Beijing Benz (officially Beijing Benz Automotive Co., Ltd) is an automotive manufacturing company headquartered in Beijing, China, and a joint venture between BAIC Motor and Mercedes-Benz Group.It was originally established in January 1984 as a joint-venture with BAIC Motor of Beijing, China and American Motors Corporation (AMC) of Michigan, USA therefore named as Beijing Jeep Corporation (Chinese: 北京吉普汽车有限公司). It was the first Chinese auto-making joint venture with a Western partner. The joint venture continued after AMC was acquired by Chrysler in 1987, when Chrysler merged with German automaker Daimler-Benz AG in 1998 and finally ended their partnership in 2005. Jeep later returned to the Chinese market in March 2010 under GAC Fiat where the company produces the Jeep Cherokee (K4) in Changsha from 2015 onwards. The Jeep Commander/Jeep Grand Commander (K8) were added in 2018 for assembling at the Changsha plant and is an exclusive model for the Chinese market. The Jeep Compass (M4) and Jeep Renegade (BQ) are produced in Guangzhou from 2016 onwards.Mercedes-Benz was firstly involved in a short lived joint venture from 1987 until 1988 with FAW (First Automobile Works) where 828 vehicles were produced consisting of the Mercedes-Benz (W123) 200 and 230E Lang, most of which were made from CKD kits. It was later established in August 2005 where Mercedes Benz was reintroduced to the Chinese market as Beijing Benz and assembled its first car, a Mercedes-Benz E-Class in December 2005.As of 2016, Beijing Benz assembles and manufactures the Mercedes-Benz E-Class (long wheelbase), and C-Class in China.Shortly afterwards, in 2017, Beijing Benz began manufacturing 58% of the parts for the Mercedes-Benz GLA-Class, Mercedes-Benz GLB-Class and Mercedes-Benz GLC-Class in China for those parts to be exported afterwards for assembly. As well as Beijing, there is also a subsidiary in Fuzhou, Fujian under the name Fujian Benz established in 2007 where they have produced light commercial vehicles such as the Vito since April 2011 and the Sprinter in November 2011. The Viano commenced in the same month as the Vito and ended production in April 2015. The V-Class was launched in March 2016.Since the 2014 sales of Chrysler and Jeep to FIAT, the Jeep-like vehicles and their derivatives have been produced by the BAIC subsidiary Beijing Automobile Works Co., Ltd. (BAW).
In addressing the difficulties encountered by settlement prediction models for earth-rock dams—such as the susceptibility of regression models to multicollinearity, as well as challenges like overfitting, local minima traps, and the complexity of tuning hyperparameters in neural network models—this study introduces an enhanced approach that incorporates Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) neural networks. To begin with, EMD is applied to decompose the time series data obtained from Global Navigation Satellite System (GNSS) measurement points at multiple scales, allowing for the extraction of both trend and periodic components. After that, Principal Component Analysis (PCA) is conducted to select key factors influencing the model, thus reducing the data’s dimensionality and improving its ability to generalize. Lastly, an LSTM-based time series model is developed, and its hyperparameters are optimized through the Whale Optimization Algorithm (WOA), which enhances both the model’s accuracy in prediction and its convergence speed. The experimental outcomes demonstrate that the proposed model significantly outperforms traditional settlement prediction methods for earth-rock dams, with a Mean Squared Error (MSE) of 7.0701 and a Mean Absolute Error (MAE) of 1.8859, alongside a coefficient of determination (R2) of 99.83%. Compared to existing techniques, this model exhibits considerable improvements in noise reduction, feature extraction, and hyperparameter tuning, offering a highly accurate and dependable approach for predicting settlement in earth-rock dams.
Lithium-ion batteries are considered as a promising energy source due to the high energy density and long cycle life. In this paper, the authors propose a model of a lithium-ion battery capacity fade and the electrochemical reactions in the battery. To improve the properties of the electrolyte and the lithium/electrolyte interface, the additive tris-(2,2,2-trifluoroethyl)-borate (TTFEB) is added into the carbonatebased electrolyte. The authors also study the factors influencing the battery capacity, including the resistance of solid electrolyte interface (SEI), the active material particle radius, the discharging rate, and the ambient temperature. The simulation results show that the lithium-ion battery life is determined by the capacity decay rate, the electrochemical mechanism, and the physicochemical parameters.
With the impact of global climate change on extreme weather and the rapid expansion of human activity range, the cryogenic freezing rain and snow disaster is becoming more frequent, bringing quite adverse effects on transportation, electric power and other industries, thus seriously affecting social and economic development and people’s daily life. Implementation of risk early warning is one of the most effective means to prevent the cryogenic freezing rain and snow disaster. This paper, taking Guizhou Province as an example, studies the early warning technology and method for the cryogenic freezing rain and snow disaster based on the demand for prevention of the cryogenic freezing rain and snow disaster, and considering meteorological factors, topography, social economy, power transportation and other factors. On this basis, the weight of risk factors is determined by subjective weighting method, the risk early warning model for the cryogenic freezing rain and snow disaster based on AHP is constructed, and the four-level risk early warning indexes are determined. Trial application is realized in Guizhou Province.
The 5754 aluminum alloy has been widely used in the automotive industry to reduce the weight of vehicles. The weld-bonding (WB) process comprising resistance spot welding and adhesive bonding processes effectively improves the mechanical properties of joints. However, it is still a great challenge in the WB process to obtain high-quality and defect-free nuggets of aluminum alloys. In this study, the parameters of the WB process are optimized and the mechanism of generation of defects during WB is analyzed. The results show that the welding parameters have a significant effect on the nugget sizes, among which the welding current plays the most important role. The residual adhesive can easily cause defects during welding, e.g., expulsion and porosity in the nugget. This can be effectively avoided by optimizing the welding parameters. In addition, the gas in the joints is effectively reduced by adding an appropriate preheating pulse prior to welding, thus lowering the damage degree of the adhesive layer. As a result, welded joints with better weld nugget quality and more stable mechanical properties are obtained.
