Mobile Machines Research Articles

КОРРЕКТИРОВАНИЕ ПЕРИОДИЧНОСТИ ОПЕРАЦИЙ ТЕХНИЧЕСКОГО СЕРВИСА МОБИЛЬНЫХ МАШИН С УЧЕТОМ ЖЕСТКОСТИ КЛИМАТА

Based on the analysis of the main operational factors, among which, for specific areas of equipment supply, one of the main ones is the climatic factor, as well as the main damaging degradation processes during the operation of machines, an effective mechanism for planning the frequency of maintenance operations is proposed, which helps to ensure a high level of technical readiness of machines. The operability potential was analyzed, its active, reserve and passive parts were considered, which made it possible to present a model for changing and restoring the operability potential of a mobile machine. Mathematical dependences are given for certain models of machines of the same functional purpose, as well as characterizing processes of reducing the potential of their performance in a certain external environment, their transformation is noted when changing natural and climatic operating conditions. The construction of a priori diagram of the ranks of climatic factors made it possible to note that the most significant adverse effect is exerted by temperature, humidity and solar radiation in the operating area. Taking into account the distribution of zones of technical rigidity of the climate, its score and the coefficient of continentality, a method is proposed for correcting the frequency of operations of technical service of transport-technological machines for the zone of their actual operation.

Multiposture leg tracking for temporarily vision restricted environments based on fusion of laser and radar sensor data

AbstractLeg tracking is an established field in mobile robotics and machine vision in general. These algorithms, however, only distinguish the scene between leg and nonleg detections. In application fields like firefighting, where people tend to choose squatting or crouching over standing postures, those methods will inevitably fail. Further, tracking based on a single sensor system may reduce the overall reliability if brought to outdoor or complex environments with limited vision on the target objectives. Therefore, we extend our recent work to a multiposture detection system based on laser and radar sensors, that are fused to allow for maximal reliability and accuracy in scenarios as complex as indoor firefighting with vastly limited vision. The proposed tracking pipeline is trained and extensively validated on a new data set. We show that the radar tracker reaches state‐of‐the‐art performance, and that laser and fusion tracker outperform recent methods.

Case study of vibration in a mobile milling machine

Many industries rely on heavy-duty machinery such as cranes, pipelines, motors, which are subject to wear and tear. Milling of such parts using conventional machine tools presents many drawbacks and is impossible in some cases. In such situations, mobile machine tools can be used to machine directly on site. However, mobile machines are flexible, and are prone to detrimental vibrations during cutting operations. Vibrational stability in the surfacing process has been extensively studied in conventional milling. However, the focus of this article is an original study on a mobile milling machine used for on-site surfacing operations. An experimental investigation into the vibrational behavior of the machine is carried out and a sensorless method is used to detect the onset of chatter. Machine flexibility is discussed using a tool-point frequency response function and references from the literature. Milling operations are carried out over a range machining parameter to determine an experimental stability limit. Internal sensors for spindle motor consumption and spindle motor encoder are monitored during the machining process. A threshold is established for determining the appearance of chatter using only the machine’s internal sensors.

Pedestrian Localization with Stride-Wise Error Estimation and Compensation by Fusion of UWB and IMU Data.

Indoor positioning enables mobile machines to perform tasks (semi-)automatically, such as following an operator. However, the usefulness and safety of these applications depends on the reliability of the estimated operator localization. Thus, quantifying the accuracy of positioning at runtime is critical for the application in real-world industrial contexts. In this paper, we present a method that produces an estimate of the current positioning error for each user stride. To accomplish this, we construct a virtual stride vector from Ultra-Wideband (UWB) position measurements. The virtual vectors are then compared to stride vectors from a foot-mounted Inertial Measurement Unit (IMU). Using these independent measurements, we estimate the current reliability of the UWB measurements. Positioning errors are mitigated through loosely coupled filtering of both vector types. We evaluate our method in three environments, showing that it improves positioning accuracy, especially in challenging conditions with obstructed line of sight and sparse UWB infrastructure. Additionally, we demonstrate the mitigation of simulated spoofing attacks on UWB positioning. Our findings indicate that positioning quality can be judged at runtime by comparing user strides reconstructed from UWB and IMU measurements. Our method is independent of situation- or environment-specific parameter tuning, and as such represents a promising approach for detecting both known and unknown positioning error states.

