A State of Health (SOH) estimation of lithium-ion batteries is a core function of battery management systems, directly affecting the safe operation, lifetime prediction, and economic efficiency of batteries. However, existing methods still face challenges in balancing feature robustness and model generalization ability; for instance, some studies rely on features whose physical correlation with SOH lacks strict verification, or the models struggle to simultaneously capture the temporal dynamics of health factors and nonlinear mapping relationships. To address this, this paper proposes an SOH estimation method based on incremental capacity (IC) curves and a Temporal Convolutional Network—Relevance Vector Machine (TCN-RVM) model, with core innovations reflected in two aspects. Firstly, five health factors are extracted from IC curves, and the strong correlation between these features and SOH is verified using both Pearson and Spearman coefficients, ensuring the physical rationality and statistical significance of feature selection. Secondly, the TCN-RVM model is constructed to achieve complementary advantages. The dilated causal convolution of TCN is used to extract temporal local features of health factors, addressing the insufficient capture of long-range dependencies in traditional models; meanwhile, the Bayesian inference framework of RVM is integrated to enhance the nonlinear mapping capability and small-sample generalization, avoiding the overfitting tendency of complex models. Experimental validation is conducted using the lithium-ion battery dataset from the University of Maryland. The results show that the mean absolute error of the SOH estimation using the proposed method does not exceed 0.72%, which is significantly superior to comparative models such as CNN-GRU, KELM, and SVM, demonstrating higher accuracy and reliability compared with other models.

Existing machine tool thermal error mitigation relying on passive structural optimization and conventional feedforward PID decoupling poorly addresses strong multi-temperature-field coupling, large time delays, and nonlinear thermal characteristics in large precision horizontal machining centers. These methods lack predictive optimization, fail to suppress the long-term temperature drift of key structural components, and cannot realize active thermal intervention, leaving a clear research gap. This paper develops a three-layer closed-loop active thermal control framework with temperature sensing, numerical decoupling, and executive regulation. S-shaped hollow aluminum temperature control plates are optimally arranged on the bed, column, and beam, and a multi-temperature zone coupling transfer function model is established. A hybrid control strategy integrating feedforward decoupling, MPC prediction, and PID steady-state compensation is proposed; MPC is introduced to handle multivariable coupling, time lag, and actuator constraints beyond the capability of traditional PID. Comparative experiments show that the MPC-based scheme reduces key point temperature variation rates by 31.47%, 14.56%, 16.06% and 44.86%. This study focuses on temperature stabilization (rather than the direct measurement of the spindle drift or geometric deformation). The proposed method provides an effective active temperature balance solution for large precision machine tools.

A (FPE-LG) is a new type of vehicle waste heat recovery device based on an organic Rankine cycle. It is expected to achieve the reuse of vehicle internal combustion engine waste heat and improve the comprehensive utilization rate of energy. To enable the FPE-LG to recover exhaust gas to a greater extent in practical applications, based on the FPE-LG coupling gas storage tank test platform, the gas is discharged from the expander cylinder. This paper analyzes the influence of differences to the tank volume, intake pressure, intake duration time, expansion duration time and exhaust duration time on piston motion characteristics and gas storage and release time during the cycle, and verifies the feasibility of gas working as a recycling medium. The results showed that the energy storage of lithium batteries increases with the increase of intake pressure, and the energy stored in lithium batteries during gas release is higher than that during gas storage; the intake duration time, expansion duration time and exhaust duration time have little effect on the storage of lithium battery energy during the cycle. When the intake pressure is 0.5 MPa, the volume of the gas tank is 30 L, and the intake duration time, expansion duration time and exhaust duration time are 50 ms, 80 ms and 30 ms, respectively, the maximum actual stroke of the piston can reach 89.592 mm.