Energy consumption ratio and heat output comparison of battery electric vehicles and diesel-powered machines: Results of field trials in European Union and Canadian underground mines

The use of diesel engine powered equipment in underground mines across the globe has been increased considerably in the recent past to enhance the productivity and safety standards. However, the extensive use of diesel engine powered equipment caused severe health hazards because of the exposure to diesel particulate matter (DPM) and toxic gases discharged from the exhausts of these equipment. NIOH and IARC, USA, reported diesel exhausts including DPM are suspected as human carcinogen. The number of diesel equipment deployed in Indian underground mine has also increased exponentially, which resulted into significant level of DPM exposure. There have not been any comprehensive study on the exposure of DPM as well as any stipulation in the current mine safety legislation in India so far except a guideline from DGMS. The concentration of DPM depends upon many factors such as ventilation, engine designs, maintenance, types of fuel, condition of roadways, exhaust treatment arrangements etc. In the present study, field investigations were accomplished in the three underground metalliferous mines. Different parameters like quantity of air, power and life of engine and gradient of roadways were taken as independent variables to predict the concentration of DPM. Multivariate regression analysis was carried out for establishing the relation between DPM and these independent parameters, and a significant empirical equation has been derived. Thereafter, DPM values were measured by Airtec DPM monitoring instrument and the values predicted from the multivariate regression model and measured values from the instrument were validated through chi square test.

A major part of the European Union’s (EU) project Sustainable Intelligent Mining System (SIMS) is investigating the development of diesel-free/carbon–neutral underground mines in order to ensure sustainable underground mining in the future. Replacing diesel machines with electric vehicles in underground hard rock mines has been widely acknowledged by the mining industry worldwide as a critical step to improve working conditions by reducing diesel exhaust–related contaminants, to reduce mine ventilation electrical power cost by reducing mine airflow quantity, and to reduce mine greenhouse gas emissions. All of these are major requirements to achieve sustainable future underground mining practices. A field trial of Epiroc’s 2nd generation of Battery Electric Vehicles (BEVs) at Agnico Eagle Finland’s Kittilä mine was conducted during 2019–2020. Vehicles tested were MT42 mine truck, ST14 Load-Haul-Dump (LHD), and Boomer E2 jumbo drill rig. This paper outlines the improvement of the working conditions observed in the field trial, and the opinions of the mine personnel at Kittilä mine on using BEVs instead of diesel machines. Measurements of atmospheric contaminants and air temperatures taken during the field trial clearly demonstrated a significant improvement of working conditions when BEVs were operating as opposed to diesel machines. This field observation was supported by the opinion of the majority of the Kittilä mine workers. However, some remaining concerns must be addressed before BEVs can replace diesel machines.

Diesel-powered load haul dump machines have been the backbone of underground mining loading and hauling operations for over six decades. However, as mines get deeper, and regulations become more rigorous, the adoption of battery electric vehicles (BEVs) has the potential to enhance energy efficiency and provide a healthier environment for miners. Electric engines have a significantly higher energy efficiency and produce no exhaust gases or diesel particulate matter. The use of BEVs in underground operations introduces additional factors to consider, such as battery swapping and the required number of batteries, swapping, and charging stations. This study conducted a discrete event simulation using Arena simulation software with a focus on queueing and its relationship to different numbers of machines, batteries, and swapping times in an underground block cave mine. The results suggest that when there are six, eight, or 12 LHDs and four swapping and charging stations with an unlimited number of batteries, the queueing time to swap the batteries remains minimal. In scenarios with eight LHDs and a limited number of batteries, depending on the battery swapping time, 2–2.5 batteries per machine are required to achieve maximum production with minimal queueing. However, when there are too few batteries, queueing becomes significant. Moreover, when the number of working groups (machines going for a battery swap around the same time) is less than the ratio between the battery operational time and the swapping time, the queueing remains low.

Recent and ongoing developments in electric battery technology has provided the opportunity to consider Battery Electric Vehicle (BEV) technologies for primary and secondary mobile equipment in underground mines at planning and operational stages. At the planning stage of new or upgraded mines, the option to consider BEV equipment appears to provide some capital and operating cost savings on the peripheral systems (development, production, ventilation infrastructure, main fans, refrigeration systems, etc.). However, under the current cost models for the BEV equipment and other limiting conditions in the mine such as minimum air velocity, the conversion to BEVs may not provide significant cost savings and, more likely, may be more expensive than conventional diesel-powered cases. This paper presents a short study that compares several BEV cases relative to diesel-powered base cases and the conclusion that the BEV cases generally result in higher overall costs, which happen to remain within the diesel-based cost estimate confidence. In the current situation, cost may not be a driving factor to select a BEV option, but the costs are competitive enough that other factors such as environmental considerations may be the driving factor to select BEV opportunities.

