Simulation study comparing battery swapping to fast charging for electric underground mine haul trucks

Diesel-based vehicles are prevalent in underground mines all over the world. These mines require extensive ventilation due to toxic gas and heat emissions from diesel engines. Thus, mining is a prime candidate for electrification. This research performs an investigation of battery swapping and fast charging for purely battery-powered underground haul trucks, focusing on productivity and cost. The results show that battery swapping with the largest feasible battery size (348 kWh pack) gives 2.8% more productivity than the best fast charging option (600 kW charge rate with 228 kWh battery pack), but costs 65% more over the five-year time span considered. With a 228 kWh pack, battery swapping gives equal productivity as fast charging, but costs 48% more.

The feasibility of heavy battery electric trucks

Currently, the focus of integrated energy system scheduling research is the multi-objective’s optimized operational strategies that take into account the economic benefits, carbon emissions, and new energy consumption rates of such systems. The integration of electric trucks with battery charging and swapping capabilities, along with their corresponding battery swapping stations, into an integrated energy system can not only optimize system operation, but also reduce investment costs associated with building energy storage equipment. This study first constructs an operational model for the electric trucks, as well as an electric truck battery swapping station, of the flexible charging and discharging; then, an optimized scheduling model of an integrated energy system is proposed, including an electric truck battery swapping station and using stepped carbon trading. On the basis of meeting the charging and battery swapping needs of electric trucks and coordinating the system’s electrical, thermal and cooling energies, the goal of the optimized scheduling model is to reduce the system’s carbon emissions, improve its economics, and optimize its ability to absorb new energy. Finally, a simulation model of the integrated energy system including an electric truck battery swapping station is built on the MATLAB platform, and commercial software package CPLEX is used to solve the model. In the calculation example, compared to the integrated energy system of disorderly charging and battery swapping of electric trucks and electric truck battery swapping stations, the proposed optimized model of the integrated energy system with the flexible charging and discharging of electric trucks and electric truck battery swapping stations reduces the operating costs by CNY 819, reduces carbon emissions by 414 kg, improves the utilization rate of wind and solar power by 0.3%, and fully utilizes wind and photovoltaic power. Therefore, the rational dispatching of the electric trucks and their battery swapping stations with flexible charging and discharging mentioned in this article can effectively optimize system operations.

State of health estimation for fast-charging lithium-ion battery based on incremental capacity analysis

The dynamic conditions and internal states of portable energy storage system (PESS), such as temperature, electricity price, state of charge (SOC), and state of health (SOH), significantly impact battery degradation. Current decision-making models for PESS operation often oversimplify the modeling of battery degradation. To address this, we introduce an environment-adaptive online learning framework that effectively integrates deep neural networks and reinforcement learning to exploit and explore external environments (i.e., electricity prices and temperature) and internal dynamics (i.e., battery degradation), providing decision support for PESS operation. This framework dynamically updates battery degradation and decision-making models in real-time, enhancing adaptive responses to external changes. Specifically, we developed a neural network based on porous electrode theory that considers multi-physical factors, such as charging power, initial and terminal SOC, SOH, and temperature to accurately assess battery degradation. This network is embedded within a deep reinforcement learning algorithm, enabling real-time, adaptive decision-making for PESS amidst varying environmental conditions. Furthermore, to navigate complex operational environments, a fine-tuning mechanism is incorporated into the degradation neural network. Application of this framework to the energy arbitrage of PESS in the California power grid demonstrates an average benefit increase of 37% compared to traditional degradation assessment models. Note to Practitioners—In this work, we develop a novel approach to addressing the critical issue of battery degradation in PESS. Existing models often oversimplify degradation, hindering accurate assessments of performance and lifespan projections. More recently, learning-based algorithms have demonstrated outstanding performance in both battery degradation modeling and real-time decision-making. In this sense, we introduce a sophisticated neural network model grounded in a porous electrode model. This model considers multi-physics factors involving charging/discharging power, initial and terminal SOC, SOH, and temperature. Complementing this, we integrate the aforementioned model into an online learning framework, enabling real-time decision-making for PESS. Furthermore, to tackle the challenges of complex operating environments, a fine-tuning mechanism is incorporated into the battery degradation neural network. We validate the effectiveness of the proposed methods through an energy arbitrage application of PESS and also reveal its potential for on-demand applications in energy and transportation systems. This note anticipates a positive impact on PESS management and contributes significantly to the evolution of energy storage systems, offering practitioners invaluable decision support for commercial applications involving battery sharing, trading, and renting.

