Cost modelling and key drivers in lithium-ion battery recycling
Abstract
As the global deployment of lithium-ion batteries (LIBs) accelerates, efficient and cost-effective recycling strategies are becoming critical to ensure material circularity and supply security. However, although the technical principles of LIB recycling are broadly understood, the economic modelling of recycling processes remains fragmented. In this Review, we examine how recycling costs are assessed across pyrometallurgical, hydrometallurgical and direct recycling routes. Profit margins can vary from US$0.4–3.3 kg−1 (hydrometallurgy) and US$0.5–4.0 kg−1 (pyrometallurgy) to US$2.0–14.4 kg−1 (direct recycling), depending on the process conditions, the cost categories considered and the number and type of recovered products. Models reflect the battery chemistry, scale and regional context. However, many models omit key cost elements such as transport, disassembly or capital expenditures, leading to a general underestimation of costs. These modelling inconsistencies hinder comparability and might misrepresent the economic potential of emerging technologies. Thus, more transparent, geographically diverse and scale-sensitive cost assessments are needed to guide future research and support informed decision-making in industry and policy, especially in light of evolving battery chemistries and regulatory demands.
Key points
Recycling costs of lithium-ion batteries can vary from US$1.64 kg−1 to US$22.4 kg−1 depending on the route, feedstock and scale.
Cost models often lack transparency and rarely provide full documentation of assumptions, input data and model parameters, limiting reproducibility.
Recycling cost models differ in scope: cluster-level models focus on a single process cluster (such as pretreatment, hydrometallurgy or direct recycling), whereas full-route models capture the entire value chain from the battery pack or cell to material recovery.
Despite the importance of capital and transport costs for industrial scalability, fixed investment and logistics are frequently omitted from both cluster-level and full-route recycling models.
To support industrial planning and regulation, future cost models should be transparent and open-source, and include evolving battery chemistries, regional differences and scale effects.
Introduction
The global manufacturing capacity for lithium-ion batteries (LIBs) reached approximately 3 TWh in 2024 and is projected to triple within the next 5 years1. This expansion is accompanied by a sharp rise in both production scrap and end-of-life batteries2,3. If not properly collected and treated, LIB waste poses environmental and safety risks owing to its toxic components and flammability4,5. At the same time, LIBs contain a range of critical raw materials, such as lithium, cobalt and nickel, whose supply is geographically concentrated and exposed to geopolitical and environmental vulnerabilities6,7. Efficient recycling strategies can mitigate environmental impacts, secure access to strategic materials and reduce dependence on primary mining8,9.
The end-of-life value chain of LIBs includes collection, transport, discharging, separation and material recovery, which are combined in varying ways depending on the recycling route10,11. The recycling routes are typically classified into three main approaches: pyrometallurgy12,13, which uses high-temperature (1,400–1,700 °C) smelting to recover metals such as cobalt and nickel; hydrometallurgy14,15, which relies on aqueous chemical processing for selective metal recovery; and direct recycling15,16, which aims to regenerate cathode materials for reuse in new batteries. Each route presents distinct trade-offs in terms of recovery efficiency, material and energy consumption, scalability and economic performance17,18. Moreover, whereas several pyrometallurgical and mechanical-hydrometallurgical plants are already operational at an industrial scale, direct recycling remains in the earlier stages of development, with ongoing efforts to scale up from pilot projects19,20.
The economic viability of recycling processes is shaped by a complex interplay of factors. Recyclers must manage a wide range of battery formats and system designs21,22, as well as evolving cell chemistries such as nickel-rich LiNixCoyMnzO2 (NMC) or LiFePO4 (LFP) batteries, which alter the composition of the input stream11,23. Post-LIB technologies such as sodium-ion and solid-state batteries present additional challenges24,25, and differences in process configurations further influence both costs and recovery outcomes26,27.
Despite growing industrial activity28, the economic modelling of LIB recycling tends to focus on technical processes and provides only selective insights into economic viability10,29. However, techno-economic models are essential to assess whether emerging processes can be scaled cost-effectively and deployed at an industrial level30,31. Without a clear understanding of cost drivers and system boundaries, implementation remains uncertain2,32. This gap is becoming increasingly relevant as LIB recycling moves towards global industrialization, with large-scale facilities being commissioned and pilot lines expanded across Europe33, North America20 and Asia34.
In this Review, we discuss cost models for LIB recycling using a process-cluster framework. We distinguish between different recycling approaches and examine how they are evaluated from an economic perspective. We focus on methodological diversity and transparency in modelling parameters, including process costs, cost categories and key variables such as cell chemistry, process design and regional context. We offer a comprehensive overview of modelling practices and highlight opportunities to improve consistency, comparability and relevance in future techno-economic assessments.
Recycling processes and clusters
Understanding the technical foundation of LIB recycling is essential for evaluating the associated costs10,20. Although the specific design of recycling routes can vary substantially, most approaches follow a sequence of recurring steps35. These steps can be grouped into seven functional process clusters: transport, preparation, pretreatment, pyrometallurgy, hydrometallurgy, direct recycling and emerging methods (Fig. 1). Each cluster represents a specific stage in the value chain, with its own materials, process conditions and economic characteristics. Models assessing recycling economics focus either on individual process clusters or on entire process routes, defined as the full chain from end-of-life battery or production scrap to a saleable (intermediate) product. Full recycling routes are referred to by the name of their main process cluster, such as pyrometallurgical, hydrometallurgical or direct recycling routes. This section provides a brief overview of the core recycling steps included in each cluster.
Process clusters include transport, preparation, pretreatment, pyrometallurgy, hydrometallurgy, direct recycling and emerging methods. Emerging methods refers to approaches that differ substantially from the established clusters pyrometallurgy, hydrometallurgy and direct recycling. Process clusters, which can be flexibly combined to yield specific products, are highlighted in dark teal. Example processes or steps within each cluster are shown in teal, whereas inputs, intermediates and end-products of the process series are displayed in blue. A complete recycling route starts with an end-of-life battery and ends with a commercially viable product, consisting of two to four process clusters. LIB, lithium-ion battery. Adapted from ref. 38, CC BY 4.0.