The real-time positioning and information feedback of the underground workers and equipment play a key role to achieve the safety digital mine because of the complex work scenario of non-coal mining in China, especially the injuries and collisions caused by the alternate operation between operators and vehicle equipment in underground mining. There are relevant requirements in the safety hazard called “six systems” which includes the personnel positioning, communication links, monitoring and controls, but the results are not prominent after investment and construction for its ambiguous definition of the scope of the standard and reasonable defects. This paper aims to study the application of Ultra-Wide Band (UWB) in underground personnel positioning, by comparing and analyzing traditional underground positioning technology. Combined with 3D scanning three-dimensional modeling technology, the UWB signal can also be used to construct 3D digital interactive underground map to enhance the accuracy of positioning information and the intuitiveness of monitoring perspective in control center. The framework of a 3D dynamic comprehensive prevention and control system (hereinafter referred to as 3D-DCPCS) based on UWB positioning in non-coal mining is proposed, depending on the supports of these technologies. Among them, UWB is a non-carrier communication technology. Its signal has strong anti-jamming performance and high transmission rate, which can improve the accuracy of real-time underground positioning. And the interactive 3D modeling technology is a new type of media, which can enhance the user's sense of reality through the transformation of pilot and perspective view. This framework of 3D-DCPCS is designed to have some specific modules, including user identity management module, underground three-dimensional map module, 3D interactive monitoring window module, underground risk assessment module, anti-collision module of personnel and vehicle, safety information statistical management module and other functional modules. This system can achieve the functions of different users' identity role management, underground safety risk identification and grade assessment, map of underground risk area, precise positioning and dynamic control of underground operators’ vehicles, remote control and communication of underground equipment and personnel and 3D dynamic monitoring of underground operation scenarios. Meanwhile, these functions can be targeted to update through a period of security information statistical feedback. The system can be applied to comprehensive prevention and control monitoring of correlative complicated operating environment, by relating to the mentioned framework and specific situation of non-coal mine. It would be of practical values to improve the safety of underground personnel, standard safe operation of mechanical equipment and man-machine orderly cooperation in underground non-coal mine if it would be built and put into application.

DPM simulation in an underground entry: Comparison between particle and species models

This study assesses the potential use of Battery Electric Vehicles (BEVs) in place of conventional Internal Combustion Engine Vehicles (ICEVs) at the household level. The assumption is that travelers will change their travel pattern and behaviors to overcome the limitations of BEVs, such as limited range and long charging times. We generate four scenarios representing different levels of traveler adaptation and apply them to sample households from the California Statewide Travel Survey. These scenarios are defined to assess the effect of changes when one of household vehicles is replaced with a BEV, in particular scheduling flexibilities and intra-household interactions on activity and vehicle allocation. These changes in their travel behavior are simulated by the Household Activity Pattern Problem with Electric vehicle (HAPPEV) model which is a variant of the well-known Household Activity Pattern Problem (HAPP). A sequential activity allocation and insertion heuristic is developed to solve the HAPPEV. In addition to testing different levels of scheduling flexibility and travel behavior, we conduct a scenario analysis on activity-travel pattern flexibility, fuel/electricity prices, BEV range and charging location availability. The results show that (1) BEVs when used as household secondary vehicles provide better environmental/economic benefits, (2) work place charging improves activity-travel pattern feasibility and travel disutility (3) electricity/fuel price impact is not significant, (4) current BEV incentives should be continued, and (5) BEV range is still a significant factor.

The use of battery electric vehicles (BEVs) is increasing in household vehicle fleets and carsharing systems. However, the car rental industry has only timidly adopted this technology. This paper presents a mathematical model for optimal trip assignment of electric and conventional vehicles (CVs) in a regional car rental company. The model was built by using a time–space network, and all BEV charging constraints were part of the formulation. The model needed to satisfy all existing demand, and two objectives were considered: maximizing the number of trips by BEVs and maximizing profit. The model was applied to the Portuguese central region, and the conclusion was that BEVs were considerably less profitable than CVs, because BEVs were less useful for intercity trips. Even if the costs and revenues were the same as for CVs, BEVs were still outperformed by CVs because the recharging time was a drawback in some trips. However, BEVs do not necessarily imply a financial loss for the companies, and a cost–benefit analysis may well prove the existence of considerable profit from including these vehicles, because marketing and environmental effects are not being valued at the moment.

Well-to-wheel water footprints of conventional versus electric vehicles in the United States: A state-based comparative analysis

There are increasing interests in improving public transportation systems. One of the proposed strategies for this improvement is the use of Battery Electric Vehicles (BEVs). This approach leads to a new challenge as the BEVs’ routing is exposed to the traditional routing problems of conventional vehicles, as well as the particular requirements of the electrical technologies of BEVs. Examples of BEVs’ routing problems include the autonomy, battery degradation, and charge process. This work presents a differential evolution algorithm for solving an electric vehicle routing problem (EVRP). The formulation of the EVRP to be solved is based on a scheme to coordinate the BEVs’ routing and recharge scheduling, considering operation and battery degradation costs. A model based on the longitudinal dynamics equation of motion estimates the energy consumption of each BEV. A case study, consisting of an airport shuttle service scenario, is used to illustrate the proposed methodology. For this transport service, the BEV energy consumption is estimated based on experimentally measured driving patterns.

