Intelligent Vehicle Path Planning Based on Optimized A* Algorithm.

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent.

RBS and ABS Coordinated Control Strategy Based on Explicit Model Predictive Control.

During the braking process of electric vehicles, both the regenerative braking system (RBS) and anti-lock braking system (ABS) modulate the hydraulic braking force, leading to control conflict that impacts the effectiveness and real-time capability of coordinated control. Aiming to enhance the coordinated control effectiveness of RBS and ABS within the electro-hydraulic composite braking system, this paper proposes a coordinated control strategy based on explicit model predictive control (eMPC-CCS). Initially, a comprehensive braking control framework is established, combining offline adaptive control law generation, online optimized control law application, and state compensation to effectively coordinate braking force through the electro-hydraulic system. During offline processing, eMPC generates a real-time-oriented state feedback control law based on real-world micro trip segments, improving the adaptiveness of the braking strategy across different driving conditions. In the online implementation, the developed three-dimensional eMPC control laws, corresponding to current driving conditions, are invoked, thereby enhancing the potential for real-time braking strategy implementation. Moreover, the state error compensator is integrated into eMPC-CCS, yielding a state gain matrix that optimizes the vehicle braking status and ensures robustness across diverse braking conditions. Lastly, simulation evaluation and hardware-in-the-loop (HIL) testing manifest that the proposed eMPC-CCS effectively coordinates the regenerative and hydraulic braking systems, outperforming other CCSs in terms of braking energy recovery and real-time capability.

A Dual-Adaptive Equivalent Consumption Minimization Strategy for 4WD Plug-In Hybrid Electric Vehicles.

Energy management strategies are vitally important to give full play to energy-saving four-wheel-drive plug-in hybrid electric vehicles (4WD PHEV). This paper proposes a novel dual-adaptive equivalent consumption minimization strategy (DA-ECMS) for the complex multi-energy system in the 4WD PHEV. In this strategy, management of the multi-energy system is optimized by introducing the categories of future driving conditions to adjust the equivalent factors and improving the adaptability and economy of driving conditions. Firstly, a self-organizing neural network (SOM) and grey wolf optimizer (GWO) are adopted to classify the driving condition categories and optimize the multi-dimensional equivalent factors offline. Secondly, SOM is adopted to identify driving condition categories and the multi-dimensional equivalent factors are matched. Finally, the DA-ECMS completes the multi-energy optimization management of the front axle multi-energy sources and the electric driving system and releases the energy-saving potential of the 4WD PHEV. Simulation results show that, compared with the rule-based strategy, the economy in the DA-ECMS is improved by 13.31%.

State-of-Health Prediction Using Transfer Learning and a Multi-Feature Fusion Model.

Existing data-driven technology for prediction of state of health (SOH) has insufficient feature extraction capability and limited application scope. To deal with this challenge, this paper proposes a battery SOH prediction model based on multi-feature fusion. The model is based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). The CNN can learn the cycle features in the battery data, the LSTM can learn the aging features of the battery over time, and regression prediction can be made through the full-connection layer (FC). In addition, for the aging differences caused by different battery operating conditions, this paper introduces transfer learning (TL) to improve the prediction effect. Across cycle data of the same battery under 12 different charging conditions, the fusion model in this paper shows higher prediction accuracy than with either LSTM and CNN in isolation, reducing RMSPE by 0.21% and 0.19%, respectively.

State of Health Prediction of Lithium-Ion Battery Based on Deep Dilated Convolution.

A battery's charging data include the timing information with respect to the charge. However, the existing State of Health (SOH) prediction methods rarely consider this information. This paper proposes a dilated convolution-based SOH prediction model to verify the influence of charging timing information on SOH prediction results. The model uses holes to fill in the standard convolutional kernel in order to expand the receptive field without adding parameters, thereby obtaining a wider range of charging timing information. Experimental data from six batteries of the same battery type were used to verify the model's effectiveness under different experimental conditions. The proposed method is able to accurately predict the battery SOH value in any range of voltage input through cross-validation, and the SDE (standard deviation of the error) is at least 0.28% lower than other methods. In addition, the influence of the position and length of the range of input voltage on the model's prediction ability is studied as well. The results of our analysis show that the proposed method is robust to different sampling positions and different sampling lengths of input data, which solves the problem of the original data being difficult to obtain due to the uncertainty of charging-discharging behaviour in actual operation.

A Dual Distribution Control Method for Multi-Power Components Energy Output of 4WD Electric Vehicles.