Transport and preparation
Transport refers to the collection and movement of end-of-life batteries from their point of use to recycling facilities. It includes logistical coordination and compliance with regulations for the safe handling and shipment of hazardous materials36,37. Upon arrival at the recycling facility, the preparation phase begins. To reduce safety risks, end-of-life batteries are usually deep discharged as an initial preparation step38,39. For easier handling, battery packs are disassembled to the module or cell level, depending on the subsequent processing requirements38. In addition to modules or cells, disassembly also separates non-cell components such as housings, electronics, the battery management system and module connectors40. These components are not part of the subsequent cell-focused recycling steps and are typically removed prior to further material processing41.
Pretreatment and pyrometallurgical recycling
After preparation, the pretreatment phase begins, aiming to separate the active materials from casings, current collectors and other structural components. This separation is achieved through mechanical or thermal techniques, such as shredding, sieving, magnetic separation or pyrolysis, depending on the specific process configuration39,42. The goal of pretreatment is to obtain an intermediate product known as black mass. Black mass is a concentrated mixture of cathode active material (CAM) and anode active materials that serves as a key input for subsequent material recovery steps37.
Alternatively, pyrometallurgy can be used instead of mechanical processes in the pretreatment stage. Pyrometallurgy refers to metallurgical processes for the extraction and refining of metals at elevated temperatures43. In LIB recycling, a pyrometallurgical pretreatment typically includes high-temperature smelting (1,400–1,700 °C (ref. 12)), salt roasting or thermal reduction processes44. The pyrometallurgical process yields metallic alloys containing nickel, cobalt, iron and copper, along with slag and gases10. Lithium, manganese and aluminium are deposited in the slag and can be further processed hydrometallurgically45. Pyrometallurgical approaches also aim to capture lithium in the flue dust, facilitating its recovery and reducing the need for hydrometallurgical treatment of the slag46,47. During the high-temperature (880–1,200 °C (ref. 48)) process, components such as plastics, electrolytes and graphite anodes are pyrolysed and used to supply energy to the process44,49.
Hydrometallurgical recycling
Hydrometallurgy is a key approach in LIB recycling due to its ability to obtain battery-grade purity metal salts from complex mixtures, and to recover many battery components15,37. Hydrometallurgical treatment is commonly applied to the black mass obtained from mechanical pretreatment but can also be used to process metal alloys or slag generated in pyrometallurgical recycling50. Operating at lower temperatures, up to 150 °C (ref. 51), the process starts with leaching of input material using mineral acids49,52. Key process parameters include the pH, temperature, reaction time, pressure, particle density and solid-to-leachate ratio10. Metals are then recovered through chemical processes such as precipitation and solvent extraction53. Owing to their similar chemical properties, separating metals such as aluminium, copper, iron, manganese, nickel and cobalt remains challenging45.
Direct recycling
Direct recycling has gained increasing attention as a resource-efficient strategy to close the loop on cathode materials and reduce the environmental footprint of LIBs54,55. Unlike pyrometallurgy and hydrometallurgy, direct recycling maintains the cathode’s crystal structure, enabling the direct reuse of cathode material in a new cell56,57. This approach typically involves several steps, including intensive mechanical comminution, electrolyte recovery, separation of anode and cathode materials, binder removal and relithiation51,56. Relithiation restores the lithium ions consumed during the battery use phase10. Because the materials are separated during disassembly and not fully degraded in the subsequent processing steps, direct recycling can recover almost all components of spent LIBs, with particular focus on the cathode58,59. Other recoverable materials include the anode60, separator61, current collectors62 and other components63,64. Relithiation processes operate across a broad temperature range, depending on the technique used29. Solid-state methods require high temperatures above 600 °C, often exceeding 900 °C, with long processing times65,66. Molten-salt methods enable relithiation at moderate temperatures (300–500 °C)67. Hydrothermal and ionothermal methods operate at lower temperatures, typically between 150 and 250 °C (refs. 68,69), under high pressure or in ionic liquids70. Electrochemical relithiation occurs at near-ambient temperatures but usually requires additional post-treatment steps29,71.
Emerging recycling methods
Additional approaches are being explored to improve the environmental performance or cost-efficiency of LIB recycling. Emerging methods do not form a separate category in a strict sense, as they often build on or modify elements of pyrometallurgy, hydrometallurgy or direct recycling. However, they introduce distinct reaction mechanisms, input materials or integration strategies that set them apart from established routes. Examples include solvometallurgical72 systems, electrochemical leaching73, bioleaching74 and microwave-assisted75 processing. Within the cluster framework, they are treated as a separate group to account for their reaction pathways and process configurations.
Single clusters and full-route models
The economics of battery recycling depend on how costs are defined, calculated and contextualized. Although recycling pathways and technologies have been widely researched, cost modelling remains fragmented and often lacks transparency. In this section, we explore how models approach the economic assessment of LIB recycling. We discuss models that focus on single process clusters and full recycling routes. We also examine the role of the model EverBatt and evaluate the overall transparency and comparability of modelling approaches.
Single process clusters
Many economic assessments do not capture the full recycling route and instead focus on individual steps within the value chain. These cluster-level models are typically used to analyse new processes, specific reaction stages or individual cost components such as chemical use or energy demand. Many models assess hydrometallurgical76,77 recycling processes, whereas pyrometallurgy78 and direct recycling79 are less commonly modelled (Supplementary Fig. 1). Here, we summarize key modelling approaches and findings for different clusters.
Transport and preparation
Early-stage processes such as the transport of end-of-life batteries80, discharging and battery pack disassembly81 are rarely evaluated as stand-alone elements in cost modelling (Supplementary Fig. 1). However, when included as a cost factor in broader assessments, substantial variation emerges due to regulatory restrictions and logistical assumptions43. Transport costs can range from US$0.24 kg−1 to US$5.51 kg−1, accounting for up to 41% of total recycling costs in some cases36.
