With the rapid development of clean energy, new energy vehicles have gradually entered the market. As an energy storage device and a critical component of these vehicles, the performance of power batteries inevitably degrades over time and under varying operating conditions, eventually leading to retirement. These retired batteries, however, still retain 70%–80% of their initial capacity and contain valuable metal resources. By replacing components, screening, and recombining them, these batteries can be applied in other energy storage scenarios, thereby enhancing their full-lifecycle value. This cascade utilization not only improves resource and equipment utilization rates but also reduces costs and creates higher economic value. My research aims to quantitatively analyze both the social and economic benefits of a cascade utilization battery energy storage system (BESS), providing recommendations for the sustainable development of power batteries.
In recent years, the global sales of electric vehicles have surged. China has placed great emphasis on the development of the new energy vehicle industry, issuing multiple batches of enterprise directories and a series of policies to encourage and regulate the cascade utilization of retired power batteries. Many countries are actively conducting research on this topic. Foreign examples include the use of retired batteries in commercial storage, participation in power market services, and household power supply. Domestically, several demonstration projects have been completed, such as grid energy storage, backup power, and peak shaving. Current research primarily focuses on economic analysis of cascade utilization battery systems, with studies on life-cycle cost (LCC), net present value (NPV), internal rate of return, and levelized cost of electricity (LCOE). However, environmental impact assessments are relatively scarce. This study addresses this gap by combining life-cycle assessment (LCA) and life-cycle cost analysis to comprehensively evaluate the social and economic benefits of a retired battery energy storage system.
This paper takes a 10 MW/30 MW·h energy storage station as the research object. The station uses retired lithium iron phosphate (LFP) batteries and is designed to improve wind farm power generation while meeting grid stability requirements. I established an LCA model covering production, first use, reassembly, second use, and recycling stages. Four scenarios were considered: (1) second use for wind energy storage + wet recycling, (2) second use for wind energy storage + physical recycling, (3) second use for photovoltaic (PV) energy storage + wet recycling, and (4) second use for PV energy storage + physical recycling. The LCC model was used to calculate the net present value (NPV) and levelized cost of electricity (LCOE) for the 10 MW/30 MW·h system, considering initial investment, operation and maintenance costs, charging costs, replacement costs, and residual value. Sensitivity analyses were performed on key parameters.
LCA Model for Battery Energy Storage Systems
I adopted the life-cycle assessment methodology following the ISO 14000 standards. The goal was to evaluate the environmental impacts of LFP batteries over their entire life cycle, including production, first use (in electric vehicles), reassembly, second use (in stationary storage), and final recycling. The functional unit (FU) was defined as a battery pack with a nominal capacity of 1 kW·h. The system boundary covered five main stages: production, first use, reassembly, second use, and recycling. The production stage considered the manufacturing of cathode and anode materials, assembly, electrolyte filling, and integration. The first use stage accounted for electrical losses due to charging/discharging efficiency and battery weight. The reassembly stage included replacement of components such as cables, steel shells, and battery management systems (BMS). The second use stage considered the battery’s capacity degradation over time and electrical losses from inverter efficiency, station operation, and self-discharge. The recycling stage compared two technologies: wet recycling (hydrometallurgical) and physical recycling (direct mechanical separation).
Based on environmental impact reports from a lithium battery construction project, I compiled the production inventory per FU, as shown in Table 1.
| Category | Material/Energy | Value |
|---|---|---|
| Input | Lithium iron phosphate (kg) | 2.67 |
| Cathode conductive carbon (kg) | 1.16 | |
| PVDF (kg) | 0.12 | |
| NMP (kg) | 0.12 | |
| Deionized water (kg) | 15.40 | |
| CMC (kg) | 0.02 | |
| SBR (kg) | 0.58 | |
| Copper foil (kg) | 1.00 | |
| Graphite (kg) | 0.07 | |
| Separator (kg) | 0.02 | |
| Aluminum shell (kg) | 0.67 | |
| PET (kg) | 0.02 | |
| BMS (kg) | 0.16 | |
| Water (m³) | 2.27 | |
| LiPF₆ (kg) | 0.38 | |
| DMC (kg) | 1.07 | |
| EC (kg) | 0.65 | |
| Energy | Electricity (kW·h) | 35.50 |
| Natural gas (m³) | 1.67 | |
| Transport | Diesel truck transport (kg·km) | 3.30 |
| Wastewater | Total wastewater (kg) | 12.60 |
| COD (kg) | 6.30×10⁻⁴ | |
| SS (kg) | 1.26×10⁻⁴ | |
| Ammonia nitrogen (kg) | 9.58×10⁻⁵ | |
| Total phosphorus (kg) | 2.58×10⁻⁶ | |
| CO (kg) | 4.33×10⁻⁶ | |
| SO₂ (kg) | 1.75×10⁻⁵ | |
| Non-methane hydrocarbons (kg) | 1.32×10⁻² | |
| Exhaust | NOx (kg) | 3.12×10⁻³ |
| Dust (kg) | 4.19×10⁻⁴ | |
| Output | LFP battery (kg) | 9.38 |
For the first use phase, I used the average parameters of pure electric passenger vehicles from the latest Chinese catalogue: driving range 469 km, curb weight 1836 kg, battery mass 419 kg, battery capacity 66 kW·h, and energy consumption limit 17 kW·h per 100 km. The electrical loss due to charging/discharging efficiency was calculated as:
$$E_e = F_v \times D_v \times (1 – \eta_{k1})$$
where \(F_v\) is the energy consumption limit (kW·h/100 km), \(D_v\) is the total driving distance (km), and \(\eta_{k1}\) is the average charging/discharging efficiency in first use (assumed 90%). The electrical loss due to battery weight was:
$$E_m = k \times F_v \times D_v \times (m / m_T)$$
with \(k = 0.49\) as the powertrain distribution coefficient, \(m\) the battery mass, and \(m_T\) the vehicle curb mass.
