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For intelligent vehicles, a robust perception system relies on training datasets with a large variety of scenes. The architecture of federated learning allows for efficient collaborative model iteration while ensuring privacy and security by leveraging data from multiple parties. However, the local data from different participants is often not independent and identically distributed, significantly affecting the training effectiveness of autonomous driving perception models in the context of federated learning. Unlike the well-studied issues of label distribution discrepancies in previous work, we focus on the challenges posed by scene heterogeneity in the context of federated learning for intelligent vehicles and the inadequacy of a single scene for training multi-task perception models. In this paper, we propose a federated learning-based perception model training system.

To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers.

Autonomous vehicles require the collaborative operation of multiple modules during their journey, and enhancing tracking performance is a key focus in the field of planning and control. To address this challenge, we propose a cooperative control strategy, which is designed based on the integration of model predictive control (MPC) and a dual proportional–integral–derivative approach, referred to as collaborative control of MPC and double PID (CMDP for short in this article).The CMDP controller accomplishes the execution of actions based on information from perception and planning modules. For lateral control, the MPC algorithm is employed, transforming the MPC’s optimal problem into a standard quadratic programming problem. Simultaneously, a fuzzy control is designed to achieve adaptive changes in the constraint values for steering angles.

The main objective of platoon control is coordinated motion of autonomous vehicle platooning with small intervehicle spacing while maintaining the same speed and acceleration as the leading vehicle, which can save energy consumption and improve traffic throughput. The conventional platoon control methods are confronted with the problem of manual parameter tuning. In order to addres this isue, a novel bifold platoon control approach leveraging a deep reinforcement learning-based model is proposed, which enables the platoon adapt to the complex traffic environment, and guarantees the safety of platoon. The upper layer controller based on the TD3 tuned PID algorithm outputs the desired acceleration. This integration mitigates the inconvenience of frequent manual parameter tuning asociated with the conventional PID algorithm. The lower layer controller tracks the desired acceleration based on the inverse vehicle dynamics model and feedback control.

The phenomenon of liquid transfer in the liquid tank of the semi-trailer vehicle for transporting dangerous cargo (SVTDC) during braking is analyzed and the relevant mathematical model is established. The braking dynamic model of the SVTDC considering the liquid sloshing in the tank is established, and the model is verified based on the co-simulation method. Based on the typical conditions, the braking deceleration and axle load calculation functions of the model are simulated and analyzed, and the application prospect of the model in the development of driving automation control strategy is discussed.

Good heat dissipation of Lithium battery can prevent the battery from shortening its life due to rapid aging or thermal runaway. In this paper, an air-cooled structure of 5 series and 3 parallel battery packs is designed, which combines the advantages of series and parallel air ducts and optimizes the heat dissipation effect and the space ratio of air ducts. First, the heat generation model of NCR18650PF lithium battery is established, and the heat generation rate and time under different discharge rates are calculated. Combined with the working conditions of the battery itself, the necessity of battery pack heat dissipation was found.

In recent years, research on car-like robots has received more attention due to the rapid development of artificial intelligence from diverse disciplines. As essential parts, path planning and lateral path tracking control are the basis for car-like robots to complete automation tasks. Based on the two-degree-of-freedom vehicle dynamic model, this study profoundly analyzes the car-like robots’ path planning and lateral path tracking control. Three objectives: path length, path smoothness, and path safety, are defined and used to construct a multi-objective path planning model. By introducing an adaptive factor, redefining the selection of reference points, and using the cubic spline interpolation for path determination, an improved NGSA-III is proposed, which is mostly adapted in solving the multi-objective path planning problem.

This paper proposed a model predictive control(MPC) based car-following control strategy for electric vehicles considering comfort, in order to improve the comfort of the car-following control system of electric vehicles. The MPC algorithm is improved in the following three aspects to improve the comfort: Firstly, a five-state longitudinal car-following model is adopted, so that the MPC algorithm can optimize the acceleration and acceleration change rate of the ego vehicle. Secondly, for the weight coefficients of the output vector and the input vector of the objective function, the fixed weight coefficients are changed into variable weight coefficients by the way of Nash equilibrium game, so that the control system can improve the weight of the parameters used to control the comfort under suitable driving conditions.