Similarly, disassembling battery packs to the cell level increases the concentration of cobalt, nickel and lithium in the feedstock, as these metals are primarily located in the CAM81. However, owing to the wide variety of battery pack designs, dismantling is often still carried out manually, which complicates standardization and limits the scalability of economic modelling23,40.
Pretreatment and pyrometallurgical recycling
Stand-alone economic evaluations of pretreatment82 and pyrometallurgical83 processes are uncommon, although both can have a substantial impact on overall cost structures84 (Supplementary Fig. 1). In the pretreatment stage, mechanical separation methods can improve the material quality and reduce downstream processing costs77. For example, the chemical-free separation of LFP cathodes and aluminium current collectors via ball milling or ultrasonic delamination costs less than 10% of the recovered material value82. Cost estimates often focus on direct operational costs such as energy and water consumption, whereas fixed costs for labour and equipment tend to be neglected82.
Isolated economic evaluations of pyrometallurgical recycling highlight a strong dependence on feedstock composition and metal content78,85. Profitability is highest when valuable metals such as cobalt and nickel are present in the alloy fraction, whereas LIBs with lower cobalt content have low economic viability78. Energy consumption accounts for approximately 45% of total operating costs, primarily driven by the high energy demand of electric arc furnaces (1,600 °C) used in pyrometallurgical processing78.
Hydrometallurgical recycling
Hydrometallurgical processes are the most modelled cluster in economic assessments of LIB recycling86. Modelling of this cluster typically starts with black mass73,87 or pre-separated cathode materials86,88 as input (Supplementary Fig. 2). In LFP recycling, some models also assess the recovery potential of the residual lithium extraction slag (LES), typically composed of iron phosphate (FePO4)89. The valorization of this residue has been proposed as an additional economic benefit of LFP-specific processes90,91. However, the economic assessment of hydrometallurgy is mainly related to the leaching and separation processes, particularly reagent consumption, reaction efficiency and metal recovery yields92,93.
The leaching agent strongly influences operating costs, with sulfuric acid94 typically resulting in lower costs compared with hydrochloric acid95, or organic acids such as citric acid96, maleic acid92 or acetic acid92. Reaction parameters such as temperature, residence time and solid-to-liquid ratios can also affect both energy demand and reagent use76,97. Most techno-economic models are built for laboratory-scale processes and include only operational costs, limiting their direct transferability to industrial applications. Economic performance is particularly sensitive when targeting materials with lower market value, such as lithium or manganese79,98 .
Direct recycling
Similar to hydrometallurgical processes, economic evaluations of direct recycling at the cluster level start with black mass or pre-separated cathode material as input. These estimates generally do not include the economics of preceding mechanical processes required for material separation and purity99,100. However, some models have started to assess the potential cost impacts of chemical purification processes at a laboratory scale, such as aluminium removal101. For example, initial alkaline leaching steps can detach aluminium foil (98.6%) from the active material, enhancing the performance of regenerated cathode materials to achieve battery-grade capacity101.
Direct recycling might be useful in LFP recycling due to the low raw material value and limited viability of pyrometallurgical or hydrometallurgical alternatives87,102. Economic assessments suggest that direct recycling offers both cost and environmental benefits as it enables the direct recovery of cathode materials. It bypasses primary production, reduces virgin resource use and lowers energy demand, warranting further development towards industrial implementation80,103.
Emerging recycling methods
Emerging recycling methods aim to improve both the economic and environmental performance of LIB recycling, often by replacing conventional hydrometallurgical steps104. Cost modelling of these clusters starts at the level of black mass73,74, CAM105,106 or LES107 (Supplementary Fig. 2). Emerging methods are often benchmarked against hydrometallurgical processes based on material and energy use, whereas broader economic factors such as labour or maintenance costs and capital expenditures are often excluded108. Profitability is highly sensitive to the purchase price of black mass, which is largely determined by upstream collection, transport and pretreatment costs74. However, these cost drivers are rarely captured in early-stage techno-economic models, despite their influence on economic feasibility74.
Emerging approaches such as mechanochemical processes have demonstrated lower energy and reagent consumption compared with hydrometallurgical processes105. Electrochemical-assisted leaching methods show up to 80% reductions in both energy and chemical use compared with traditional hydrometallurgy73. Bioleaching is another alternative under investigation74. It is considered economically advantageous owing to its minimal use of chemical reagents, and generation of less hazardous waste compared with hydrometallurgical recycling methods109.
In addition to new reaction pathways, some emerging approaches focus on process integration to improve the overall profitability of battery recycling by combining material recovery with additional resource and emission savings110. For example, coupling wastewater electrodialysis with CO2 capture has been proposed to enhance both economic viability and resource efficiency110.
Anode recycling
Anode materials have received less attention in LIB recycling due to the lower economic value of graphite111 compared with cathode metals such as cobalt112, nickel113 or lithium114. However, from a resource circularity perspective, recovering anode materials can reduce the need for virgin graphite115. One possible approach involves repurposing spent graphite using carbon-based coatings116. Other approaches focus on upcycling, in which high-quality graphene is obtained from spent LIBs at lower cost than commercial production routes while also enabling lithium recovery117. Graphite has also been successfully converted into graphene using a modified Hummers’ method, further expanding the scope of value recovery from the anode118.
Full-route models
In contrast to cluster-level models, full-route assessments aim to capture the entire recycling value chain, from battery pack or cell to a commercially viable material. These models combine multiple process clusters into a holistic cost framework, typically including preparation119,120, pretreatment121,122 and hydrometallurgical123,124 processing as core components (Supplementary Figs. 1 and 2). Many of these models are designed at or near the industrial scale125,126 and simulate realistic plant configurations124,127 by integrating several interdependent steps such as discharging125, mechanical processing119,126, pyrometallurgical treatment128,129 and hydrometallurgical refining38,104, which are typically modelled in sequence as the output of one step becomes the input for the next. Full recycling routes are usually modelled using either individually built frameworks or the Argonne National Laboratory publicly available tool EverBatt. Both approaches offer valuable insights, yet differ in terms of transparency, scope and assumptions.
