Pathway decisions for reuse and recycling of retired lithium-ion batteries considering economic and environmental functions

Rapid growth in electric vehicle (EV) deployment has driven a surge in lithium-ion battery (LIB) production and retirement, creating pressing challenges for end-of-life management. Improper handling of retired LIBs risks environmental pollution, resource losses, waste management pressures, and supply chain vulnerabilities. Reuse and recycling are widely advocated to mitigate these risks. Prior work has examined reuse within energy storage systems (ESS), as well as holistic and component-level life cycle assessments (LCAs). However, key gaps remain: the lack of a unified assessment methodology across diverse second-life applications, limited consideration of capacity configuration impacts on economics and emissions, and insufficient integration of state of health (SOH) in recycling pathway choices. This study addresses these gaps by proposing a pathway decision strategy that jointly optimizes economic performance and environmental impact across cradle-to-grave stages for prominent cathode chemistries (NMC and LFP), multiple initial SOHs (70%, 80%, 90%), diverse reuse scenarios (ESS, communication base stations, low-speed vehicles) with capacity configurations, and alternative recycling routes (hydrometallurgical, direct, pyrometallurgical) at varying SOHs at end-of-life. The goal is to identify optimized reuse–recycling combinations that enhance profitability while reducing carbon footprints.

The literature highlights a broad set of reuse opportunities for retired EV batteries, including ESS for renewable integration, communication base stations (CBSs), and low-speed vehicles (LSVs). Studies have assessed environmental, economic, and sustainability outcomes of second-life batteries (SLBs), with many finding that reuse reduces life cycle environmental impacts relative to immediate recycling, though contrasting evidence exists. Research has advanced inconsistency detection, SOH estimation, and material sorting to better allocate batteries. Despite this progress, several gaps persist: capacity configuration is often overlooked despite its decisive role in real-world deployment; variations in capacity influence load patterns and environmental emissions; and there is no unified methodology for rational reuse across applications with different requirements. On recycling, hydrometallurgical, pyrometallurgical, and direct recycling (and their integrations) are established or emerging options, but the critical role of SOH on recycling outcomes is underexplored, as is the influence of technological innovation on the trade-offs between reuse and recycling. These gaps motivate a comprehensive, LCA-based strategy to guide pathway decisions for retired LIBs that integrate economic and environmental criteria and reflect SOH-dependent performance.

The study applies a process-based life cycle assessment and economic analysis over cradle-to-grave system boundaries: cell material extraction and refinement, cell production, module/pack assembly, first use in EVs, refurbishment (collection, testing, repurposing), reuse in multiple applications (ESS, CBS, LSV) with capacity configuration optimization, and end-of-life (EOL) pretreatment and recycling. Two chemistries are considered (NMC, LFP). Initial SOHs at retirement from EVs are 70%, 80%, and 90%. Reuse scenarios include ESS (multiple load profiles), CBS (commercial, railway, urban, rural), and LSV (sightseeing), with system battery capacity configured to maximize profit while considering degradation. EOL recycling technologies include hydrometallurgical, direct, and pyrometallurgical recycling, evaluated across SOHs of 40%, 50%, 60%, 70%, 80%, and 90%. Economic analysis: Economic performance is measured as unit battery profit ($/kWh battery), comprising investment (battery purchase and refurbishment), maintenance, and operation. Pricing models for new and retired batteries, maintenance cost, and operation with time-of-use energy prices and degradation are detailed. Capacity configuration is optimized for profit (ESS and CBS: total profit; LSV: daily average profit). Recycling economics consider costs (e.g., labor, utilities, disassembly) and revenues from recovered materials, with sensitivity to lithium carbonate (Li2CO3) prices. Environmental analysis: The environmental indicator is carbon footprint (GWP100), calculated per kWh battery and per kWh life cycle electricity delivery, using Ecoinvent v3.9 factors. The baseline context is Guangdong, China (2023), without temporal or spatial grid variation in the base case. Cell/module/pack manufacturing, refurbishment, reuse operations (including renewable generation and grid mix), and recycling impacts are included. Allocation of recovered materials assumes an open-loop market with no disequilibrium. Sensitivity analyses cover Li2CO3 price variability, electricity carbon intensity (0.3140–0.7488 kg CO2-eq/kWh), SOH ranges during reuse and at EOL, and accessory configuration effects. Direct recycling is included due to its rapidly developing status and potential for lower energy, cost, and emissions than conventional routes.