Electric vehicle (EV) fast charging systems are rapidly evolving to meet the demands of a growing electric mobility landscape. This paper provides a comprehensive overview of various fast charging techniques, advanced infrastructure, control strategies, and emerging challenges and future trends in EV fast charging. It discusses various fast charging techniques, including inductive charging, ultra-fast charging (UFC), DC fast charging (DCFC), Tesla Superchargers, bidirectional charging integration, and battery swapping, analysing their advantages and limitations. Advanced infrastructure for DC fast charging is explored, covering charging standards, connector types, communication protocols, power levels, and charging modes control strategies. Electric vehicle battery chargers are categorized into on-board and off-board systems, with detailed functionalities provided. The status of DC fast charging station DC-DC converters classification is presented, emphasizing their role in optimizing charging efficiency. Control strategies for EV systems are analysed, focusing on effective charging management while ensuring safety and performance. Challenges and future trends in EV fast charging are thoroughly explored, highlighting infrastructure limitations, standardization efforts, battery technology advancements, and energy optimization through smart grid solutions and bidirectional chargers. The paper advocates for global collaboration to establish universal standards and interoperability among charging systems to facilitate widespread EV adoption. Future research areas include faster charging, infrastructure improvements, standardization, and energy optimization. Encouragement is given for advancements in battery technology, wireless charging, battery swapping, and user experience enhancement to further advance the EV fast charging ecosystem. In summary, this paper offers valuable insights into the current state, challenges, and future directions of EV fast charging, providing a comprehensive examination of technological advancements and emerging trends in the field.

As the electrification of heavy-duty trucks accelerates, conventional charging methods face challenges, including long charging durations and reduced transportation efficiency. This paper compares and evaluates various charging methods for electric heavy-duty trucks (EHDTs), including slow charging, fast charging, battery swapping, and electric roads, from both technological and economic perspectives. A case study in a harbor setting further examines the cost and efficiency implications of a 22 kW slow charger, a 150 kW fast charger, and battery swapping (the swappable battery is charged with 150 kW). The analysis provides insights into selecting the most suitable charging solution by assessing annual charging costs, truck and infrastructure cost amortization, and downtime across different scenarios. The findings of this paper indicate that slow charging is cost-effective in low-demand operations but becomes impractical as operational demand increases, leading to excessive downtime exceeding 37,000 h annually in high-demand scenarios. Fast charging significantly reduces downtime but requires higher infrastructure investment and charging costs. Battery swapping minimizes downtime to less than 300 h annually in high-demand scenarios, and, despite a higher initial infrastructure cost, it emerges as the most cost-effective option over five years for medium- and high-utilization fleets, with a total cost of approximately €1.67 million in the studied harbor case. Thus, selecting a suitable charging solution depends on operational priorities, such as minimizing cost or maximizing fleet availability within a specific use-case context.

The US Advanced Battery Consortium goals for 2023 call for low cost, fast charge, electric vehicle (EV) batteries with a 15-minute charge time at 80% pack capacity.[1] This milestone would have a huge impact on the universal market adoption of EVs. Unfortunately, fast charging at rates above 1C, defined as the current at which it takes one hour for a battery to charge/discharge to full capacity, imposes extensive deleterious effects on current lithium-ion battery (LiB) chemistries. High charging rates aggressively accelerate degradation mechanisms owed to structural damage due to both increased cycling temperature and inhomogeneous Li ion mass transport properties. The induced electrode damage can cause void formation between the active material and conductive-binder matrix and delamination from the current collecting foil. The now electronically isolated active material results in large costs on the capacity retention of the battery.In this study, we report measurements via operando X-ray microtomography of cylindrical cells used in EVs under simulated fast charge and drive cycles. Three batteries were measured during cycling after antecedent fast charging cycles, the 3rd, 5th, and 81st - 82nd cycles were analyzed to track morphological damage at different battery life points. To track the spatial and morphological degradation of both the anode and cathode structures, we employed a deep learning segmentation method using the U-Net convolutional neural network (CNN). Using a Euclidean Distance Mapping method, void formation is tracked spatially in 3-dimensions within the electrode coating. Insight into how fast charging induces structural damage will better inform research into fast-charge protocols and new battery chemistries for electrolytes, electrolyte additives, and novel electrode architectures. [1]Liu, Y.; Zhu, Y.; Cui, Y. Challenges and Opportunities towards Fast-Charging Battery Materials. Nat. Energy 2019, 4 (7), 540–550. https://doi.org/10.1038/s41560-019-0405-3.

