Search Results (349)

Background: The global transition to low-carbon energy systems has intensified the need for circular approaches in energy supply chains, yet studies on second-life EV battery ecosystems in emerging economies remain fragmented between barrier prioritization and efficiency assessment. Methods: This study addresses this gap by integrating the Best–Worst Method (BWM) and Data Envelopment Analysis (DEA) to connect subjective expert-based prioritization with objective efficiency benchmarking. Using expert panel inputs and scenario-based circular energy configurations representing emerging economy conditions, the results indicate that technical barriers (28.4%) and economic barriers (24.9%) dominate the priority structure, with battery performance uncertainty and high initial investment as the most critical constraints. Results: DEA results show that configurations with formal reverse logistics and certification mechanisms achieve frontier efficiency (θ = 1.000), whereas fragmented informal configurations exhibit the lowest efficiency (θ = 0.712). High-tech configurations with weak regulation demonstrate that technological investment alone is insufficient without institutional development. Conclusions: The novelty lies in developing a context-sensitive BWM–DEA framework that embeds barrier priorities into efficiency evaluation, an approach rarely explored in prior circular supply chain research. The study provides a holistic decision-support tool for policymakers and industry stakeholders seeking to accelerate circular energy transitions in emerging economies. Full article

The rising penetration of renewable energy introduces greater volatility and uncertainty into energy systems. Energy storage systems (ESS) play a vital role in enhancing system flexibility and stability. However, frequent charge–discharge cycles lead to significant degradation of storage devices, reducing their economic efficiency and lifespan. This paper proposes a two-stage robust optimization framework under high renewable penetration, explicitly considering battery degradation. The first stage determines the optimal capacity configuration of distributed energy resources, including PV, wind, gas turbines, and ESSs. The second stage optimizes operational strategies under worst-case uncertainty in renewable generation and load, while accounting for the degradation cost and cycle life of the ESS. A linearized degradation model is developed based on depth-of-discharge (DoD), and the overall problem is solved using a Column-and-Constraint Generation (C&CG) algorithm. Simulation results demonstrate that the proposed approach effectively balances investment and operation, reduces degradation-related costs, and ensures reliable performance under uncertainty. Full article

The battery management system (BMS) is the core of ensuring the safety and performance of new energy vehicles, and real-time high-precision estimation of battery state of charge (SOC) is its key function, which directly affects battery safety, endurance, and service life. Faced with the challenges brought by high energy density and ultra-fast charging technology, lithium-ion batteries exhibit strong nonlinear and time-varying characteristics, making it difficult for existing SOC estimation methods to balance computational efficiency and accuracy. This study proposes a Bayesian-based Hammerstein multi-model (MM) fusion algorithm for accurate lithium battery SOC estimation across a wide temperature range, especially under low-temperature conditions. First, two Hammerstein SOC submodels are constructed: a traditional polynomial Hammerstein model and a TPA-Hammerstein model incorporating the temporal pattern attention mechanism. Second, KV-ADAM is employed for parameter training and identification of the submodels. Finally, a Bayesian weighted fusion strategy is used to dynamically integrate the outputs of the two submodels. The experimental results show that this method significantly improves the accuracy and robustness of SOC estimation, overcomes the limitations of a single model under complex dynamic conditions, provides an effective solution for lithium battery SOC estimation, and helps the safe operation of electric vehicles and the sustainable development of the industry. Full article

Lithium iron phosphate batteries have seen a recent rise in usage in electric vehicles and battery energy storage systems. For these applications, reliability is of paramount importance, influences long-term adoption and high return on investment, especially regarding battery replacement. Remaining Useful Life (RUL) prediction is at the core of avoiding unexpected failure and enabling proactive battery maintenance. Physics-based and data-driven methods have been explored by researchers, whilst Physics-Informed Neural Networks (PINNs) can combine their strengths in estimating battery RUL. This paper investigates the integration of the Gompertz function, an inherently interpretable white-box model, into Long Short-Term Memory (LSTM) networks to follow the physical laws of degradation, capture downward monotonic behavior and long-term dependencies from data resulting in Gompertz-Informed LSTMs (GILSTMs). Pure LSTMs are regarded as black box systems and critical infrastructure operators such as battery energy storage system (BESS) operators may refrain from using such systems. Gray-box models such as GILSTMs may get over this hurdle by increasing model interpretability and helping industry adopters know when they will benefit from data-driven modeling. This study explores two GILSTM architectures. The first uses an LSTM to predict Gompertz parameters, which are then converted into RUL via the inverse Gompertz equation. The second uses the inverse Gompertz equation as a verification step to cross-check the RUL values generated by the LSTM. The first type of GILSTM was constrained by both a physics loss and an inverse Gompertz layer to predict RUL while the second verified the results of an LSTM, despite that the GILSTMs failed to generalize. The first type of GILSTM achieved an average RMSE of 22.97%, while the second type achieved an average RMSE of 26.99%. The models in this paper are also benchmarked on the first 100 cycles, a current state of art for battery degradation testing. The best overall implementation was an LSTM that predicted RUL by recursively predicting SoH achieving an average RMSE per cycle of 9.18% and a 100th cycle RMSE of 17.02%. This study evaluates the trade-off between the predictive accuracy of black-box LSTMs and physical interpretability of Gompertz models. While pure LSTMs provide superior accuracy, the Gompertz parameters stabilize by 85% SoH. This 85% threshold serves as an interpretable confidence trigger, informing BESS operators when to rely on LSTM RUL forecasts. Full article