Burden of Pulmonary Rifampicin-Resistant Tuberculosis in Kajiado, Kenya: An Observational Study.

Rifampicin resistance (RR) is a major challenge in the clinical management of tuberculosis (TB), but data on its prevalence are still sparse in many countries. Our study aimed at estimating the prevalence of RR-TB in Kajiado County, Kenya. Secondary objectives were to estimate the incidence of pulmonary TB in adults and the rate of HIV-TB coinfection. We conducted an observational study in the context of the ATI-TB Project, carried out in Kajiado. The project was based on an active-case-finding campaign implemented with the aid of village chiefs, traditional healers and community health volunteers. Diagnosis relied on Xpert MTB/RIF, including a mobile machine that could be used to cover areas where testing would otherwise be difficult. In sum, 3840 adults were screened for active TB during the campaign. RR cases among all TB diagnoses were 4.6%. The annual incidence of pulmonary TB among adults was 521 cases per 100,000 population. The rate of HIV coinfection was 22.2% among pulmonary TB diagnoses. The prevalence of RR-TB was four times that what could be inferred from official notifications in Kajiado, and higher than overall prevalence in Kenya. In addition, our estimate of incidence of pulmonary TB in adults in Kajiado significantly differed from cases notified in the same area. In contrast, the rate of HIV coinfection was in line with national and regional data. TB diagnostic capability must be strengthened in Kajiado to improve patients' management and public health interventions.

Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study.

Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 drinks for men per occasion) in young adults but need to be optimized for timing and content. Delivering just-in-time support messages in the hours prior to BDEs could improve intervention impact. We aimed to determine the feasibility of developing a machine learning (ML) model to accurately predict future, that is, same-day BDEs 1 to 6 hours prior BDEs, using smartphone sensor data and to identify the most informative phone sensor features associated with BDEs on weekends and weekdays to determine the key features that explain prediction model performance. We collected phone sensor data from 75 young adults (aged 21 to 25 years; mean 22.4, SD 1.9 years) with risky drinking behavior who reported their drinking behavior over 14 weeks. The participants in this secondary analysis were enrolled in a clinical trial. We developed ML models testing different algorithms (eg, extreme gradient boosting [XGBoost] and decision tree) to predict same-day BDEs (vs low-risk drinking events and non-drinking periods) using smartphone sensor data (eg, accelerometer and GPS). We tested various "prediction distance" time windows (more proximal: 1 hour; distant: 6 hours) from drinking onset. We also tested various analysis time windows (ie, the amount of data to be analyzed), ranging from 1 to 12 hours prior to drinking onset, because this determines the amount of data that needs to be stored on the phone to compute the model. Explainable artificial intelligence was used to explore interactions among the most informative phone sensor features contributing to the prediction of BDEs. The XGBoost model performed the best in predicting imminent same-day BDEs, with 95% accuracy on weekends and 94.3% accuracy on weekdays (F1-score=0.95 and 0.94, respectively). This XGBoost model needed 12 and 9 hours of phone sensor data at 3- and 6-hour prediction distance from the onset of drinking on weekends and weekdays, respectively, prior to predicting same-day BDEs. The most informative phone sensor features for BDE prediction were time (eg, time of day) and GPS-derived features, such as the radius of gyration (an indicator of travel). Interactions among key features (eg, time of day and GPS-derived features) contributed to the prediction of same-day BDEs. We demonstrated the feasibility and potential use of smartphone sensor data and ML for accurately predicting imminent (same-day) BDEs in young adults. The prediction model provides "windows of opportunity," and with the adoption of explainable artificial intelligence, we identified "key contributing features" to trigger just-in-time adaptive intervention prior to the onset of BDEs, which has the potential to reduce the likelihood of BDEs in young adults. ClinicalTrials.gov NCT02918565; https://clinicaltrials.gov/ct2/show/NCT02918565.