For cooperative adaptive cruise control (CACC) system, a robust following control algorithm based on fuzzy PID principle is adopted in this paper. Firstly, a nonlinear vehicle dynamics model considering the lag of driving force and acceleration constraints was established. Then, with the vehicle’s control hierarchic, the upper controller takes the relative speed between vehicles and the spacing error as inputs to output the following vehicle's target acceleration, while the lower controller takes the target acceleration as inputs and the throttle opening and brake master cylinder pressure as outputs. For the setting of target spacing, this paper additionally considers the relative speed between vehicles and the acceleration of the front vehicle. Through testing, compared with the traditional variable safety distance model, the average distance reduces by 5.43% when leading vehicle is accelerating, while increases by 2.74% in deceleration.

Intelligent sweeper vehicle is gradually applied to human life, in which the accuracy of garbage identification and classification can improve cleaning efficiency and save labor cost. Although Deep Learning has made significant progress in computer vision and the application of semantic network segmentation can improve waste identification rate and classification accuracy. Due to the loss of some spatial information during the convolution process, coupled with the lack of specific datasets for garbage identification, the training of the network and the improvement of recognition and classification accuracy are affected. Based on the Unet algorithm, in this paper we adjust the number of input and output channels in the convolutional layer to improve the speed during the feature extraction part. In addition, manually generated datasets are used to greatly improve the robustness of the model.

A flow channel design of the battery liquid cooling plate is carried out through the variable density topology optimization method according to the heat dissipation requirements of lithium-ion power batteries under actual working conditions. Firstly, given the non-uniform heat generation of lithium battery cells, the heat generation mechanism is studied so that the battery electro-thermal model is established, then the distribution regularity of heat generation rate in the cell at different discharge rates is obtained. Subsequently, through COMSOL Multiphysics simulation software, the multi-objective topology optimization of the primary configuration radiator is conducted. The weights of the optimization objectives minimum temperature and minimum flow resistance are determined by practical engineering application. Finally, an optimized model with a volume fraction of 50% was obtained.

Regenerative braking is an effective way to increase the cruising range of vehicles. In commercial vehicles with large vehicle mass, regenerative braking can be maintained in a high-power working state for a long time theoretically because of the large braking torque and long braking time. But in fact, it is often impossible to run at full power because of battery safety problems. In this paper, a control strategy is designed to maintain the maximum power operation of regenerative braking as much as possible. The maximum charging power of the battery is obtained through the battery model, and it is set as the battery limiting parameter. The regenerative braking torque and power are obtained by using the motor model. The eddy current retarder is used to absorb the excess power that the battery can't bear, and the braking torque of the eddy current retarder is calculated. Finally, mechanical braking is used to make up the insufficient braking torque.

Accurate surrounding vehicle motion prediction is critical for enabling safe, high quality autonomous driving decision-making and motion planning. Aiming at the problem that the current deep learning-based trajectory prediction methods are not accurate and effective for extracting the interaction between vehicles and the road environment information, we design a target vehicle intention-aware dual attention network (IDAN), which establishes a multi-task learning framework combining intention network and trajectory prediction network, imposing dual constraints. The intention network generates an intention encoding representing the driver’s intention information. It inputs it into the attention module of the trajectory prediction network to assist the trajectory prediction network to achieve better prediction accuracy.

The battery liquid cooling system can ensure that the battery works within a suitable temperature range, improve the safety performance of the battery system, and ensure the cruising range. This paper introduces a design scheme of a stamped double-parallel liquid cooling plate. Based on the STAR-CCM+ simulation software, a thermal simulation model of the battery management system is established to analyze the thermal behavior of the battery system and to study the effect of the inlet mass flow rate on the temperature of the top surface of the batteries. At the same time, with the analysis of the proportion of pressure drop of each component in the liquid cooling plate, an optimization of inserted part in the liquid cooling plate is proposed. The numerical analysis results are compared with the experimental results of the pressure drop to improve the effectiveness of the optimization scheme.