Energy management strategies are vitally important to give full play to the energy-saving of the four-wheel drive electric vehicle (4WD EV). The cooperative output of multi-power components is involved in the process of driving and braking energy recovery of 4WD EV. This paper proposes a novel energy management strategy of dual equivalent consumption minimization strategy (D-ECMS) to improve the economy of the vehicle. According to the different driving and braking states of the vehicle, D-ECMS can realize the proportional control of the energy cooperative output among the multi-power components. Under the premise of satisfying the dynamic performance of the vehicle, the operating points of the power components are distributed more in the high-efficiency range, and the economy and driving range of the vehicle are optimized. In order to achieve the effectiveness of D-ECMS, MATLAB/Simulink is used to realize the simulation of the vehicle. Compared with the rule-based strategy, the economy of D-ECMS increased by 4.35%.

Study on the Complex Electrical Response Characteristics of Loaded Coal and the Control Mechanism of the Main Fracture System Structure.

The structure of coal seam fractures is the main physical property of coalbed methane reservoir evaluation, and the complex resistivity method is a potential geophysical evaluation method for coal seam fractures. In this study, cylindrical coal samples with axial directions perpendicular to the bedding, face cleat, and butt cleat were prepared. The complex electrical parameters of the loaded specimens were tested with test frequencies ranging from 1 Hz to 10 kHz. The complex electrical response characteristics of the loaded coal are summarized, and the control mechanism of the main fracture system structure is analyzed. The results indicated that (1) as the loading pressure increased, the resistance R and the absolute values of reactance X(|X|) gradually decreased, especially in the frequency band where R slowly decreased and the characteristic frequency of X, the decreased amplitude was more significant, and the cutoff frequency of R and the characteristic frequency of X all gradually increased. (2) The complex electrical properties of coal show obvious anisotropic characteristics. Both R and |X| decreased sequentially according to the direction perpendicular to the bedding, face cleat, and butt cleat; the cutoff frequency of R and the characteristic frequency of X all increased sequentially. (3) The dispersion phenomenon of the complex electrical properties of coal is attributed to the induced polarization; the elevated loading stress enhances the polarization effects of the molecular-induced moments of the coal skeleton, and the anisotropic difference of the complex electrical properties is due to the difficulty in the degree of transport of charged particles induced by structural differences of the main fracture system. (4) The resistance R3 and capacitance Xc were selected as the complex electrically sensitive parameters of the loaded coal orthogonal fracture structures. A logarithmic inversion model reflecting the main fracture system structure of coal was constructed. This provides a certain theoretical basis for efficient electrical exploration of coal reservoir fracture structures.

Geological Controls on the Gas Content and Permeability of Coal Reservoirs in the Daning Block, Southern Qinshui Basin.

The gas content and permeability of the coal reservoir are the key factors affecting coalbed methane (CBM) productivity. To investigate the geological controls on the permeability and gas content of coal reservoirs in the Daning block, southern Qinshui Basin, geological surveys combined with laboratory experiments, including coal petrology analysis, proximate analysis, and methane isothermal adsorption experiments, were carried out. The results show that the gas content of coals in the Daning block ranges from 5.56 to 17.57 (avg. 12.83) m3/t, and the coal permeability is generally above 0.1 mD, averaging 0.96 mD. The gas content of coal reservoirs shows decreasing trends with the increase in ash yield and moisture content, while tends to increase with the increase of vitrinite content; however, the correlation coefficients are all extremely low. The gas content presents a strong positive correlation with the burial depth of coal seams, but overall poorly correlates with the coal thickness. The CBM-rich areas are generally located at the hinge zones of secondary synclines, while the lower gas content areas commonly occur at the hinge zones of secondary anticlines. The normal faults are developed in the Daning block, and as expected, the gas content of coal seams that are near the normal faults is commonly lower. It was found that the well testing permeability of coal reservoirs in the Daning block decreases exponentially with the increase of the minimum horizontal stress (σh) and the maximum horizontal principal stress (σH). With the increase of the burial depth, the coal permeability also decreases exponentially. The primary and cataclastic structure coals generally have a higher hydro-fracturing permeability than the granulitic and mylonitic structure coals. This work can serve as a guide for the target area selections of CBM enrichment and high production in the Daning block.

Experimental Studies on the Enhancement of Permeability of Anthracite by Acidizing: A Case Study in the Daning Block, Southern Qinshui Basin.

