Search Results (113)

The electrification of road vehicles is a relatively mature sector, while other areas of mobility, such as construction machinery, are just beginning their transition to electric solutions. This work presents the design and realization of an integrated drive system specifically developed for retrofitting fan drives in heavy machinery, like bulldozers and tractors, utilizing existing 48 VDC batteries. By replacing or complementing internal combustion and hydraulic technologies with electric solutions, significant advantages in efficiency, reduced environmental impact, and versatility can be achieved. Focusing on the fan drive system addresses the critical challenge of thermal management in high ambient temperatures and harsh environments, particularly given the high current requirements for $3\mathrm{k}\mathrm{W}$-class applications. A sensorless architecture has been selected to enhance reliability by eliminating mechanical position sensors. The developed fan drive has been extensively tested both on a braking bench and in real-world applications, demonstrating its effectiveness and robustness. Future work will extend this prototype to electrify additional onboard hydraulic motors in these machines, further advancing the electrification of heavy-duty equipment and improving overall efficiency and environmental impact. Full article

The global urgency to mitigate climate change by reducing transport-related emissions drives the accelerated electrification of road freight transport. This paper presents a comprehensive meta-study forecasting the development and corresponding power demand of electric trucks and tractor units in Hamburg up to 2050, emphasizing the shift from conventional to electric vehicles. Utilizing historical registration data and existing commercial and institutional reports from 2007 to 2024, the analysis estimates future distributions of electric heavy-duty vehicles across Hamburg’s 103 city quarters. Distinct approaches are evaluated to explore potential heavy-duty vehicle distribution in the city, employing Mixed-Integer Linear Programming to quantify and minimize distribution uncertainties. Power demand forecasts at this detailed geographical level enable effective infrastructure planning and strategy development. The findings serve as a foundation for Hamburg’s transition to electric heavy-duty vehicles, ensuring a sustainable, efficient, and reliable energy supply aligned with the city’s growing electrification requirements. Full article

This study develops a real-time energy-efficient control strategy for distributed-drive electric tractors (DDETs) to minimize electrical energy consumption during traction operations. Taking a four-wheel independently driven DDET as the research object, we conduct dynamic analysis of draft operations and establish dynamic models of individual components in the tractor’s drive and transmission system. A backpropagation (BP) neural network-based operational speed prediction model is constructed to forecast operational speed within a finite prediction horizon. Within the model predictive control (MPC) framework, a real-time energy-efficient control strategy is formulated, employing a dynamic programming algorithm for receding horizon optimization of energy consumption minimization. Through plowing operation simulation with comparative analysis against a conventional equal torque distribution strategy, the results indicate that the proposed real-time energy-efficient control strategy exhibits superior performance across all evaluation metrics, providing valuable technical guidance for future research on energy-efficient control strategies in agricultural electric vehicles. Full article

Hybrid tractors, as an efficient and environmentally friendly power system, are gradually becoming an important technical choice in the agricultural field. Compared to conventional powertrain systems, hybrid electric powertrains can achieve a 15–40% reduction in fuel consumption. By optimizing the engine operating range and incorporating electric-only driving modes, these systems further contribute to a 20–35% decline in CO₂ emissions, along with a significant mitigation of nitrogen oxides (NOx) and particulate matter (PM) emissions. In this paper, the energy management technology of hybrid tractors is reviewed, with emphasis on the energy scheduling between the internal combustion engine and electric motor, the optimization control algorithm, and its practical performance in agricultural applications. Firstly, the basic configuration and working principle of hybrid tractors are introduced, and the cooperative working mode of the internal combustion engine and electric motor is expounded. Secondly, the research progress of energy management strategies is discussed. Then, the application status and challenges of hybrid power systems in agricultural machinery are discussed, and the development trend of hybrid tractors in the fields of intelligence, low carbonization, and high efficiency in the future is prospected. This paper extracts many experiences and methods from the references over the years and provides a comprehensive evaluation. Full article

