In order to enhance the vehicle braking energy recovery efficiency and maintain braking stability, this paper proposes a comprehensive energy recovery method that takes the drivers’ styles and road adhesion characteristics into account. Firstly, the driving style feature parameters are extracted from different drivers’ data, and the driving style recognition model is trained based on the Support Vector Machine (SVM). Then, the road images are preprocessed using the U-Net network, and the lightweight network MobileNet V3 is trained to recognize the road surface efficiently. Finally, combined with the recognition results of driving styles and road surfaces, the variable ratio of braking force of the front and rear axles of the vehicle is allocated, and a method is proposed to determine the regenerative braking force considering the weight of driving styles and road adhesion conditions, the braking energy recovery strategy is formulated on this basis. The simulation results show that the braking efficiency and stability are significantly improved for different road surfaces; the SOC of the battery is improved by 2.13 percentage points and 1.02 percentage points in the WLTC and NEDC cycle conditions respectively, further improving the overall braking stability and economy of vehicles.

To Improve the comprehensive economy of Hybrid Electric Velicle (HEV) in driring, aiming at the problem of insufficient adaptability of existing HEV energy management strategies to drivers with different driving styles an adaptive equivalent consumption minimization energy management strategy based on real-time recognition of driving styles is proposed. Firstly, the changes in driving demand caused by the real-time pedal signal variation are analyzed, and the real-time driving styles in the rolling time window by data characteristic extraction is identified. Then, in the equivalent consumption minimization strategy considering the cost of battery degradation, and an adaptive function of equivalent factor is constructed by quantifying the driving style coefficient, and the optimal equivalent factor and battery degradation cost weighting coefficient corresponding to different driving styles are solved. Based on this, an adaptive law for equivalent factor based on artificial potential field method is designed. The simulation results showed that, compared with the equivalent energy consumption minimization without consideration of driving style, the proposed method improves fuel economy by 2.82% while reducing the battery loss by 25% under the same driving condition.

To address the issues of a low recovery rate of motor braking energy and significant fluctuations in the DC bus voltage, a braking energy tracking strategy is put forward. This strategy can adjust the reference value of the supercapacitor current in real time by considering multiple parameters, including motor power, the efficiency of the bidirectional DC/DC converter, and the supercapacitor voltage. The discrete state space model of the energy storage unit is used to predict the current and voltage of the supercapacitor, and the current reference value of the supercapacitor is calculated by combining the other parameters in the system. The current loop based on Proportional-Integral (PI) is used to control the current of the supercapacitor and track the reference value of the current in real time. Simulation and experiments compared the tracking of regenerative energy under two different control strategies. The results indicate that the proposed strategy enhances the efficiency of regenerative energy recovery from 55.93% to 86.76%, and it restricts the voltage fluctuation of the DC bus within 0.9%.

With the increasing applications of new technologies such as smart driving, autonomous driving and Internet connectivity, traditional in-vehicle networks are difficult to meet the Quality of Service (QoS) demands of diverse applications. In order to improve the data transmission rate and guarantee the QoS demand of services in the in-vehicle network, a deep reinforcement learning QoS routing algorithm based on SDVN is designed in combination with Software-Defined Vehicular Network (SDVN) technology. The algorithm can realize intelligent control and optimized management of data transmission in the in-vehicle network to ensure the control, distribution and monitoring of in-vehicle network traffic and improve the quality and efficiency of in-vehicle data transmission. The experimental results show that the routing algorithm can better reduce the delay of the in-vehicle network and has better optimization performance compared to the traditional routing algorithm.

In order to improve the quality and reliability of in-vehicle communication, in-vehicle visible light communication technology is introduced by analyzing the influence of infrastructure-vehicle channel transmission model and road infrastructure parameters. Based on the line-of-sight channel between the infrastructure and the vehicle, a model is established to analyze the communication loss during signal transmission. Closed-form analytical equation of the system performance are derived by using the line-of-sight communication Bit Error Rate (BER), downtime probability, and the Block Error Rate (BLER). The effect of the composite pointing error on BER, downtime probability, and BLER of the line-of-sight communication system is analyzed. The experimental results show that: under the no-fading and line-of-sight transmission conditions, increase the height of the street light reduces the BER, while increasing the communication link distance increases the BER; raising the downtime threshold reduces the downtime probability of the line-of-sight link; increasing in the number of transmitted bit blocks helps to improve the performance of BLER; when the number of BER blocks is increased, the system’s BLER decreases rapidly; after considering the pointing errors, BER, downtime probability and BLER all increase, which indicates that pointing error has a debilitating effect on the system communication.

A three-segment Pseudo-Random Frequency Active Zero State Pulse Width Modulation (PRF-AZSPWM) strategy based on Markov process is proposed to address the common mode interference problem in electric vehicle electric drive systems. Based on the equivalent zero vector effect, this strategy introduces a binary Markov chain to generate random synthetic signals, allowing the carrier frequency to vary randomly within a certain range, while selecting appropriate transmission probabilities to spread the harmonic energy to a wider frequency range. The simulation results show that the proposed scheme effectively reduces the common-mode interference level of the electric drive system, and the effectiveness of the proposed strategy is further confirmed through prototype testing.

To weaken the vibration and noise of automotive synchronous motors, this paper proposes a combined rotor slotting design scheme and electromagnetic noise forward optimization design method. Firstly, the mechanism of electromagnetic vibration noise is explored, then based on Maxwell tensor method and finite element method, the time-space distribution characteristics of radial electromagnetic force wave are studied, and the main electromagnetic force harmonic components causing electromagnetic noise are determined. Secondly, an improved design scheme of combined rotor slotting is proposed, and the optimal solution of structural parameters of slotting scheme is determined by combining the optimal prediction meta-model and strength Pareto evolutionary algorithm. Finally, the electromagnetic simulation model of the motor is established, and its line back electromotive force, cogging torque and output torque are compared and evaluated. The results show that the rotor slotting design can effectively suppress the spatial 0-order 12f electromagnetic force harmonic amplitude, improve the back EMF waveform, reduce the cogging torque and torque ripple, and thus reduce the vibration noise. Compared with the original prototype, the harmonic amplitude of the 0-order 12f electromagnetic force is weakened by 81.51%, the torque ripple is reduced by 44.98%.

The master cylinder pressure estimation of the Electro-Hydraulic Brake (EHB) system is crucial to reduce the sensor dependence of EHB. In this paper, the master cylinder pressure is estimated based on BP neural network. First, a real-vehicle road test is carried out and data such as vehicle speed, master cylinder piston displacement, master cylinder piston speed and master cylinder pressure are collected. Second, a BP neural network is established using the master cylinder piston displacement and master cylinder piston speed as feature inputs and the real master cylinder pressure as target output. Third, the BP neural network is trained by the training dataset and gradient-descent algorithm. Finally, the pressure estimation performance is verified by the testing dataset. The results show that the proposed algorithm reduces the estimation error by 38% and 15%, compared with the dynamic pressure-displacement model and the LSTM-based estimation algorithm, respectively.