The transition from conventional vehicles to battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) is expected to significantly contribute to reducing greenhouse gas emissions in the transportation sector. However, the effectiveness of this transition depends on how BEVs and PHEVs are used compared to internal combustion engine vehicles (ICEVs) and hybrid electric vehicles (HEVs). This paper analyzes data from the 2018–2020 Dutch National Travel Surveys to assess travel behavior of single-car households across four vehicle types: ICEVs, HEVs, PHEVs, and BEVs. Specifically, we focus on daily trip frequency and vehicle kilometers traveled (VKT) for both commuting and non-commuting purposes, while examining how these vehicle usage patterns correlate with vehicle attributes, socioeconomic and demographic factors, and the built environment. Our descriptive analysis shows that BEV and PHEV users have significantly longer daily VKT for both commuting and non-commuting travel compared to ICEV and HEV users. The model results reveal that after controlloing for various factors, BEVs are associated with shorter daily VKT for non-commuting travel compared to other powertrain types, while a pattern not observed for commuting travel. Notably, there is no evidence of a rebound effect linked to the use of BEV and PHEV powertrains. Additionally, leased or company vehicles, regardless of powertrain type, are associated with higher daily VKT and a higher probability of trip-making compared to privately owned vehicles. This higher daily VKT observed for BEV and PHEV users is largely due to the higher prevalence of their vehicles being leased or company cars, rather than the powertrain type itself.

PurposeThis paper aims to forecast the availability of used but operational electric vehicle (EV) batteries to integrate them into a circular economy concept of EVs' end-of-life (EOL) phase. Since EVs currently on the roads will become obsolete after 2030, this study focuses on the 2030–2040 period and links future renewable electricity production with the potential for storing it into used EVs' batteries. Even though battery capacity decreases by 80% or less, these batteries will remain operational and can still be seen as a valuable solution for storing peaks of renewable energy production beyond EV EOL.Design/methodology/approachStoring renewable electricity is gaining as much attention as increasing its production and share. However, storing it in new batteries can be expensive as well as material and energy-intensive; therefore, existing capacities should be considered. The use of battery electric vehicles (BEVs) is among the most exciting concepts on how to achieve it. Since reduced battery capacity decreases car manufacturers' interest in battery reuse and recycling is environmentally hazardous, these batteries should be integrated into the future electricity storage system. Extending the life cycle of batteries from EVs beyond the EV's life cycle is identified as a potential solution for both BEVEOL and electricity storage.FindingsResults revealed a rise of photovoltaic (PV) solar power plants and an increasing number of EVs EOL that will have to be considered. It was forecasted that 6.27–7.22% of electricity from PV systems in scenario A (if EV lifetime is predicted to be 20 years) and 18.82–21.68% of electricity from PV systems in scenario B (if EV lifetime is predicted to be 20 years) could be stored in batteries. Storing electricity in EV batteries beyond EV EOL would significantly decrease the need for raw materials, increase energy system and EV sustainability performance simultaneously and enable leaner and more efficient electricity production and distribution network.Practical implicationsStoring electricity in used batteries would significantly decrease the need for primary materials as well as optimizing lean and efficient electricity production network.Originality/valueEnergy storage is one of the priorities of energy companies but can be expensive as well as material and energy-intensive. The use of BEV is among the most interesting concepts on how to achieve it, but they are considered only when in the use phase as vehicle to grid (V2G) concept. Because reduced battery capacity decreases the interest of car manufacturers to reuse batteries and recycling is environmentally risky, these batteries should be used for storing, especially renewable electricity peaks. Extending the life cycle of batteries beyond the EV's life cycle is identified as a potential solution for both BEV EOL and energy system sustainability, enabling more efficient energy management performance. The idea itself along with forecasting its potential is the main novelty of this paper.

Ergonomic Guidelines for Designing and Maintaining Underground Coal Mining Equipment

Electric vehicles (EVs) are related to various symbols, identities, and beliefs, and are considered much more than a means of transport. Existing literature has investigated the contribution of financial incentives and various psychological factors to the EV purchase decision. However, few studies investigate the effect of psychological factors on post-purchase EV use. We emphasize that the ultimate success in the widespread acceptance of EVs depends acutely on their post-purchase use. This study empirically addressed the effect of perceived attributes related to EVs, perceived accidental risk, self-environmental identity, and general environmental beliefs on the annual vehicle kilometres travelled (VKT) by battery electric vehicle (BEV) owners. Drivers who own only BEVs and those who own both internal combustion engine vehicles and BEVs were compared to identify the role of psychological factors in BEV use in a Norwegian sample. The dataset was analysed using an ordinary least squared regression model. The socio-demographic characteristics and mobility patterns of the two groups are investigated. The findings indicate that economic aspects are positively associated with annual VKT for sole BEV owners, whereas perceived operating barriers have a negative effect on annual VKT for the other group. The results suggest the inclusion of psychological factors in predicting a more precise model of the induced travel demand of EV owners, which, in turn, is necessary to estimate energy demand accurately and to take steps in establishing the required infrastructure.

Numerical study on DPM dispersion and distribution in an underground development face based on dynamic mesh