Individual modelling
Techno-economic assessments of full recycling routes show that profitability is sensitive to battery chemistry and the number of products recovered. For example, total cost of ownership analyses indicate that recycling LFP-based batteries remains economically unviable under pyrometallurgical conditions due to their low metal value85. Similarly, efforts to recover multiple co-products do not necessarily improve economic outcomes as the additional costs associated with chemical inputs and separation steps might outweigh the comparatively low market value of materials, such as manganese or lithium119.
Beyond chemistry and co-product recovery, economic outcomes are also shaped by the process scale and system design. Larger facilities benefit from economies of scale124, and integration of recycling processes directly into LIB gigafactories can improve cost stability and enhance resilience to raw material price volatility125. Similar effects are expected from strategic capacity expansions, such as the proposed scaling of the European hydrometallurgical infrastructure — a network of industrial recycling plants that predominantly applies hydrometallurgical processes to recover valuable metals from end-of-life.
Within this broader context, direct cathode recycling has emerged as a cost-effective route compared with hydrometallurgical and pyrometallurgical processes, particularly under laboratory-scale conditions using production scrap130. For example, a molten salt-based approach demonstrated up to 53% savings in electricity and water use compared with hydrometallurgical alternatives131, and manufacturing costs can be reduced when CAM recovery is directly reintegrated into cell production, depending on the break-even costs for recovered materials132. Using deep eutectic solvents in direct cathode recycling is less economically favourable than pyrometallurgy and hydrometallurgy, and has higher environmental impacts, particularly in terms of global warming potential and ozone depletion72. Hybrid systems that combine lithium recovery with hydrogen production have also been proposed to enhance both economic and environmental benefits133. Reported benefits include a reduction in electricity consumption (from 6.3 kWh m−3 to 3.6 kWh m−3 for hydrogen production), high-purity lithium recovery and the generation of hydrogen fuel. In parallel, lithium recovery systems combined with brine desalination offer additional advantages, such as eliminating the need for corrosive chemicals, minimizing greenhouse gas emissions and secondary waste, and enabling the production of fresh water alongside lithium recovery134.
Despite their holistic scope, only a minority of full-route models explicitly include transport costs127,135. Many models consider a broad range of operational expenditures, including direct production costs, indirect process-related expenses and general overheads. However, fixed investments such as capital equipment and buildings remain underexplored. This omission limits the transferability of results and underscores the need for more transparent and scalable modelling approaches.
EverBatt model
The EverBatt model is a publicly available tool designed for comprehensive cost and environmental impact analysis136. The model was developed by Argonne National Laboratory and integrates two in-house tools: the Battery Performance and Cost (BatPac)137,138 model for cost calculation and GREET139 for environmental lifecycle assessment. It supports comparisons across various recycling routes (pyrometallurgical, hydrometallurgical and direct recycling) and different global locations (the United States, China and Korea). The model covers a range of battery chemistries, such as lithium cobalt oxide LiCoO2 (LCO), LiMn2O4 (LMO), LFP, NMC and LiNi0.8Co0.15Al0.05O2 (NCA), and includes battery pack production and disassembly, single and mixed feedstock recycling from end-of-life batteries, manufacturing scrap and black mass.
Cost estimates using EverBatt consistently highlight cost advantages linked to regional and process-specific conditions135. In the United States, recycling becomes profitable only at higher throughput levels (~7,000 tonnes per year), whereas lower labour costs in China enable economic viability at smaller scales (~3,000 tonnes per year)120. Similarly, combining decentralized preprocessing with a central hydrometallurgy hub reduces transport costs for end-of-life batteries121. Hydrometallurgy emerged as the most cost-efficient recycling route across regions, with the lowest overall costs reported in China135. Processing costs account for the majority of total expenses (75–90%), whereas dismantling costs vary considerably, ranging from just 2% in China to 21% in Belgium135. These differences underscore the impact of labour costs and process design on regional cost structures. Direct recycling can reduce manufacturing costs by 25–36% and achieve greenhouse gas emission reductions of around 30%140, indicating that direct recycling is more profitable and less emission-intensive than pyrometallurgical and hydrometallurgical alternatives141.
EverBatt also enables the adaptation and comparison of novel processes with existing routes126,142. For instance, if a pyrometallurgical process design is maintained but only the input material changes, updated material prices can easily be integrated whereas other process assumptions remain unchanged129,143. This modelling flexibility allows for a rapid economic evaluation without the need for extensive data collection or detailed process modelling144. Another common application is the use of EverBatt’s generic hydrometallurgical and pyrometallurgical process templates as a baseline for quick benchmarking145,146. This approach enables the evaluation of the economic potential of new processes without having to build full-scale models from scratch.
Modelling practices
Transparent modelling of LIB recycling costs remains a limitation as full-route model metrics often are not published in a documented and accessible format124,131,132 (Supplementary Fig. 2). Costs are frequently mentioned, together with a general description of the model parameters, without offering full transparency27,104.
Models focusing on single process clusters often rely on simplified linear approaches, using basic parameter multiplication (Supplementary Fig. 2). This simplified linear approach means that costs are typically estimated by multiplying unit prices with material or process quantities, assuming a direct linear relationship between inputs and costs (for example, price per kilogram multiplied by processed mass)147. These models are primarily laboratory-based, draw on primary data and serve as early-stage cost approximations rather than scalable economic frameworks88,148.
To enhance comparability and robustness in future assessments, more open, documented and scalable modelling tools are needed. Underlying assumptions, data sources and calculation structures are needed to improve the interpretability of results and support broader application across regions and technologies.
