Vehicle detection is an indispensable part of environmental perception technology for smart cars. Aiming at the issues that conventional vehicle detection can be easily restricted by environmental conditions and cannot have accuracy and real-time performance, this article proposes a front vehicle detection algorithm for smart car based on improved SSD model. Single shot multibox detector (SSD) is one of the current mainstream object detection frameworks based on deep learning. This work first briefly introduces the SSD network model and analyzes and summarizes its problems and shortcomings in vehicle detection. Then, targeted improvements are performed to the SSD network model, including major advancements to the basic structure of the SSD model, the use of weighted mask in network training, and enhancement to the loss function. Finally, vehicle detection experiments are carried out on the basis of the KITTI vision benchmark suite and self-made vehicle dataset to observe the algorithm performance in different complicated environments and weather conditions. The test results based on the KITTI dataset show that the mAP value reaches 92.18%, and the average processing time per frame is 15 ms. Compared with the existing deep learning-based detection methods, the proposed algorithm can obtain accuracy and real-time performance simultaneously. Meanwhile, the algorithm has excellent robustness and environmental adaptability for complicated traffic environments and anti-jamming capabilities for bad weather conditions. These factors are of great significance to ensure the accurate and efficient operation of smart cars in real traffic scenarios and are beneficial to vastly reduce the incidence of traffic accidents and fully protect people’s lives and property.

To improve the accuracy of insulation monitoring between the battery pack and chassis of electric vehicles, we established a serial battery pack model composed of first-order resistor-capacitor (RC) circuit battery cells. We then designed a low-voltage, low-frequency insulation monitoring model based on this serial battery pack model. An extended Kalman filter (EKF) was designed for this non-linear system to filter the measured results, thus mitigating the influence of noise. Experimental and simulation results show that the proposed monitoring model and extended Kalman filtering algorithm for insulation resistance monitoring present satisfactory estimation accuracy and robustness.

To address the problems of low efficiency and high fuel consumption during the cold start and warm-up processes of internal combustion engines, a series hybrid electric vehicle was selected as the research object and two optimal control strategies were designed. A bench test was performed to determine the following: (a) the influence of engine coolant temperature on effective thermal efficiency; and (b) the relationship between engine operating conditions and coolant temperature increase rate. On the basis of the test results, two sets of warm-up process optimization control strategies were designed using a dynamic programming method and a fuzzy control method based on equivalent consumption minimization strategy (ECMS). The test results show that the fuzzy control method for the coolant temperature can effectively shorten the time required to warm up the engine, and the energy consumption of warm-up process can be reduced by nearly 10% through the dynamic programming method.

Vehicle dynamic management (VDM) is a vehicle chassis integrated control system based on electronic stability program (ESP) and continuous damping control (CDC) that has been developed in recent years. In this work, the ideal yaw angle rate and sideslip angle of the mass center are calculated deriving an ideal monorail model with two degrees of freedom. Then, a direct yaw moment proportional-integral-differential control strategy for ESP is proposed as the foundation of VDM. In addition, a multi-objective fuzzy continuous damping control (MFCDC) is proposed to achieve comfort, handling stability, and rollover prevention. The effect of the MFCDC strategy is analyzed and verified through a sine wave steer input test, double line change test, and fishhook test. The results indicate that MFCDC-ESP has a significant advantage in preventing rollover. MFCDC-ESP can maintain the optimized distribution of damping force through its own compensation under possible instability and predict the critical stable state to some extent. MFCDC-ESP exhibits strong real-time sensitivity to the control state of the damping force of each wheel. Hence, it can ensure the comfort of passengers under good driving conditions and exert strong adaptability and control effects under extreme working conditions.

This paper is mainly based on the college formula race car, through the application of CAN bus communication interface settings, the layout and adjustment of the car sensors, and then carry out the data collection and analysis of the car. CAN bus is the local area network of controller and is one of the field bus which is put forward by German Bosch company and applied most widely in the world. The development of the data analysis system is mainly based on the MoTeC i2 data analysis software. MoTeC i2 data analysis software has been developed for many years under the continuous research of professional teams all over the world. According to the data analysis requirement of FSAE undergraduate Formula 1 teams, this paper introduces a series of data analysis methods closely related to FSAE, mainly including data channel research. This paper expounds the reliability, suspension, tire temperature, steering, and braking data analysis, respectively. Based on the data analysis template created by Motec i2, this paper conducts analysis and basic application according to the data of the real car, so as to meet the requirements of verifying design made by simulation model and tuning the real car scientifically. By means of wheel speed sensor, acceleration sensor, displacement sensor, multichannel temperature sensor, and so on, we conduct a series of tests on 2020 season car, including endurance test, linear acceleration experiment, steady rotation, etc., evaluate the data collected, and then carry on a reasonable set‐up of racing, ultimately improving lap speed.

The energy management control strategy of hybrid electric vehicles is a critical research field. During driving, a vehicle encounters various uncertain and even intricate driving cycles for a vehicle. Although reinforcement learning has clear advantages in energy management control strategies, fuel consumption remains high in the driving cycles. To solve these problems, this study proposes an uncertainty-based reinforcement learning method, which uses a distributed uncertainty function to select the action when faced with intricate regions of driving cycles. The algorithm implementation processes are as follows: First, uncertainty is calculated by the uncertainty module; Then, the action values, in situations of low uncertainty, are derived from the original reinforcement learning algorithm, while in situations of high uncertainty, they are obtained from the uncertainty module. Results indicate that through the application of uncertainty-based reinforcement learning method, the fuel economy is improved by about 5% compared to the Deep Deterministic Policy Gradient (DDPG) algorithm and about 11% higher than the Deep Q-Network (DQN) algorithm under intricate driving cycles.