This paper presents a quasi-static model for assessing potential energy savings through partial electrification of a land leveler implement. The quasi-static model simulates the behavior of the hydraulic circuit components, including the pump and a spool-type flow divider, for a commercial land leveler used in agricultural applications. Two electrification schemes are presented. In the first scheme, the pump, originally driven at fixed speed by the PTO, is driven at variable speed by an electric drive, with no changes in the hydraulic circuit. In the second electrification scheme, the decentralization of the hydraulic system is implemented by using separate variable-speed pumps for each actuator. Results show significant potential energy savings of 9–22% with the first electrification scheme and 45–53% with the second scheme, compared to the traditional non-electrified setup. Our findings demonstrate that electrification could be a strategic choice to improve the efficiency of tractor implements and agricultural machinery. Full article

To achieve shorter path length and lower repetition rate for robotic complete coverage path planning, a complete-coverage path-planning algorithm based on transition probability and learning perturbation operator (CCPP-TPLP) is proposed. Firstly, according to the adjacency information between nodes, the distance matrix and transition probability matrix of the accessible grid are established, and the optimal initialization path is generated by applying greedy strategy on the transition probability matrix. Secondly, the population is divided into four subgroups, and different degrees of learning perturbation operations are carried out on subgroups to update each path in the population. CCPP-TPLP was tested against five algorithms in different map environments and in the working map environment of electric tractors with height information The results show that CCPP-TPLP can optimize the selection of path nodes, reduce the total length and repetition rate of the path, and significantly improve the planning efficiency and quality of complete coverage path planning. Full article

LiFePO₄ batteries are widely used in hybrid electric tractors due to their high energy density, stable working voltage, low self-discharge rate, long cycle life, absence of memory effect, environmental friendliness, and flexible sizing. Accurate State of Charge (SOC) estimation is crucial for Battery Management Systems (BMSs). This study utilizes a LiFePO₄ battery dataset from the University of Maryland to improve SOC estimation accuracy. The forgetting factor recursive least squares method was employed for parameter identification, and a temperature-dependent second-order RC equivalent circuit model was developed in MATLAB R2024a/Simulink. The proposed SAO-LSTM model demonstrated superior SOC estimation performance compared to traditional ampere-hour integration, achieving a 98.23% error reduction. Evaluation results showed 0.39% and 0.31% decreases in root mean square error and mean absolute error, respectively, confirming the model’s robustness and high estimation accuracy for LiFePO₄ batteries in hybrid electric tractors. Full article

To address the issue of over-regulation of the temperature of a liquid-cooled power battery thermal management system under the plowing condition of electric tractors, which leads to high energy consumption, a nonlinear model prediction control (NMPC) algorithm for the thermal management system of the power battery of electric tractors applicable to the plowing condition is proposed. Firstly, a control-oriented electric tractor power battery heat production model and a heat transfer model were established based on the tractor operating conditions and Bernardi’s theory of battery heat production. Secondly, in order to improve the accuracy of temperature prediction, a prediction method of future working condition information based on the moving average theory is proposed. Finally, a nonlinear model predictive control cooling optimization strategy is proposed, with the optimization objectives of quickly achieving battery temperature regulation and reducing compressor energy consumption. The proposed control strategy is validated by simulation and a hardware-in-the-loop (HIL) testbed. The results show that the proposed NMPC strategy can control the battery temperature better, that in the holding phase the proposed control strategy reduces the compressor speed variation range by 24.6% compared with PID, and that it reduces the compressor energy consumption by 23.1% in the whole temperature control phase. Full article

Pure electric tractors face challenges in complex operating conditions, including the excessive peak motor torque caused by frequent start–stop cycles and insufficient energy utilization. To address these issues, this study proposes a hydraulic–electric hybrid drive system for tractor traveling systems which is based on a range-extended hybrid architecture. By combining the high-torque characteristics of hydraulic drive systems with the high control precision of electric motors, a hydraulic–electric dual-power coupling model was constructed. A logic-threshold-based operating mode division strategy and a hierarchical braking energy recovery mechanism were developed. The start–stop control dynamics and energy recovery efficiency of the system during plowing and transport operations were thoroughly analyzed. The simulation results demonstrate that while maintaining its acceleration and braking performance, the proposed system achieves 18.8% and 35.7% reductions in its peak motor torque during plowing and transport operations, respectively. Its braking energy recovery efficiency improved to 48.3% and 66.4% in the two scenarios; 18.5% and 25.7% reductions in overall energy consumption were seen. Full article

