Merchants Chongqing Communications Research and Design Institute

Lithium-ion batteries are highly sensitive to temperature, with excessive heat leading to accelerated aging, reduced cycle life, and safety risks such as thermal runaway. Traditional thermal management systems often struggle to maintain optimal battery temperature, especially under high ambient temperatures or dynamic operating conditions. This paper proposes a novel reinforcement learning (RL)-based control method for the coupled thermal management system of a power battery and air conditioning system in a Plug-in Hybrid Electric Vehicle (PHEV). The system integrates the battery cooling circuit with the air conditioning system via a heat exchanger, eliminating the need for a separate radiator. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed to control the water pump speed, optimizing battery temperature in real-time. Experimental results under New European Driving Cycle (NEDC) conditions at 40°C ambient temperature demonstrate that the RL-controlled pump responds faster than the Model Predictive Control (MPC) algorithm, allowing the battery temperature to reach the target temperature of 28°C 1 min earlier with a maximum fluctuation of only 0.8°C. The RL-controlled pump consumes 4 W h more energy than the MPC-controlled pump, while the compressor energy consumption is only 1.68% higher. Additionally, the temperature fluctuations in the passenger cabin under RL control are only 0.1°C higher than under MPC, ensuring passenger comfort. This study highlights the potential of RL-based control for improving the efficiency and stability of integrated thermal management systems in electric vehicles.

Quantized deep neural networks (QDNNs) provide an efficient solution for real-time human activity recognition (HAR) on resource-constrained edge devices by addressing computational and memory limitations while maintaining high classification accuracy. This study proposes a QDNN-based framework utilizing advanced quantization techniques, including weight pruning, fixed-point quantization, and layer compression, to optimize model size, inference speed, and resource efficiency. Additionally, the integration of optical sensors for high-resolution data acquisition further enhances the robustness and sensitivity of activity recognition systems. An adaptive quantization-aware training strategy is integrated into the framework to mitigate accuracy loss caused by quantization and ensure robust performance in dynamic environments. The framework was evaluated on benchmark datasets, UCI HAR and WISDM, using Raspberry Pi and NVIDIA Jetson Nano as deployment platforms. Experimental results demonstrate a 45% reduction in memory consumption, a 38% decrease in inference latency, and up to a 42% reduction in computational complexity compared to baseline DNNs, while maintaining over 98% classification accuracy across various quantization levels. These results highlight the practicality of QDNNs for enabling real-time HAR in wearable health monitoring, smart homes, and other edge-based applications, offering a scalable and efficient approach to deploying machine learning models in resource-limited settings.

To address issues such as data fragmentation, low visualization, and delayed early warning in traditional bridge construction monitoring, an intelligent monitoring system for bridge construction that integrates BIM (Building Information Modeling) and IoT (Internet of Things) technologies is proposed. Firstly, the core application requirements and technical challenges of BIM and IoT technologies in the field of bridge engineering are outlined. Secondly, a four-tier architecture system consisting of the “perception layer–transmission layer–data layer–application layer” was constructed, with a focus on explaining the functional modules, hardware selection, and software integration solutions at each level to achieve real-time collection, dynamic modeling, and intelligent analysis of key parameters such as structural stress, settlement, temperature, and equipment operational status during the construction process. Finally, based on an actual bridge engineering project, system application verification is conducted. By comparing the application effects of traditional monitoring methods with those of the intelligent monitoring system, the feasibility and superiority of the system in enhancing construction monitoring accuracy, efficiency, and safety management capabilities are validated.

To mitigate the position divergence resulting from attitude error amplification in conventional fully coupled architectures, this study proposes a decoupled navigation architecture based on the Versatile Quaternion-based Filter (VQF). This architecture removes attitude estimation from the state vector, forming a two-layer structure comprising an independent attitude module and a navigation filter. The VQF is integrated as a standalone attitude module via a standardized interface. An uncertainty quantification model is developed by extracting the VQF's internal correction states, which maps deviations among intermediate quaternion values to a measurable uncertainty metric. To compensate for the loss of cross-covariance induced by decoupling, a dual-layer compensation mechanism is introduced: a base layer adjusts the overall uncertainty using innovation statistics, while a compensation layer explicitly propagates attitude uncertainty through parameterized noise matrices. Experimental results demonstrate that the proposed method achieves notable improvements in positioning accuracy and significantly suppresses extreme errors in high-curvature scenarios. The approach is particularly effective for high-curvature, high-dynamic applications where process noise modeling is inherently difficult. Compared to traditional fully coupled architectures, the decoupled architecture offers enhanced robustness. The complementary characteristics identified between the two architectures provide valuable insights for expanding the operational envelope of underwater navigation systems.