The lithium-ion battery is the power source of an electric vehicle, so it is of great significance to estimate the state of charge (SOC) of lithium-ion batteries accurately to ensure vehicle safety. To improve the accuracy of the parameters of the equivalent circuit model for batteries, a second-order RC model for ternary Li-ion batteries is established, and the model parameters are identified online based on the forgetting factor recursive least squares (FFRLS) estimator. To improve the accuracy of SOC estimation, a novel fusion method, IGA-BP-AEKF, is proposed. Firstly, an adaptive extended Kalman filter (AEKF) is used to predict the SOC. Then, an optimization method for BP neural networks (BPNNs) based on an improved genetic algorithm (IGA) is proposed, in which pertinent parameters affecting AEKF estimation are utilized for BPNN training. Furthermore, a method with evaluation error compensation for AEKF based on such a trained BPNN is proposed to enhance SOC evaluation precision. The excellent accuracy and stability of the suggested method are confirmed by the experimental data under FUDS working conditions, which indicates that the proposed IGA-BP-EKF algorithm is superior, with the highest error of 0.0119, MAE of 0.0083, and RMSE of 0.0088.

In mobile robot path planning, the traditional A* algorithm suffers from high path redundancy and poor smoothness, while the Dynamic Window Approach (DWA) tends to deviate from the global optimal path and has low efficiency in avoiding dynamic obstacles when integrated with global path planning. To address these issues, a smoothing optimised A*-guided DWA fusion algorithm (SOA-DWA) is proposed in this paper. Firstly, the A* algorithm was improved by introducing a path smoothing strategy and path pruning mechanism, generating a globally optimal path that complied with the vehicle kinematic constraints. Secondly, three sub-functions were introduced into the evaluation function of the DWA algorithm: the distance evaluation between the reference trajectory and the global path, the path direction evaluation, and the dynamic obstacle avoidance evaluation, to enhance the real-time performance of dynamic obstacle avoidance and the consistency of the global path. The SOA-DWA algorithm ensured that the mobile robot could effectively avoid obstacles in complex environments without deviating from the global optimal path. Thirdly, experimental results show that in a static environment, the path length and turning angle of the SOA-DWA algorithm are reduced by an average of 13.3% and 16.25%, respectively, compared with the traditional algorithm. In a dynamic environment, the path length and turning angle are reduced by an average of 10.5% and 14.5% compared to the traditional DWA algorithm, respectively, significantly improving the smoothness of the path and driving safety. Compared to the existing fusion algorithm, the SOA-DWA algorithm reduces the path length by an average of 10.1%, improves planning efficiency by an average of 42%, and effectively enhances obstacle avoidance efficiency. Finally, the effectiveness of the improved algorithm proposed in this paper was further verified by mobile robot experiments.

The advancement of autonomous driving technology has urgently necessitated enhanced safety testing measures. Traditional road testing methods face significant challenges due to their high costs and prolonged durations. In response to the inefficiencies of manual scenario construction and the difficulties in selecting effective scenarios using common scenario generation methods in autonomous driving safety testing, this study proposes an innovative automatic SG system based on PreScan2021.1.0. The SG process is significantly simplified by this system’s capability to swiftly and accurately generate a vast array of specific scenarios through the input of scene parameters. The results indicate that this system achieves SG at a rate 2.5-fold faster than manual methods, alongside substantial improvements in accuracy. This system introduces a novel approach to virtual simulation, which is vital for the progress of autonomous driving safety. It offers a new paradigm for quickly and precisely generating test scenarios for autonomous driving.

Aiming at to avoid the security drawbacks of the authentication protocol in Long Term Evolution-Wireless Local Area Network (LTE-WLAN) heterogeneous converged network proposed by the 3rd Generation Partnership Project (3GPP), an improved protocol based on hybrid cryptosystem is proposed to achieve access authentication for WLAN user equipment(UE) with identity privacy protection. The security analysis shows that by using certificateless signcryption(CLSC) scheme without pairing calculation based on Elliptic Curve Cryptography (ECC), hash chain and identity index mechanism, the proposed authentication protocol provides the following ten kinds of security properties: anonymous protection for International Mobile Subscriber Identity (IMSI), update on shared keys, protection for master session key(MSK), resistance to impersonation attack, replay attack, man-in-the-middle attack, redirect attack and Denial of Service (DoS) attack, mutual authentication between communication entities, and without framework modification from the original protocol. The performance analysis shows that the approximate calculation time of all the communication entities is 79ms in total and that of UE is 266us. Thus, our proposed protocol is superior to some other related improved protocols in terms of security and efficiency.