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An accurate state of charge (SOC) can provide effective judgment for the BMS, which is conducive for prolonging battery life and protecting the working state of the entire battery pack. In this study, the first-order RC battery model is used as the research object and two parameter identification methods based on the least square method (RLS) are analyzed and discussed in detail. The simulation results show that the model parameters identified under the Federal Urban Driving Schedule (HPPC) condition are not suitable for the Federal Urban Driving Schedule (FUDS) condition. The parameters of the model are not universal through the HPPC condition. A multitimescale prediction model is also proposed to estimate the SOC of the battery. That is, the extended Kalman filter (EKF) is adopted to update the model parameters and the adaptive unscented Kalman filter (AUKF) is used to predict the battery SOC. The experimental results at different temperatures show that the EKF-AUKF method is superior to other methods. The algorithm is simulated and verified under different initial SOC errors. In the whole FUDS operating condition, the RSME of the SOC is within 1%, and that of the voltage is within 0.01 V. It indicates that the proposed algorithm can obtain accurate estimation results and has strong robustness. Moreover, the simulation results after adding noise errors to the current and voltage values reveal that the algorithm can eliminate the sensor accuracy effect to a certain extent. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The battery monitoring system (BMS) plays a crucial role in maintaining the safe operation of the lithium battery electric bicycle and prolonging the life of the battery pack. This paper designed a set of new battery monitoring systems based on the Android system and ARM single-chip microcomputer to enable direct management of the lithium battery pack and convenient monitoring of the state of the battery pack. The BMS realizes the goal of monitoring the voltage, current, and ambient temperature of lithium batteries, estimating the state of charge (SOC) and state of health (SOH), protecting the battery from abuse during charging or discharging, and ensuring the consistency of the batteries by integrating the passive equalization circuit. The BMS was proven effective and feasible through several tests, including charging/discharging, estimation accuracy, and communication tests. The results indicated that the BMS could be used in the design and application of the electric bicycle. [ABSTRACT FROM AUTHOR]

An accurate state-of-charge (SOC) can not only provide a safe and reliable guarantee for the entirety of equipment but also extend the service life of the battery pack. Given that the chemical reaction inside the lithium-ion battery is a highly nonlinear dynamic system, obtaining an accurate SOC for the battery management system is very challenging. This paper proposed a gated recurrent unit recurrent neural network model with activation function layers (GRU-ATL) to estimate battery SOC. The model used deep learning technology to establish the nonlinear relationship between current, voltage, and temperature measurement signals and battery SOC. Then the online SOC estimation was carried out on different testing sets using the trained model. The experiments in this paper showed that the GRU-ATL network model could realize online SOC estimation under different working conditions without relying on an accurate battery model. Compared with the gated recurrent unit recurrent neural (GRU) network model and long short-term memory (LSTM) network model, the GRU-ATL network model had more stable and accurate SOC prediction performance. When the measurement data contained noise, the experimental results showed that the SOC prediction accuracy of GRU-ATL model was 0.1–0.4% higher than the GRU model and 0.3–0.7% higher than the LSTM model. The mean absolute error (MAE) of SOC predicted by the GRU-ATL model was stable in the range of 0.7–1.4%, and root mean square error (RMSE) was stable between 1.2–1.9%. The model still had high prediction accuracy and robustness, which could meet the SOC estimation in complex vehicle working conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance. [ABSTRACT FROM AUTHOR]

Traffic sign detection and recognition are crucial in the development of intelligent vehicles. An improved traffic sign detection and recognition algorithm for intelligent vehicles is proposed to address problems such as how easily affected traditional traffic sign detection is by the environment, and poor real-time performance of deep learning-based methodologies for traffic sign recognition. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, the model is considerably improved on the basis of the classical LeNet-5 convolutional neural network model by using Gabor kernel as the initial convolutional kernel, adding the batch normalization processing after the pooling layer and selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the German Traffic Sign Recognition Benchmark. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. Experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. Compared with other algorithms, the proposed algorithm has remarkable accuracy and real-time performance, strong generalization ability and high training efficiency. The accurate recognition rate and average processing time are markedly improved. This improvement is of considerable importance to reduce the accident rate and enhance the road traffic safety situation, providing a strong technical guarantee for the steady development of intelligent vehicle driving assistance. [ABSTRACT FROM AUTHOR]

Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles. [ABSTRACT FROM AUTHOR]

This study designs an active equilibrium control strategy based on model predictive control (MPC) for series battery packs. To shorten equalisation time and reduce unnecessary energy consumption, bidirectional active equalisation is modelled and analysed, and the model predictive control algorithm is then applied to the established state space equation. The optimisation problem that minimises the equilibrium time is transformed to a linear programming form in each cycle. By solving the linear programming problem online, a group of control optimal solutions is found and the series equalisation problem is decoupled. The equalisation time is shortened by dynamically adjusting the equalisation current. Simulation results show that the MPC algorithm can avoid unnecessary energy transfer and shorten equalisation time. The bench experimental result shows that the equilibrium time is reduced by 31%, verifying the rationality of the MPC strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]