Lithium-ion batteries serve as the primary sources of power for electric vehicles (EVs) and hybrid electric vehicles (HEVs). For vehicle applications, battery management systems (BMSs) are nec-essary to protect lithium-ion batteries from overheating and to ensure optimum vehicle perfor-mance. Our approach to developing a BMS was based on recent advances in the application of phase field models for lithium-ion batteries. In particular, our reduced-order model (ROM) uti-lized a dataset generated from the COMSOL® Multiphysics simulation of the Cahn–Hilliard equation for a single particle of a lithium iron phosphate (LiFePO4) cathode: an example of using a reduced-order model (ROM) based on a single-particle model (SPM). The main innovation of our ROM is that the SPM is fully coupled to a heat transfer model at the battery cell level. We utilized principal component analysis to identify a lower-order model that could reproduce the battery’s voltage and temperature response for ambient temperatures ranging from 253 to 298 K and for discharge rates ranging from 1 C to 20.5 C. The reduced-order dataset was then fitted to the ex-perimental data for an A123 Systems 26650 2.3 Ah cylindrical battery using deep neural network (DNN) regression.

(1) Background: Heavy and chronic alcohol intake causes altered gut-permeability and dysfunction; and exhibits a unique pro-inflammatory state. Thyroid-associated hormones and proteins may be dysregulated by alcohol administration; however, the impact of altered gut-derived changes on thyroid function is unclear. This study investigated the role of gut-dysfunction and pro-inflammatory activity on thyroid function in patients with alcohol use disorder (AUD). (2) Methods: Male and female AUD patients (n=44) were grouped as Gr.1 with normal thyroid stimulating hormone (TSH) levels (n=28, 0.8≤TSH≤3 mIU/L); and Gr.2 with clinically elevated TSH levels (n=16, TSH> 3 mIU/l). Demographics, drinking measures, comprehensive metabolic panel, and candidate thyroid markers (TSH, circulating triiodothyronine [T3] and free thyroxine [fT4]) were tested. Plasma-derived gut-dysfunction associated markers (lipopolysaccharide [LPS], LPS-binding protein [LBP], and LPS-induced pathogen-associated protein [CD14]), and cytokine profile (IL1-β, TNF-α, IL-6, IL-8, MCP-1, PAI-1) were analyzed and compared with the thyroid, demographic, and drinking markers. (3) Results: Both groups presented with a borderline overweight category of BMI. Gr.2 presented with numerically higher level of chronic and heavy drinking patterns vs Gr.1. fT4 levels were elevated while T3 was within normal limits in both the groups. Gut-dysfunction markers LBP and CD14 were numerically elevated in Gr.2 vs Gr.1 suggesting subtle ongoing changes; however, the difference was not statistically significant. All pro-inflammatory cytokines were significantly elevated in Gr.2 among IL1-, MCP-1, and PAI-1. Gr.2 showed a strong and statistically significant effect of gut-immune-pituitary response (r=0.896, p=0.002) on TSH levels in a multivariate regression model with LBP, CD14, and PAI-1 levels as upstream variables; this assessment was not significant in Gr.1. In addition, AUROC analysis demonstrated that many of the cytokines strongly predicted TSH in Gr.2, including IL-6 (area=0.774, p<0.001) and TNF- (area=0.708, p=0.017) among others. This was not observed in Gr.1. Gr.2 demonstrated elevated fT4 as well as TSH, which suggests that there was subclinical thyroiditis with underlying CNS dysfunction and lack of a negative feedback loop. (4) Conclusions: These findings reveal the toxic effects of heavy and chronic drinking that play a pathological role in thyroid gland dysregulation employing the gut-brain axis. These results also strongly emphasize potential directions to strongly consider thyroid dysregulation in the overall medical management of AUD.