In order to address the challenges of range anxiety and optimizing energy efficiency in the electric vehicle sector, this study proposes a mechatronic flywheel power system design based on a dual-planetary gear arrangement. This system integrates the control motor, drive motor, flywheel mechanism and power output shaft within a dual planetary gear set via a mechanical coupling mechanism. It leverages the power-splitting characteristics of planetary gears to achieve multi-energy synergistic control. Compared to conventional single planetary gearset configurations, the dual planetary gearset architecture allows for more efficient coordination between the motor and flywheel during power transitions. Optimizing the gear configuration delivers a broader gear ratio range and enhanced torque modulation capability, which significantly improves transmission efficiency and the precision of energy management. Research findings indicate that, compared to single planetary gear systems, the dual planetary gear system significantly enhances system stability under UDDS conditions by optimizing the flywheelbattery synergistic operation mechanism. This results in flywheel rotational speed fluctuations being reduced by approximately 30%, and a higher proportion of the drive motor operating within the 80%-90% efficiency range. In addition, the efficiency of the dual planetary gear system has increased by 5% to 10%, the power loss of the battery has decreased by 31%, the current fluctuation has been reduced by 29.6%, and the proportion of time during which the battery operates in the high-efficiency range has increased by 6.9%. Meanwhile, the dual planetary gear system reduces the power consumption per 100 km to 12.43 kW & sdot;h, which is a 6.26% reduction compared to the single planetary gear system. The research results provide a more efficient, stable and economically viable hybrid power solution for electric vehicles, with broad application prospects.

To address the issues of torque fluctuation and transient power fluctuation in new energy commercial vehicles, this study proposes an integrated flywheel electric drive system. A unified matching and control method is presented, which integrates six working modes, flywheel parameter design, matching of the main drive motor and control motor, and torque distribution based on quadratic programming into a framework for correlation. Firstly, the flywheel rotational inertia is determined based on representative driving cycle statistical data. Then, the parameters of the main drive motor and control motor are successively matched. Subsequently, the motor loss diagram is embedded in the optimization objective. The joint simulation platform results under the CHTC cycle show that the flywheel rotational inertia under the optimized configuration is 0.13 kg m2, the peak torque of the main drive motor is 224 N m, and the peak torque of the control motor is 75 N m. Compared with the system without the flywheel, this method reduces the peak torque of the main drive motor from 277 to 224 N m, reduces the torque standard deviation from 42.94 to 36.33, and keeps the speed tracking error within ±2 km/h, thereby improving torque stability and overall energy utilization efficiency.

This work presents the development of a hierarchical lateral control strategy for intelligent vehicles, aimed at addressing the degradation of trajectory tracking accuracy and lateral stability caused by time-varying tire cornering stiffness—changes that typically arise under nonlinear tire conditions and dynamic load transfer scenarios. A two-layer estimation architecture is designed to enable real-time and accurate identification of tire cornering stiffness: the upper layer leverages a Lyapunov-based sliding mode observer to estimate lateral force and vehicle sideslip angle, while the lower layer integrates these estimated states into an adaptive Kalman filter (AKF) with noise covariance updating rules. This adaptive rule allows the AKF to adapt to nonlinear operating conditions where tire stiffness varies significantly, ensuring reliable stiffness identification results. The identified cornering stiffness values are then incorporated into a Linear Quadratic Regulation (LQR) controller to optimize front-wheel steering input, thereby enhancing the controller’s robustness against model uncertainties induced by stiffness changes. The performance of the proposed LQR with Stiffness Identification (LSI-LQR) controller is validated through CarSim–Simulink co-simulations and hardware-in-the-loop (HIL) experiments. Test scenarios include asphalt pavements with high (0.85) and low (0.45) adhesion coefficients, multi-lane change maneuvers, and vehicle speeds of 72 km/h and 54 km/h. Experimental results show that, compared with feedforward LQR and feedforward + preview LQR controllers, the LSI-LQR controller improves tracking accuracy by an average of 35.997

