This paper presents HAC-DBAT, a normal-data-only dual-branch Transformer framework for record-level fault recognition and faulty-cell localization in heterogeneous electric-vehicle battery packs. The dataset comprises 88 vehicle records, including 34 normal training records and a held-out test partition of 50 records containing seven labeled faults and 13 labeled faulty cells. On the test partition, the record-level F1-score is 0.9333, the conditional faulty-cell F1-score is 0.8889, and the end-to-end faulty-cell F1-score is 0.8276. The framework integrates validity-masked pack alignment, residual-conditioned dual-branch reconstruction, normal-reference thresholding and hotspot-aware cell ranking. Its evaluation concerns fault-record identification and faulty-cell localization, rather than verified thermal-runaway lead time.
Aiming to mitigate the issues of heat buildup and excessive flow resistance in traditional cold plates applied to lithium-ion batteries, this work develops a topology optimization framework and produces two optimized layouts (Case III and Case IV) with differentiated multi-objective weight assignments. Their thermo-hydraulic performances were systematically evaluated against parallel (Case I) and serpentine (Case II) channels under various inlet velocities, coolant temperatures, and discharge rates. The results indicate that biomimetic bifurcations in topological channels effectively significantly reduce the extent of low-velocity regions and equalize flow distribution. Among all configurations, Case IV demonstrates the best overall performance: under a 1C discharging rate with an inlet flow velocity of 0.2 m/s, it achieves the lowest maximum temperature (28.97 °C), minimal temperature differential (3.97 °C), and lowest equivalent thermal resistance (0.305 °C/W), while reducing the hydraulic pressure drop by approximately 79.3% (from 436.83 Pa to 90.57 Pa) compared to serpentine channels (Case II). The analyses reveal that increasing inlet velocity beyond 0.3 m/s yields diminishing cooling returns due to internal conductive bottlenecks. Variations in inlet temperature exert negligible impacts on thermal resistance, whereas resistance across discharge rates follows Rth,2C > Rth,3C > Rth,1C > Rth,0.5C, governing transient thermal transport characteristics during high-rate discharge.
Aqueous organic flow batteries (AOFBs) are a promising technology for large-scale energy storage. The electrolyte solution is significant in determining the battery’s performance, which has so far remained poorly understood, especially for concentrated electrolyte. In this work, by combining nuclear magnetic resonance (NMR) and molecular dynamic simulations, we explored ion pairing behavior in bipyridinium-based concentrated electrolytes for AOFBs. Through 1H NMR and 35Cl NMR investigation, the anisotropy of ion pairing behavior between the bipyridinium dications and the chloride counterions was demonstrated. The relationship between bipyridinium-based electrolyte structures and their solubility is illustrated. The volume-specific capacity of bipyridinium-based flow batteries varies and differs nearly threefold for 2.50 M electrolytes with different configurations. This work provides new insights for designing electroactive materials and optimizing the performance of AOFBs.
A growing number of second-life battery companies are entering North America’s stationary energy storage market as the demand for both electric vehicles and electricity grid energy storage continues to rise. These companies have many potential revenue streams available to them, but the unique characteristics of second-life batteries also present many challenges beyond those faced by conventional battery developers. To explore these challenges, this study engages directly with industry stakeholders, including eight North American second-life battery companies and five Canadian electrical utilities and electricity system operators. Based on a series of interviews with these stakeholders, this study compiles an aggregate perspective on the current state of the North American second-life battery industry as well as its present opportunities and challenges. The study concludes that although the industry is largely still in an exploratory stage, with nascent supply chains and minimal progress towards competing in utility-scale applications, second-life battery companies are working to overcome major regulatory barriers that are inhibiting industry growth and developing innovative technological and commercial solutions to compete with conventional battery energy storage. The study finds that utilities and electricity system operators seem open to, and certainly not discriminatory toward, this technology and its inclusion in the grid. Even so, the second-life battery industry still has a lengthy developmental curve ahead, subject to dynamic market forces. A set of 17 actionable steps is recommended for governments seeking to support the growth of this industry.
Accurate state of charge (SOC) estimation for lithium-ion batteries (LIBs) is essential. To overcome the susceptibility of conventional data-driven models to local optima and terminal divergence under complex conditions, this paper proposes a novel joint estimation algorithm. Using a second-order resistor–capacitor (RC) model, the slime mould algorithm (SMA) identifies parameters offline at temperature chamber setpoints of 0 °C, 25 °C, and 45 °C for a modified extended Kalman filter (MEKF). Subsequently, a backpropagation (BP) neural network optimized by the sparrow search algorithm (SSA) is introduced to perform feedforward compensation for nonlinear residuals, addressing the error accumulation caused by the randomness of initial network weights, collectively constituting the proposed SSA-BP-MEKF algorithm. Validation at 0 °C, 25 °C, and 45 °C under the Dynamic Stress Test (DST), US06, Federal Urban Driving Schedule (FUDS), and Beijing Dynamic Stress Test (BJDST) demonstrates that the maximum absolute error (MaxAE) remains within 2.21% at 25 °C. At 0 °C and 45 °C, the algorithm effectively suppresses the local divergence of conventional BP compensation, restricting the MaxAE for the demanding BJDST cycle to 2.65% and 1.49%, respectively. Ultimately, the proposed approach significantly enhances estimation accuracy and robustness in complex operating environments.