The second-life application of vehicle batteries is getting attention as millions of battery systems, modules, or cells are going to enter the market in the coming decade. The performance uncertainty with or without historical knowledge of the batteries’ vehicle usage is a concern. Moreover, detailed studies on second-life battery cell behavior is sparse and an improved understanding is required for reuse/repurpose. In this work, two second-life battery packs are dismantled, and the extracted prismatic and pouch Nickel–Manganese–Cobalt (NMC) cells with 141 Ah and 65 Ah, respectively, are extensively investigated to understand the second-life degradation behavior. The one-and-a-half-year-long test campaign has followed dedicated suitable stationary test matrices, generating a valuable dataset. The aging dataset is then filtered with the most correlated features via Pearson correlation analysis (PCA) and used to train different machine learning algorithms, resulting in a root-mean-square-error (RMSE) of 0.065 and 0.109 for prismatic and pouch cells, respectively, with the best-performing ElasticNet model validated against real-life stationary profiles. The developed framework is suitable for edge computation where the SoH could be evaluated online, facilitating state-based performance and lifetime extension. Full article

Electric two-wheelers (E2Ws) are promoted as lower-emission options in emerging economies. Their long-term cost competitiveness depends mainly on battery durability and how batteries are managed at the end of their life. This research examines Li-ion and nickel-cobalt-manganese (NCM)-type batteries versus the previously common lead-acid batteries in these markets. The study uses a 12-year total cost of ownership (TCO) framework that includes battery degradation, estimated first-life duration, and alternative lifecycle pathways. It covers three sensitivity analysis cases: conservative, base case, and optimistic. Three scenarios are evaluated: (1) no lifecycle management, (2) refurbishment for first-life extension, and (3) integrated lifecycle management with refurbishment, second-life utilisation, and recycling. Results show that managing the battery lifecycle can reduce TCO. The amount of reduction depends on first-life duration, ownership horizon, refurbishment cost, downstream residual value, and use intensity. The greatest TCO gains are found in battery categories with short first-life duration, allowing substantial residual value recovery during ownership. Batteries with first-life durations of 12 years or more provide smaller benefits. These findings support optimising lifecycle pathways for maximum residual value. Improved TCO performance, along with supportive infrastructure, policies, and market development, is critical for broader E2W adoption. Full article

Reliable state-of-health (SOH) estimation is a key prerequisite for the safe and effective reuse of second-life lithium-ion batteries. However, practical assessment during early-stage screening is often constrained by extremely limited cycling data, where only a few discharge cycles are available due to time and cost limitations. This study investigates SOH estimation under an extreme sparse-cycling scenario in which only three discharge cycles per battery are available, reflecting realistic constraints in early-stage second-life battery screening. Under such severe data limitations, conventional data-driven models become unreliable, motivating the need for data-efficient and interpretable approaches. To address this challenge, a physics-aware and explainable machine learning framework is proposed, integrating physically interpretable feature extraction with lightweight regression models and Shapley Additive exPlanations SHAP-based interpretability analysis. Electrochemically motivated and mathematically derived features are extracted from voltage, current, and capacity measurements to ensure robustness under severe data scarcity. Multiple model classes, including linear regression, support vector regression, tree-based ensembles, and deep learning architectures, are systematically evaluated to assess their suitability in this constrained regime. Experimental results on real second-life battery datasets demonstrate that physics-aware linear models provide stable and interpretable SOH estimates under extreme data sparsity, whereas more complex nonlinear and deep learning models exhibit higher variability due to insufficient training data. These findings highlight that model suitability is strongly dependent on data availability and support the adoption of interpretable, physics-aware approaches for early-stage second-life battery screening rather than long-term degradation modeling. Full article