The use of mobile thermal imaging and machine learning technology for the detection of early surgical site infections

BackgroundSurgical Site Infections (SSI) yield subtle, early signs that are not readily identifiable. This study sought to develop a machine learning algorithm that could identify early SSIs based on thermal images. MethodsImages were taken of surgical incisions on 193 patients who underwent a variety of surgical procedures. Two neural network models were generated to detect SSIs, one using RGB images, and one incorporating thermal images. Accuracy and Jaccard Index were the primary metrics by which models were evaluated. ResultsOnly 5 patients in our cohort developed SSIs (2.8%). Models were instead generated to demarcate the wound site. The models had 89–92% accuracy in predicting pixel class. The Jaccard indices for the RGB and RGB ​+ ​Thermal models were 66% and 64%, respectively. ConclusionsAlthough the low infection rate precluded the ability of our models to identify surgical site infections, we were able to generate two models to successfully segment wounds. This proof-of-concept study demonstrates that computer vision has the potential to support future surgical applications.

Condition Monitoring of an Axial Piston Pump on a Mini Excavator

The focus of this paper is to show the process of developing a condition monitoring system for an axial piston pump mounted on a mini excavator. This work outlines some previous condition monitoring work on axial piston pumps but addresses the lack of research conducted on mobile hydraulics. The valve plate of the pump is chosen as a case study to demonstrate varying degrees of wear and damage to represent healthy and faulty pump conditions. The wear and damage of these valve plates is measured using an optical profilometer, and efficiency measurements were conducted to characterize the fault levels. Once the faults were characterized, the mini excavator was introduced and instrumented to demonstrate what parameters were being considered. Next, three duty cycles were introduced: controlled, digging, and different operator cycles. The controlled cycles are a very repeatable condition that eliminated the need of an operator. The digging cycle was more of a realistic cycle where an operator dug into a loose pile of soil. The different operator cycle is the same as the digging cycle, but a different operator was employed. The sensors that proved to be the most useful in detecting valve plate faults were the drain pressure, pump port pressures, engine speed, and pump displacement. Fault detectability accuracies of 100% were achievable under the controlled cycle utilizing the Fine KNN classification machine learning algorithm. The digging cycle could achieve a fault detection accuracy of 93.6% using the same algorithm and sensors. Finally, the cross-compatibility between a model trained under once cycle and using data from another cycle as an input was investigated. This study showed that a model trained under the controlled duty cycle does not give reliable and accurate fault detectability for data run in a digging cycle, below 60% accuracies. However, cross-compatibility may be achievable if more extreme faults are present. This work concluded by recommending a diagnostic function for mobile machines to perform a preprogrammed operation to reliably and accurately detect pump faults.

A Mode-Driven Control Strategy to Reduce Electric Drive Peak Power of Hybrid Wheel Loader Propulsion System

