Due to the divergence of accuracy caused by inertial measurement unit (IMU) cumulative error, it is difficult for a single IMU equipment to realize vehicle positioning. Therefore, this paper proposes an IMU pose state estimation algorithm based on modulation long short-term memory-unscented Kalman filter (ML-UKF) algorithm. First, the algorithm improves the memory mode of LSTM network by using Modulation LSTM neural network and establishes IMU state model and observation model. Then, in order to adapt to the application of deep learning algorithm in UKF, an equal spacing sigma sampling method is proposed. Finally, the effect of IMU pose state estimation is verified by experiments. Results show that the root mean square error of the ML-UKF algorithm is decreases by 65.43% relative to the state of the art, further verifying the effectiveness of the proposed algorithm.

It is difficult to extract effective weak signals from complex noise environment in many engineering applications. To address the problem, an improved unscented Kalman filter (UKF) algorithm based on a radial basis function-neural network (RBF-NN) is proposed in this article. This algorithm achieves effective extraction of weak signals under the condition of low signal-to-noise ratio (SNR). It uses RBF-NN to establish a distributed system of state and observation models to replace the traditional UKF modeling method. Then, the RBF-NN is applied to build a constant coefficient model to modify the UKF filtering deviation (DE) value, and thus, the accuracy of the algorithm is improved. Finally, simulation experiments are conducted at different SNRs of surface nuclear magnetic resonance (SNMR) signals. Results show that effective weak signals can still be obtained when SNR = −10 dB, verifying the effectiveness of the algorithm.

During the wireless power transmission of electric vehicle, due to the influence of compensation circuit’s manufacturing process, driver’s operating technique and other factors, the wireless power transfer efficiency features strong uncertainty. The paper chooses coil group’s spatial parameters as well as compensation circuit’s parameters as input variables, and then builds the deep learning network as the framework of uncertainty quantification, so as to obtain the relevant statistical characteristic parameters of wireless power transfer efficiency, through comparing with the calculation results of classical Monte Carlo, it can be verified that the uncertainty quantification based on deep learning has almost the same solution accuracy and higher computational efficiency. At the same time, the paper adopts tent chaotic mapping and adaptive inertia weight strategy to improve the traditional Aquila Optimization(AO) algorithm, then uses the improved multi-objective AO algorithm to accomplish the optimization of wireless power transfer system’s structure. Through the comparison of improved AO and other optimization algorithms, it can be concluded that the improved AO can search out the optimal design parameter group with faster speed and higher accuracy, which provides effective guidance for the actual optimized design of electric vehicle’s wireless power transfer system.

With the rapid development of electric vehicles (EVs), the wireless power transfer (WPT) technology of EV is becoming more and more mature. The safety of human electromagnetic exposure caused by the WPT system of EV has become the focus of research. Considering the practical application of EV WPT system, this paper studies the electromagnetic exposure of human body based on the uncertainty quantification theory. A sparse polynomial chaos expansion method is used to establish the surrogate model of the mean value of some human organs’ SAR, and the probability distribution function of SAR is calculated. The Sobol is used to quantify the impact of relevant variables on SAR. The final calculation results show that when the person is on the side of the EV, the offset between the magnetic coupling coils of the WPT and the lateral displacement of the human body on the side of the EV have a greater impact; When a person is at the rear of the EV, the vertical displacement of the magnetic coupling coils of the WPT has a greater impact on the longitudinal displacement of the human body at the rear of the vehicle. Combined with the above analysis, this paper can provide reasonable suggestions for the safety protection of electromagnetic exposure of human body in the WPT of EVs.

Precise and robust extrinsic parameter calibration is fundamental for LiDAR-camera multi-modal sensing applications. However, most existing methods assume that sensors have the same orientation, limiting their effectiveness in feature extraction and feature alignment from different angle of view in multi-angle sensing scenarios. Moreover, the calibration accuracy of existing methods is insufficient for high-performance applications. To address these limitations, we propose a novel automatic extrinsic parameter calibration method utilizing a spherical target. We propose the Curvature Consistency Spherical Detection (CCSD) algorithm for LiDAR point cloud sphere recognition. The CCSD leverages the sphere's structural attributes, enabling robust detection against noise and partial occlusion. To improve camera sphere detection, we present an enhanced ellipse detection technique and compensate the eccentricity error arising from spherical projection based on the principle of perspective transformation. Extensive simulations and real-world experiments demonstrate the proposed method's superiority in accuracy and practicality over state-of-the-art (SOTA) methods.