With the rapid growth of electric vehicle (EV) ownership, attentions have been paid to the foundation of EVs, the electric vehicle supply equipment (EVSE). Different approaches of effort, among which battery swapping and fast charging are the two most well studied, have been made to solve the tradeoff problem between the battery charging speed and battery lifetime. There has been considerable debate over development strategy between charging and battery swapping. In passenger vehicles, the EV charging mode seems to dominate. But, does it mean that the battery swap mode is a dead-end? The answer should be “No”. There are use cases showing that battery swap can have great potentials for some particular uses, such as taxis and buses. This paper uses Monte Carlo simulations of vehicle behaviors to compare the service capacities and earnings of EV charging and battery swapping for both taxi and bus fleets. Stochastic models of taxis, buses, charging stations (CSs) and battery swapping systems are set up. Subsequently, service capacities of the EVSE are compared. The impact of factors on the service capacity, such as the size of the vehicle's battery, vehicle's moving speed, the power of the CS, and the price of the swapping service is investigated. Finally, possible reasons of today's less prevalence of battery swapping stations are discussed. The results of the analysis, which can be helpful to policymakers and industry investors, show that with same service capacity, an EV battery swapping station could provide significantly more financial and social benefits for the vehicle operators and EVSE service providers than that of an EV CS.

Life cycle optimization framework of charging–swapping integrated energy supply systems for multi-type vehicles

ABSTRACTIn this article, we review the worldwide developments of battery-electric buses (a) from medium-sized vehicles (e.g., 6.7 m) to heavy-duty vehicles (e.g., 11 m), and (b) from the slow-charging mode (e.g., 6 hours) to the fast-charging mode (e.g., 10 minutes). We also review the worldwide operations of battery-electric buses (a) from 1907 in London, England, the early 1980s in Denver, Colorado, and the early 1990s in Santa Barbara, California, and Chattanooga, Tennessee, to various international cities now, and (b) from less than 20 vehicles in a transit agency to more than 1,000 vehicles. We summarize the experiences and lessons learned from real-world operations. We examine key technical specifications that are critical to the operations of electric bus systems, in particular the operational distance and charging time. Due to a limited operational range of battery-electric buses, three range remedy methods have been proposed: (a) regular (slow) battery charging with backup vehicles equipped with fully charged batteries; (2) battery swapping; and (3) fast opportunity charging during the layover period. We conduct a qualitative analysis on the strengths and weaknesses of each range remedy method. We also analyze the vehicle cost, energy cost, and emissions of transit buses powered by different sources, and examine potential impacts of fast-charging electric buses on the electric grid.