Cost drivers
Understanding cost estimates in LIB recycling requires examining absolute values and the assumptions and modelling boundaries that shape them. In this section, we compare recycling costs, with a particular focus on variations by process route and input format. To contextualize these findings, we then explore broader influences on costs, including regional cost structures and the types of cost categories considered in techno-economic models. Finally, we benchmark literature values against outputs from the EverBatt model to assess the plausibility and completeness of current cost estimates, and to offer a more transparent view of the cost structure in industrial-scale LIB recycling.
Pyrometallurgical recycling
The cost estimates for recycling LIBs vary widely, reflecting differences in feedstock, process configurations and methodology. Processing costs in pyrometallurgical recycling are insensitive to the input format, as dismantling the battery pack is typically not required in pyrometallurgical processing149. Reported costs, averaging US$4.30 kg−1 for battery packs and US$4.26 kg−1 for cells (Fig. 2a), reflect this similarity.
a–d, Costs, revenues and profits of pyrometallurgical (panel a), hydrometallurgical (panel b), direct recycling (panel c) and emerging recycling (panel d) methods based on the starting point of the recycling process. Full-process models start at the pack or cell level, and cluster-level models begin at the black mass, cathode active material (CAM) or lithium extraction slag (LES) stage. Hatched areas indicate the mean values across reported estimates. For hydrometallurgical recycling starting from CAM, the median is shown due to the high variability in reported values. Further details on assumptions, cell chemistries, process conditions and products underlying each data point are provided in Supplementary Tables 6–23 and from refs. 72,73,74,75,76,77,79,80,81,82,86,87,88,89,90,91,92,93,94,95,96,97,98,101,102,103,104,105,106,107,108,115,116,117,118,119,120,121,122,123,124,125,127,128,129,130,131,132,133,134,135,140,141,142,143,144,145,146,148,152,154,155,175,188.
Pyrometallurgical processes are categorized into roasting (or calcination) and smelting designs13. At the pack level, only smelting processes using slag-forming agents are modelled, whereas cell-level models consider both smelting and, to a lesser extent, roasting routes (Supplementary Tables 6 and 7). A key economic advantage of smelting with slag formation is the use of low-cost reagents such as sand (US$0.06 kg−1 (ref. 136)) and calcium oxide (US$0.13 kg−1 (ref. 136)). Despite the use of low-cost reagents, several economic drawbacks limit the feasibility of pyrometallurgical recycling. The high energy demand required to maintain operating temperatures between 800 °C and 1,300 °C represents a major cost driver in most process designs78,150.
Capital cost estimates for pyrometallurgical recycling can vary widely. For a 25,000 tonnes per year facility, an investment of US$4.07 million for two key process components has been reported. This investment includes a 1.5 tonnes h−1 shaft furnace (US$398,900) and a 0.5 tonnes h−1 gas scrubbing system (US$3.67 million)124. However, the EverBatt model, assuming the same annual throughput but larger equipment capacities (3.0 tonnes h−1 furnace and 5.9 tonnes h−1 gas scrubbing), reports US$21.5 million and US$1.0 million for the respective components136. The large differences illustrate the sensitivity of capital expenditure estimates to assumptions about equipment sizing, modelling approaches, data sources and regional cost variations. For instance, EverBatt does not differentiate capital costs by location, whereas the US$4.07 million estimate was based on a German context124,136. This heterogeneity complicates cross-study comparisons and limits the generalization of capital expenditure benchmarks in pyrometallurgical modelling.
Revenue generation in pyrometallurgical recycling is highly dependent on feedstock composition and downstream processing. As materials such as cables, steel casings and cathode components are not separated during smelting, they are incorporated into a mixed metal alloy151. This alloy requires further processing to meet specifications for reuse in battery manufacturing10. Although price volatility affects all recycling routes, the undifferentiated nature of pyrometallurgical outputs amplifies revenue uncertainty, as the value of mixed metal alloys depends strongly on fluctuating cobalt and nickel prices85. This price dependence poses a particular challenge for cobalt-lean and cobalt-free chemistries, which yield lower-value alloys and lack additional revenue streams from separable components40,85.
Revenue assumptions are frequently based on subsequent hydrometallurgical treatment of the alloy (Supplementary Tables 6 and 7). Revenues average US$4.69 kg−1 for pack-based recycling and US$7.61 kg−1 for cell-based approaches, with profits varying between US$0.39 kg−1 (pack level) and US$3.35 kg−1 (cell level) (Fig. 2a). The lower average at pack level is largely driven by the inclusion of a broader range of cell chemistries141. Only cobalt-rich chemistries such as LCO and LiNi0.33Mn0.33Co0.33O2 (NMC111) were found to be profitable, whereas others (for example, LiNi0.5Mn0.3Co0.2O2 (NMC532), LiNi0.6Mn0.2Co0.2O2 (NMC622), LiNi0.8Mn0.1Co0.1O2 (NMC811), NCA and LMO) showed net losses ranging from –US$0.19 kg−1 to –US$3.23 kg−1 (ref. 141). This difference in profitability explains why cell-level studies, which predominantly assess cobalt-rich batteries72,145, tend to report higher profitability.
Finally, a major limitation of pyrometallurgical recycling is the loss of lithium, which typically ends up in the slag, which is rarely processed further10,37. Although subsequent hydrometallurgical recovery has been proposed, the quality of recovered lithium salts is often unspecified72,152. This limited lithium recovery reduces overall material recovery efficiency and limits the competitiveness of pyrometallurgical routes considering future regulatory targets for lithium recovery153. For example, the European Union has set mandatory minimum recycling rates of 50% for lithium by 2027 and 80% by 2031 (ref. 153).
Hydrometallurgical recycling
Cost estimates for hydrometallurgical recycling, as examined here, are from both full-route models, starting at the battery pack or cell level, and single cluster-level models that begin with preprocessed materials such as black mass, CAM or LES. In this section, we focus on cost estimates rather than modelling methodologies. The distinction between full-route and cluster-level models is mentioned only to clarify differences in system boundaries and included process steps, as these factors strongly affect reported costs.