The present paper investigates the comparative impact of vibrations on operator safety for two diesel and electric agricultural tractors under real operating conditions. Vibrations were measured using four triaxial accelerometers installed at critical points, including the seat base, backrest, floor, and operator’s head. Tests were conducted on two comparable tractor models, a diesel New Holland TCE 50 and an electric prototype TE-0, across four types of terrains (concrete, grass, uneven agricultural road, and plowed land) and at two working speeds (5 km/h and 10 km/h). The root mean square (RMS) accelerations, seat-to-head transmissibility, and isolation efficiency were calculated in compliance with ISO 2631 standards to evaluate the effects on operator health and comfort. The results showed superior vibration isolation efficiency for the electric tractor, particularly within the critical frequency range of 4–12 Hz, where human health risks are most significant and a better isolation efficiency of 98%, significantly reducing operator exposure to harmful vibrations. These findings highlight the potential of electric tractors to improve operator comfort, safety, and long-term health in agricultural applications. Full article

In 2023, the EU set the target to reduce greenhouse gas (GHG) emissions by 55% until 2030 compared to 1990. The European Transport Policy sees battery–electric vehicles as a key technology to decarbonize the transport sector, so governments support the adoption through dedicated funding programs. Battery–electric trucks hold great potential to decarbonize the transport sector, especially for high-impact, heavy-duty trucks. Theoretical life-cycle assessments (LCA) predict a lower CO₂e emission impact from battery–electric trucks compared to conventional diesel trucks. Yet, one concern repeatedly mentioned by potential users is the doubt about the ecological advantage of battery–electric vehicles. This is rooted in the problem of a much higher CO₂e impact of the lithium-ion batteries production process. As heavy-duty trucks have a much larger battery, the hypothec in the construction phase of the vehicle is significantly higher, which must be regained during the use phase. Although theoretical assessments exist, CO₂e evaluations using real-world application data are almost nonexistent, as the technology is at the very start of the adoption curve. Exemplary is the fact that there were only 72 registered battery–electric heavy-duty tractor trucks throughout the whole of Germany at the start of 2023. This paper aims to deliver one of the first real-world quantifications using operational data for the actual reduction impact of battery–electric heavy-duty trucks compared to diesel trucks. This study uses the methodology of the life-cycle assessment approach according to ISO 14040/14044 to gain a systematic and holistic technology comparison. For this LCA, the system boundaries are considered from cradle to cradle. This includes the production of raw materials and energy, the manufacturing of the trucks, the use phase, and the recycling afterward. The research objects of this study are battery–electric and diesel Volvo FM trucks, which have been in use by the German freight company Nord-Spedition GmbH since May 2023. The GREET^® database is used to assess the emission impact of the material production and manufacturing process. The Volvo tractor trucks resemble a critical case, as the vehicles have a battery size of 540 kWh—around 11 times larger than a usual passenger car. The operation data is directly provided by the logistics company to observe fuel/electricity consumption. Other factors are assessed through company interviews as well as a wide literature research. Finally, a large question mark concerning total emissions lies in the cradle-to-cradle capabilities of large-scale lithium-ion batteries and the electricity grid mix. Different scenarios are being considered to assess potential disposal or recycling paths as well as different electricity grid developments and their impact on the overall balance. The findings estimate the total emissions reduction potential to range between 34% and 69%, varying with assumptions on the electricity grid transition and recycling opportunities. This study displays one of the first successful early-stage integrations of battery–electric heavy-duty trucks into the daily operation of a freight company and can be used to showcase the ecological advantage of the technology. Full article

In Japan, the decline in the number of agricultural workers and the aging of the workforce are problems, and there is a demand for more efficient and labor-saving work. Furthermore, in order to correct the rising price of fertilizer and the increasing burden on the environment caused by fertilizer, there is a demand for more efficient fertilization. Therefore, we aim to develop an electric soil sampling robot that can run autonomously using Global Navigation Satellite System (GNSS) information. GNSS and the Inertial Measurement Unit (IMU) are used as navigation sensors. The work machine is a crawler type that reduces soil compaction. In addition, a route map was generated in advance using the coordinate values of the field, with soil sampling positions set at 10 m intervals. In the experiment, the robot traveled along the route map and stopped automatically. The standard deviation of the standard deviation of lateral error was about 0.032 m, and the standard deviation of the interval between soil sampling positions was also less than 0.05 m. Therefore, it can be said that the accuracy is sufficient for soil sampling. It can also be said that even higher density sampling is possible by setting the intervals for soil sampling at finer intervals. Full article