Stationary energy storage (SES) is increasingly needed to integrate variable renewable generation and improve consumer self-consumption, but technology choices involve associated trade-offs between cost, efficiency, and life-cycle impacts. This study evaluates the role of second-life lithium-ion (Li-ion) batteries repurposed from electric vehicles for stationary applications, compared to lead-acid (Pb-acid) batteries and power-to-hydrogen-to-power (PtH₂P) systems. We develop an optimization-based sizing and dispatch framework using measured PV–load profiles and hourly market electricity prices, and evaluate performance per 1 MWh delivered to the load over a 10-year life cycle. Economic performance is quantified through discounted cash flows equal to levelized cost of storage (LCOS), while environmental performance is assessed through life-cycle metrics with explicit representation of recycling and second-life credits. In addition to global warming potential (GWP), the analysis considers additional resource and impact metrics, as well as key operational efficiency metrics, including bidirectional consumption efficiency, autonomy, and share of self-consumption/export of photovoltaic systems. Scenario and sensitivity analyses examine the impact of policy and financial parameters, in particular feed-in tariff remuneration and discount rate, on the comparative ranking of technologies. The results highlight how circular economy pathways, especially second-life distribution for Li-ion batteries and high end-of-life recovery for lead-acid batteries, have a significant impact on the life-cycle burden for delivered energy, while market-driven conditions for dispatching and export activities shape economic outcomes. Overall, the proposed workflow provides a transparent, circularity-aware basis for selecting stationary storage technologies associated with photovoltaic systems, under realistic operational constraints. Full article

Optimized dispatch of microgrids is crucial for improving the economic performance and long-term sustainability of modern low-carbon power systems. In particular, accurate economic dispatch modeling for battery energy storage systems (BESSs) is essential for properly evaluating the operational benefits and lifetime costs of microgrids. However, when both battery cycle aging and calendar aging are considered, the resulting scheduling model becomes highly nonlinear, high-dimensional, non-convex, and multimodal, which poses substantial challenges to conventional optimization methods. To alleviate the above problem, a symmetry-guided multi-strategy differential hybrid slime mold algorithm (MDHSMA) is introduced for the day-ahead economic dispatch of microgrids under a refined battery degradation framework. First, a chaotic bimodal mirrored Latin hypercube sampling strategy is designed to exploit symmetry during population initialization, thereby enhancing diversity and improving structured coverage of the search space. Second, a history-driven adaptive differential evolution mechanism is integrated to balance global exploration and local exploitation more effectively during the iterative search process. Third, a state-aware stagnation handling framework is incorporated to maintain population vitality and further improve convergence accuracy and robustness. MDHSMA is evaluated against 12 state-of-the-art optimizers on the CEC2017 and CEC2022 benchmark suites and two representative engineering optimization problems to verify its overall performance. In addition, it is applied to a microgrid case study with refined BESS degradation modeling. The results show that MDHSMA achieves the lowest comprehensive operating cost by effectively coordinating electricity arbitrage and battery life consumption. Moreover, it guides the energy storage system toward shallow charge–-discharge patterns, thereby mitigating accelerated degradation caused by excessive cycling. These results confirm the effectiveness and practical value of the proposed method for sustainable microgrid dispatch in complex nonconvex optimization scenarios. Full article

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

The increasing deployment of off-grid photovoltaic–battery energy storage systems (PV–BESSs) has intensified operational demands on battery energy storage, particularly when second-life lithium-ion batteries are employed. Due to aging-induced increases in internal resistance and reduced thermal margins, second-life batteries are more vulnerable to high-current operation at a low state-of-charge (SOC), which aggravates heat generation and accelerates degradation. In this study, an SOC-dependent soft current limiting strategy is proposed that reshapes the discharge current reference under low-SOC conditions while maintaining fixed SOC limits, thereby targeting current-domain protection rather than SOC-boundary adaptation for reliable off-grid operation. The proposed method introduces two SOC thresholds to gradually derate the allowable discharge current, preventing abrupt current changes near the lower SOC bound. A unified MATLAB/Simulink-based framework is developed for a 24 h representative off-grid PV–BESS scenario using a second-order equivalent circuit model coupled with a lumped thermal model. Simulation results show that the proposed current shaping reduces low-SOC current stress and associated Joule heating, leading to moderated temperature rise, while only slightly affecting the unmet load under the tested conditions. These findings indicate that SOC-dependent current shaping can provide a control-oriented means to reduce low-SOC electro-thermal stress in second-life batteries within the studied off-grid PV–BESS framework. Full article

