendly power facilities across the USA, encompassing historical operational data, sensor readings, and environmental parameters. Through predictive analytics, the system identifies patterns and trends within these data sets to forecast equipment failures and performance degradation accurately. By prioritizing maintenance tasks based on risk assessment models and condition monitoring, the system enables organizations to optimize resource allocation, minimize downtime, and extend asset lifespan. Embracing the Frameworks of EcoPFM Predictive Maintenance System holds immense promise for organizations operating eco-friendly power facilities in the United States. By harnessing data-driven insights and proactive maintenance strategies, this system offers a pathway towards enhanced operational efficiency, cost reduction, and sustainability, ultimately contributing to the advancement of eco-friendly energy infrastructure in the nation.
 Keywords: Predictive Maintenance, System, ECOPFM, Technology.

Assessing the cost-effectiveness of electric trucks in Indian food supply chains

Abstract Fostering green sustainable transportation in the Indian food supply chain with the adoption of electric trucks (ETs) is seemingly a promising alternative. However, before widescale adoption, a thorough cost-benefit analysis is essential for making an apt decision towards transitioning to ETs. Utilizing data of 13 ETs, a total cost of ownership (TCO) model has been developed that outlines the economic benefits of ETs by evaluating the impact of factors like energy consumption, charging strategy, annual driven distances, route optimization, etc. With acquisition costs of ETs constituting 50–55 % of TCO the study outlines that ETs have significantly lower energy costs of 6–12 % (50–58 % for IC-engine trucks) and maintenance costs of 6–8% (13–18 % for IC-engine trucks), thereby reducing the overall TCO. Subsequently, a fleet-level analysis of ET adoption in a food supply chain comprising of four heavy-duty trucks and 16 medium-duty trucks has been performed with emphasis on assessment of supply chain-specific parameters like battery-mass penalty, level of utilization, etc. Overall, this study provides insight for fleet managers, policymakers and industry leaders to promote sustainable through an extended use of ET in the present scenario for the food supply chain.

A fractional order model of auxiliary power batteries suitable for hydrogen fuel cell hybrid systems heavy-duty trucks

Aiming at the problem that the existing battery fractional order model is not suitable for the auxiliary power battery of hydrogen fuel cell heavy-duty trucks, considering the characteristics of multiple influencing factors and nonlinearity in actual driving processes, a fractional order model of auxiliary power batteries is proposed based on the hydrogen fuel cell heavy-duty trucks model and the batteries' fractional order model. Then, to solve the charge state estimation error increases due to the high frequency and large rate charging and discharging, H∞ filter is introduced into the adaptive unscented Kalman filter to optimize the charge state estimation in the model. Additionally, the model's parameters are identified using the evolutionary algorithm and the recursive least squares technique based on the forgetting factor. Finally, the model is verified by dynamic conditions. The results show that compared with the auxiliary power batteries' fractional order model, the optimized model reduces the MAE by 0.08% and the RMSE by 0.22% when estimating voltage, and reduces the MAE by 0.25% and the RMSE by 2.66% when estimating SOC. Therefore, the optimized fractional order model of auxiliary power battery has higher accuracy and applicability.

Fuel consumption estimation in heavy-duty trucks: Integrating vehicle weight into deep-learning frameworks

Insufficient consideration of vehicle weight dynamics during real-world driving could lead to inaccurate fuel consumption estimates. This study examined the impact of vehicle weight on fuel consumption rate (FCR) by analyzing extensive, high-resolution operating data obtained from 162 heavy-duty trucks (HDTs). An engine output power-based (EOP) model, an artificial neural network (ANN) model, and a long short-term memory-convolutional (LSTM-Conv) model were developed and contrasted with conventional vehicle specific power (VSP) and Virginia Tech Microscopic (VT-Micro) models. The results indicated a significant, non-linear relationship between weight and FCR. Compared to 5-ton trucks, FCR for trucks weighing 15–25 tons and 45–55 tons increased by 290% and 755%, respectively, under low-speed and positive acceleration conditions. The LSTM-Conv model outperformed the VSP, VT-Micro, and EOP models, achieving MAPEs of 9.81% for FCR and 1.49% for trip fuel economy estimation. The deep-learning models exhibited enhanced stability across varying speeds, accelerations, and vehicle weights.

Comparative Life Cycle Assessment of Battery and Fuel Cell Electric Cars, Trucks, and Buses

Addressing the pressing challenge of global warming, reducing greenhouse gas emissions in the transportation sector is a critical imperative. Battery and fuel cell electric vehicles have emerged as promising solutions for curbing emissions in this sector. In this study, we conducted a comprehensive life cycle assessment (LCA) for typical passenger vehicles, heavy-duty trucks, and city buses using either proton-exchange membrane fuel cells or Li-ion batteries with different cell chemistries. To ensure accuracy, we supplemented existing studies with data from the literature, particularly for the recycling phase, as database limitations were encountered. Our results highlight that fuel cell and battery systems exhibit large emissions in the production phase. Recycling can significantly offset some of these emissions, but a comparison of the technologies examined revealed considerable differences. Overall, battery electric vehicles consistently outperform fuel cell electric vehicles regarding absolute greenhouse gas emissions. Hence, we recommend prioritizing battery electric over fuel cell vehicles. However, deploying fuel cell electric vehicles could become attractive in a hydrogen economy scenario where other factors, e.g., the conversion and storage of surplus renewable electricity via electrolysis, become important.