Flying cars are emerging as a key technological solution for developing Urban Air Mobility (UAM) due to their three-dimensional transportation capabilities. Various aerodynamic layout forms have been developed for flying cars. Among them, multi-rotor layout is highly coupled between control force and torque due to the underdrive characteristics, while fixed-wing and thrust vector layouts face structural complexity and weight challenges. To address the above problems, this paper proposes a novel tiltable ducted fan flying car for UAM. The design adopts a cross-shaped aerodynamic layout with four independently tilting ducted fans arranged at the end of the arm. The system realizes precise control of fan speed and inclination angle through distributed drive, while providing both control force and torque. This design combines structural compactness and omnidirectional drive capability. Then, the paper covers powertrain architecture planning, aerodynamic parameter design and structural design of the tilting mechanism. A dynamic model of the system is established, and an over-actuated control strategy based on the pseudo-inverse method is proposed to realize the decoupled control of force and torque. Finally, simulations using MATLAB/Simulink demonstrate that the position tracking errors in the x, y, and z axes are less than 1 m, and the attitude angle error is below 0.5 degrees. These results validate the excellent performance of the designed flying car in terms of attitude control accuracy and dynamic response.

The lifespan of lithium-ion batteries is closely linked to the total cost of ownership of electrified vehicles, making it critical to elucidate the mechanisms by which powertrain load fluctuations impact battery degradation. In this study, real-world driving profiles were acquired using a high-frequency data acquisition system. Frequency-domain parameterization analysis revealed that vehicle discharge currents are predominantly concentrated in the 0-1 Hz range. Accordingly, parameterized frequency-domain discharge protocols were formulated to conduct cycle testing on commercial NMC/graphite lithium-ion cells. The results demonstrate significant frequency-dependent degradation behavior within the 0-1 Hz range: after 900 equivalent full cycles, cells subjected to low-frequency discharge protocols exhibited superior capacity retention and slower internal resistance growth compared to those under higher-frequency driving dynamics. Non-destructive electrochemical analysis revealed that the loss of active material at the anode, induced by high-frequency fluctuating stress, is the primary cause of the observed cell-level degradation divergence. These findings provide a theoretical basis for optimizing energy management strategies in hybrid electric vehicles, offering valuable insights for effectively extending battery system life and reducing the total cost of ownership.

To confirm the efficiency of HESS affected by supercapacitor and purely electric flywheel batteries. First, the operation modes of lithium battery-supercapacitor and lithium battery-flywheel hybrid energy systems are designed in this study. Based on this, a new energy management strategy based on Harr wavelet using variable decomposition layers is proposed. Finally, the test platform for two hybrid energy systems is constructed and the performance tests are completed. The test results show that under the UDDS condition, the average efficiency of Li-ion battery condition is improved by 4.13 % and the average efficiency of Li-ion battery-supercapacitor composite energy system condition is improved by 3.30 % compared with the single-energy Li-ion battery scheme affected by the instantaneous high power regulation of the supercapacitor. The average efficiency of the lithium battery condition is increased by 4.46 % with the purely electric flywheel, however, the average efficiency of the lithium battery-purely electric flywheel composite energy system condition is instead reduced by 9.90 % due to the low efficiency of the purely electric flywheel.

To improve the tracking accuracy and the adaptability of intelligent vehicles in various road conditions, an adaptive model predictive controller combining reinforcement learning is proposed in this paper. Firstly, to solve the problem of control accuracy decline caused by a fixed prediction time domain, a low-computational-cost adaptive prediction horizon strategy based on a two-dimensional Gaussian function is designed to realize the real-time adjustment of prediction time domain change with vehicle speed and road curvature. Secondly, to address the problem of tracking stability reduction under complex road conditions, the Deep Q-Network (DQN) algorithm is used to adjust the weight matrix of the Model Predictive Control (MPC) algorithm; then, the convergence speed and control effectiveness of the tracking controller are improved. Finally, hardware-in-the-loop tests and real vehicle tests are conducted. The results show that the proposed adaptive predictive horizon controller (DQN-AP-MPC) solves the problem of poor control performance caused by fixed predictive time domain and fixed weight matrix values, significantly improving the tracking accuracy of intelligent vehicles under different road conditions. Especially under variable curvature and high-speed conditions, the proposed controller reduces the maximum lateral error by 76.81% compared to the unimproved MPC controller, and reduces the average absolute error by 64.44%. The proposed controller has a faster convergence speed and better trajectory tracking performance when tested on variable curvature road conditions and double lane roads.

