Retired EV Batteries Find New Life in Grid Storage
As the global electric vehicle (EV) market surges forward, a new challenge emerges: what to do with millions of retired lithium-ion batteries. With lifespans typically ending when capacity drops below 80%, these batteries still retain significant energy potential—often 60% or more of their original capability. Rather than consigning them to landfills or recycling facilities prematurely, researchers and engineers are turning their attention to second-life applications, particularly in grid-scale energy storage. This shift not only promises economic benefits but also aligns with broader sustainability goals by extending the useful life of valuable materials and reducing environmental impact.
Among the most promising reuse pathways is the integration of retired EV batteries into stationary energy storage systems (ESS) for power grids. These systems can help balance supply and demand, integrate renewable energy sources, and enhance grid stability. However, selecting the right application scenario for a given batch of retired batteries has remained a complex and often subjective process. Without a standardized, data-driven methodology, project developers and utilities face uncertainty in assessing technical feasibility, economic viability, and safety risks.
A recent study published in the Journal of Global Energy Interconnection offers a breakthrough in this domain. Led by Xie Hua from the School of Electrical Engineering at Beijing Jiaotong University, along with colleagues Liu Zhe, Kong Depeng, and Lei Bo, the research introduces a comprehensive evaluation framework designed to determine the most suitable grid storage applications for retired power batteries. The paper, titled “Scenario Applicability Evaluation Method of Second-life Batteries for the Grid Energy Storage Based on the Improved VIKOR Algorithm,” presents a systematic approach that combines technical, economic, and safety performance metrics to guide decision-making in real-world projects.
The growing volume of retired EV batteries underscores the urgency of this work. In China alone, where EV adoption has skyrocketed in recent years, battery production and deployment have reached unprecedented levels. According to industry data, lithium iron phosphate (LFP) and ternary lithium batteries account for 97.18% of all installed power batteries. As these vehicles reach end-of-life, the number of retired units is expected to grow exponentially. Without effective reuse strategies, this could lead to a waste management crisis and missed opportunities for resource recovery.
The traditional approach to evaluating second-life battery applications has often been fragmented. Some studies focus narrowly on technical performance, such as cycle life and degradation rates, while others emphasize economic models like net present value (NPV) or internal rate of return (IRR). Safety assessments, though critical, are sometimes treated as an afterthought. The lack of an integrated framework has made it difficult to compare different use cases objectively or to prioritize investments based on holistic criteria.
Xie Hua and her team address this gap by proposing a three-dimensional evaluation system that considers technical performance, economic performance, and safety performance as equally important pillars. Each dimension is supported by a set of quantifiable indicators, allowing for a balanced and transparent assessment. This multi-criteria decision-making model is not only more robust than single-factor analyses but also better reflects the complexity of real-world energy projects.
On the technical side, the researchers identify three key parameters: capacity retention rate, capacity deviation, and capacity degradation rate. The capacity retention rate measures how much usable energy a battery retains relative to its original capacity, providing a snapshot of its current health. Capacity deviation reflects the inconsistency among individual cells within a battery pack—a critical factor in system reliability, as mismatched cells can lead to imbalanced charging and accelerated aging. The capacity degradation rate captures how quickly the battery loses performance over time under specific operating conditions, which varies significantly depending on the application.
For economic evaluation, the study incorporates standard financial metrics widely used in energy project analysis. Net present value (NPV) estimates the total profitability of a storage system over its lifetime, accounting for both revenue streams and operational costs. Internal rate of return (IRR) indicates the project’s efficiency in generating returns relative to the initial investment, serving as a benchmark for comparing alternative uses of capital. Dynamic payback period measures how long it takes for cumulative earnings to offset the upfront expenditure, offering insight into financial risk and liquidity.
Safety, often the most sensitive aspect of battery reuse, is assessed through a tiered classification system. Drawing on existing safety standards, the researchers define four levels of battery safety status, ranging from “normal operation” to “prohibited use” due to thermal runaway or severe electrolyte loss. They also introduce a battery aging degree index that correlates capacity fade with increased risk of failure. By quantifying safety as a measurable indicator rather than a qualitative judgment, the model enables more objective risk comparisons across different scenarios.
What sets this research apart is not just the breadth of its evaluation framework, but also the sophistication of its analytical methodology. The team employs an enhanced version of the VIKOR algorithm—a multi-attribute decision-making technique known for balancing group utility and individual regret. Unlike conventional ranking methods that simply order alternatives by score, VIKOR seeks a compromise solution that maximizes collective satisfaction while minimizing dissatisfaction among stakeholders.
To refine the weighting of each evaluation criterion, the researchers combine two established techniques: the CRITIC method and the entropy weight method. The CRITIC (Criteria Importance Through Intercriteria Correlation) method evaluates the contrast intensity and conflict between indicators, assigning higher weights to those that carry more discriminative power. The entropy weight method, rooted in information theory, determines weights based on the dispersion of data—indicators with greater variability receive higher importance. By integrating these two approaches, the model achieves a more balanced and less biased weighting scheme than either method could provide alone.