Within the past decade, the average price for automotive lithium-ion battery (LIB) packs has fallen roughly by 80% [1]. This has played a substantial role in increasing the driving range of mass market electric drive vehicles (EDVs) and the demand for EDVs [2]. Additionally, the increasing availability of direct current fast charging (DCFC) stations is working synergistically in aiding EDV adoption and utility. For instance, a 25% annual increase in electric vehicle miles was documented in areas where 50 to 120-kW DCFC stations were available [3, 4]. Thus, continued DCFC network expansion, along with faster charging, could significantly increase the utility of battery electric vehicles and alleviate consumers’ range anxiety to a comfortable level. EDV charging speeds are not yet comparable to the fueling speed of conventional gasoline engines, which is typically less than 10 minutes [5]. Higher- power charging stations up to 400 kW are necessary to achieve a 10-minute recharge [5]. Additional challenges will be encountered in realizing this extreme charging speed, from battery cells to vehicle systems, and from charging infrastructure hardware to charging network economic feasibility. On the battery side, the increased charging rate associated with extreme fast charging could adversely affect battery performance and life (i.e., state of health [SOH]). Besides the cell-level aging, additional pack-level aging factors could come into play under fast charge conditions. Thus, it is paramount to understand the effects of fast charging on LIB’s SOH, from the pack to cell level and to identify the most critical factors affecting battery SOH. This understanding would benefit battery developers, automotive original equipment manufacturers, and electric vehicle supply equipment developers, allowing for sensible design and management of the LIB pack to satisfy target life requirements in a cost-effective way. This presentation will discuss some of the implications associated with different charging protocols, e.g., alternating current level 2 (AC L2), direct current fast charging (DCFC), and combined AC L2 and DCFC, on cells as well as full packs at different temperatures. The effect of delayed fast charging, in which charging completes shortly before the next discharge, on the battery SOH will also be shown. Finally, pack design considerations that require understanding of aspects extending beyond scaling the performance at the cell level will be discussed. References S. M. Knupfer, R. Hensley, P. Hertzke, P. Schaufuss, Electrifying insights: How automakers can drive electrified vehicle sales and profitability, McKinsey & Company (2017)B. Nykvist, M. Nilsson, Rapidly falling costs of battery packs for electric vehicles, Nat. Clim. Change 5, 329-332 (2015)N. Lutsey, S. Searle, S. Chambliss, A. Bandivadekar, Assessment of leading electric vehicle promotion activities in United States cities, Int. Counc. Clean Transp., July 2015.M. McCarthy. California ZEV policy update- SAE 2017 Government/Industry meeting presentation, Washington DC, Jan 2017.S. Ahmed, I. Bloom, A. N. Jansen, T. Tanim, E. Dufek, A. Pesaran, A. Burnham, R. B. Carlson, F. Dias, K. Hardy, M. Keyser, C. Kreuzer, A. Markel, A. Meintz, C. Michelbacher, M. Mohanpurkar, P. A. Nelson, D. C. Robertson, D. Scoffield, M. Shirk, T. Stephens, R. Vijayagopal, J. Zhang, Enabling fast charging – A battery technology gap assessment, J. Power Sources, 367, 250-262 (2017)

In order to meet the growing demand of extended lifetime and fast charging capability, Li-ion batteries (LiBs) have been optimized and further developed since the beginning. From a wide choice of LiB technologies, Li4Ti5O12 (LTO) anode are known for its fast charging characteristics which is a key requirement of present electrical vehicles (EVs). The longer lifetime is a crucial criterion too to make it a popular choice for EVs and hybrid electric vehicles (HEVs). Since, the aging of Li-ion batteries depend on several degradation factors related mostly to used active materials, electrolyte etc., two types of LTO battery cells are selected for cycle life aging investigation in this research. Fast charging is selected as the key variation in the cycling conditions to understand the battery aging under accelerated lithiation process. Electrochemical impedance spectroscopy (EIS) is used along with typical capacity check during tests to characterize the batteries during lifetime.In this study, two types of LTO batteries were cycled under a long cycling campaign. Both the cells are power optimized and commercially available. The pouch shaped EIG 5Ah cell has a LiNixCoyAl1-x-yO2 (NCA) cathode material and a specific power of more than 2000 W/kg. On the other hand, TOSHIBA manufactured LTO is a prismatic cell, which operates at a nominal voltage of 2.3V, has a specific power of more than 1200 W/kg and it has a cathode material possibly of LiNixCoyMn1-x-yO2 (NMC). These batteries were cycled with four fast charging conditions at room temperature and with 90% and 80% depth-of-discharge (DoD), respectively. LTO 5Ah cell was cycled with 0.5C to 5C charging currents against 1C discharge current, where C-rate refers to the cell’s nominal capacity. Whereas, LTO 23Ah was tested in the range of 1C to 8C charging current versus 2C discharge rate. The ongoing lifetime test campaign is already more than 2-yr long, however, aging characteristics is quite different. The lifetime tests and the capacity measurement are done with PEC ACT0550 battery cycler and the EIS is performed with the Biologic’s MPG-205 equipment. The performance tests were done in the frequency based on full equivalent cycles (FEC) of which, FEC is taken as the nominal charge Ah throughput from a single charge-discharge cycle. All the tests were performed inside controlled environment temperature chambers (CTS).The capacity fade characteristics during aging, is found to be insignificant for the EIG cell as it has a negative degradation which basically means an improved state of health (SoH). A SoH can be defined as the percentile of actual or latest capacity divided by the beginning of life (BoL) value. All the aging conditions have experienced capacity gain for the EIG cell and even after 4000 FEC, no degradation is found. This proves both the fast charging suitability of this cell and longer lifetime as well. On the contrary, TOSHIBA LTO 23Ah cell has shown charging C-rate dependency on the capacity fade. Cells cycled with 2C rate aged faster (6%) than the other conditions. This is little unusual thus further investigation is necessary. While other conditions with different charging speed have moderate degradation with the maximum of 2.5% fade for 4C charge-cycling after 4800 FEC. The ongoing cycling investigation of LTO 23 Ah cell will have better illustration of the degradation characteristics in future.The impedance growth is analyzed after obtaining the parameters through an equivalent circuit model (ECM). The quantitative black box type modeling was done by EC-lab software by choosing a one-RQ circuit where the Nyquist plot is fitted by ohmic resistance (R0), charge transfer resistance (Rct) and constant phase elements (CPEs, Q). The results show that LTO 5Ah has a maximum ohmic resistance increase of 26% for 1C charge condition while the charge transfer resistance growth is gradually higher for the higher C-rate cycling eventually reaching 84% growth for this cell. To the opposite, the TOSHIBA cells have insignificant R0 increase and have decreasing Rct growth by C-rate.In general, the lower impedance growth in both the cells indicate their long lifetime and good performance under fast charging conditions. The characterization parameters in terms of capacity and power fade investigated in this study, can be used as model input parameters to do prognosis and/or diagnosis of LTO-anode based cells as state of charge/health/power/energy prediction. Figure 1

