To enhance the braking stability of electric vehicles and maximize braking energy recovery, this paper proposes a regenerative braking force distribution strategy based on Electro-Mechanical Braking (EMB) in a single-pedal mode. The braking intention during single-pedal operation is identified using an Adaptive Neuro-Fuzzy Inference System (ANFIS), with the effectiveness of this method validated through data collection and analysis on a six-degree-of-freedom test rig, showing significant improvement in intention recognition accuracy. An innovative distribution method for front and rear axle braking forces is developed, and a fuzzy controller is designed with battery State of Charge ( SOC ), vehicle velocity ( v ), braking intensity ( z ), and braking intention ( I ) as inputs, and the regenerative braking ratio coefficient ( k ) as the output. The controller is optimized using the Sparrow Search Algorithm (SSA), further enhancing braking energy recovery efficiency. Co-simulation with Simulink and AVL Cruise software demonstrates the strategy’s effectiveness. Results indicate that under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) and China Light-Duty Vehicle Test Cycle (CLTC) conditions, the braking energy recovery efficiency of the proposed strategy reaches 21.54% and 25.39%, respectively. These findings confirm that the EMB-based single-pedal regenerative braking force distribution strategy significantly improves both braking stability and energy recovery efficiency in electric vehicles, offering valuable insights for future braking strategy development.

This research delves deeply into the traffic light intersection control issue for connected autonomous vehicles (CAVs). Firstly, we employed the vehicle dynamics simulation software, CarSim, to model the vehicle, and utilized the intelligent driving simulation software, PreScan, to establish a road environment model. Subsequently, we designed a rule-based traffic light crossing controller (RB-TLCC) for CAVs in Matlab/Simulink and implemented a co-simulation with CarSim and PreScan. Furthermore, we conducted driver-in-the-loop studies using the Logitech G27 driving simulator kit and compared the experimental results with those from RB-TLCC. Our findings indicate that the RB-TLCC enhances regenerative braking energy by 23.5%, improves traffic efficiency by 17.9%, and increases driving smoothness by 50.7%. Additionally, based on Markov Chain theory, we proposed a multi-objective optimization model (MO-OM) for CAVs traffic light intersection crossing using the Proximal Policy Optimization algorithm (PPO). A comparison was conducted under two different operating conditions between RB-TLCC and dynamic programming algorithms (DP). The results indicate that the MO-OM proposed in this paper exhibits the best comprehensive control performance among the three control methods. While adhering to traffic regulations, it enhances the recuperation of braking energy, the efficiency of vehicle passage, and the smoothness of travel at traffic light intersections. This study offers effective methodologies for enhancing the performance of CAVs in traffic light intersection control and holds significant reference value for the advancement of future intelligent transportation systems (ITS).

To address the challenge of nondestructively identifying watercore disease in apples during growth and maturation, a portable device was developed for real-time grading of apple watercore using visible/near-infrared (Vis/NIR) spectroscopy combined with a one-dimensional quadratic convolutional neural network (1DQCNN). The instrument enables rapid, nondestructive, and accurate detection of apple watercore grades. The AI-OX2000-13 micro-spectrometer is used as the core data acquisition unit, and an ARM processing system is built with the STM32F103VET6 as the main control chip. A 4G wireless communication module enables efficient and stable data transmission between the processor and computer, meeting the real-time detection needs of apple watercore content in orchard environments. To improve the scientific and accurate classification of watercore grades, this paper combines the BiSeNet and RIFE algorithms to construct a 3D model of apple watercore, allowing quantification of the degree of watercore and classification into four levels. Based on this, quadratic convolution operations are incorporated into a one-dimensional convolutional neural network (1DCNN), leading to the development of the 1D quadratic convolutional neural network (1DQCNN) model for watercore grade classification. Experimental results indicate that the model achieves a classification accuracy of 98.05%, outperforming traditional methods and conventional CNN models. The designed portable instrument demonstrates excellent accuracy and practicality in real-world applications.

Fully leveraging the four-wheel independent drive characteristics of distributed-drive electric vehicles has become essential for enhancing their driving range. However, conventional regenerative braking strategies applied to such vehicles often fail to consider individual wheel slip ratios, which can easily lead to wheel lock and low energy recovery efficiency. To address these issues, this paper proposes a novel energy management method that integrates hybrid braking control with intelligent connected speed planning. A hierarchical control strategy for the hybrid braking system is first developed, explicitly accounting for the slip ratio of each wheel. The upper-level controller calculates the slip ratio for each wheel based on vehicle speed and wheel speed information and subsequently determines the braking torque distribution between the front and rear axles. The lower-level controller then allocates the motor braking torque and hydraulic braking torque to each wheel, subject to system constraints such as battery status and motor torque limits. Building on this framework, vehicle state and road information are incorporated as inputs to formulate a Markov decision process, which optimizes traffic efficiency, energy economy, and ride comfort as multiple objectives. The deep deterministic policy gradient (DDPG) algorithm is employed to achieve collaborative optimization of speed planning and energy management. Simulation results demonstrate that the proposed DDPG-based control strategy outperforms both rule-based control methods and classical dynamic programming algorithms in terms of comprehensive performance across traffic efficiency, energy consumption, and ride comfort. These findings validate its superiority in complex traffic conditions.

Cold spell in late spring often occur during the flowering period of fruit trees, causing irreversible damage to flower buds and significant economic losses to apple orchards. To address this challenge, we propose an optimized frost protection system based on the Hippo Optimization Algorithm (HOA) and the k-minimum spanning tree (k-MST) model. A green solar frost protection scheme is first developed. Then, the number and location of smoke machines are optimized using HOA. The wiring layout is further optimized using the k-MST model based on mixed integer linear programming (MILP), where the number of deployment points derived by HOA is used as the input parameter k. Simulation results show that compared with traditional methods, this optimization approach can achieve a maximum reduction of 55.55% in the number of smoke machines, and an even more significant saving of up to 44.16% in wiring length. The optimization method provides a cost-effective strategy for agricultural frost protection.

Late spring frost poses a serious threat to orchard production, especially in mountainous orchards where timely frost monitoring and adaptive protection are difficult to implement. To address this problem, this study developed an intelligent smoke-based anti-frost machine integrating a LoRa-based wireless temperature monitoring system, a smoke actuation unit, and a closed-loop control terminal. To overcome the slow response and large overshoot of conventional PID control under nonlinear field conditions, a fuzzy PID control strategy optimized by the Hippopotamus Optimization Algorithm (HOA) was proposed to regulate smoke release in real time. Comparative simulations were conducted using conventional PID, fuzzy PID, and HOA-fuzzy PID controllers, and field experiments were performed in an apple orchard. The results showed that the HOA-fuzzy PID controller achieved the best dynamic performance. Compared with conventional PID, the overshoot, rise time, and settling time were reduced by 60.12%, 33.21%, and 61.94%, respectively; compared with fuzzy PID, they were reduced by 22.85%, 50.45%, and 60.11%, respectively. Disturbance simulation further indicated improved control robustness. Field experiments showed that the prototype increased the orchard canopy temperature by 0.9–3.0 K, and the PM_2.5 distribution in the operational area indicated improved smoke coverage. The adaptive regulation strategy also avoided continuous fixed-output operation, suggesting its potential to improve energy-use efficiency. Overall, the proposed system provides a feasible field-operable approach for improving canopy thermal conditions and reducing frost-risk exposure in mountainous orchards, although further biological validation is still required.