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In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts prediction accuracy. Addressing these challenges, this study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity, which is then processed by the RCVAE model. Coupled with customized training and inference algorithms, this model can generate specific electrochemical data for EOL and ECL under supervised conditions. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.

Decentralized multi-energy systems (MESs) are a key element of a future low-carbon energy supply. Here, we address the crucial role of grid-connected and off-grid MESs in achieving a low-carbon future, particularly relevant for regions like the Mediterranean with high renewable energy potential and carbon-intensive grid networks, using a mixed integer linear program for optimal economic and environmental MES design considering location-specific regulations. The results reveal that substantial cost (up to 30%, potentially saving 1.6 million) and greenhouse gas (GHG) emission reductions can be reached in Mediterranean regions with sufficient solar and wind energy resources currently relying on fossil fuel-based generators. However, our case study shows that the actual cost and emission reductions are most likely limited due to location-specific regulations (limiting cost savings to 0.8 million), especially those that constrain solar photovoltaic and onshore wind. Off-grid energy systems might be suitable decarbonization options in Mediterranean regions, to avoid absorbing current GHG-intensive power from the local power grid, under marginal cost increase (15%) compared to grid-connected cost optimization. However, off-grid MESs require substantial upfront investments and exhibit some environmental trade-offs, especially on material utilization, which could be overcome by balanced autonomy. Overall, truly sustainable and secure decentralized energy systems can only be reached by considering life cycle environmental impacts, social acceptance, and regulations during the design phase. Applied Energy, 377 ISSN:0306-2619 ISSN:1872-9118

Optimally scheduling multi-energy flow is an effective method to utilize renewable energy sources (RES) and improve the stability and economy of integrated energy systems (IES). However, the stable demand-supply of IES faces challenges from uncertainties that arise from RES and loads, as well as the increasing impact of cyber-attacks with advanced information and communication technologies adoption. To address these challenges, this paper proposes an innovative model-free resilience scheduling method based on state-adversarial deep reinforcement learning (DRL) for integrated demand response (IDR)-enabled IES. The proposed method designs an IDR program to explore the interaction ability of electricity-gas-heat flexible loads. Additionally, the state-adversarial Markov decision process (SA-MDP) model characterizes the energy scheduling problem of IES under cyber-attack, incorporating cyber-attacks as adversaries directly into the scheduling process. The state-adversarial soft actor-critic (SA-SAC) algorithm is proposed to mitigate the impact of cyber-attacks on the scheduling strategy, integrating adversarial training into the learning process to against cyber-attacks. Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources. Moreover, the proposed method demonstrates resilience against cyber-attacks. Compared to the original soft actor-critic (SAC) algorithm, it achieves a 10% improvement in economic performance under cyber-attack scenarios. Accepted by Applied Energy, Manuscript ID: APEN-D-24-03080