Ge He

Yiwei Qiu, Jiatong Li, Yangjun Zeng et al.

An emerging approach for large-scale renewable hydrogen production is integrating multiple alkaline water electrolysis (AWE) stacks into one balance-of-plant (BoP) system, sharing gas-lye separation and lye circulation components. While this configuration, termed $N$-in-1, reduces cost and complexity, its dynamic performance under fluctuating power remains unclear compared with conventional 1-in-1 systems. This paper develops a state-space model of the multi-stack AWE system, capturing lye circulation, temperature, and hydrogen-to-oxygen (HTO) dynamics, calibrated via experiments on a 4,000 Nm$^3$/h-rated 4-in-1 system. A nonlinear model predictive controller (NMPC) is then designed to coordinate inter-stack current distribution, lye flow, and cooling for load tracking and operational stability. Simulations on the experimental-validated model show that a $4$-in-1 system can achieve very similar performance compared to four parallel 1-in-1 systems. Differences in load-tracking error, temperature stabilization, and specific energy consumption remain below 0.015 MW, 0.346 K, and 0.001 kWh/Nm$^3$ under wind power supply.

Yiwei Qiu, Qingjie Sun, Yangjun Zeng et al.

In renewable power-to-ammonia (ReP2A) systems, the intermittency of wind and solar generation propagates through electrolytic hydrogen production and induces thermal instability in the ammonia synthesis reactor (ASR). The resulting temperature cycling accelerates fatigue and shortens service life, while reactor thermal inertia limits flexible start-up, shutdown, and load adjustment. To address this issue, this study integrates molten-salt thermal energy storage (MS-TES) into the Haber-Bosch synthesis loop and develops a coordinated electro-hydrogen-thermal scheduling framework. MS-TES decouples hydrogen supply fluctuations from reactor thermal dynamics by enabling hot standby operation and sustained thermal support during start-up and low-load conditions. A state-space model is established to capture the thermal dynamics of the ASR and MS-TES. Based on this model, an optimal scheduling program coordinates ammonia synthesis operation with hydrogen production, battery energy storage (BES), and hydrogen storage (HS). The problem is formulated as a mixed-integer linear program (MILP) and extended with information gap decision theory (IGDT) to address renewable uncertainty. Case studies based on an industrial-scale project in northern China show that MS-TES enhances reactor thermal stability and system-level flexibility, while diminishing the marginal benefit of large BES capacity. As a result, a configuration combining small BES, HS, and MS-TES achieves near-equivalent performance to large-BES systems, with lower investment and improved economic returns. Year-round simulations further show that MS-TES avoids ASR start-up and shutdown and delivers consistently higher net revenue under variable renewable conditions.

Lidang Jiang, Changyan Hu, Sibei Ji et al.

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.

Lidang Jiang, Zhuoxiang Li, Changyan Hu et al.

The early prediction of battery life (EPBL) is vital for enhancing the efficiency and extending the lifespan of lithium batteries. Traditional models with fixed architectures often encounter underfitting or overfitting issues due to the diverse data distributions in different EPBL tasks. An interpretable deep learning model of flexible parallel neural network (FPNN) is proposed, which includes an InceptionBlock, a 3D convolutional neural network (CNN), a 2D CNN, and a dual-stream network. The proposed model effectively extracts electrochemical features from video-like formatted data using the 3D CNN and achieves advanced multi-scale feature abstraction through the InceptionBlock. The FPNN can adaptively adjust the number of InceptionBlocks to flexibly handle tasks of varying complexity in EPBL. The test on the MIT dataset shows that the FPNN model achieves outstanding predictive accuracy in EPBL tasks, with MAPEs of 2.47%, 1.29%, 1.08%, and 0.88% when the input cyclic data volumes are 10, 20, 30, and 40, respectively. The interpretability of the FPNN is mainly reflected in its flexible unit structure and parameter selection: its diverse branching structure enables the model to capture features at different scales, thus allowing the machine to learn informative features. The approach presented herein provides an accurate, adaptable, and comprehensible solution for early life prediction of lithium batteries, opening new possibilities in the field of battery health monitoring.