The reassembly stage inventory is presented in Table 2.
| Category | Material/Energy | Value |
|---|---|---|
| Input | Retired LFP battery (kg) | 1.00 |
| Cable (copper) (kg) | 1.21×10⁻² | |
| Steel shell (kg) | 3.23×10⁻² | |
| BMS (kg) | 8.08×10⁻³ | |
| Nickel (metal) (kg) | 4.04×10⁻³ | |
| Energy | Electricity (kW·h) | 6.74×10⁻² |
| Output | Second-use battery (kg) | 1.00 |
For the second use phase, the battery energy storage system was assumed to have a cycle life of 3000 cycles, depth of discharge (DoD) 90%, average capacity retention 0.8, service life 3 years (per battery pack), daily cycle time 4 hours, annual usage 300 days, battery efficiency 92%, monthly self-discharge rate 3%, inverter efficiency 96%, and station power consumption 15 W. Capacity loss was modeled using the semi-empirical equation:
$$\xi = A \times e^{-(E_a / R_t)} \times L^z$$
where \(A = 0.1825\), \(E_a / R_t = 4.443\) K, and \(z = 0.5878\) for LFP. The round-trip electrical loss was:
$$E_{\text{round-trip}} = \sum_{l=1}^{L} E \times (1 – \xi) \times DoD \times (1 – \eta_T \times \eta_{k2})$$
where \(E\) is initial capacity in second use, \(\eta_T\) is transmission efficiency, \(\eta_{k2}\) is charging/discharging efficiency in second use (92%), and \(l\) is the cycle index. The station operation loss was:
$$E_{\text{op}} = L \times P_{\text{con}} \times t / f$$
with \(P_{\text{con}} = 15\) W, \(t\) discharge duration, and \(f\) cycles per day. Self-discharge loss:
$$E_{\text{self}} = E \times \eta_{\text{self}} \times t_{\text{stor}}$$
where \(\eta_{\text{self}} = 3\%\) per month and \(t_{\text{stor}}\) is storage time.
Two recycling technologies were considered: wet recycling (using hydrochloric acid, hydrogen peroxide, etc.) and physical recycling (using liquid nitrogen and mechanical separation). The LCA was performed using SimaPro software with the ReCiPe 2016 midpoint method. Five impact categories were selected: global warming potential (GWP), fine particulate matter formation (FPMF), terrestrial acidification (TA), marine eutrophication potential (MEP), and fossil resource scarcity (FRS).
LCC Model for Battery Energy Storage Systems
The economic analysis was based on the life-cycle cost theory. The total cost net present value (NPV) of the 10 MW/30 MW·h battery energy storage system was calculated using the formula:
$$C_{\text{total}} = C_{\text{inv}} + \sum_{n=1}^{N} \frac{C_{\text{COM}} + C_C}{(1+r)^n} + \frac{C_R}{(1+r)^{N_1}} + \frac{C_{\text{Rec}}}{(1+r)^N}$$
where \(C_{\text{inv}}\) is the initial investment cost, \(C_{\text{COM}}\) is the annual operation and maintenance cost, \(C_C\) is the annual charging cost, \(C_R\) is the replacement cost (every 5 years, when the battery pack reaches end of second life), \(C_{\text{Rec}}\) is the residual value at the end of the system life, \(r\) is the discount rate (8%), \(N\) is the system lifetime (5, 10, or 15 years), and \(N_1\) is the replacement time. The initial investment included the cost of retired batteries, BMS, power conversion system, and other balance-of-system components, totalling 4691.0 × 10⁴ yuan. Annual O&M cost was 57.4 × 10⁴ yuan, replacement cost was 1476.38 × 10⁴ yuan per replacement, and residual value rate was 0.5%.