As the global demand for lithium-ion batteries (LIBs) continues to rise, battery recycling has become a critical strategy for mitigating resource depletion, minimising environmental impact, and advancing a circular economy. However, recycled electrode materials, particularly cathode and anode powders, often contain residual impurities such as transition metals (e.g., Cu, Fe, Al), polymeric binders (e.g., PVDF), and electrolyte decomposition products. These contaminants can significantly impair the electrochemical performance, thermal stability, and overall safety of newly manufactured cells. This study aims to systematically investigate the nature, origin, and impact of impurities in recycled cathode and anode materials. A suite of analytical techniques, including inductively coupled plasma mass spectrometry (ICP-MS), infrared spectroscopy (IR), scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS), and thermogravimetric analysis (TGA), will be employed to quantify impurity levels and assess material integrity across various recycling streams. The findings are expected to inform the establishment of impurity threshold limits for battery-grade recycled materials and guide the development of enhanced purification protocols. Ultimately, this research will support the production of safer and more reliable second-life batteries, offering valuable insights to recyclers, manufacturers, and regulatory bodies committed to sustainable energy storage technologies. Full article

Isothermal dispatch models for battery energy storage systems (BESSs) systematically underestimate degradation costs because dispatch-induced Joule heating elevates cell temperature and accelerates ageing through Arrhenius-type kinetics. This paper proposes three integrated contributions. First, a thermal–electrochemical coupling loop embeds a first-order lumped thermal model within the dispatch simulation: cell temperature is updated from I²R heat generation and Newton cooling at each time step, and the resulting temperature trajectory feeds into the Arrhenius stress factors of a semi-empirical degradation model combining $\sqrt{\Delta t}$-based calendar ageing with Rainflow-based cycle ageing, enabling the optimiser to discover thermally self-regulating strategies. This coupling is critical because, as the results demonstrate, ignoring it leads to systematic underestimation of degradation costs by up to 13%. Second, the resulting four-objective problem (negative profit, thermally coupled degradation cost, SOC deviation, and CVaR imbalance penalty) is solved by a hypervolume-contribution-enhanced NSGA-III (HVC-NSGA-III), which augments reference-point selection with an archive pruned by removing the solution of the smallest individual hypervolume contribution, concentrating Pareto resolution in the knee region. Third, an SOH-adaptive knee-point selection assigns the degradation weight as a monotone function of ageing degree $(1-\mathrm{SOH})/(1-{\mathrm{SOH}}_{\mathrm{EOL}})$, automatically tightening dispatch conservatism as remaining useful life diminishes. Simulations on ENTSO-E data over 96 h show the following: (i) thermal coupling shifts the Pareto front by 8–15% in the degradation dimension with temperature excursions up to 7 K; (ii) HVC-NSGA-III improves hypervolume by 8.7% over standard NSGA-III; (iii) SOH-adaptive selection reduces capacity loss by 27.4% at only 9.1% revenue cost; and (iv) ablation confirms Rainflow (24.8%) and thermal coupling (13.1%) as the two largest contributors. Full article

To promote the sustainable development of power battery recycling, this study investigates the strategic interplay between altruistic preferences and channel structure. Addressing divergent interests and the dynamic evolution of recycling scale and brand reputation, a differential game model with two state variables is constructed to analyze four decision modes: resale/agency under selfish/altruistic scenarios. The results reveal that altruistic preferences induce Pareto improvements, reconciling the recycler’s utility with the partner’s profit growth. Notably, altruism acts as a moderating mechanism that reshapes channel advantages, enabling the Resale–Altruistic (RA) mode to surpass the agency mode as the system-wide optimal state. Furthermore, a substitutive compensation effect between altruistic preference and revenue-sharing contracts is identified. This research provides a quantitative framework for optimizing behavioral contract design and governance in battery recycling ecosystems. Full article

Lithium-ion batteries (LIBs) can considerably improve their lifespan by optimising operating conditions. This may entail ensuring optimal operating temperature, limiting the state-of-charge (SoC) window, reducing cycling current, and changing the physical orientation of the uncompressed LIB cell. In this study, we examine how these four conditions and some of their combinations impact degradation in both 1st life as well as in second life. The cell analysed in this investigation was the Xalt 31 HE cell, an energy-optimised Li-ion pouch cell with a capacity of 31 Ah and an NMC433-graphite chemistry. As a follow-up study of previously reported results, a total of 18 cells were investigated. We report results focusing on improving cycle life and ensuring safety before second life. The optimal conditions for first-life cycling in the full SoC window were found at room temperature, when cycled with a lower current and the cells oriented horizontally. We observed that under the same cycling conditions, a vertical alignment of cells resulted in an increased degradation rate compared to horizontal alignment. The best second-life capacity retention was found for cells initially cycled at room temperature, then later cycled with a reduced SoC window, at a lower current and in a horizontal orientation. If the cells were cycled at an elevated temperature in first life, the second-life compatibility was reduced considerably. An incremental capacity analysis (ICA) of the first-life ageing data revealed a possible indicator for ensuring safety and cycleability into second-life use. Full article