To enhance the accuracy and reliability of estimating the State Of Charge (SOC) for lithium-ion power batteries, a representative Thevenin model for lithium batteries is selected for study. The modeling and parameter identification methods for this model are elaborated. By analyzing the main limitations of existing SOC estimation techniques, a novel method based on the Thevenin equivalent circuit model combined with an unscented Kalman filter (UKF) is proposed. Verification results demonstrate that this method exhibits good tracking performance, maintains errors within an acceptable range, and delivers overall excellent performance.

In order to improve the energy efficiency of pure eletric vehicles and a chieve precise and efficient vehicle control, explore the front axle disconnection device of a four-wheel drive pure electric vehicle equipped with dual permanent magnet synchronous motors, which realizes the dynamic switching between two-wheel drive and four-wheel drive modes by controlling the combination and separation of the front axle. The actual vehicle verification results show that this strategy effectively reduces the zero torque loss of the electric motor and minimizes the impact on drivability, while significantly improving economy and extending driving range.

To investigate the economic feasibility of hydrogen-powered heavy-duty trucks in both subsidized and non-subsidized markets, a hydrogen vehicle performance simulation model and a Total Cost of Ownership (TCO) model are established. Through comparative analysis of different powertrain configurations meeting dynamic performance requirements under user-specific operating conditions, as well as sensitivity studies on hydrogen pricing and energy consumption per 100 km, several critical findings are obtained as following. Powertrain configuration significantly impacts total vehicle cost, with higher fuel cell system power leading to increased overall costs. Hydrogen-powered heavy trucks require scenario-specific adaptations in both market types. While profitability is achievable in subsidized markets, the margin remains narrow. In non-subsidized markets, economic viability requires hydrogen prices below 21 RMB/kg. The study reveals substantial sensitivity to hydrogen consumption, with profitability thresholds established at <12 kg/100 km in subsidized markets and <10 kg/100 km in non-subsidized environments. Notably, when consumption falls below 9 kg/100 km in subsidized markets, economic performance becomes comparable to conventional diesel trucks. These findings provide theoretical foundations and technical references for advancing hydrogen vehicle commercialization, offering valuable insights for industry development.

With the rapid development of intelligent vehicles and new energy vehicles, there are higher requirements for the dynamic responsiveness, multi-working condition adaptability and safety of the suspension system. In order to break through the limitations of traditional suspension and semi-active suspension in body posture adjustment and complex road condition adaptation, this paper outlines the working principle of the full-active suspension and makes an in-depth analysis of the characteristics, laws and advantages of the full-active suspension. Combined with the domestic and foreign research status, the advantages and limitations of various control strategies are summarized. On this basis, the development direction of the vehicle full-active suspension technology and its control strategies is proposed, in order to provide a reference for the in-depth development and wide application of the full-active suspension technology.

To enhance the management capabilities for the State of Health (SOH) of power batteries and strengthen their contribution to carbon emission reduction, this paper conducts a detailed review centered on lithium-ion power batteries—a core component of electric vehicles. The review encompasses the industry overview, supportive policies, existing challenges, SOH management of in-vehicle power batteries, and future developments. The study demonstrates that SOH management of power batteries exhibits the following key trends: (1) Strengthening SOH management can effectively improve battery performance and lifespan, thereby reducing the carbon emissions of the entire vehicle; (2) Refined battery management technologies based on SOH optimization (e.g state estimation, optimal control) represent the core direction for future development; (3) Battery full-lifecycle management strategies incorporating cascade utilization can significantly enhance the overall carbon reduction capability of lithium-ion batteries.

A battery State of Health (SOH) estimation method based on Gated Recurrent Unit (GRU) neural network is proposed to address the issue of low accuracy in estimating the SOH of new energy vehicle power batteries. This method extracts multidimensional input features based on battery charging data, performs data cleaning and normalization on the features, and trains a GRU network to construct a battery SOH estimation model. The results indicate that the proposed method can achieve an average absolute error of 0.26% in estimating battery SOH, which is 1.04% lower than traditional calculation methods. This method can achieve a more accurate estimation of battery SOH and can be used for evaluating the aging status of electric vehicles.

Artificial intelligence (AI) models, with their strong generalization and multi-task learning capabilities, have demonstrated extensive application potential in intelligent connected vehicles. This paper summarizes the challenges of the application of AI models in driving automation, analyzes the technical route of driving automation models, and the supporting platform technology of driving automation model development and validation, summarizes the application of intelligent cockpit, and explores the method for constructing scenario generation models based on large language models. From the perspectives of AI security and data governance, this paper summarizes the security governance practices associated with the application directions for AI models, providing a reference for the safety assessment and management of AI-related applications.

To enhance lateral active safety in intelligent connected vehicles, the identification and definition of unintended functional safety scenarios for the Electric Power Steering (EPS) system are integrated into system design and development process. By combining scenario domain analysis, equivalence partitioning, and boundary value analysis with the deconstruction of vehicle model variants and lateral motion scenarios related to EPS, more potential hazardous operational scenarios are expanded from known operational scenarios. Meanwhile, the Driver-In-the-Loop (DIL) system based on hardware-in-the-loop simulation is utilized. Relying on its advantages of “driver-vehicle-environment” closed-loop, more unknown risk scenarios are transformed into valid known scenarios. Subsequently, the existing safety technical requirements are optimized and the overall performance of the EPS system is further improved.

To optimize the opening and closing forces of the automotive Power Lift Gate (PLG) system, the literature on algorithms related to lift gate forces is reviewed, and a mechanical model of the lift gate system is established. The expressions for the moment exerted by the support rod on the lift gate and the moment exerted by gravity on the lift gate are derived, and the relationships between the length of the support rod, the force arm of the support rod, and the opening angle are analyzed. A simulation model is created using simulation software to perform calculations, and the rationality of the expressions is verified. This study provides a theoretical foundation and optimization direction for the design and improvement of PLG systems.

The Noise Vibration Harshness (NVH) performance of vehicles is one of the key indicators of overall vehicle qualities. To enhance ride comfort and meet increasingly stringent NVH requirements, this paper reviews the application of Artificial Intelligence (AI) algorithms in NVH optimization, both domestically and internationally. It analyzes feasible approaches for improving NVH performance using AI-based methods and discusses future trends and challenges in AI-driven NVH optimization. The study aims to provide valuable insights for leveraging intelligent algorithms to address automotive performance enhancement.