The cars that debuted at the Beijing Auto Show demonstrate that the Chinese market is now at the forefront of electrification and intelligence. These are the 19 most intriguing models we saw. The newest concept car from Lynk & Co was revealed at the 2026 Beijing Auto Show. While major motor shows in Europe and the United States are being forced to downsize or change their format, those in China continue to expand. With 1,451 vehicles on display, including 181 world premieres, the 2026 Beijing International Automotive Exhibition 2026 (also known as Auto China 2026) has become the largest auto show in history--and that's in terms of both exhibition space and the number of vehicles on display. This fact itself reflects a shift in the center of gravity of the automotive industry, but that's not all. A much larger structural transformation is actually taking place in China today. Previously, the focus was on low-priced electric vehicle models, but now price is no longer the primary point of competition.

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Tesla says its annual revenue has fallen for the first time as the electric vehicle (EV) maker shifts it focus to artificial intelligence (AI) and robotics. The company, which is run by multi-billionaire Elon Musk, reported a 3% decline in total revenues in 2025, while profits fell 61% in the last three months of the year. Tesla also announced plans to end production of its Model S and Model X vehicles. It will now use the manufacturing plant in California that made those cars to produce its line of humanoid robots - known as Optimus. In January, China's BYD overtook Tesla as the world's biggest EV maker, while Musk's involvement in politics both in the US and abroad has proved controversial.

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The pivot doesn't look out of place at CES, where Chinese electronics companies are increasingly applying their manufacturing prowess to new industries. If you've never been to Shenzhen, China's electronics capital, the annual CES trade show in Las Vegas is the next best thing. I'm reporting this week from the sprawling event, surrounded by fancy, strange, and often unnecessary gadgets, and despite my sore legs, I've barely scratched the surface. There are at least 900 Chinese tech companies attending CES this year, almost a quarter of the total exhibitors, according to an analysis of the conference's exhibitor directory. I even saw two Chinese humanoid robots at different booths dancing to the same viral Chinese rap song five minutes apart.

If a major disaster like Fukushima or Chernobyl ever happens again, the world would know almost straight away, thanks to an array of government and DIY radiation-monitoring programs running globally.

Accurate electric vehicle (EV) charging demand forecasting is essential for stable grid operation and proactive EV participation in electricity market. Existing forecasting methods, particularly those based on graph neural networks, are often limited to modeling pairwise relationships between stations, failing to capture the complex, group-wise dynamics inherent in urban charging networks. To address this gap, we develop a novel forecasting framework namely HyperCast, leveraging the expressive power of hypergraphs to model the higher-order spatiotemporal dependencies hidden in EV charging patterns. HyperCast integrates multi-view hypergraphs, which capture both static geographical proximity and dynamic demand-based functional similarities, along with multi-timescale inputs to differentiate between recent trends and weekly periodicities. The framework employs specialized hyper-spatiotemporal blocks and tailored cross-attention mechanisms to effectively fuse information from these diverse sources: views and timescales. Extensive experiments on four public datasets demonstrate that HyperCast significantly outperforms a wide array of state-of-the-art baselines, demonstrating the effectiveness of explicitly modeling collective charging behaviors for more accurate forecasting.

The increasing labor costs in agriculture have accelerated the adoption of multi-robot systems for orchard harvesting. However, efficiently coordinating these systems is challenging due to the complex interplay between makespan and energy consumption, particularly under practical constraints like load-dependent speed variations and battery limitations. This paper defines the multi-objective agricultural multi-electrical-robot task allocation (AMERTA) problem, which systematically incorporates these often-overlooked real-world constraints. To address this problem, we propose a hybrid hierarchical route reconstruction algorithm (HRRA) that integrates several innovative mechanisms, including a hierarchical encoding structure, a dual-phase initialization method, task sequence optimizers, and specialized route reconstruction operators. Extensive experiments on 45 test instances demonstrate HRRA's superior performance against seven state-of-the-art algorithms. Statistical analysis, including the Wilcoxon signed-rank and Friedman tests, empirically validates HRRA's competitiveness and its unique ability to explore previously inaccessible regions of the solution space. In general, this research contributes to the theoretical understanding of multi-robot coordination by offering a novel problem formulation and an effective algorithm, thereby also providing practical insights for agricultural automation.

Abstract--The rapid adoption of electric vehicles (EVs) introduces major challenges for decentralized charging control. Existing decentralized approaches efficiently coordinate a large number of EVs to select charging stations while reducing energy costs, preventing power peak and preserving driver privacy. These situations create competition for limited charging slots, resulting in long queues and reduced driver comfort. T o address these limitations, we propose a novel collective learning-based coordination framework that allows EVs to balance individual comfort on their selections against system-wide efficiency, i.e., the overall queues across all stations. In the framework, EVs are recommended for adaptive charging behaviors that shift priority between comfort and efficiency, achieving Pareto-optimal trade-offs under varying station capacities and dynamic spatiotemporal EV distribution. Experiments using real-world data from EVs and charging stations show that the proposed approach outperforms baseline methods, significantly reducing travel and queuing time. The results reveal that, under uncertain charging conditions, EV drivers that behave selfishly or altruistically at the right moments achieve shorter waiting time than those maintaining moderate behavior throughout. Our findings under high fractions of station outages and adversarial EVs further demonstrate improved resilience and trustworthiness of decentralized EV charging infrastructure. LECTRIC vehicles (EVs) are becoming a preferred option in intelligent transportation systems due to their energy efficiency and reduced emissions, critical in addressing environmental concerns and fuel shortages. According to recent global market reports, EV sales are projected to surpass 17 million units in 2024 (over 20% market share), with over 20 million expected in 2025 [1]. As governments expand public charging infrastructure to meet soaring demand, centralized charging management faces limitations in scalability, cost, and resilience (e.g., single points of failure) [2], [3]. A promising alternative lies in decentralized charging control among EVs. It aims to allow EVs to manage their charging based on local conditions, user preference and grid/station needs without a central authority.

The growing demand for electric vehicle (EV) charging infrastructure presents significant planning challenges, requiring efficient strategies for investment and operation to deliver cost-effective charging services. However, the potential benefits of EV charging assignment, particularly in response to varying spatial-temporal patterns of charging demand, remain under-explored in infrastructure planning. This paper proposes an integrated approach that jointly optimizes investment decisions and charging assignments while accounting for spatial-temporal demand dynamics and their interdependencies. To support efficient model development, we leverage a large language model (LLM) to assist in generating and refining the mathematical formulation from structured natural-language descriptions, significantly reducing the modeling burden. The resulting optimization model enables optimal joint decision-making for investment and operation. Additionally, we propose a distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) to address computational complexity in high-dimensional scenarios, which can be executed on standard computing platforms. We validate our approach through a case study using 1.5 million real-world travel records from Chengdu, China, demonstrating a 30% reduction in total cost compared to a baseline without EV assignment.