Powertrain electrification is an effective solution to reducing emission of mobile machines. In this paper, an electric-hydraulic hybrid drive for wheel loader propulsion system is studied, where it combines high energy density of electric drive and high power density of hydraulic drive. The braking energy is captured hydraulically and stored in an accumulator. This reduces the peak power of electric drive due to large vehicle acceleration and deceleration, making the peak electric power reduction possible. The energy management strategy is essential for hybrid powertrain since it determines how efficiently the power is transferred between different sources. The existing control strategies such as thermostat strategy and power follower strategy are largely dependent on the cycles and it is challenging to achieve satisfactory results when the cycle changes. The wheel loader loading cycle is a characterized cycle consisting of multiple predictable operating modes, giving some opportunities to develop a mode-driven strategy. In this paper a mode-driven control strategy for electric-hydraulic hybrid wheel loader is proposed to achieve peak electric power reduction over power follower strategy (baseline strategy), without sacrificing electric energy use and vehicle operation hours. In the strategy four modes are defined and the expected hydraulic SOC (system pressure) profile in each mode is scheduled so that it can provide power assist and capture regenerative braking energy as much as possible. By setting the pressure profile, the hydraulic charge sustaining is guaranteed. The system operation with mode-driven strategy is compared with power follower strategy through simulation studies. Results show that peak powers of battery and electric motor with mode-driven strategy are reduced by nearly 30% compared to power follower strategy. Results also show that the vehicle operation hour has been slightly increased by 3% by using mode-driven strategy. These results verify the effectiveness of proposed strategy for electric-hydraulic hybrid wheel loader.

The Future of Mine Safety: A Comprehensive Review of Anti-Collision Systems Based on Computer Vision in Underground Mines.

Underground mining operations present critical safety hazards due to limited visibility and blind areas, which can lead to collisions between mobile machines and vehicles or persons, causing accidents and fatalities. This paper aims to survey the existing literature on anti-collision systems based on computer vision for pedestrian detection in underground mines, categorize them based on the types of sensors used, and evaluate their effectiveness in deep underground environments. A systematic review of the literature was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify relevant research work on anti-collision systems for underground mining. The selected studies were analyzed and categorized based on the types of sensors used and their advantages and limitations in deep underground environments. This study provides an overview of the anti-collision systems used in underground mining, including cameras and lidar sensors, and their effectiveness in detecting pedestrians in deep underground environments. Anti-collision systems based on computer vision are effective in reducing accidents and fatalities in underground mining operations. However, their performance is influenced by factors, such as lighting conditions, sensor placement, and sensor range. The findings of this study have significant implications for the mining industry and could help improve safety in underground mining operations. This review and analysis of existing anti-collision systems can guide mining companies in selecting the most suitable system for their specific needs, ultimately reducing the risk of accidents and fatalities.

Predicting highly dynamic traffic noise using rotating mobile monitoring and machine learning method

Traffic noise, characterized by its highly fluctuating nature, is the second biggest environmental problem in the world. Highly dynamic noise maps are indispensable for managing traffic noise pollution, but two key difficulties exist in generating these maps: the lack of large amounts of fine-scale noise monitoring data and the ability to predict noise levels in the absence of noise monitoring data. This study proposed a new noise monitoring method, the Rotating Mobile Monitoring method, that combines the advantages of stationary and mobile monitoring methods and expands the spatial extent and temporal resolution of noise data. A monitoring campaign was conducted in the Haidian District of Beijing, covering 54.79 km of roads and a total area of 22.15 km2, and gathered 18,213 A-weighted equivalent noise (LAeq) measurements at 1-s intervals from 152 stationary sampling sites. Additionally, street view images, meteorological data and built environment data were collected from all roads and stationary sites. Using computer vision and GIS analysis tools, 49 predictor variables were measured in four categories, including microscopic traffic composition, street form, land use and meteorology. Six machine learning models and linear regression models were trained to predict LAeq, with random forest performing the best (R2 = 0.72, RMSE = 3.28 dB), followed by K-nearest neighbors regression (R2 = 0.66, RMSE = 3.43 dB). The optimal random forest model identified distance to the major road, tree view index, and the maximum field of view index of cars in the last 3 s as the top three contributors. Finally, the model was applied to generate a 9-day traffic noise map of the study area at both the point and street levels. The study is easily replicable and can be extended to a larger spatial scale to obtain highly dynamic noise maps.

