Jilei Ye

With high renewable penetration, independent user-side storage suffers from low utilization and high investment. Shared energy storage (SES) addresses this by aggregating multi-user demand. This paper proposes a bi-level optimal configuration method for user-side SES considering the conflicting interests between the investor and users. The upper level maximizes the investor’s annual revenue by optimizing SES capacity and power, while the lower level minimizes users’ comprehensive energy cost via charge/discharge scheduling and load shifting. The bi-level model is transformed into a single-level mixed-integer linear programming (MILP) problem using strong duality theory, and the Shapley value method ensures fair cost allocation based on marginal contributions. Case studies on a community with three typical user groups show that, under the considered time-of-use price and cost parameter settings, the proposed method outperforms independent storage in the studied scenarios, reducing total user costs by 12.8% and increasing investor revenue by 1.9%; with a 30% load shifting capability, investor revenue rises by 13.2% and user costs drop by 0.78%. The Shapley value-based allocation guarantees fairness while quantifying the actual costs of multi-category users jointly using SES.

To support low-carbon smart buildings, this study proposes a multi-objective capacity optimization method for PV-energy storage systems considering the flexibility potential of orderly electric vehicle (EV) charging loads. First, an orderly EV charging model based on price-guided charging quantifies the flexibility potential of EV charging loads. Then, a bi-level multi-objective capacity configuration model is proposed. In the upper level, the building operator minimizes both economic and carbon emission costs, jointly optimizing PV-energy storage capacity configuration and EV charging prices. In the lower level, EV users optimize their charging schedules to minimize their charging costs in response to the charging prices. To solve this bi-level multi-objective problem, the two objectives are normalized and weighted into a single-objective function, and then a heuristic solution method based on a genetic algorithm is developed. Case study results show that the proposed mode reduces building economic cost and electricity-related carbon emissions, demonstrating the benefits of incorporating carbon emissions into demand-side optimization under the “dual-carbon” targets.

State-of-health (SOH) estimation is a prerequisite for the safe and efficient operation of electrochemical energy storage systems (ESS), but practical deployment remains limited by incomplete observability, inconsistent health labels, domain shift, and insufficient trustworthiness under changing operating conditions. This review synthesizes machine-learning-based SOH estimation across electrochemical ESS, covering lithium-ion, sodium-ion, and aqueous batteries, supercapacitors, and battery-supercapacitor hybrid configurations. The literature is organized along an end-to-end machine-learning-driven pipeline, including health target definition and labeling, field data and observability, health-indicator representation, model learning and generalization, benchmarking and trustworthiness, and deployment-oriented integration. The review shows that reliable SOH estimation does not depend solely on model architecture, but on the alignment among target semantics, signal availability, feature representation, supervision regime, evaluation design, and operational objective. It further highlights that health semantics are device-dependent and should not be transferred mechanically across batteries and supercapacitors. Representative learning strategies are discussed, including structured regression, temporal deep models, transfer and online adaptation, and physics-guided or uncertainty-aware learning. The review also extends the discussion from component-level diagnosis to system-level integration in batteries, supercapacitors, and hybrid ESS. Future progress will depend on observability-aware target design, robust evaluation, and trustworthy deployment rather than algorithmic novelty alone.

Addressing the challenge of power supply stability caused by the intermittent nature of photovoltaic power generation in off-grid microgrids, this study uses a commercial park in Wuhan as a case study and optimizes the capacity configuration of a photovoltaic–storage–hydrogen fuel cell hybrid microgrid system based on HOMER Pro software. First, a topology of the off-grid microgrid is constructed, comprising photovoltaic (PV), lithium-ion batteries, hydrogen fuel cells, and a diesel generator as backup. The power output characteristics, efficiency curves, and life-cycle cost models of each component are accurately established. On this basis, two typical operation strategies, namely Load Following (LF) and Cycle Charging (CC), are proposed and compared. The influence of different strategies on the optimal capacity configuration and operational economics is systematically analyzed, and the Cycle Charging strategy is identified as the optimal operation strategy for this scenario. Subsequently, a multi-scenario capacity optimization design is further conducted based on the optimal operation strategy. The minimization of net present cost (NPC) is taken as the primary objective, while multiple evaluation indicators such as renewable fraction (RF), levelized cost of electricity (LCOE), energy storage cycle life degradation, and system redundancy rate are comprehensively considered. The results show that, while ensuring 100% power supply reliability, the proposed model reduces the net present cost (NPC) by approximately 14.4% compared with the conventional PV-storage scheme. The renewable fraction (RF) reaches 95.8%, while the reliance on lithium-ion battery capacity is significantly reduced (battery capacity configuration decreased by 24.3%). This effectively extends the energy storage lifespan and enhances the overall economic and environmental benefits. The results provide a theoretical basis and technical reference for the planning and design of off-grid microgrids with high penetration of renewable energy.