Knowing the energy balance in agricultural systems is essential for a holistic understanding of sustainability, productivity and economic return. The aim of this study was to estimate the cumulative energy demand (CED), greenhouse gas (GHG) emissions and carbon footprint in industrial potato and tomato production systems in the Southeast region of Brazil, identifying mitigation strategies in different scenarios. The Life Cycle Analysis methodology was used, and two functional units were defined: one hectare of cultivation and one kilogram of vegetable produced. The CEDs for tomato and potato production were 59,553.56 MJ ha^–1 (or 0.54 MJ kg^–1) and 57,992.02 MJ ha^–1 (or 1.45 MJ kg^–1), respectively. The GHG emissions were 5425.13 kg CO₂ eq ha^–1 for potato production and 5270.9 kg CO₂ eq ha^–1 for tomato production, resulting in carbon footprints of 0.135 and 0.042 kg CO₂ eq kg^–1, respectively. Fertilizers, diesel and pesticides were the main contributors to CED and GHG emissions. Thus, in order to achieve greater sustainability in the production of these vegetables and mitigate the impacts on the environment generated by the high demand for energy and GHG emissions, it is necessary to replace synthetic fertilizers with organic sources, chemical pesticides with biological pesticides, diesel with biodiesel or the use of electric vehicles and tractors, resulting in reductions of up to 39 and 52% in the GHG emissions for potatoes and tomatoes, respectively. Full article

To address the challenges faced by fuel cell hybrid electric tractors (FCHETs) equipped with a battery and supercapacitor, including the complex coordination of multiple energy sources, low power allocation efficiency, and unclear optimal energy consumption, this paper proposes two energy management strategies (EMSs): one based on hierarchical instantaneous optimization (HIO) and the other based on multi-dimensional dynamic programming with final state constraints (MDDP-FSC). The proposed HIO-based EMS utilizes a low-pass filter and fuzzy logic correction in its upper-level strategy to manage high-frequency dynamic power using the supercapacitor. The lower-level strategy optimizes fuel cell efficiency by allocating low-frequency stable power based on the principle of minimizing equivalent consumption. Validation using a hardware-in-the-loop (HIL) simulation platform and comparative analysis demonstrate that the HIO-based EMS effectively improves the transient operating conditions of the battery and fuel cell, extending their lifespan and enhancing system efficiency. Furthermore, the HIO-based EMS achieves a 95.20% level of hydrogen consumption compared to the MDDP-FSC-based EMS, validating its superiority. The MDDP-FSC-based EMS effectively avoids the extensive debugging efforts required to achieve a final state equilibrium, while providing valuable insights into the global optimal energy consumption potential of multi-energy source FCHETs. Full article

In farmland environments where GNSS signals are obstructed, such as forested areas or in adverse weather conditions, traditional GNSS/INS integrated navigation systems suffer from positioning errors and instability. To address this, a model-assisted integrated navigation system is proposed, combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The CNN-BiLSTM model is trained under normal GNSS conditions and used to predict positioning when GNSS signals are interrupted, effectively replacing GNSS to ensure stable and accurate navigation. Experimental validation is conducted using field data from tractor simulations. The results show that, during a 100-s GNSS denial, the CNN-BiLSTM model reduces the average position error by 79.3% compared to pure inertial navigation and by 5.4% compared to traditional LSTM. In a 30-s GNSS denial, the average position error is reduced by 41% compared to inertial navigation and 6.2% compared to LSTM. The model maintains positioning accuracy within 3% of the GNSS/INS output under normal conditions, demonstrating its feasibility and effectiveness. This approach offers a promising solution for autonomous tractor navigation in GNSS-denied agricultural environments, contributing to precision agriculture. Full article