An exploratory study of software engineering in heavy-duty mobile machine automation

As the amount and complexity of software for automating heavy-duty mobile machinery is increasing, software engineering in this domain is becoming more important. To characterize the industry’s current state of software engineering and its issues to guide future research, we performed an empirical exploratory study. We interviewed 16 software engineering professionals from 13 different companies conducting business in heavy-duty mobile machines and their automation. The interviews were analyzed qualitatively, and quantification of the analysis results is presented. We first create an overview of software engineering in the heavy-duty mobile machinery industry. We then identify problem areas affecting software development and discuss some of the possible solutions found in literature. Our findings indicate that the major problem areas faced in the industry that require more research are its digital transformation, autonomous machine functional safety, low availability of workforce for developing software for robotic mobile machines and the lack of established software standards.

Optimizing internal transport in terms of technical parameters in a mining company

The research scientific article summarizes the arguments within the scientific discussion on the issue of internal transport and machine park in mining companies. The machine park in mining companies is base of efective mining processes and optimal results of mining. The machine park in the mining company introduces a combination of stagnant and active equipment in the process of mining. The goal of this article is to reflect the innovative approach in the creation of internal transport by machine park in the mining company with the aim of increasing the reliability, safety, failures, and efficiency of mining equipment. The entity of the investigation was the selected mining company in Slovakia focused on limestone mining. Approach of research were focused on using comparative analysis of the technical parameters of mining machines and their technical performance. The relevance of the decision of this scientific research was connected with the innovation of a mining machine park focused on mobile machines with a combination of stationary machines. The article presents the results pointing to significant improvements in the machine park of the mining company. In this paper we suggest two alternatives (two models that monitor the factors as quality, costs, safety, reliability, and optimization of company resources, which are important for the optimal arrangement of equipments) of the composition of the machine park for a selected mining company with Mobil crusher Sandvik QJ341+, Mobile sorter McCloskey S130, Wheel loader CAT 972 M, and tracked excavator CAT 336F and second alternative Mobil crusher Hartl 12/65J, Mobile sorter Anaconda SR514, Wheel loader CAT 966 M and tracked excavator CAT 329E. The results of two alternatives of the machine park in a mining company can be recommendations for other mining companies in the area of building the machine park for mining processes and its optimization.

Special Vehicles and Equipment in Fire Operations Used in Different Regions

Different parts of the world have different disasters or fire and technical rescue events. Economic, infrastructural and natural environmental differences cause great variances. The nations employ various organisations, depending on the available possibilities, with diverse technical solutions to eliminate the sources of fire, disaster, incident dangers. In case of fires and technical rescue occasions that have already occurred and require intervention and pose a direct threat to human life, physical integrity and property, these tasks are typically performed by fire brigade organisations. These organisations also carry out more or less extensive activities in their areas of responsibility; however, national solutions may involve the use of firefighting vehicles and equipment that differ significantly from one neighbouring country to another.The article presents the general firefighting vehicles used internationally and in Hungary and their characteristic capabilities, together with the widely used technical equipment and devices worldwide, built-in or mobile firefighting machines. In this paper a wide-ranging inspection of firefighting vehicles are demonstrated, and the description of the firefighting equipment can be interpreted together with the carrier vehicles. By special fire engines, the author here means vehicles that are on standby in each country, but only in small numbers and only alerted a few times a year. An important aspect in this category is that the design and equipment of these firefighting vehicles are planned to eliminate special incident situations that are unlikely to occur, but pose a great threat to human life, physical integrity or the property of the country and citizens, even in the short term. The article focuses also on these less frequent, more specialised firefighting vehicles and technical devices, with a description of their typical deployment conditions.When drawing the conclusions, the ideas and solutions found during the international outlook worthy of Hungarian adaptation are presented. Here the national adaptation possibilities of each international, special firefighting vehicle, the advantages they can provide and the expected disadvantages are examined. The content of the article is a general summary that fills a gap due to the small amount of international literature, which can also be used during research, investigation and education in this research field.

Research on the evaluation method of agricultural intelligent robot design solutions.