As the most active and top producing area of coalbed methane (CBM) in China, the southern Qinshui Basin (SQB) is dominated by anthracite. Due to the low permeability of coals, plenty of non-gas-producing and low production CBM wells exist in the SQB. The permeability enhancement through some technological means is the key to increasing the CBM production of this area. In this paper, some typical anthracites were selected from the Daning block of the SQB to assess the effect of acidification treatments on permeability enhancement. The maceral composition determination shows that approximately 15% of minerals exist in the collected coal samples, and the X-ray diffractometer (XRD) results reveal that the minerals consist primarily of clay minerals, along with a little amount of quartz, calcite, and dolomite. Two types of acidizing fluids were used to conduct acidification treatments on the anthracites for different lengths of time. The N2 permeability of the anthracites before and after acidification was measured and compared. The results show that the original samples exhibit low permeability. As the acidification time increases, the permeability of all of the samples shows an increasing trend, and the acid sensitivity index I a increases rapidly first and then levels off, and finally approaches 1. After 48 h of acidification, the samples show an increase ranging from 8.75 to 22.67 times (avg. 14.3 times) the original permeability. The permeability enhancement of the SQB anthracites is mainly attributed to the dissolution of acid-soluble minerals in the cleat system of coal. The minerals in the cleats are completely or partially dissolved by the acids, generating some soluble and insoluble substances; when the fluid flows through, the cleat space is reallocated. Overall, the cleat demineralization by acids frees up a lot of cleat spaces, leading to an increase in cleat connectivity. As a result, the fluid movement becomes smooth and the permeability of coal improves.

Construction of Functionally Compartmental Inorganic Photocatalyst-Enzyme System via Imitating Chloroplast for Efficient Photoreduction of CO2 to Formic Acid.

Inorganic photocatalyst-enzyme systems are a prominent platform for the photoreduction of CO2 to value-added chemicals and fuels. However, poor electron transfer kinetics and enzyme deactivation by reactive oxygen species in the photoexcitation process severely limit catalytic efficiency. In chloroplast, enzymatic CO2 reduction and photoexcitation are compartmentalized by the thylakoid membrane, which protects enzymes from photodamage, while the tightly integrated photosystem facilitates electron transfer, promoting photocatalysis. By mimicking this strategy, we constructed a novel functionally compartmental inorganic photocatalyst-enzyme system for CO2 reduction to formate. To accomplish efficient electron transfer, we first synthesized an integrated artificial photosystem by conjugation of the cocatalyst (a Rh complex) onto thiophene-modified C3N4 (TPE-C3N4), demonstrating an NADH regeneration rate of 9.33 µM·min-1, 2.33 times higher than that of a homogeneous counterpart. The enhanced NADH regeneration activity was caused by the tightly conjugated structure of the artificial photosystem, enabling rapid electron transfer from TPE-C3N4 to the Rh complex. To protect formate dehydrogenase (FDH) from photoinduced deactivation, FDH was encapsulated into MAF-7, a metal-organic framework (MOF) material, to compartmentalize FDH from the toxic photoexcitation process, similar to the function of the thylakoid membrane. Moreover, the triazole linkers of MAF-7 possess both hydrophilicity and pH-buffering capacity providing a stable microenvironment for FDH, which could enhance enzyme stability in photosynthesis. The synergy between the enhanced electron transfer of TPE-C3N4 for NADH cofactor regeneration and MOF-protection of the redox enzyme enables the construction of a functionally compartmental inorganic photocatalyst-enzyme association system, promoting CO2 photoconversion to formic acid with a yield of 16.75 mM after 9 h of illumination, 3.24 times greater than that of the homogeneous reaction counterpart.

Fourier transform infrared spectroscopy combined with deep learning and data enhancement for quick diagnosis of abnormal thyroid function.

BACKGROUND: To evaluate the Fourier transform infrared spectroscopy (FT-IR) combined with deep learning models to allow for quick diagnosis of abnormal thyroid function. MATERIALS AND METHODS: Serum samples of 199 patients with abnormal thyroid function and 183 healthy patients were collected by infrared spectroscopy data and combined with different decibel noise for data expansion. The data were directly imported into three deep models: multilayer perceptron (MLP), a long-short-term memory network (LSTM), and a convolutional neural network (CNN), and 10-fold cross-validation was used to evaluate the performance of the model. RESULTS: The accuracy rates of the three models using the original data were 91.3 %, 88.6 % and 89.3 %, and the accuracy rates of the three models after data enhancement were 92.7 %, 93.6 % and 95.1 %. CONCLUSION: The results of this study indicated that the use of large sample serum infrared spectroscopy data combined with deep learning algorithms is a promising method for the diagnosis of abnormal thyroid function.