With the rapid advancement of renewable energy, the energy storage battery industry has witnessed remarkable growth, achieving an installed capacity of 21 GW in 2023. Currently, lithium-ion and lead-acid batteries dominate this sector, jointly accounting for approximately 97% of the market share. This study employs a full life cycle assessment (LCA) methodology to conduct a comparative analysis of the environmental impacts associated with five typical energy storage cell types: Lithium Iron Phosphate (LFP), Lithium Nickel Cobalt Manganese Oxide (NCM), Lead-Acid Battery (LAB), and their respective secondary-use counterparts (SULFP and SUNCM). The functional unit is defined as the delivery of 1 kWh of energy, which integrates both the nominal capacity and the cycle life of the energy storage cell. The CML-IA baseline method is utilized to evaluate eight environmental impact categories: abiotic depletion potential (ADP), abiotic depletion potential-fossil fuels (ADP-ff), global warming potential (GWP), human toxicity (HT), acidification potential (AP), eutrophication potential (EP), ozone layer depletion potential (ODP), and photochemical oxidation (PO). The analysis encompasses the entire life cycle, including production, use, and end-of-life recycling stages for each energy storage battery.
1. Goal and Scope Definition
The primary goal of this assessment is threefold: first, to establish comprehensive life cycle inventories and contrast the average environmental impacts of the five energy storage cell technologies within a Chinese context; second, to identify the most impactful life cycle stages (hotspots) for each energy storage battery; and third, to explore the influence of allocation methods, operational parameters, and future energy mix scenarios on the overall environmental footprint. The core function of an energy storage cell is to serve as a medium for energy transfer. Therefore, the functional unit is precisely defined as the delivery of 1 kWh of electrical energy over the entire service life of the battery pack. This approach provides a more accurate and comparable basis for evaluation than using simply 1 kWh of nominal capacity, as it inherently accounts for the performance and longevity differences among various energy storage cell chemistries.
2. Life Cycle Inventory and Modeling
The system boundary for new energy storage batteries (LFP, NCM, LAB) spans from raw material extraction (cradle) through battery production and assembly, the use phase, to final recycling or disposal (grave). For secondary-use energy storage cells (SULFP, SUNCM), the boundary starts with the repurposing process of retired electric vehicle battery packs, which includes dismantling, component refurbishment or replacement, and re-assembly, followed by the second-life use phase and eventual end-of-life management. The environmental burdens from the initial vehicle-use phase are allocated to the first life. Key performance parameters for the modeled energy storage batteries are summarized in Table 1.
| Parameter | Unit | LFP | NCM | SULFP | SUNCM | LAB |
|---|---|---|---|---|---|---|
| Initial Capacity | % | 100 | 100 | 80 | 80 | 100 |
| End-of-Life Capacity | % | 60 | 60 | 60 | 60 | 60 |
| Nominal Capacity | kWh | 1 | 1 | 1 | 1 | 1 |
| Charge/Discharge Efficiency | % | 90 | 90 | 88 | 88 | 77.5 |
| Depth of Discharge (DoD) | % | 80 | 80 | 80 | 80 | 80 |
The capacity fade of lithium-based energy storage cells over cycles is modeled using an empirical equation:
$$ \xi = A \cdot e^{-\frac{E_a}{R \cdot T}} \cdot n^z $$
where \(\xi\) is the relative capacity loss (%), \(A\) is a constant, \(E_a\) is the activation energy (J·mol⁻¹), \(R\) is the gas constant (J·(mol·K)⁻¹), \(T\) is the temperature (K), \(n\) is the cycle number, and \(z\) is the power-law coefficient. For the LFP energy storage cell, parameters are \(A = 0.1825\), \(E_a/R = -1324.65/T\) at \(T=298.15\) K, and \(z = 0.5878\). For the NCM energy storage cell, \(A \cdot e^{-E_a/(R \cdot T)} = 0.00362\) and \(z = 0.588\).
The total energy input (\(E_{In}\)), output (\(E_{Out}\)), and loss (\(E_{Loss}\)) over the life of a lithium-based energy storage battery are calculated as follows:
$$ E_{In} = \sum_{i=1}^{n} (1 – \xi_i) \cdot E_I \cdot DoD \cdot \alpha^{-1} $$
$$ E_{Out} = \sum_{i=1}^{n} (1 – \xi_i) \cdot E_I \cdot DoD \cdot \alpha $$
$$ E_{Loss} = E_{In} – E_{Out} = \sum_{i=1}^{n} (1 – \xi_i) \cdot E_I \cdot DoD \cdot \frac{1-\alpha^2}{\alpha} $$
where \(E_I\) is the initial nominal energy capacity (kWh) of the energy storage cell, and \(\alpha\) is the round-trip charge/discharge efficiency. For the LAB, a linear capacity degradation is assumed, and its energy loss is calculated accordingly. The environmental impact per delivered energy, termed Levelized Environmental Impact of Energy (LEIOE), is computed as:
$$ LEIOE = \frac{EI_{Unit}}{E_{Out}} $$
where \(EI_{Unit}\) represents the total life cycle environmental impact of a unit energy storage battery (with 1 kWh nominal capacity), encompassing impacts from production (\(EI_{pro}\)), use (\(EI_{use}\)), and end-of-life management. The end-of-life impact is a weighted average of formal recycling (\(EI_{rec}\)) and disposal (\(EI_{dis}\)), based on the formal collection/recycling rate (\(\rho\)).
