Lithium-ion batteries (LIBs) are widely used in electrical vehicles (EVs), but safety issues with LIBs still occur frequently. State of charge (SOC) and state of health (SOH) are two crucial parameters for describing the state of LIBs. However, due to inconsistencies that may occur among hundreds to thousands of battery cells connected in series and parallel in the battery pack, these parameters can be difficult to estimate accurately. To address this problem, this paper proposes a joint SOC and SOH estimation method based on the nonlinear state space reconstruction (NSSR) and long short-term memory (LSTM) neural network. An experiment testbed was set up to measure the SOC and SOH of battery packs under different criteria and configurations, and thousands of charging/discharging cycles were recorded. The joint estimation algorithms were validated using testbed data, and the errors for SOC and SOH estimation were found to be within 2.5% and 1.3%, respectively, which is smaller than the errors obtained using traditional Ah-Integral and LSTM-only algorithms.

Urban thermal environments, characterized by the interplay between indoor and outdoor conditions, pose growing challenges in high-density coastal cities. This study proposes a multi-scale, integrative framework that couples a satellite-derived land surface temperature (LST) analysis with microscale building performance simulations to holistically evaluate the high-density urban thermal environment in subtropical climates. The results reveal that compact, high-density morphologies reduce outdoor heat stress (UTCI) through self-shading but lead to significantly higher cooling loads, energy use intensity (EUI), and poorer daylight autonomy (DA) due to restricted ventilation and limited sky exposure. In contrast, more open, vegetation-rich forms improve ventilation and reduce indoor energy demand, yet exhibit higher UTCI values in exposed areas and increased lighting energy use in poorly oriented spaces. This study also proposes actionable design strategies, including optimal building spacing (≥15 m), façade orientation (30–60° offset from west), SVF regulation (0.4–0.6), and the integration of vertical greenery to balance solar access, ventilation, and shading. These findings offer evidence-based guidance for embedding morphological performance metrics into planning policies and building design codes. This work advances the integration of outdoor and indoor performance evaluation and supports climate-adaptive urban form design through quantitative, policy-relevant insights.

In recent years, lithium-ion batteries (LIBs) have gained immense popularity and widespread adoption in various applications. Due to the voltage and capacity requirements of these applications, a single LIB is often inadequate. To meet these demands, LIBs are interconnected in series and parallel configurations to achieve higher voltage and capacity levels. To improve performance and prolong the lifespan of battery packs, battery equalization methods are employed to ensure balanced state-of-charge (SOC) and voltage levels among individual battery cells. Passive battery equalization methods are commonly utilized in industrial applications due to their simplicity and cost-effectiveness. The implementation of complex techniques to enhance performance may not be suitable for passive equalization methods, as it would introduce complications and increase implementation costs. This paper introduces two innovative equalization methods: the rank-sum ratio (RSR) based method and the piecewise function based (PFSO) method. These methods leverage real-time internal resistance measurement devices to enhance the equalization process. To evaluate their effectiveness, the simulation model of battery module is developed based on the Thévenin-based equivalent circuit, utilizing data obtained from hybrid pulse power characteristic (HPPC) tests. Simulation results demonstrate that the proposed methods outperform other passive equalization methods, including the SOC-based and voltage-based methods. In a setting with a 16-cell battery module possessing a total capacity of 51.2V and 104Ah, the RSR and PFSO methods can equalize the battery module, adjusting the capacity from 98.97Ah to 102.56Ah and 101.12Ah, respectively, as opposed to the 100.90Ah and 100.09Ah achieved by the SOC-based and voltage-based methods.

Accurate State of Charge (SOC) estimation is critical for optimizing the performance and longevity of lithium-ion batteries (LIBs), which are widely used in applications ranging from electric vehicles to renewable energy storage. Traditional SOC estimation methods, such as Coulomb counting and open-circuit voltage measurement, suffer from cumulative errors and slow response times. This paper proposes a novel machine learning-based approach for SOC estimation by integrating Electrochemical Impedance Spectroscopy (EIS) with the SHapley Additive exPlanations (SHAP) method, Atom Search Optimization (ASO), and Light Gradient Boosting Machine (LightGBM). This study focuses on large-capacity lithium iron phosphate (LFP) batteries (3.2 V, 104 Ah), addressing a gap in existing research. EIS data collected at various SOC levels and temperatures were processed using SHAP for feature extraction (FE), and the ASO–LightGBM model was employed for SOC prediction. Experimental results demonstrate that the proposed SHAP–ASO–LightGBM method significantly improves estimation accuracy, achieving an RMSE of 3.3%, MAE of 1.86%, and R² of 0.99, outperforming traditional methods like LSTM and DNN. The findings highlight the potential of EIS and machine learning (ML) for robust SOC estimation in large-capacity LIBs.

Lithium Iron Phosphate (LFP) batteries are characterized by a flat voltage profile across a wide state-of-charge (SOC) range, which reduces the effectiveness of open-circuit voltage (OCV) curves for SOC estimation, particularly in the mid-SOC region. To address this limitation, comprehensive Electrochemical Impedance Spectroscopy (EIS) experiments were performed on LFP cells across the full SOC range and under multiple temperature conditions. From the experimental dataset, key impedance features were identified using a Random Forest algorithm, and a convolutional neural network with elastic net regularization (CNN-EN) was developed to model SOC. The CNN-EN model achieved high predictive accuracy, with an MSE of 0.0012, MAE of 0.02753, R ² of 0.9820, and MAPE of 0.0607. Notably, a single frequency point at 0.0125 Hz delivered performance comparable to the full EIS spectra, reducing data collection time by approximately 85% and model training time by 6% while maintaining accuracy. These results demonstrate that experimentally validated EIS features, when combined with machine learning, provide a robust, efficient, and accurate pathway for SOC estimation in LFP batteries, with strong potential for integration into real-time battery management systems (BMS).

Urban morphology critically shapes environmental performance, yet few studies integrate multiple sustainability targets within a unified modeling framework for its design optimization. This study proposes a data-driven, multi-scale approach that combines parametric simulation, artificial neural network-based multi-task learning (MTL), SHAP interpretability, and NSGA-II optimization to assess and optimize urban form across 18 districts in Hong Kong. Four key sustainability targets—photovoltaic generation (PVG), accumulated urban heat island intensity (AUHII), indoor overheating degree (IOD), and carbon emission intensity (CEI)—were jointly predicted using an artificial neural network-based MTL model. The prediction results outperform single-task models, achieving R² values of 0.710 (PVG), 0.559 (AUHII), 0.819 (IOD), and 0.405 (CEI), respectively. SHAP analysis identifies building height, density, and orientation as the most important design factors, revealing trade-offs between solar access, thermal stress, and emissions. Urban form design strategies are informed by the multi-objective optimization, with the optimal solution featuring a building height of 72.11 m, building centroid distance of 109.92 m, and east-facing orientation (183°). The optimal configuration yields the highest PVG (55.26 kWh/m²), lowest CEI (359.76 kg/m²/y), and relatively acceptable AUHII (294.13 °C·y) and IOD (92.74 °C·h). This study offers a balanced path toward carbon reduction, thermal resilience, and renewable energy utilization in compact cities for either new town planning or existing district renovation.