Policy scenario analysis of achieving the "dual carbon" goal for the road transportation sector in China

The goal of "dual carbon" has brought the pressure of reducing carbon emissions to the road transportation sector in China. This study aims to identify and analyze possible policy measures that would help to achieve this goal from the perspective of the whole life cycle. We build a bottom-up computing framework based on the LEAP model. Six policy scenarios are designed to quantitatively assess their effects. The results show that promoting pure electric vehicles (PEV) and plug-in hybrid electric vehicles (PHEV) is appropriate for the passenger vehicles (PV), light-duty trucks (LDT), and mini trucks (MT) sub-sectors, while hydrogen fuel cell vehicles (FCV) are more suitable for the promotion of the heavy-duty trucks (HDT), heavy-duty buses (HDB), and medium-duty buses (MDB) sub-sectors. Additionally, the recycling of vehicle materials can effectively reduce the carbon emissions during the life cycle of motor vehicles. Furthermore, reducing the carbon emissions from the vehicle cycle of PV and the fuel cycle of HDT should be taken seriously. The main contribution of this study lies in establishing a comprehensive list of vehicle cycle carbon emission factors, covering various sub-sectors of China's road transportation sector. Besides, this study considers China's strategic planning and "dual carbon" goal, and proposes policy recommendations specifically tailored to the practical issues faced by China.

Theoretical Development and Numerical Validation of an Asymmetric Linear Bilateral Control Model- Case Study for an Automated Truck Platoon

In this paper, we theoretically develop and numerically validate an asymmetric linear bilateral control model (LBCM) , in which the motion information (e.g., position and speed) from the immediate leading vehicle and the immediate following vehicle of a subject vehicle in an automated vehicle platoon are weighted differently to determine the control inputs to the subject vehicle. The novelty of the asymmetric LBCM is that using this model all the follower vehicles in a platoon can adjust their acceleration and deceleration to closely follow a constant desired time gap to improve platoon operational efficiency while maintaining local and string stability. We theoretically analyze the local stability of the asymmetric LBCM using the condition for asymptotic stability of a linear time-invariant system and prove the string stability of the asymmetric LBCM using a space gap error attenuation approach. Then, we evaluate the efficacy of the asymmetric LBCM by simulating a closely coupled cooperative adaptive cruise control (CACC) platoon of fully automated trucks in various non-linear acceleration and deceleration states. We choose automated truck platooning as a case study since heavy-duty trucks experience higher delays and lags in the powertrain system, and limited acceleration and deceleration capabilities than passenger cars. To evaluate the platoon operational efficiency of the asymmetric LBCM, we compare the performance of the asymmetric LBCM to a baseline model, i.e., the symmetric LBCM, for different powertrain delays and lags. Our analyses showed that the asymmetric LBCM can handle any combined powertrain delays and lags up to 0.6 sec while maintaining a constant desired time gap during a stable platoon operation, whereas the symmetric LBCM fails to ensure stable platoon operation as well as maintain a constant desired time gap for any combined powertrain delays and lags over 0.2 sec. These findings demonstrate the potential of the asymmetric LBCM in improving platoon operational efficiency and stability of an automated truck platoon.

Evaluating Fuel Cell vs. Battery Electric Trucks: Economic Perspectives in Alignment with China’s Carbon Neutrality Target

The electrification of heavy-duty trucks stands as a critical and challenging cornerstone in the low-carbon transition of the transportation sector. This paper employs the total cost of ownership (TCO) as the economic evaluation metric, framed within the context of China’s ambitious goals for heavy truck electrification by 2035. A detailed TCO model is developed, encompassing not only the vehicles but also their related energy replenishing infrastructures. This comprehensive approach enables a sophisticated examination of the economic feasibility for different deployment contexts of both fuel cell and battery electric heavy-duty trucks, emphasizing renewable energy utilization. This study demonstrates that in the context where both fuel cell components and hydrogen energy are costly, fuel cell trucks (FCTs) exhibit a significantly higher TCO compared to battery electric trucks (BETs). Specifically, for a 16 ton truck with a 500 km range, the TCO for the FCT is 0.034 USD/tkm, representing a 122% increase over its BET counterpart. In the case of a 49 ton truck designed for a 1000 km range, the TCO for the FCT is 0.024 USD/tkm, marking a 36% premium compared to the BET model. The technological roadmap suggests a narrowing cost disparity between FCTs and BETs by 2035. For the aforementioned 16 ton truck model, the projected TCO for the FCT is expected to be 0.016 USD/tkm, which is 58% above the BET, and for the 49 ton variant, it is anticipated at 0.012 USD per ton-kilometer, narrowing the difference to just 4.5% relative to BET. Further analysis within this study on the influences of renewable energy pricing and operational range on FCT and BET costs highlights a pivotal finding: for the 49 ton truck, achieving TCO parity between FCTs and BETs is feasible when renewable energy electricity prices fall to 0.022 USD/kWh or when the operational range extends to 1890 km. This underscores the critical role of energy costs and efficiency in bridging the cost gap between FCTs and BETs.

