Limited driving range is one of the major obstacles to the widespread application of electric vehicles (EVs). Accurately predicting the remaining driving range can effectively reduce the range anxiety of drivers. In this paper, a blended machine learning model was proposed to predict the remaining driving range of EVs based on real-world historical driving data. The blended model fuses two advanced machine learning algorithms of Extreme Gradient Boosting Regression Tree (XGBoost) and Light Gradient Boosting Regression Tree (LightGBM). The proposed model was trained to “learn” the relationship between the driving distance and the proposed features such as cumulative output energy of the motor and the battery, different driving patterns, and temperature of the battery). In addition, an “anchor (baseline) based” strategy was proposed and was seen to be able to effectively eliminate the unbalance distribution of dataset. The results of experiments suggest that our proposed anchor-based blended model has better performances with a smaller prediction error range of -0.8, 0.8 as compared with previous methods.

As the dominant choice for powering the Electric Vehicles (EVs), it is crucial to estimate its state of health (SOH) and predict its remaining useful life (RUL). This paper proposes a novel machine learning-based prognostic method for lithium-ion batteries with real-world driving and charging data. A SOH evaluation system and a cluster interpolation correction method are applied to address the various data problems. Based on the capacity estimation method, select the voltage ranges through Dynamic Non-dominated Sorting Genetic Algorithm II (D-NSGA-II), which can dynamically capture the optimal ranges in different environments. A multi-dimensional input fusion model (GM-LSTM) is proposed to predict RUL, overcoming the problem of limited data. Additionally, several experiments based on EVs are implemented to verify the proposed method. The experimental results demonstrate the effectiveness of the proposed methodology, with the average relative error for SOH estimates and RUL forecasts are 1.53% and 1.34%.

It is challenging to plan paths for autonomous vehicles on half-structured roads because of the vast planning area and complex environmental constraints. This work aims to plan optimized paths with high accuracy and efficiency. A two-step path planning strategy is proposed. The classic planning problem is divided into two simpler planning problems: reduction problems for a vast planning area and solving problems for weighted directed graphs. The original planning area is first reduced using an RRT (Rapidly Exploring Random Tree) based guideline planner. Second, the path planning problem in the smaller planning region is expanded into a weighted directed graph and transformed into a discrete multi-source cost optimization problem, in which a potential energy field based discrete cost assessment function was designed considering obstacles, lanes, vehicle kinematics, and collision avoidance performances, etc. The output path is then obtained by applying a Dijkstra optimizer. Comparative simulations are conducted to assess the effectiveness of the proposed strategy. The results shows that the designed strategy balances efficiency and accuracy with enough planning flexibility and a 22% improvement in real-time performance compared to the classic Lattice planner, without significant loss of accuracy.

With the gradual popularization of autonomous driving technology, how to obtain traffic sign information efficiently and accurately is very important for subsequent decision-making and planning tasks. Traffic sign detection and recognition (TSDR) algorithms include color-based, shape-based, and machine learning based. However, the algorithms mentioned above are insufficient for traffic sign detection tasks in complex environments. In this paper, we propose a traffic sign detection and recognition paradigm based on deep learning algorithms. First, to solve the problem of insufficient spatial information in high-level features of small traffic signs, the parallel deformable convolution module (PDCM) is proposed in this paper. PDCM adaptively acquires the corresponding receptive field preserving the integrity of the abstract information through symmetrical branches thereby improving the feature extraction capability. Simultaneously, we propose sub-pixel convolution attention module (SCAM) based on the attention mechanism to alleviate the influence of scale distribution. Distinguishing itself from other feature fusion, our proposed method can better focus on the information of scale distribution through the attention module. Eventually, we introduce GSConv to further reduce the computational complexity of our proposed algorithm, better satisfying industrial application. Experimental results demonstrate that our proposed methods can effectively improve performance, both in detection accuracy and mAP@0.5. Specifically, when the proposed PDCM, SCAM, and GSConv are applied to the Yolov5, it achieves 89.2% mAP@0.5 in TT100K, which exceeds the benchmark network by 4.9%.