During the cold-start operation of proton exchange membrane fuel cells (PEMFC) at a low temperature, the product water from electrochemical reactions tends to freeze, blocking porous electrodes and ultimately causing cold start failure. To effectively overcome this challenge, a comprehensive three-dimensional, transient, multi-physics coupled numerical simulation model was strategically developed using COMSOL Multiphysics to thoroughly analyze PEMFC cold-start behavior. The model enables systematic investigation of how proton exchange membrane (PEM) thickness affects current density distribution, ice formation dynamics, and thermal evolution characteristics during subzero cold-start processes. Experimental validation was conducted with three PEM thicknesses (0.05 mm, 0.127 mm and 0.183 mm) at -20 degrees C, Data analysis revealing that when the PEM thickness increased from 0.05 mm to 0.183 mm, the maximum current density of the PEMFC decreased from 0.126 Acm(-2 )to 0.061 Acm(-2), while the cold-start duration extended from 37.9 s to 76 s. Significantly, the 0.127-mm PEM demonstrated the highest temperature rise among the three configurations, reaching 4.17 K. Although thicker PEM prolonged cold-start survival time and enhanced water retention, excessive thickness diminishes the cell's thermal mass and impairs temperature rise efficiency.

To improve the tracking performance of intelligent vehicles, a lateral controller based on linear quadratic regulator (LQR) theory and a longitudinal controller based on backstepping sliding mode control (SMC) theory are proposed in this paper. Firstly, a feedforward LQR controller was established based on a two-degree-of-freedom vehicle dynamics model. To solve the stability reduce problem of feedforward LQR controller caused by model linearization, the controller was improved based on the constant turn rate and velocity model. To further improve the predictive controller, an adaptive prediction time mechanism based on the particle swarm optimization algorithm was established. Finally, a longitudinal tracking algorithm based on backstepping SMC was proposed. To verify the performance of the proposed controllers, co-simulation and hardware in loop experiments were conducted. The results show that the proposed controllers have both stability and accuracy, which can significantly improve tracking performance.

Time delay feedback control has been widely studied in vehicle body vibration reduction, but the influence of different feedback states on stability and vibration reduction is not fully understood. In this paper, a simplified vehicle suspension model is used to investigate different time delay feedback control states on suspension stability and vibration reduction, to clarify which feedback state can effectively improve vehicle comfort and driving safety. Six different time delay feedback control systems are established by combining two feedback objects (vehicle body and wheel) with three feedback variables (displacement, velocity, and acceleration). The stable region independent of time delay (SRITD) and switching stability regions of time delay control systems with six different feedback states are investigated using the polynomial solution method and the Routh-Hurwitz criterion. Subsequently, the control parameters of the six model sets were optimized within their stable regions using a variable-weight particle swarm optimization (VWPSO) program. The vibration reduction performance and stability region laws are derived by comparing the frequency response characteristics and the root mean square (RMS) values of vehicle vibration. The results show that the two states based on wheel velocity and body acceleration have obvious vibration reduction effects, and have a wide vibration reduction frequency band. This study guides the selection of different feedback state variables for time delay control.

The increase in rotational speed is the key to enhancing the energy storage capacity of flywheel batteries. Non-contact bearings can ensure reliable operation of high-speed flywheel batteries. To design electromagnetic bearings that meet the requirements of high-speed flywheel batteries. Firstly, a multi parameter and multi-objective optimization model for the radial electromagnetic bearing structure was designed with the optimization objectives of maximum electromagnetic force and minimum volume. On the basis of ensuring maximum electromagnetic force, the minimum spatial volume is reduced by 16.58%. Furthermore, the influence of bias current and control current on the electromagnetic force and rotor displacement of radial electromagnetic bearings was analyzed. The bias current and control current that satisfy the current stiffness and displacement stiffness were determined. Then, a mathematical model for differential control of radial magnetic bearings was constructed and a magnetic bearing controller based on adaptive discrete sliding mode control was designed. Finally, flywheel battery bench testing and data analysis were conducted. The results show that the designed radial magnetic bearing can achieve low response error, low system oscillation, and high robustness operation under the conditions of buoyancy step, foundation excitation, and random excitation. Compared with the sliding mode control method, the maximum difference in displacement tracking error was reduced by 62.31%, the average error was reduced by 42.74%, and the energy consumption was reduced by 49.32% under random excitation test conditions.