Database-driven lightweight design for axle housing of heavy-duty truck

Rapid and efficient design methods of axle housing are an increasingly important area for the lightweight design of heavy-duty trucks. Here, a database-driven lightweight design method for axle housing is developed. A theoretical analysis is conducted to explore the relationship between the shape and maximum stress of cross-sections. Subsequently, A double super-ellipse equation is applied to generate massive cross-sections with different shapes and sizes. The secondary development module is constructed to achieve the parametric modeling, finite element analysis, and result analysis of axel housing. Finally, the proposed database-driven lightweight design is employed to carry out the lightweight design of axel housing under different design criteria. The results show that compared to their counterpart, the mass of axel housing using Q345 and Q420 is decreased by 10.3% and 18.9%, respectively, and their maximum stress are declined by 32.9% and 20.8%, respectively, under the design criteria of equal strength. Under the design criteria of specified safety factors, the mass and maximum stress of axel housing are decreased by 11.7% and 25.0%, respectively. This investigation offers some important insights into the lightweight design of tube components.

Levelized cost of driving for medium and heavy-duty battery electric trucks

The total cost of ownership (TCO) of trucks is known as one of the main decision-making factors by logistics operators for adopting alternative powertrains such as battery electric trucks (BETs). In this study, we develop a very detailed levelized cost of driving (LCOD) model to analyse the TCO of BETs and conventional trucks (CTs) in medium and heavy-duty truck weight classes. The model has methodological advancements such as developing opportunity costs for charging activities, using a detailed operational time calculation, and analysing the optimum driving ranges or battery sizing. By implementing an extensive sensitivity analysis of LCOD for CTs and BETs over 43 variables, it is revealed that the key parameters such as operational driving range, battery pack price, state of charge of battery, driver cost, “mid-shift” charging power, ambient temperature, opportunity charging, and driving speed have major impacts on the cost competitiveness of BETs vs. CTs. In addition, the impact of battery and charging technology improvements as well as designing optimum driving ranges are examined in three different operational trip profiles (urban, short-haul or regional, long-haul). The result shows that: 1) BETs in urban trip profiles with the current and/or short-term battery technology might be economically viable alternatives for CTs without the help of the policy measures, 2) BETs with below 40 t gross vehicle weight and the long-term improvements in battery technology in all the operational trip profiles might be economically viable alternatives for CTs without the help of the policy measures, and 3) the implementation of policy measures affecting the relative costs of CTs and BETs and development of fast-charging facilities would be needed to support the above 40 t BETs in short-haul and long-haul trips for the current and/or short-term as well as mid-term battery technologies.

Generalized thermodynamic modeling of hydrogen storage tanks for truck application

Hydrogen-driven heavy-duty trucks are a promising technology for reducing CO2 emissions in the transportation sector. Thus, storing hydrogen efficiently onboard is vital. The three available or currently developed physical hydrogen storage technologies (compressed gaseous, subcooled liquid, and cryo-compressed hydrogen) are promising solutions. For a profound thermodynamic comparison of these storage systems, a universally applicable model is required. Thus, this article introduces a generalized thermodynamic model and conducts thermodynamic comparisons in terms of typical drive cycle scenarios. Therefore, a model introduced by Hamacher et al. [1] for cryo-compressed hydrogen tanks is generalized by means of an explicit model formulation using the property cv2P from REFPROP [2], which is understood as a generic specific isochoric two-phase heat capacity. Due to an implemented decision logic, minor changes to the equation system are automatically made whenever the operation mode or phase of the tank changes. The resulting model can simulate all three storage tank systems in all operating scenarios and conditions in the single- and two-phase region. Additionally, the explicit model formulation provides deeper insights into the thermodynamic processes in the tank. The model is applied to the three physical hydrogen storage technologies to compare drive cycles, heat requirement, dormancy behavior, and optimal usable density. The highest driving ranges were achieved with cryo-compressed hydrogen, however, it also comes with higher heating requirements compared to subcooled liquid hydrogen.

Heavy-Duty Transportation in 10 Charts

This article illustrates the key facts and figures about the transportation sector and its contribution to emissions, in particular the heavy-duty trucking, in terms of the current state of sector in the U.S. in light of increasing recent focus on decarbonization efforts with zero emission trucking. Heavy-duty trucks contribute significantly more to annual mileage traveled, fuel consumption, and greenhouse gas emissions on per vehicle basis, compared to light-duty vehicles and passenger cars. Given the essential role of heavy-duty trucking in freight movement demanding high energy, decarbonizing trucking sector comes with unique challenges overcoming which will have an important impact on reducing global emissions as well as mitigating the adverse health effects arising from these emissions.