Due to their high energy density, long cycle life, and high efficiency, lithium‐ion batteries have become one of the most widely used electrochemical energy‐storage technologies. This work addresses the challenge of extracting informative internal indicators from operational data and accurately estimating the state of health (SOH) of lithium‐ion batteries. Based on multidimensional operating measurements, we construct a set of health factors that are strongly correlated with capacity from four perspectives: time, charged energy, power, and temperature. Building on these factors, a SOH estimation model is developed by integrating particle swarm optimization (PSO) with a long short‐term memory (LSTM) network and an attention mechanism (PSO‐LSTM‐Attention). The model is trained and validated on NASA's public aging datasets, and its performance is evaluated using root mean square error (RMSE) and mean absolute percentage error (MAPE). Across the tested cells (B0005, B0006, B0007, B0029, and B0030) and operating conditions (24°C–43°C), the proposed method achieves accurate capacity estimation, with all RMSE values below 1% and average errors around 0.5%. These results demonstrate that the proposed framework can effectively exploit multidimensional operational data for SOH estimation and has the potential to support battery operation optimization and safety management in practical applications.

Estimating the State of Health (SOH) of lithium batteries is vital for the safe management of new energy systems. This study leverages voltage, current, and temperature data from the NASA battery dataset to extract health features. The relationship between these features and battery capacity is evaluated using mutual information analysis. An integrated Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Squeeze-and-Excitation (SE) model (CNN-GRU-SE) for estimating battery SOH is proposed. The CNN module identifies local features from the input data, the SE module emphasizes key features, and the GRU module captures temporal dependencies, effectively tracking the battery’s health trend over time. The estimation results indicate that the CNN-GRU-SE model reduces the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) by approximately 3% to 10% compared to the CNN, GRU, and CNN-GRU models. These results confirm the superior estimation capability of the integrated CNN-GRU-SE model. Furthermore, the study underscores the effective integration of the strengths of CNN, SE modules, and GRU, demonstrating its potential application in battery health management.

In situ growth of N-doped bamboo-like carbon nanotubes embedded with FeNi nanoparticles on carbon cloth (FeNi@NBCNTs/CC) has been reported, along with the demonstration of the direct application of FeNi@NBCNTs/CC as self-standing bifunctional air cathodes in rechargeable zinc–air batteries (RZABs). The resulting FeNi@NBCNTs/CC, with 3D integrated conductive frameworks, large surface area, hierarchically porous architecture and embedded FeNi nanoparticles, provided sufficient accessible reaction sites and an optimal charge/mass transfer environment. Thus, the FeNi@NBCNTs/CC exhibited excellent electrocatalytic performance toward the oxygen reduction reaction (ORR) with a positive half-wave potential of 0.90 V and toward the oxygen reduction reaction (OER) with a low potential of 1.52 V at 10 mA cm⁻², outperforming commercial Pt/C and Ru/C catalysts, respectively. Impressively, they served as self-standing air cathodes for liquid and flexible quasi-solid-state RZABs, demonstrating outstanding battery performance with high energy density, robust durability with a low rate of energy loss and favorable flexibility. This work provides a useful strategy for fabricating low-cost and efficient electrodes without the use of polymeric binders and noble metals for metal–air batteries and other related fields.

Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The EWOA selects features by initially assigning weights based on importance scores from a random forest model. Gaussian noise increases population diversity, while a dynamic threshold method optimizes the selection process, preventing local optima. The selected features construct the SVR model for SOH estimation. This method is validated using four aging datasets from the NASA database, conducting 50 prediction experiments per battery. The results indicate optimal average absolute error (MAE) and root mean square error (RMSE) within 0.41% and 0.71%, respectively, with average errors below 1% and 1.3%. This method enhances automation and accuracy in feature selection while ensuring efficient SOH estimation, providing valuable insights for practical LIB applications.

Lithium-ion batteries are widely applied in the form of new energy electric vehicles and large-scale battery energy storage systems to improve the cleanliness and greenness of energy supply systems. Accurately estimating the state of power (SOP) of lithium-ion batteries ensures long-term, efficient, safe and reliable battery operation. Considering the influence of the parameter identification accuracy on the results of state of power estimation, this paper presents a systematic review of model parameter identification and state of power estimation methods for lithium-ion batteries. The parameter identification methods include the voltage response curve analysis method, the least squares method and so on. On this basis, the methods used for modeling and estimating the SOP of battery cells and battery packs are classified and elaborated, focusing on summarizing the research progress observed regarding the joint estimation method for multiple states of battery cells. In conclusion, future methods for estimating the SOP of lithium-ion batteries and their improvement targets are envisioned based on the application requirements for the safe management of lithium-ion batteries.

The battery power state (SOP) is the basic indicator for the Battery management system (BMS) of the battery energy storage system (BESS) to formulate control strategies. Although there have been many studies on state estimation of lithium-ion batteries (LIBs), aging and temperature variation are seldom considered in peak power prediction during the whole life of the battery. To fill this gap, this paper aims to propose an adaptive peak power prediction method for power lithium-ion batteries considering temperature and aging is proposed. First, the Thevenin equivalent circuit model is used to jointly estimate the state of charge (SOC) and SOP of the lithium-ion power battery, and the variable forgetting factor recursive least squares (VFF-RLS) algorithm and extended Kalman filter (EKF) are utilized to identify the battery parameters online. Then, multiple constraint parameters including current, voltage, and SOC were derived, considering the dependence of the polarization resistance of the battery on the battery current. Finally, the verification experiment was carried out with LiFePO4 battery. The experimental results under FUDS operating conditions show that the maximum SOC estimation error is 1.94%. And the power prediction errors at 20%, 50%, and 70% SOC were 5.0%, 8.1% and 4.5%, respectively. Our further work will focus on the joint estimation of battery state to further improve the accuracy.