To address the cold start problem of proton exchange membrane fuel cell, this paper investigates the influence of ambient temperature and cathode inlet relative humidity on the cold start performance of PEMFC. First, a mathematical model of PEMFC cold start was established, and then a three-dimensional, dynamic, multi-parameter, and multi-physical field coupling simulation model was designed for the analysis of PEMFC cold start performance based on COMSOL software platform. Based on the model, the effects of ambient temperature and cathode inlet relative humidity on the characteristics of PEMFC, such as current density, temperature change and icing condition are analyzed during the cold start process. In this study, only the PEMFC at-7 degrees C successfully self-started, while the PEMFCs at-20 degrees C and-10 degrees C failed due to severe icing and performance deterioration. The results show that a higher ambient temperature helps to stabilize the output performance of the cold start process, delays icing, and prolongs the survival time under cold start conditions. In the-10 degrees C environment, the PEMFC failed to achieve a cold start under all three cathode inlet relative humidity conditions of 0%, 30%, and 60%, indicating that the cathode humidification has a very limited improvement on the fuel cell power and heat production performance, while increasing the risk of ice accumulation.

This article investigates the effects of gas diffusion layer thickness and porosity on the cold start performance of PEMFC to address the issues of liquid water freezing and cold start failure in low-temperature environments. Based on the cold start mathematical model and three-dimensional physical field model, a three-dimensional multiphase flow and multi physical field coupled cold start transient simulation model of PEMFC applied to cold start was established using COMSOL software. Three GDL thicknesses of 0.15mm, 0.25mm, and 0.35mm, as well as three porosity rates of 0.35, 0.55, and 0.75, were selected to investigate their effects on current density, ice volume fraction, ice space distribution, temperature rise changes, and temperature distribution performance during cold start. The simulation results indicate that ice formation first occurs near the membrane side of the cathode catalytic layer, and the amount of ice formation in the catalytic layer region under the ridge is higher than that in the channel region. The highest temperature of the battery occurs in the central region of the membrane electrode, and the cathode side is higher than the anode side. Increasing the thickness of the gas diffusion layer helps to delay ice formation and improve cold start performance. A larger porosity can also extend current output and cold start time.

The regenerative suspension plays an important role in reducing the energy consumption of vehicle. This paper proposes an optimized design of an Integrated Electromagnetic Linear Energy Regenerative Suspension System (IELERS) to capture the energy dissipated by traditional vehicle suspension systems. The IELERS employs a moving-coil electromagnetic linear actuator instead of conventional dampers. This actuator provides damping force to reduce vibration while also recovering kinetic energy generated by the suspension’s reciprocating motion. The IELERS features two operational modes: energy regeneration and Linear Quadratic Regulator (LQR) controlled active damping. Suspension system models were developed for each mode. In the energy regeneration mode, structural parameters of the IELERS were optimized by establishing a dimensionless hybrid optimization objective. This objective balances comfort and safety probabilities, resolving conflicts in suspension performance indicators and inconsistencies in dimensional scales. The integration of the Taguchi method with the neighborhood particle swarm algorithm improved optimization efficiency, while multi-condition optimization ensured adaptability across various driving scenarios. Finally, the dynamic performance changes of the IELERS before and after optimization were analyzed, and a prototype was developed for bench experiments. The results indicate that the optimized IELERS improves ride comfort without compromising handling stability. Compared with the energy regeneration mode, the body acceleration and suspension working space in active damping mode decreased by 22.5% and 33.8%, respectively, while tire dynamic deformation increased by 25%. Under Class B road, the vehicle suspension system generates average energy regeneration powers of approximately 63 W at a driving speed of 72 km/h.