Heavy-duty vehicles are a major contributor to CO2 emissions in the transportation sector, and it is necessary to develop clean and green technologies to replace diesel trucks. Electric trucks have not reached a breakthrough in the German market. In addition to technology development, customer acceptance of new technologies is a critical factor in the success of sustainable transportation policies. This study aims to fill this knowledge gap by investigating the perceptions regarding electric trucks and providing insights into the acceptance of these technologies. Data and arguments on the expected risks and benefits of heavy-duty electric trucks, with a special focus on the battery swapping solution, were collected through a survey and expert interviews in the German commercial transport sector. The authors collected a sample of 146 qualitative responses and 61 individual statements on the expected risks and benefits of electric trucks and battery swapping. While the responses to the classified questions are overwhelmingly positive, the individual statements show that there are still many open questions.

This study aims to calculate the betweenness centrality according to the slope of the three-dimensional street network by taking in the metabolic conversion distance. Furthermore it aims to grasp the importance of the streets that compose the network according to the tendency of human route selection. Therefore, the four centralities are defined. as follows: 1)Betweenness Centrality;Number of Links：The route with the minimum number of links passing from any start point to any end point is defined as the shortest route. The evaluation value is the frequency that any link is passed on all the shortest routes. 2)Betweenness Centrality;Distance：Based on a two-dimensional map, the index is based on the distance between each street. The route with the minimum sum of the distances from any start point to any end point is defined as the shortest route, and the evaluation value is the frequency of passing through any link in all the shortest distance routes. 3)Betweenness Centrality;Slope：This centrality considers the slope of the street. The shortest route is defined as previously noted, and the evaluation value is the frequency passing through any link in all the smallest slopes routes. 4)Betweenness Centrality;Metabolism：This value in total considers the distance and slope in a three-dimensional street network. The route with the minimum sum of metabolic conversion distances from any start point to any end point is defined as the shortest route, and the evaluation value is the frequency of passing through any link in all of the shortest metabolic conversion distance routes. These were applied to two simplified models and actual streets, and the method's effectiveness of the method was demonstrated. First, based on Betweenness Centrality;Number of Links, using the number of links as an index, links in the center of the street network tend to be selected. Based on Betweenness Centrality;Distance, using the two-dimensional distance as an index, links having a short two-dimensional distance tend to be selected. Based on Betweenness Centrality;Slope, links with a small slope tend to be selected. Based on Betweenness Centrality;Metabolism, links with a short three-dimensional distance and a low slope tend to be selected. Therefore, it can be said that way finding Betweenness Centrality;Metabolism is valid in selecting a route to calculated the moving cost is calculated according to the distance and slope when a person moves. Next, applying Betweenness Centrality;Metabolism to Chuo-ku, Kobe, the value of streets with relatively short distances and small slopes in the site became higher. Subsequently, it was shown that Betweenness Centrality;Metabolism is an index suitable for the current situation of Kobe. Finally, the importance of the street was visualized using Betweenness Centrality;Metabolism, assuming the case of traveling around a spot or passing through a proposed street. A new important area was proposed by designating a street adjacent to the tourist attraction "Sorakuen". Those are useful in focusing on commerce and tourism planning.