At present, agricultural robots are produced in large quantities and used in agricultural planting, and the traditional agricultural model is gradually shifting to rely on the Internet of Things and sensors to accurately detect crop growth information. The scientific and rational design of agricultural robots plays a huge role in planting and production efficiency, however, the factors affecting their design are complex and ambiguous, so it is necessary to use a rational evaluation system to make a preferential decision among multiple design options. In order to reduce the subjectivity and blindness of program selection in the process of agricultural robot design, make the decision more objective and reasonable, and thus enhance the practicality and scientificity of the program, a new comprehensive evaluation method based on user requirements is proposed. First, after researching and interviewing users and farming operations, obtaining raw information on requirements, using the Kano model to classify the requirements and establishing an evaluation index system. Secondly, the combination of hierarchical analysis(AHP) and entropy weighting method is used to assign weights to the evaluation index system, calculate the weight value and importance ranking of each index, and carry out various program designs based on the ranking. Finally, the VIKOR method was applied to evaluate and rank the design solutions. The new evaluation method can better complete the preferential decision of the agricultural robot design scheme and get a more perfect design scheme, which reduces the influence of human subjective thinking in the decision-making process. The method not only corrects the traditional evaluation method, but also effectively improves the accuracy and comprehensiveness of the design evaluation process. It also provides a reference for designers to preferably select design solutions and promotes the development of small mobile machines in the context of smart agriculture.

ПРЕДЕЛ ГЛУБИННОЙ КОНТАКТНОЙ ВЫНОСЛИВОСТИ ПОВЕРХНОСТНО-УПРОЧНЕННЫХ ЗУБЧАТЫХ КОЛЕС

The reliability and durability of gears in most cases depend on the contact endurance of the material of the parts. Analysis of the stress state of surface-hardened gears has shown that under contact loading, the source of primary damage can be located both on the surface and in the depth of the hardened layer. The greatest danger is deep destruction. To prevent deep pitting, it is necessary to ensure a sufficient amount of the maximum allowable stresses in the depth of the layer, exceeding the effective equivalent stresses. The value of the allowable stresses is determined by the value of the contact endurance limit, depending on the mechanical properties of the material. The article discusses the results of experimental studies of the contact fatigue resistance of transmission gears of energy-saturated mobile machines made of cemented steel grades. It is established that the limit of contact endurance is related to the hardness of the material, determined by the Rockwell method, by a linear dependence. This dependence is taken as a basis for a comparative analysis of the calculated lifetime and experimentally established in the process of bench tests of gears. Taking into account the nonlinear nature of the ratio of hardness values determined by the Rockwell and Vickers scales, the relationship is obtained between the limit of deep contact endurance and the value of hardness measured by the Vickers scale with a load of 1.961 N. It is shown that the results of comparison of calculated and experimental data are greatly influenced by the limiting state of the deep contact pitting of the teeth of the tested gears. Statistical processing of calculated and experimental data was carried out. It is found that with the identity of the limiting state of the active surfaces of the gear teeth, the results of the lifetime calculation correspond to experimental data with a correlation coefficient of 0.964 and an average standard deviation of 13 %. The obtained expression for calculating the limit of deep contact endurance contributes to increasing the reliability of calculating the lifetime of gears provided by the resistance of deep contact fatigue.

A novel hydraulic load-sensing control solution to working functions of mobile machine

The hydraulic load-sensing system is a more efficient system solution than constant pressure system in mobile machine. However, the overall system efficiency for multiple actuators is still low since this centralized hydraulic control system only adapts to the highest load pressure. Therefore, in this article, a distributed hydraulic control system where each function is powered by a hydraulic free piston engine is proposed. Compared with centralized solution, it is more efficient since the power of hydraulic free piston engine is tailored to each function. By integrating small inertia linear engine and pump piston in one unit, the hydraulic free piston engine is an efficient and fast response hydraulic power source. Since the hydraulic free piston engine is essentially a digital hydraulic power source, a digital load-sensing control is proposed in this study. The system architecture, modeling, controller design and performance of the digital hydraulic load-sensing control system are presented. A test bench is developed to verify the system feasibility and performance.