The total electricity delivered over the lifetime, discounted, was:
$$E_{\text{total}} = \sum_{n=1}^{N} \frac{Q_E \times (1 – \eta_{\text{self}}) \times SOC \times DoD \times \eta_{\text{dis}} \times N_y}{(1+r)^n}$$
where \(Q_E = 30\) MW·h, \(SOC = 0.8\) (average), \(DoD = 0.9\), \(\eta_{\text{dis}} = 0.92\) (discharge efficiency), and \(N_y = 300\) cycles per year. The levelized cost of electricity was then:
$$C_{\text{LCOE}} = \frac{C_{\text{total}}}{E_{\text{total}}}$$
Environmental Impact Results
The LCA results for the four scenarios (1: wind + wet; 2: wind + physical; 3: PV + wet; 4: PV + physical) are summarized in Table 3.
| Impact category | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 |
|---|---|---|---|---|
| GWP (kg CO₂ eq) | 194.0 | 251.0 | 212.0 | 268.0 |
| FPMF (kg PM2.5 eq) | 0.394 | 0.491 | 0.405 | 0.502 |
| TA (kg SO₂ eq) | 0.943 | 1.132 | 0.928 | 1.116 |
| MEP (kg P eq) | -0.011 | -0.008 | -0.010 | -0.009 |
| FRS (kg oil eq) | 43.7 | 57.7 | 49.5 | 63.4 |
Generally, Scenario 1 (wind energy storage with wet recycling) exhibited the lowest environmental impacts across most categories, except for TA where Scenario 3 (PV + wet) was slightly better. The production phase and the first use phase contributed most to GWP, FPMF, TA, and FRS. The reassembly phase had negligible influence. The second use phase for wind energy storage had lower GWP than for PV because wind power has a cleaner grid mix. Wet recycling provided positive environmental credits by recovering materials (e.g., lithium chloride), thereby offsetting some impacts. Physical recycling, while simpler, resulted in higher net impacts due to energy-intensive processes and lower material recovery rates.
To further illustrate the contributions of different stages, Table 4 breaks down the GWP by life-cycle phase for Scenario 1.
| Life-cycle phase | GWP contribution |
|---|---|
| Production | 97.5 |
| First use | 89.2 |
| Reassembly | 1.5 |
| Second use (wind) | 18.3 |
| Recycling (wet) | -12.5 |
| Total | 194.0 |
The largest contributor in the production phase was electricity consumption (32%), followed by lithium iron phosphate production (24%) and BMS manufacturing (16%). In the recycling phase, wet recycling electricity accounted for 46% of the recycling stage impact, with calcium chloride and other reagents also significant.
Sensitivity Analysis of Environmental Impacts
I conducted a sensitivity analysis on GWP for Scenario 1 by varying four parameters: production energy consumption (\(E_p\)), first-use charging/discharging efficiency (\(\eta_{k1}\)), first-use energy consumption limit (\(F_v\)), and second-use charging/discharging efficiency (\(\eta_{k2}\)). Each parameter was varied by ±10% and ±15%. The results are presented in Table 5.
| Parameter | Change -15% | Change -10% | Change +10% | Change +15% |
|---|---|---|---|---|
| \(E_p\) | 188.5 | 191.0 | 197.0 | 199.5 |
| \(\eta_{k1}\) | 209.6 | 205.0 | 183.0 | 178.5 |
| \(F_v\) | 188.0 | 190.0 | 198.0 | 200.0 |
| \(\eta_{k2}\) | 198.5 | 197.0 | 191.0 | 189.5 |
The first-use charging/discharging efficiency had the greatest influence: a 10% increase reduced GWP by 11 units (from 194 to 183). The second-use efficiency had a smaller but still significant effect. Production energy consumption and first-use energy consumption limit had moderate effects. This indicates that improvements in battery efficiency during the first use (e.g., in electric vehicles) are crucial for reducing the overall environmental footprint of cascade utilization battery energy storage systems.
Economic Analysis Results
The NPV calculation for the 10 MW/30 MW·h battery energy storage system after 1000 cycles (first year of operation) is shown in Table 6.
| Item | Amount (10⁴ yuan) |
|---|---|
| Initial investment cost | 4,691.0 |
| Operation and maintenance cost (annual, discounted) | 185.8 |
| Revenue (electricity sales, discounted) | 655.5 |
| Residual value | 14.8 |
| Net present value (NPV) | -4,206.6 |
The NPV is negative, mainly due to the high initial investment. As technology matures, the cost of retired batteries and repurposing is expected to decrease, potentially making such projects economically viable.
