Despite significant advancements in roll stability control for individual vehicle types, comparative research across on-road and off-road vehicles remains limited, hindering cross-disciplinary innovation. This study bridges this gap by systematically analyzing roll stability control in both vehicle categories, focusing on theoretical foundations, key technologies, and experimental validation methods. On-road vehicles rely on mature technologies like active suspension, braking, and steering, which enhance safety through sensor monitoring, rollover prediction, and integrated stability control. Validation is primarily performed through hardware-in-the-loop simulations and on-road testing. Off-road vehicles, operating in more complex environments with dynamic load changes and rugged terrain, emphasize adaptive leveling, direct torque control, and active steering. Their stability control strategies must also account for terrain irregularities, real-time load shifts, and extreme slopes, validated through scaled-model tests and field trials. Comparative analysis reveals that while both vehicle types face similar challenges, their control strategies differ significantly: on-road vehicles focus on handling and high-speed stability, while off-road vehicles require more robust, adaptive mechanisms to manage environmental uncertainties. Future research should explore multi-system collaborative control, such as integrating active suspension with intelligent terrain perception, to improve adaptability and robustness across both vehicle categories. Furthermore, the integration of machine learning and advanced predictive algorithms promises to enhance the intelligence and versatility of roll stability control systems.

To address the limited capability of conventional hydro-pneumatic suspensions in coordinated damping–stiffness regulation, this paper proposes a new semi-active hydro-pneumatic suspension (SAHPS) system based on a dual-valve shock absorber. A damping valve architecture composed of a spring check valve–solenoid proportional valve–spring check valve is arranged between the rod and rodless chambers of the hydraulic cylinder, enabling coordinated adjustment of suspension damping and equivalent stiffness. Furthermore, a genetic algorithm optimization with model predictive control (GA-MPC) is designed to enhance the overall dynamic performance of the suspension while effectively reducing the operating frequency of the solenoid proportional valve. Finally, AMESim–Simulink co-simulations and hardware-in-the-loop (HIL) experiments are conducted under bumpy road excitation and Class C random road conditions. Under Class C random road conditions, compared with passive hydro-pneumatic suspension and semi-active suspension with conventional MPC, the proposed method achieves maximum reductions of 11%, 25%, and 12.9% in the root mean square values of body acceleration, suspension working space, and dynamic tire load, respectively. The discrepancies between experimental and simulation results remain below 7%, confirming the effectiveness of the proposed system and control strategy. This study provides a new technical guidance for low-frequency vibration suppression in vehicle suspension systems.

In view of the deterioration of vehicle dynamic performance caused by the increased radial stiffness of a non-pneumatic wheel and its nonlinearity, a semi-active suspension error tracking backstepping control strategy based on the model reference method is proposed. Firstly, the segmental linearization of the nonlinear stiffness of the non-pneumatic wheels is carried out, and a quarter-vehicle system model integrating the non-pneumatic wheel and the semi-active suspension is established. Subsequently, a model reference system based on the $H_{\infty }$ control theory is designed. On this basis, a semi-active suspension error tracking backstepping controller based on the reference model is developed. Finally, comparative dynamics simulations are carried out to verify the effectiveness of the controller. The results indicate that the designed controller exhibits a superior control effect compared with the existing controllers. Specifically, the proposed control method reduces the root-mean-square (RMS) value of sprung mass acceleration by 20.0% under random road excitation compared to passive suspension while ensuring system constraints, and reduces the peak-to-peak (P-P) values of sprung mass acceleration and dynamic wheel load by 60.6% and 42.5% under bumpy road excitation, respectively.

In current research, there are excessive assumptions and simplifications in the mathematical models developed for thermoelectric generators. In this study, a comprehensive mathematical model was developed based on a plate-type thermoelectric generator divided into multiple thermoelectric units. The model takes into account temperature-dependent thermoelectric material parameters and fluid flow. The model was validated, and a maximum error of 6.4% was determined. Moreover, the model was compared and analyzed with a numerical model, with a maximum discrepancy of 7.2%. The model revealed the factors and their degree of influence on the performance of the thermoelectric generator unit. In addition, differences in temperature distribution, output power, and conversion efficiency between multiple thermoelectric units were clearly studied. This study can guide modeling and some optimization measures to improve the overall performance of thermoelectric generators.

To dynamically track the maximum power of an automotive thermoelectric generator (ATEG) system in real-time, this study introduces a novel maximum power point tracking (MPPT) algorithm that integrates Kalman filtering and fuzzy control. Employing a two-phase interleaved parallel DC–DC boost converter in the MPPT controller effectively reduces current ripple and switch loss. Results demonstrated a significant improvement in tracking time compared to the traditional incremental conductance algorithm, attributed to the elimination of high-frequency components in output power by the Kalman filter. The novel algorithm exhibits enhanced tracking stability through the application of fuzzy control. Ultimately, the tracking accuracy of the novel algorithm surpasses that of the incremental conductance algorithm by 5.2%, achieving an impressive 94.9%. This study, therefore, presents a valuable contribution to a novel MPPT algorithm for precisely and rapidly tracking the global maximum power points of the ATEG system throughout the entire vehicle driving cycle.