Average costs are US$4.14 kg−1 for pack-based recycling and US$4.71 kg−1 for cell-based approaches (Fig. 2b). These cost differences are largely driven by process scale assumptions, with pack-level models often assuming industrial-scale facilities124,127,135, whereas cell-level models cover a broader mix of laboratory154,155, pilot134 and industrial-scale settings121,125.This scale effect likely offsets the higher process complexity associated with pack disassembly and contributes to the lower average cost observed for pack-level recycling. However, disassembly of battery packs remains a cost-intensive step due to the heterogeneity of pack designs and the frequent reliance on manual labour23,40. Alternatively, automation and robotic systems aim to standardize and accelerate this step. However, such technologies often entail substantial upfront investments (for example, US$200,000 for a disassembly robot23) and are not yet widely implemented in industrial settings156,157.
In full-route models, hydrometallurgical processes predominantly rely on sulfuric acid (US$0.08 kg−1 (ref. 136)) as the leaching agent and hydrogen peroxide (US$1.46 kg−1 (ref. 136)) as the reducing agent (Table 1). Although hydrogen peroxide is relatively expensive, its use improves leaching efficiency158. For example, leaching of LCO batteries improves cobalt recovery from 55% (without hydrogen peroxide) to 75% (with 10 vol% hydrogen peroxide) and 98% (with 15 vol% hydrogen peroxide) within 30 min of reaction time158. This effect is attributed to the conversion of Co(III) to the more soluble Co(II), breaking the strong Co–O bonds in the cathode material158. Metal recovery downstream is typically achieved through solvent extraction38,119 or chemical precipitation122,144 (Supplementary Tables 9 and 10).
Single cluster-level models explore a broader range of leaching agents, such as citric acid (US$1.95 kg−1 (ref. 136)), hydrochloric acid (US$0.57 kg−1 (ref. 136)) or ammonium hydroxide (US$0.53 kg−1 (ref. 136)). These systems are valued for their potential metal selectivity and lower environmental impact, enabled by milder leaching conditions and reduced need for oxidative agents159,160,161. However, despite their theoretical benefits, they do not consistently result in lower costs. Sulfuric acid remains the most cost-effective option, especially when paired with hydrogen peroxide to maximize leaching efficiency88,96. Beyond reagent choice, process parameters such as the temperature, residence time and solid-to-liquid ratio further influence both reagent consumption and energy demand158,162. Lower temperatures and shorter residence times generally reduce energy demand, whereas higher solid-to-liquid ratios typically reduce reagent consumption by requiring less leaching solution per unit of processed material163,164.
CAM recycling models are especially sensitive to scale. Laboratory-scale models often work with samples as small as 10 g and extrapolate results to a per-kilogram basis, leading to inflated costs148,154. To reduce this bias, the median value of US$4.71 kg−1 from the reported cost estimates is used for CAM recycling in cluster-level models, to minimize the influence of outliers. For black mass recycling processes, the average cost is US$3.88 kg−1, although the cost of producing black mass itself is often not considered, potentially underestimating overall process expenses76. These relatively high costs of black mass and CAM recycling, compared with values from full-route industrial models, reflect laboratory-based conditions, with expensive reagents and equipment, rather than scalable industrial settings.
In laboratory-scale LFP recycling, some models also assess the recovery potential of the residual LES, from which FePO4 can be recovered89. Average costs are an additional US$1.64 kg−1 on top of the hydrometallurgical recycling costs for lithium recovery from LFP, with the process yielding a modest profit of US$0.69 kg−1, offering an additional economic opportunity in closed-loop systems89,90,91 (Table 1).
Full-route models report revenues ranging from US$6.51 kg−1 (cell level119,128,145,155) to US$8.18 kg−1 (pack level127,141), resulting in average profits of US$1.80 kg−1 and US$4.04 kg−1, respectively (Table 1). The higher revenues for pack-level models might be partly explained by the inclusion of non-cell components, although this alone does not fully account for the differences observed. These non-cell components such as housing, battery management system, electronic components, cables and module connectors are separated during pack disassembly84,165. These components are sorted and forwarded to established recycling routes124. Although rarely quantified in detail, their inclusion can affect the overall revenue estimates and should be carefully considered when comparing models with differing input scopes.
Cluster-level models report average revenues of US$4.36 kg−1 for black mass recycling and a median of US$5.16 kg−1 for CAM recycling (Fig. 2b). However, many of these models only consider the leaching step and exclude downstream separation or purification, meaning revenue estimates are often missing (Supplementary Tables 11 and 12). As such, the reported profits of US$0.46 kg−1 (CAM level)77,79 to US$0.48 kg−1 (black mass level)87,98 are not broadly representative.
Several challenges limit the profitability of hydrometallurgical recycling. Wastewater volumes are considerable, and associated treatment costs are frequently excluded from economic assessments110,166. Additionally, sodium sulfate is often generated as a by-product, which requires further handling51,167. As more elements are targeted for recovery, including manganese, lithium and graphite, the equipment and reagent demands increase, yet the economic value of the additional recovered materials does not scale proportionally119,124. This cost–benefit mismatch can lead to reduced revenues compared with more selective processes that focus solely on high-value metals such as cobalt and nickel119. Cobalt-rich materials offer favourable margins, whereas cobalt-free chemistries such as LFP or LMO are often economically unprofitable127,144. Hydrometallurgical recycling can be profitable under industrial conditions, but greater transparency in modelling scope and inclusion of downstream steps is required to assess economic feasibility more consistently.