In this paper, the vehicle suspension dynamic response of time delay feedback control is analyzed considering the suspension of the seat. An active suspension vibration reduction control method based on double time-delay feedback control is proposed to further improve the ride comfort. Firstly, the double time-delay dynamic response equation of the system with three degrees of freedom is established. Then, the root mean square values of seat acceleration and body acceleration are used as the objective functions. Meanwhile, the RMS values of suspension dynamic deflection and tire dynamic displacement are used as constraints, and the optimal feedback parameters are obtained using particle swarm optimisation. The Routh-Hurwitz criterion and frequency domain scanning methods are used to verify the stability of the double time-delay system. Finally, simulation in the time domain and frequency domain is accomplished. It is verified that this feedback control method has strong robustness and anti-interference ability.

For different types of electric vehicles, improving the efficiency of on-board energy utilization to extend the range of vehicle is essential. Aiming at the efficiency reduction of lithium battery system caused by large current fluctuations due to sudden load change of vehicle, this paper investigates a composite energy system of flywheel–lithium battery. First, according to the design requirements of vehicle performance, the essential parameters of the hybrid energy storage system are designed using CPE function. Then, based on the vehicle dynamics and operating principle of the hybrid energy system, a mathematical model for performance analysis of the hybrid energy electric vehicle is established. Finally, energy management strategy for the hybrid energy system is designed with the use of wavelet algorithm. Research results show significant improvement of the storage system efficiency. Specially, compared with the original scheme, owing to the flywheel battery, the maximum current and discharge rate of the lithium battery are reduced by 6.55

Energy management is a key factor affecting the efficient distribution and utilization of energy for on-board composite energy storage system. For the composite energy storage system consisting of lithium battery and flywheel, in order to fully utilize the high-power response advantage of flywheel battery, first of all, the decoupling design of the high- and low-frequency components of the power required by vehicle is carried out based on Haar wavelet algorithm. Then, to solve the problem that the Haar wavelet is unable to adapt to the random and complex vehicle operation, caused by the design using fixed decomposition layer, support vector machine (SVM) is applied to construct the identification model for the time-varying vehicle operation. Moreover, to maintain the state of energy (SOE) of flywheel within the efficient range for adjusting the lithium battery operation, a fuzzy controller is designed to redistribute the power from Haar wavelet. Finally, the economic performance of the composite energy storage system under WLTC is tested and analyzed. Results show that, under WLTC condition, compared with the energy management strategy using Haar wavelet with fixed decomposition layer, the proposed adaptive wavelet–fuzzy energy management is able to reduce the power fluctuation of lithium battery by 26.6

The irreversible loss of active lithium ions (Li+) in lithium -ion batteries causes battery failure such as low firstcycle Coulombic efficiency and poor cycling stability. Researchers compensated for the active ion loss by the strategy of pre -embedding excess Li+ within the electrode materials or electrolytes, thus improving the electrochemical performances of the batteries. This mini review takes pre -embedded lithium as an entry point to introduce the concept, efficacies, and implementation methods of pre -embedded active ions and their applications in novel electrochemical energy storage systems. The cited instances in recent years of pre -embedding strategies are explained and commented in detail. It is believed that this short review can quickly establish the knowledge structure of pre -embedded active ions and track the latest pre -embedding strategies, and provide inspirations for their wide applications in novel electrochemical energy storage systems.

Improving energy utilization efficiency to extend the range of vehicle is the common issue concerned by various forms of electric vehicles. In order to reveal the influence of electric flywheel and electromechanical flywheel on vehicle economy, two kinds of hybrid energy systems are studied. Firstly, based on the operating characteristics of the two types of flywheels, the topology schemes of the two hybrid energy systems are designed. On this basis, to make full use of the advantages of electric flywheel and electromechanical flywheel, energy management strategies based on wavelet algorithm and logic threshold are designed. Finally, economy tests of the two hybrid energy systems are conducted. Results show that compared with the single energy scheme with lithium battery, under CLTC, as the control motor of the electric flywheel operates under high speed and low torque range frequently, the energy consumption improvement of lithium battery is not enough to compensate for the flywheel energy loss. The net loss of the lithium battery-electric flywheel energy system increases by 2.61%. Profit from efficiency improvement of lithium battery system, increase of regenerative energy recovery and better efficiency of main drive motor, the net loss of the lithium battery-electromechanical flywheel energy system decreases by 6.44%.