Compressed carbon dioxide (CO₂) energy storage (CCES) has emerged as a promising large-scale energy storage technology, characterized by high energy density, moderate critical temperature, and operational flexibility. Concurrently, carbon capture, utilization and storage (CCUS) technology represents a critical pathway toward carbon neutrality for energy systems. The integration of CCES with CCUS is attracting growing research interests due to its unique potential to synergize energy and carbon flows within a closed-loop framework. This paper provides a comprehensive literature review of technological advancements in CCES and offers a perspective on its integration with CCUS. First, the fundamental working principle, system configurations, key performance indicators, and emerging demonstration projects of CCES are introduced. Subsequently, cutting-edge research and key challenges of CCES system are reviewed, focusing on optimization of CO₂-based mixed working media, efficient liquefaction of low-pressure CO₂, development of low-cost and safe CO₂ storage facilities, enhancement of system performance through integration, and evaluation of dynamic behaviors. A central focus is placed on the integration of CCES with CCUS, highlighting how this synergy transforms CCES from a pure storage technology into a multi-functional tool for carbon management. This integration enables infrastructure sharing, dual-function storage (for energy and CO₂), and improved economics. Finally, this review identifies key directions for future research, including advancing efficient system integration, developing high-precision transient simulation models and dynamic control algorithms, ensuring long-term safety of geological reservoirs under cyclic injection-extraction operations, and establishing multi-objective optimization and multi-criteria assessment frameworks to support the commercial deployment of integrated CCES-CCUS systems.

With the rapid development of electric vehicles and energy storage systems (ESSs), accurate state-of-health (SOH) estimation for lithium-ion batteries has become crucial for ensuring safety and optimizing performance. However, SOH estimation under dynamic operating conditions remains challenging, as non-monotonic voltage profiles and irregular current patterns reduce the effectiveness of conventional measurement methods. This paper proposes a comprehensive approach that combines health feature extraction with a parallel deep learning architecture for robust SOH estimation. First, the method extracts four highly correlated health features (K, b, σ_ΔQ, and σ_δΔQ) from dynamic measurement data collected by sensors, with correlation coefficients between these features and the actual SOH exceeding 0.95. These extracted features are then processed through a novel parallel Temporal Convolutional Networks (TCN)-Transformer hybrid architecture: the TCN captures multi-scale local temporal patterns, while the Transformer models global dependencies. An attention-gated fusion module dynamically integrates complementary feature representations from the two branches and adaptively weights different paths based on degradation features. Experimental validation on three standardized battery datasets (MIT, CALCE, Oxford) shows that the method achieves an estimation accuracy with a root mean square error (RMSE) below 1% under all operating conditions, representing an 8%–70% improvement over conventional methods. Attention weight analysis reveals correlations with aging mechanisms, providing interpretability for model decisions. The proposed method enables practical real-time battery health assessment in dynamic environments.

Solid-state electrolytes are crucial for developing next-generation batteries with enhanced safety and energy density. Among them, the polymethyl methacrylate (PMMA)-based gel polymer electrolytes (GPEs) have emerged as promising materials for high-performance battery systems. However, PMMA-based electrolytes suffer from intrinsically low ionic conductivity. While blending with quaternary ammonium salts offers an effective solution, it often leads to salt deposition during cycling, compromising long-term stability. In this work, a novel GPE is developed by grafting long-chain quaternary ammonium salt (C₁₆DMAAC) onto the PMMA backbone. This molecular design simultaneously regulates polymer chain disorder and immobilizes free anions, enabling a high Li-ion transfer number of 0.59, ionic conductivity of 7.23 × 10⁻⁴ S/cm, and an expanded electrochemical stability window of 4.9 V. Moreover, the incorporated ammonium cations in the C₁₆DMAAC segment optimize the Li⁺ solvation structure, promoting the formation of a robust, inorganic-rich solid electrolyte interphase (SEI). The excellent cycling stability is demonstrated by the Li||NCM811 full cell, which retains 92% of its initial capacity over 200 cycles at 0.5 C, and 80% retention after 300 cycles at 2 C. This work presents a promising strategy for designing novel electrolyte structures by grafting quaternary ammonium salts into polymer chains to improve battery stability and lifespan.