3. Life Cycle Impact Assessment Results
The comparative life cycle impact assessment results for the five energy storage batteries, normalized to the functional unit of 1 kWh delivered, are presented in Table 2. The analysis reveals a clear hierarchy in environmental performance.
| Impact Category | Unit | LFP | NCM | SULFP | SUNCM | LAB |
|---|---|---|---|---|---|---|
| ADP | kg Sb eq | 4.37E-06 | 1.16E-05 | 4.50E-06 | 1.11E-05 | 3.06E-05 |
| ADP-ff | MJ | 2.53 | 3.03 | 3.01 | 3.44 | 7.70 |
| GWP | kg CO₂ eq | 2.70E-01 | 3.16E-01 | 3.33E-01 | 3.69E-01 | 8.21E-01 |
| HT | kg 1,4-DB eq | 1.43E-01 | 4.48E-01 | 2.24E-01 | 3.87E-01 | 5.35E-01 |
| AP | kg SO₂ eq | 1.24E-03 | 1.82E-03 | 1.56E-03 | 1.88E-03 | 4.00E-03 |
| EP | kg PO₄ eq | 3.21E-04 | 4.25E-04 | 3.79E-04 | 4.80E-04 | 8.88E-04 |
| ODP | kg CFC-11 eq | 2.50E-09 | 6.80E-09 | 2.90E-09 | 6.11E-09 | 8.04E-09 |
| PO | kg C₂H₄ eq | 5.38E-05 | 7.21E-05 | 6.09E-05 | 7.50E-05 | 1.56E-04 |
The LFP energy storage cell demonstrates the most favorable environmental profile, exhibiting the lowest impact in seven out of the eight categories. Its GWP is calculated at 0.27 kg CO₂ eq per kWh delivered. The secondary-use LFP (SULFP) energy storage battery generally ranks second, showing significant benefits over new NCM and LAB cells in most categories, except for a slightly higher GWP than new NCM. The new NCM and secondary-use NCM (SUNCM) energy storage cells show mixed results, with each outperforming the other in different categories. Notably, the LAB energy storage cell consistently presents the highest environmental burden across all impact categories, with a GWP value approximately three times that of the LFP cell. Therefore, the overall environmental impact potential from low to high is: LFP, SULFP, NCM, SUNCM, and LAB. This ranking underscores the superior environmental performance of lithium iron phosphate chemistry for energy storage applications and highlights the potential benefits of extending battery life through secondary use, albeit with performance trade-offs.
4. Contribution Analysis and Hotspot Identification
Disaggregating the impacts by life cycle stage provides crucial insights for mitigation strategies. For all types of energy storage batteries, the use phase is the dominant contributor to impacts related to fossil fuel depletion (ADP-ff), global warming (GWP), acidification (AP), eutrophication (EP), and photochemical oxidation (PO), typically accounting for 58% to 92% of the total burden in these categories. This dominance is directly attributed to the electricity consumed (and lost) during the charge/discharge cycles of the energy storage cell. The environmental footprint of this electricity is a function of the grid mix, making the use phase emissions highly dependent on the carbon intensity of the local power generation.
The production (and repurposing) stage is the primary hotspot for abiotic resource depletion (ADP, mainly due to metal use), human toxicity (HT), and ozone layer depletion (ODP), contributing between 44% and 235% of the impact in these categories. For lithium-based energy storage cells, the production of cathode materials (containing lithium, nickel, cobalt, manganese, phosphorus), copper foil, and the battery management system are key drivers. For the lead-acid energy storage battery, lead and copper consumption are major factors.
The recycling stage generally offers environmental benefits by offsetting the need for virgin material production. It shows significant net positive effects (impact reduction) in categories like ADP, HT, AP, EP, ODP, and PO, ranging from 1.8% to 143.1% reduction relative to the burdens. However, due to the significant energy inputs required for recycling processes, the net benefits in terms of fossil depletion (ADP-ff) and global warming (GWP) are relatively modest, between 0.2% and 5.7%. The formal recycling rate (\(\rho\)) is a critical parameter here, with LAB currently having a much higher formal collection rate than lithium-ion energy storage batteries in many regions, enhancing its end-of-life credit potential.
5. Sensitivity and Scenario Analysis
5.1 Influence of Allocation Methods for Secondary-Use Energy Storage Cells
The choice of method to allocate environmental burdens between the first (vehicle) and second (stationary storage) life of a repurposed energy storage battery significantly influences the results. Four common methods were analyzed: economic allocation (based on relative market value, 33% to second life), physical allocation (based on energy delivered, 59% to second life), 50/50 allocation (equal split), and the cut-off method (0% of production burden, 100% of recycling benefit to second life). Compared to the baseline economic allocation, the cut-off method yields the most favorable results for the secondary-use energy storage cell, reducing its calculated impacts by 6% to 91%, particularly in resource and toxicity-related categories. This method strongly promotes the perceived benefits of second-life applications. Conversely, the 50/50 and physical allocation methods, which assign a larger share of the initial production burden to the second life, can make the secondary-use energy storage battery appear less environmentally advantageous than a new one. The selection of an allocation method should reflect the specific goal of the study and the maturity of the second-life market.