Direct recycling
Direct recycling aims to preserve the crystal structure and functionality of CAM168,169, and is particularly suitable when high-value materials such as NMC or LCO can be directly recovered132. Economic modelling of direct recycling remains limited, both at the full-route and cluster level, largely due to the ongoing development of scalable process designs and the early technological readiness level of most proposed methods170. Nevertheless, estimates suggest that direct recycling might offer the highest profit margins among all assessed recycling routes121,132,141 (Table 1). Profits of up to US$14.39 kg−1 (CAM), US$3.60 kg−1 (black mass) and US$4.35 kg−1 (pack) have been reported (Fig. 2c). These high margins, especially for CAM estimates, are largely driven by optimistic pricing assumptions for recovered CAM and by avoiding downstream refining79,103.
The economic potential of direct recycling strongly depends on the ability to match recovered CAM with current battery demand171. A key limitation is that recovered materials can only be reused for the same cathode chemistry. For example, end-of-life NMC111 cells typically yield NMC111 CAM, even though modern battery production increasingly relies on high-nickel variants such as NMC811 (refs. 24,172). This mismatch between recovered and required materials potentially limits market demand and leads to inefficient use of critical raw materials such as cobalt121.
Direct recycling requires a high degree of mechanical separation and material purity, particularly when starting from battery packs or cells37. Electrolyte removal is often performed using supercritical CO2 to avoid thermal degradation, and relithiation is commonly conducted with lithium carbonate (Li2CO3) to restore electrochemical performance173 (Table 1). Costs vary depending on the input format: US$5.26 kg−1 for packs, US$3.55 kg−1 for cells for full-route models, US$2.69 kg−1 for black mass and US$7.67 kg−1 for CAM for single cluster-models (Fig. 2c). Higher costs in CAM-based direct recycling are associated with modified processing strategies and different reagent combinations, as illustrated by alkali-assisted or carbonate-based systems combined with glucose79,101 (Supplementary Table 17). Profits in full-route assessments are further enhanced by the inclusion of recoverable materials such as copper, steel and graphite (Supplementary Tables 14 and 15).
A main barrier to industrial implementation is process integration. Many of the mechanical pretreatment steps are adapted from hydrometallurgical processes and not optimized for direct material recovery149. Another challenge is the variability in the degree of decomposition of end-of-life materials, which has a direct impact on the quality and performance of the recovered CAM168. This uncertainty reduces the robustness and reliability of the process compared with conventional methods, in which materials are returned to their elemental form174. Achieving the required material purity and electrochemical performance often requires complex resynthesis steps, which can reduce the cost advantages of direct recovery168,170. These challenges underscore that the economic viability of direct recycling is highly dependent on technical maturity, consistent feedstock quality and the ability to reliably produce battery-grade materials that meet evolving performance requirements173.
Developing recycling methods
Emerging recycling methods show notably higher process costs than established routes and remain economically uncertain. Reported values for full-route models starting from battery cells average US$22.4 kg−1, with associated losses of –US$6.71 kg−1 (refs. 72,75,108) (Table 1). Single cluster-level estimates of black mass or CAM recycling yield lower average costs (US$17.48 kg−1)105,133 but clearly exceed those of conventional hydrometallurgy or pyrometallurgy86,123.
In a solvometallurgical full-route model, process costs vary widely depending on the solid-to-liquid ratio, ranging from US$13.10 kg−1 to US$54.66 kg−1, and estimated revenues remain modest at US$14.51 kg−1 (ref. 72). This cost gap highlights both the early development stage and the sensitivity of such systems to reaction parameters. Another emerging approach, microwave-assisted recycling, achieves profits (US$10.55 kg−1; Supplementary Table 18) and is compatible with conventional process flows, including microwave-based reduction followed by leaching75.
In cluster-level models, emerging approaches such as bioleaching74, electrochemical leaching73 or mechanochemical105 treatments are explored for black mass or CAM recycling, often at the laboratory scale and focused on individual steps such as leaching or purification (Table 1). Revenue data and downstream recovery are frequently omitted, although some methods such as bioleaching or electrodeposition show potential for higher selectivity and lower reagent demand74,175.
These methods remain economically immature, but early-stage modelling serves a strategic purpose as it helps identify concepts with cost-saving potential and avoid the scale-up of processes unlikely to compete with established technologies.
Geographical influence
Beyond process design and input format, geographical factors also shape cost estimates. Key parameters in costs of LIB recycling vary significantly by region due to differences in labour and energy costs135. In February 2025, the average electricity price was US$0.088 kWh−1 in China, US$0.082 kWh−1 in the United States and US$0.188 kWh−1 in Europe176,177,178. Labour costs show similar disparities, with average hourly wages of US$6.99 in China (2023)179, US$43.11 in the United States (2023)180 and US$34.34 in Europe (2024)181,182. These differences influence comparative cost outcomes and highlight the importance of clearly defined geographical assumptions in cost modelling. This regional variability makes location comparisons important, especially for stakeholders in the growing battery recycling industry who need to determine where to establish new recycling plants.
Interestingly, cost assessment of LIB recycling in the European Union started getting more attention from 2021 (Supplementary Fig. 5). This timeline aligns with the implementation of the EU Green Deal in 2021, which aims to make the European Union climate-neutral by 2050 and prioritizes circular value creation, and the new EU Battery Regulation, which came into force in 2023 and will require minimum material recovery rates from 2027 (refs. 153,183). However, many models do not specify their location for cost calculations, complicating result comparisons (Supplementary Fig. 2). For example, regions such as South America, Africa and Asia (excluding China) are largely overlooked. Meanwhile, Japan and South Korea also produce substantial amounts of battery production waste that need recycling184,185.
Cost types
Recycling costs vary due to differences in process design or regional context, and based on which cost categories are included in techno-economic assessments. Many estimates focus on direct operational costs, particularly material inputs and energy consumption, and omit other relevant categories such as transport, labour or capital expenditure. This selective cost inclusion can lead to substantial underestimation of total process costs, especially in models that only analyse single process clusters (Table 2).