The evaluation process is further strengthened by incorporating group decision-making principles. Recognizing that different stakeholders—engineers, financiers, regulators—may prioritize criteria differently, the model allows for adjustable weight ranges within predefined bounds. This flexibility ensures that the final recommendation reflects a consensus-oriented outcome rather than a rigid, one-size-fits-all ranking.
To validate their approach, the researchers apply it to three representative grid storage scenarios: peak-valley arbitrage, renewable energy output smoothing, and frequency modulation auxiliary services. Each scenario represents a distinct operational profile with unique demands on battery performance.
Peak-valley arbitrage involves charging batteries during off-peak hours when electricity prices are low and discharging during peak periods when prices are high. This application typically requires deep cycling—up to 80% depth of discharge (DOD)—and relatively slow charge/discharge rates (e.g., 0.2C). While economically attractive in regions with large price spreads, it subjects batteries to significant stress, accelerating degradation.
Renewable energy output smoothing aims to mitigate the variability of wind and solar generation by storing excess power during high production and releasing it during lulls. This use case usually involves moderate DOD (around 20%) but frequent cycling, sometimes multiple times per day. The goal is to provide a stable, dispatchable output to the grid, enhancing the predictability of renewable sources.
Frequency modulation, or frequency regulation, is one of the most demanding applications for battery storage. It requires rapid response to grid signals, often involving high-power bursts at 2C or higher, with hundreds of cycles per day. Although this service can generate high revenue due to its critical role in grid stability, it imposes extreme thermal and mechanical stress on batteries, leading to faster wear and higher safety risks.
Using real-world data from retired LFP batteries with an initial capacity of 0.75 kWh, the team conducts a comparative analysis across these three scenarios. Their findings reveal clear trade-offs. In terms of technical performance, the renewable smoothing application shows the highest compatibility, as the moderate cycling regime aligns well with the remaining health of the batteries. However, from an economic standpoint, peak-valley arbitrage emerges as the most profitable, thanks to favorable electricity pricing structures in the test region (a suburban industrial park in Beijing). Frequency modulation, despite its high revenue potential, scores poorly on economics due to the accelerated degradation and shorter system lifespan, which erode net returns.
Safety assessments further refine the results. All three scenarios are deemed acceptable under the model’s safety thresholds, but peak-valley arbitrage and renewable smoothing show higher safety indices due to less aggressive operating conditions. Frequency modulation, while technically feasible, carries a higher risk profile, particularly as the batteries age and their thermal management becomes less effective.
When all dimensions are integrated using the improved VIKOR algorithm, the overall applicability ranking becomes evident: peak-valley arbitrage ranks highest, followed by renewable output smoothing, with frequency modulation falling out of contention due to its negative economic score. This outcome highlights a crucial insight—the most technically suitable application is not necessarily the most economically viable or safest. Decision-makers must therefore adopt a holistic view that accounts for all three dimensions.
The implications of this research extend beyond academic interest. For battery recyclers, energy storage developers, and utility planners, the proposed evaluation method offers a practical tool for optimizing asset utilization. Instead of relying on intuition or fragmented data, stakeholders can now apply a standardized, transparent process to match retired batteries with their best-fit applications. This not only improves project economics but also enhances system reliability and public confidence in second-life battery technologies.
Moreover, the methodology supports the development of battery passport systems—digital records that track a battery’s history, performance, and suitability for reuse. As regulatory frameworks evolve, such as the European Union’s Battery Regulation, which mandates sustainability and traceability requirements, tools like the one developed by Xie Hua’s team will become essential for compliance and market access.
The study also opens avenues for future research. While the current model focuses on LFP batteries, the framework can be adapted to other chemistries, including nickel-manganese-cobalt (NMC) and sodium-ion batteries. Additionally, incorporating real-time monitoring data from battery management systems (BMS) could enable dynamic re-evaluation of applicability as battery conditions evolve over time. Machine learning enhancements might further refine predictions by identifying non-linear degradation patterns and hidden failure modes.
In conclusion, the work by Xie Hua, Liu Zhe, Kong Depeng, and Lei Bo represents a significant step forward in the sustainable management of retired EV batteries. By providing a rigorous, multi-dimensional evaluation method grounded in real-world data and advanced decision theory, they offer a much-needed solution to one of the most pressing challenges in the clean energy transition. As the world moves toward deeper decarbonization, the ability to repurpose existing assets efficiently and safely will be key to building resilient, low-carbon energy systems.
The findings underscore a broader principle: circular economy strategies in the energy sector are not just environmentally sound—they are economically smart. By extending the life of batteries through intelligent reuse, society can reduce waste, conserve resources, and unlock new value streams. This research paves the way for a future where no battery is truly “retired,” but instead finds new purpose in powering a cleaner, more sustainable grid.
Scenario Applicability Evaluation Method of Second-life Batteries for the Grid Energy Storage Based on the Improved VIKOR Algorithm by Xie Hua, Liu Zhe, Kong Depeng, Lei Bo, Journal of Global Energy Interconnection, DOI: 10.19705/j.cnki.issn2096-5125.2024.02.010