The levelized cost of electricity (LCOE) was calculated for system lifetimes of 5 years and 15 years, as shown in Table 7.
| Cost component | 5-year LCOE (yuan/(kW·h)) | 5-year share (%) | 15-year LCOE (yuan/(kW·h)) | 15-year share (%) |
|---|---|---|---|---|
| Initial cost | 3.17 | 87.40 | 1.48 | 60.78 |
| O&M cost | 0.15 | 4.23 | 0.15 | 6.31 |
| Charging cost | 0.31 | 8.66 | 0.31 | 12.91 |
| Replacement cost | 0 | 0 | 0.49 | 20.09 |
| Residual value | -0.01 | -0.30 | -0.00 | -0.10 |
| Total LCOE | 3.63 | 100.00 | 2.44 | 100.00 |
For a 5-year lifetime, the LCOE is 3.63 yuan/(kW·h), with initial cost dominating. For a 15-year lifetime, two battery replacements are needed (cost 2 × 1476.38 × 10⁴ yuan discounted), yet the LCOE drops to 2.44 yuan/(kW·h) because the fixed initial cost is spread over more electricity generated. The charging cost is based on electricity from wind (0.283 yuan/(kW·h) in this analysis).
Sensitivity Analysis of LCOE
I examined the sensitivity of LCOE to four parameters: average state of charge (SOC), depth of discharge (DoD), charging electricity price (\(p_{\text{ele}}\)), and system lifetime (\(N\)). The results are summarized in Table 8.
| Parameter | Value range | LCOE range (yuan/(kW·h)) |
|---|---|---|
| SOC | 80%–95% | 2.48 – 2.40 |
| DoD | 76%–84% | 2.55 – 2.33 |
| \(p_{\text{ele}}\) | 0.283–0.673 yuan/(kW·h) | 2.44 – 2.87 |
| System lifetime \(N\) | 5, 10, 15 years | 3.63, 2.72, 2.44 |
LCOE decreases with higher SOC and DoD because more energy is delivered per cycle, reducing the per-unit cost of initial investment and O&M. LCOE increases with higher charging electricity price. The system lifetime has a strong impact: extending from 5 to 15 years reduces LCOE by 33% because the initial cost is amortized over more output, despite additional replacement costs. The charging cost source also matters: if the system charges from a mix of 50% wind and 50% PV, the average tariff of 0.478 yuan/(kW·h) leads to an LCOE of approximately 2.65 yuan/(kW·h).
Conclusion
This study comprehensively evaluated the social and economic benefits of a cascade utilization battery energy storage system using LCA and LCC methodologies. The key findings are as follows:
First, cascade utilization of retired power batteries can significantly reduce environmental impacts compared to disposing of them directly. Among the four scenarios investigated, the combination of second use for wind energy storage and wet recycling (Scenario 1) achieved the lowest global warming potential (194 kg CO₂ eq per FU) and the best performance in most impact categories. The production phase and first-use phase are the largest contributors; therefore, improving battery efficiency during electric vehicle operation and using cleaner energy in production can further reduce the environmental footprint. Wet recycling offers environmental credits by recovering valuable materials, while physical recycling is less beneficial.
Second, from an economic perspective, the current LCOE of the 10 MW/30 MW·h retired battery energy storage system is relatively high (2.44 yuan/(kW·h) for a 15-year lifetime). The system yields a negative NPV under the assumed parameters, indicating that it is not yet profitable without subsidies or further cost reductions. The initial investment cost dominates the LCOE, followed by replacement costs for longer lifetimes. Sensitivity analyses reveal that increasing the depth of discharge, state of charge, or system lifetime can lower the LCOE. The charging electricity price is a critical driver: using low-cost renewable energy (e.g., wind) makes the system more economical.
Third, the widespread application of battery energy storage systems based on retired power batteries is a promising pathway for resource conservation and environmental protection. However, technical improvements, standardization of repurposing processes, and supportive policies are essential to enhance economic viability. Future research should focus on optimizing the balance between environmental benefits and economic costs, exploring alternative recycling technologies, and developing business models that internalize the positive externalities of cascade utilization battery systems.
In conclusion, the cascade utilization of power batteries in battery energy storage systems provides a dual advantage: it extends the useful life of batteries and reduces the environmental burden associated with battery production and disposal. By continuing to refine LCA and LCC models and by integrating real-world data, we can better guide the sustainable development of this emerging industry.