Yüksek Hızlı Tren Hatlarında Rayların Yeniden Profillendirilmesinde Döner Taşlama Metodunun Uygulanabilirliği: Ankara-Eskişehir Hattı Örneği

Demir yolu rayları uzun süreli ve emniyetli kullanım için bakım planlaması yapılması gereken en önemli üst yapı elemanlarındandır. Rayların optimum profile sahip olması, sürüş konforunun arttırılması ve ray ömrünün ekonomik olarak uzatılabilmesi için rayların belirli periyotlarla yeniden profillendirme yapılması gereklidir. Bu çalışmada Yüksek Hızlı Tren Hatlarında rayların yeniden profillendirme çalışmalarında mobil ray taşlama makinesi ile döner taşlama metodunun uygulanabilirliği Ankara-Eskişehir hattı özelinde araştırılmıştır. Çalışmada öncelikle yeniden profillendirme stratejileri ve metotları ele alınmıştır. Çalışma öncesi hattın durumu incelenerek ön ölçümler alınmıştır. Değerlendirme sonucu belirlenen tolerans ve kalite gereksinimlerine göre döner taşlama uygulaması yapılmış olup alınan ölçümler incelenmiştir. Çalışma sonu yapılan değerlendirmede söz konusu metodun Yüksek hızlı Tren Hatlarında uygulanabilir olduğu görülmüştür. Bahse konu çalışmanın literatürde yeteri kadar yer almaması karşısında Yüksek Hızlı Tren Hatlarında yeniden profillendirme stratejilerinin oluşturulmasına katkı sağlayacağı düşünülmektedir.

Experimental Investigations of Partially Valve-, Partially Displacement-Controlled Electrified Telehandler Implements

The next generation of electrified heavy-duty mobile machines (HDMMs) requires more efficient hydraulic systems—to save energy and to compensate for the limited capacities of available mobile electric energy sources. This study is experimentally demonstrating the functionality, dynamic performance, and efficiency of such a more efficient but also cost-effective system. The demonstrator is a conventional nine-tonne telehandler that has been transformed by replacing the diesel engine with an electric machine (EM) and changing the boom function from valve to displacement control. Since the system control and the resulting dynamics are not trivial, key aspects of it are explained in the paper. With the functional system, achievable consumption reductions could be obtained by measuring five different representative work cycles repeatedly and comparing the average consumption values to the consumption of a purely valve-controlled but also electrified reference version. In four of five cycles, an average reduction of 21–31% was achieved, which confirms the simulation results from previous studies and the effectiveness of the concept. However, one cycle—characterized by serial movements and longer breaks of the boom movement—showed a reduction of only 3% and that the effectiveness of the concept can also be lower in certain cases that depend mainly on the operator.

An optimal-pressure control strategy for hydrostatic wheel loader

Mobile machines consume a large amount of fossil fuel each year; therefore, it is critical to reduce their fuel consumption. A wheel loader is a typical mobile machine of great research interest. Most compact wheel loaders are usually equipped with hydrostatic transmissions. To reduce the fuel consumption of the powertrain, a min-rpm strategy where the engine speed is maintained as low as possible is usually applied. It is a rule-based control strategy, however, cannot achieve minimum engine fuel consumption. Therefore, an optimal-pressure control strategy is proposed for hydrostatic wheel loaders, where the minimum fuel consumption is achieved by selecting optimal system pressure at each time step. To show the advantages of the proposed strategy, the powertrain operation and engine fuel consumption are compared with the min-rpm strategy through simulation studies. To further verify the effectiveness of the proposed strategy, comparisons are conducted using three loading cycles (30s, 57s and 90s). Results show the engine fuel savings in three loading cycles, with fuel saving up to 27% in the 90s loading cycle.