In this paper, a high-efficiency and low-cost lithium-ion battery pack active balance system is designed. It adopts a distributed structure and consists of three parts: auxiliary power module, one-way isolated DC/DC conversion module, and a battery group. The battery single cells in the battery pack are layered and divided into m battery groups in total, and each battery group is composed of n battery single cells. Each battery group is connected to an isolated DC/DC conversion module, and all the conversion modules are connected in parallel with the auxiliary power. Taking the SOC average value of the all-single cells in one battery group as the balancing variable, the auxiliary power is controlled to charge the battery group with the lower SOC average value, so that the difference of the SOC average value of all battery groups is within the set threshold range, so as to realize the active balance of each battery group.

The plug-in hybrid electric vehicle (PHEV) gradually moves into the mainstream market with its excellent power and energy consumption control, and has become the research target of many researchers. The energy management strategy of plug-in hybrid vehicles is more complicated than conventional gasoline vehicles. Therefore, there are still many problems to be solved in terms of power source distribution and energy saving and emission reduction. This research proposes a new solution and realizes it through simulation optimization, which improves the energy consumption and emission problems of PHEV to a certain extent. First, on the basis that MATLAB software has completed the modeling of the key components of the vehicle, the fuzzy controller of the vehicle is established considering the principle of the joint control of the engine and the electric motor.

The sweeping vehicle has made a great contribution to the cleaning of urban roads. The traditional electric sweeping vehicle uses the main and auxiliary motors to drive the driving system and the operating system respectively. However, because the sweeper is in a low-speed working condition for a long time, and the drive motor must meet the demand for high power, there exist problems of low motor utilization and high cost. Aiming at this phenomenon, a dual-motor power coupling system based on planetary gears is proposed. First, analyze the driving mode of the dual-motor coupling power system according to the actual working scheme of the sweeper, and match the parameters of the motor based on this. Second, on the premise of meeting the power requirements, analyze and divide the working range of each drive mode based on the principle of minimum energy consumption, and then obtain the best drive mode switching control and speed and torque distribution strategy.

With the rapid development of new energy vehicles, the echelon utilization of retired power battery has become an important factor to promote the healthy development of this industry, while the Remaining Useful Life (RUL), as the key reference factor for the echelon utilization of retired power battery, has attracted the attention and research of many scholars in recent years. At present, most prediction methods are based on off-line data, which cannot process real-time data in time, so it is difficult to realize online prediction of RUL. In order to realize the real-time online monitoring and high-precision calculation of lithium-ion battery RUL, this paper proposes a lithium-ion battery RUL prediction method based on data-driven and multi-model fusion. The one-dimensional Convolutional Neural Network (1D_CNN) is used for fast online feature extraction of one-dimensional battery capacity time series data to mine potential hidden information.

With the rapid development of the logistics and transportation industry, heavy-duty trucks play an increasingly important role in social life. However, due to the characteristics of large cargo loads, high center of mass and relatively narrow wheelbase, the driving stability of heavy trucks are poor, and it is easy to cause rollover accidents under high-speed driving conditions, large angle steering and emergency obstacle avoidance. To improve the roll stability of heavy trucks, it is necessary to design an active anti-rollover control system, through the analysis of the yaw rate and the load transfer rate of the vehicle, driving states can be estimated during the driving process. Under the intervention of the control system, the lateral transfer rate of heavy trucks can be reduced to correct the driving posture of the vehicle body and reduce the possibility of rollover accidents.

Heat generated by friction between the brake discs and the brake pad causes the disc temperature to rise, which affects the braking performance. This flux generated from the contact surface of the vehicle brake disc not only affects the braking performance but also tends to be wasted and pollutes the environment. However, an accurate system is needed to make efficient use of this generated heat flux, which is usually wasted. Thermoelectric generators (TEGs) are solid-state gadgets utilized in the conversion of heat to electricity. Hence, the aim of this study is to convert the heat flux generated at the disc contact surface into electrical energy by employing a thermoelectric generator. In Addition, the energy harvested energy to power the battery, which in turn charges the temperature monitoring systems. Thermoelectric generators were positioned at different geometrical points of the brake discs to achieve optimal efficiency and energy storage possibilities.