5.2 Sensitivity to Operational Parameters
A sensitivity analysis was conducted on three key parameters: cycle life, charge/discharge efficiency (\(\alpha\)), and formal recycling rate (\(\rho\)). The results are starkly different. Variations in the cycle life of the energy storage cell show relatively low sensitivity (below 25% for a ±10% change) on the GWP per kWh delivered. This is because the impact per cycle during the use phase remains constant, and changes in total cycles proportionally affect both total impact and total delivered energy.
In contrast, the charge/discharge efficiency of the energy storage battery is an extremely sensitive parameter. An improvement of just 5% in \(\alpha\) can lead to a reduction in life cycle GWP by 18% to 71%, translating to a sensitivity coefficient exceeding 367%. This high sensitivity stems from a dual effect: higher efficiency directly reduces the amount of electricity wasted as heat loss per cycle, and it increases the total amount of energy delivered over the battery’s life, thereby diluting the fixed impacts from production and recycling. This highlights that technological advancements aimed at improving the round-trip efficiency of an energy storage cell are among the most effective levers for minimizing its environmental footprint.
The formal recycling rate shows the lowest sensitivity. A 5% increase in \(\rho\) reduces overall GWP by less than 0.27%, with a sensitivity below 5.5%. While increasing recycling rates is important for resource conservation and waste management, its effect on reducing the carbon footprint per unit of delivered energy is marginal compared to efficiency gains.
5.3 Emission Reduction Potential under Future Electricity Mixes
The environmental impact of the energy storage cell, particularly its GWP, is intrinsically linked to the carbon intensity of the electricity used during its operation and manufacturing. Projecting the life cycle GWP of each energy storage battery under China’s evolving grid mix towards 2025, 2035, and 2050 reveals substantial reduction potential. Compared to the 2021 baseline, the cleaner electricity mixes in 2025, 2035, and 2050 are projected to reduce the life cycle GWP of all five energy storage cell types by over 31%, 52%, and 72%, respectively. This demonstrates that the decarbonization of the power grid is a critical, system-level strategy for enhancing the environmental benefits of energy storage systems. Furthermore, in scenarios where the energy storage battery is coupled directly with a photovoltaic (PV) system, the use-phase emissions can be virtually eliminated, potentially reducing the life cycle GWP of the energy storage cell by 78% to 92%.
6. Conclusions and Recommendations
This comprehensive life cycle assessment of five typical energy storage batteries leads to several key conclusions and policy-relevant insights:
- Performance Ranking: Among the studied technologies, the Lithium Iron Phosphate (LFP) energy storage cell presents the lowest environmental impact potential across most categories. The environmental burden from low to high follows the order: LFP, secondary-use LFP, new NCM, secondary-use NCM, and lead-acid battery (LAB).
- Dominant Life Cycle Stage: For all energy storage cell types, the use phase is the predominant contributor to fossil depletion, climate change, acidification, eutrophication, and smog formation impacts, accounting for the majority of the burden.
- Critical Lever for Improvement: Enhancing the round-trip charge/discharge efficiency of the energy storage battery is the single most effective technical parameter for reducing its life cycle environmental impact, exhibiting a sensitivity over an order of magnitude greater than improving cycle life or formal recycling rates.
- System-Level Synergy: The ongoing greening of the electricity grid is a powerful driver for reducing the carbon footprint of energy storage systems. Coupling energy storage cells directly with renewable generation sources like solar PV can dramatically amplify these benefits.
Based on these findings, to promote the low-carbon and sustainable development of the energy storage industry, the following recommendations are proposed:
- Technology Prioritization: Support the development and deployment of LFP-based energy storage cell technology, given its superior environmental profile and lower critical material supply risk compared to NCM chemistries.
- Research and Development Focus: Direct substantial R&D investment towards technologies that improve the round-trip efficiency of energy storage batteries, as this yields the highest environmental return per unit of improvement.
- Policy and Infrastructure: Accelerate policies that facilitate the deep decarbonization of the power grid. Simultaneously, develop robust regulatory frameworks and infrastructure to scale up the formal, environmentally sound recycling of all types of energy storage cells, especially lithium-ion, to capture resource value and mitigate end-of-life impacts.
- System Design: Encourage the integrated design and deployment of energy storage systems alongside renewable energy sources to maximize lifecycle emission reductions.
In summary, the sustainable evolution of the energy storage sector hinges not only on selecting the right battery chemistry but also on continuous improvements in operational efficiency, supportive grid decarbonization policies, and the establishment of a circular economy for battery materials. The energy storage cell is a pivotal component in the clean energy transition, and a holistic, life-cycle-informed approach is essential to ensure its development aligns with overarching environmental sustainability goals.