- Reuse profitability: ESS and CBS scenarios are generally profitable for both NMC and LFP, with ESS yielding higher total profit and CBS higher average daily profit. LSV reuse is typically not profitable due to reliance on grid electricity without renewable integration. - LFP vs NMC in reuse: LFP SLBs often outperform NMC due to longer lifespans and higher efficiency. Higher initial SOH increases unit battery profit in ESS/CBS due to longer usable life; however, in some ESS cases, 70% SOH LFP can deliver higher daily profit than 80% SOH due to lower purchase cost (e.g., +$0.13/day in ESS-Gov and +$0.32/day in ESS-Com). - Capacity configuration: Profitability in ESS/CBS increases with larger capacities up to practical limits; CBS shows diminishing returns due to a 3-hour operational limit. LSV prefers low daily average cost configurations. Break-even from initial loss to profit in ESS/CBS occurs within 64–628 days under optimal configurations. - Recycling economics: For NMC, direct recycling is most profitable across SOH 40%–90% due to simplicity and higher value of recovered NMC; for LFP, hydrometallurgical recycling is most profitable, with LFP profits varying less with SOH than NMC. Profitability ranking is sensitive to Li2CO3 price: as lithium price increases, hydrometallurgical and pyrometallurgical options gain relative advantage (lithium is an output for hydro/pyro but an input for direct). Cost-to-revenue ratios decrease with higher SOH (NMC direct: 0.80→0.58 from 40%→90% SOH; LFP hydro: 0.92→0.60), reflecting improved material recovery and lower inputs. - Recycling composition: For NMC direct recycling, major costs include degraded NMC and Li2CO3; major revenues from regenerated NMC, graphite, and copper. For LFP hydrometallurgy, major costs include degraded LFP, labor, and water; revenues from FePO4, Li2CO3, and copper. Higher SOH increases Li2CO3 revenue share for LFP. - Carbon footprint: Recycling CF per kWh decreases with increasing SOH. NMC recycling delivers larger environmental benefits than LFP due to higher positive electrode impacts; direct recycling exhibits lower energy use and CF than hydro/pyro, making it environmentally favorable. - Integrated reuse + recycling: For NMC, delaying recycling (wider SOH reuse interval) increases lifecycle profit for ESS/CBS; LSV remains negative. Maximum lifecycle economic benefit reported is $66.67/kWh by using CBS then direct recycling from 90% to 40% SOH; the worst is −$59.26/kWh for LSV (90%→40% SOH) with direct recycling. Minimum lifecycle CF for NMC observed at 0.23 kg CO2-eq/kWh life cycle electricity delivery (ESS-Lig, 70% SOH). - Pathway optimization vs traditional: Compared with a traditional pathway (EV use then hydrometallurgical recycling), an optimized pathway including refurbishment, CBS reuse, and direct recycling (for NMC) or hydrometallurgical recycling (for LFP) reduces lifecycle costs and emissions. Example: NMC retired at 90% SOH—optimized pathway lowers final lifecycle cost to $91/kWh vs traditional, a $43/kWh reduction. For LFP (80% SOH), optimized CBS reuse + hydro-recycling yields $156/kWh profit and reduces CF by 0.15 kg CO2-eq/kWh life cycle electricity delivery; for NMC (80% SOH), $38/kWh profit and a 0.14 kg CO2-eq/kWh reduction. - System-level potential: With 150 GWh global retirements in 2025 (45% LFP, 55% NMC), maximum reuse and recycling profit could reach $13.6 billion. - Strategic insights: Users benefit more from extended reuse, whereas recyclers prefer higher-SOH inputs. Profitability-based rankings by SOH can align incentives between users and recyclers.

The findings demonstrate that jointly optimizing reuse and recycling pathways can substantially improve both economic and environmental outcomes for retired EV batteries. ESS provides the highest total profit, and CBS provides the highest average daily profit in reuse, while LSV is generally unfavorable economically. Direct recycling is most economical for NMC and hydrometallurgical for LFP, with direct recycling also offering the lowest carbon footprint for both chemistries. Integrating SOH into both reuse and recycling decisions is crucial: higher SOH increases profitability and reduces recycling CF per kWh, and delaying recycling to extend reuse typically improves lifecycle value. The work shows how combining LCA with profit optimization and capacity configuration yields actionable guidance for selecting application scenarios and recycling technologies, reconciling the interests of users (seeking long-term value) and recyclers (seeking higher-SOH feedstock). The strategy is relevant across stakeholders, informing R&D and manufacturing (e.g., materials choices, design for recycling), reuse operators (capacity configuration and scenario selection), recyclers (technology choice under material price dynamics), and policymakers (carbon footprint and circularity goals). Broader implications include the need for data transparency (battery passports), alignment of incentives, and consideration of future grid decarbonization trajectories that can shift environmental outcomes.

The study proposes and validates a universally applicable evaluation framework for pathway decisions for retired LIBs that integrates economic performance and carbon footprint over the full life cycle. For reuse, ESS maximizes total profit and CBS maximizes daily profit; for recycling, direct recycling is preferable for NMC and hydrometallurgical for LFP, with direct recycling minimizing carbon footprint for both. Optimized pathways for LFP (e.g., 80% SOH with CBS reuse plus hydrometallurgy) and NMC (e.g., 80% SOH with CBS reuse plus direct recycling) demonstrate significant profit gains and carbon reductions relative to traditional immediate-recycling pathways. At scale, optimized reuse and recycling could deliver multi-billion-dollar value. Future research should incorporate enhanced safety considerations, improved SOH and RUL prediction, automated and collaborative disassembly, user behavior impacts, expanded reuse scenarios and advanced recycling processes, business models, and policy mechanisms, as well as broader chemistry coverage to generalize sustainability insights.

- The failure rate of LIB cells during refurbishment is not modeled. - Recycling output quality (battery-grade regeneration and closed-loop integration) is not considered; open-loop allocation without market disequilibrium is assumed. - Direct recycling is rapidly evolving and modeled based on current insights; industrial-scale variability may affect results. - Baseline environmental context assumes constant electricity carbon intensity (Guangdong, China, 2023) without temporal or spatial grid variations; sensitivity shows results may shift with future decarbonization. - Only two chemistries (NMC, LFP) and specific application profiles are assessed; generalization to other chemistries and use cases may require additional data. - Some economic outcomes are sensitive to volatile material prices (e.g., Li2CO3) and to capacity configuration assumptions and degradation models. - The study does not jointly optimize multi-stakeholder constraints (e.g., supply logistics, safety compliance) beyond the modeled boundaries.