A comparison of cost categories considered in recycling cost models reveals clear trends (Supplementary Fig. 9). Material and energy costs are considered as a standard in calculating costs, reflecting their immediate relevance for process operations77,124,125. Other direct manufacturing costs such as water or waste and indirect manufacturing costs (depreciation, maintenance, labour) are more frequently accounted for in full-route models130,141 but remain largely overlooked in cluster-level assessments81,98. General expenses, such as marketing, research and development (R&D), and administrative expenses are rarely considered at all in cluster-level approaches74,78 but more in full-route models128,143. Capital expenditures are among the most under-represented cost categories122,124. Whereas some full-route models account for investments in equipment and infrastructure124,132,135, cluster-level estimates almost entirely omit them77,88,97, despite their critical importance for industrial-scale implementation and long-term economic feasibility.
Taken together, these patterns highlight that most current models focus predominantly on variable operating costs, often omitting fixed and indirect expenditures that are essential for assessing the economic performance of recycling systems. The omission of these expenditures underscores the need for more comprehensive and transparent cost assessments that reflect the full complexity of industrial LIB recycling.
Cost structure
Industrial-scale cost assessments using the EverBatt136 model provide a useful benchmark for evaluating reported process costs (Fig. 3a–c). EverBatt assumes that both hydrometallurgical and direct recycling undergo the same mechanical pretreatment. However, direct recycling might require additional pretreatment steps, leading to increased costs37. Although the EverBatt model can account for pack disassembly down to the cell level, it still assumes manual labour, resulting in different cost considerations compared with broader industrial process routes124,186. Focusing only on operational costs, an additional US$1.26 kg−1 of cells is required for pack disassembly136. It is important to note that these values do not include transport and collection costs, which are yet under-researched, particularly in terms of the necessary infrastructure.
a–c, Cost structure of pyrometallurgical (panel a), hydrometallurgical (panel b) and direct recycling (panel c) routes broken down into cost types for the recycling of 10,000 LiNi0.33Mn0.33Co0.33O2 (NMC111) cells per year136. Average values from the EverBatt model for the regions China, Korea and the United States were considered to reflect an aggregated, region-independent cost estimate. d, Comparison between mean reported cost estimates from full-model recycling models and total recycling costs estimated from the EverBatt model. Both the reported values and EverBatt estimates refer to cell-level recycling processes, ensuring consistency in the system boundaries and enabling for a direct comparison of absolute cost values. EverBatt is considered a reference model due to its broad use and transparent breakdown of capital, operational and indirect costs. The data used in the figure are from refs. 72,108,119,123,125,128,130,131,136,140,142,144,145,152,154,155 and are provided in Supplementary Tables 32–35.
Across all routes, material inputs represent the largest cost component. However, many models focus primarily on material and energy costs and omit indirect or capital expenditures. As a result, EverBatt-based estimates exceed values in literature by 9% for pyrometallurgy, 16% for hydrometallurgy and 47% for direct recycling (Fig. 3d). The discrepancy is particularly pronounced for direct recycling, reflecting both the early development stage and simplified modelling assumptions. A sensitivity analysis at the industrial scale further confirms the gap between reported estimates and industrial benchmarks. For pyrometallurgical processes, convergence with EverBatt occurs at approximately 15,000 tonnes per year, whereas hydrometallurgical models align above 25,000 tonnes per year (Supplementary Figs. 6 and 7). By contrast, cost estimates for direct recycling remain below EverBatt outputs even at 50,000 tonnes per year (Supplementary Fig. 8).
This analysis underscores the risk of systematically underestimating costs, particularly for emerging routes such as direct recycling, and highlights the need for more comprehensive models that reflect real-world process conditions. Improving access to capital cost data and consistently accounting for pretreatment, disassembly and infrastructure will strengthen the robustness and comparability of future cost assessments.
Summary and future perspectives
In this Review, we have discussed how the economics of LIB recycling are modelled and assessed. Cost estimates vary widely depending on the region, process inputs (for example pack, cell and black mass) and scale assumptions. Pyrometallurgical and hydrometallurgical processes have reached the industrial scale in some regions, whereas direct recycling remains in earlier development stages53,187. A recurring pattern in cost estimates is the under-representation of capital expenditure, transport and disassembly costs, which can lead to notable underestimation of total process costs. Economic profitability is highly dependent on the battery chemistry and the proportion of expensive metals. Evolving chemistries such as nickel-rich NMC and LFP require new recycling process parameters, which makes profitable recycling more difficult.
These findings point to three key priorities for the next generation of cost models. First, techno-economic assessments should adopt a more holistic scope that includes indirect costs, capital investment and pretreatment steps in a transparent and scalable manner. A holistic scope could be achieved by making available open-source modular modelling frameworks that allow for the flexible addition of further cost categories and process steps. Second, more geographically diverse and scale-sensitive studies are needed to ensure that cost models reflect real-world industrial conditions. We recommend combining techno-economic assessments with region-specific data on labour, energy and transport costs, and incorporating industrial-scale data from commercial facilities where available to better represent large-scale operations. Last, future research should explicitly address the impact of changing battery chemistries and the potential role of automation and design-for-recycling strategies on economic feasibility. These aspects could be addressed by including sensitivity analyses on battery material composition and by modelling different automation scenarios for disassembly, sorting and material recovery.
More consistent and transparent cost assessments can guide investment decisions, support the planning of recycling infrastructure, and inform regulatory frameworks such as minimum recycling efficiencies or extended producer responsibility schemes. Open-access models that disclose all cost categories, assumptions and data sources would be a key step towards this goal. For battery manufacturers, aligning product design with end-of-life recovery strategies offers a path to cost reduction and improved circularity. Ultimately, establishing economically viable recycling systems for LIBs will require coordinated efforts across the value chain, including open modelling practices, regionally adapted solutions and robust policy support.
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Authors and Affiliations
L.S. researched data for the article, contributed substantially to discussion of the content and wrote the article. L.S and M.G. contributed to the conceptualization of the article. J.L. supervised the work. All authors reviewed and/or edited the manuscript before submission.
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