A novel hybrid adaptive event-triggered platoon control strategy is proposed to achieve the balanced coordination between communication resource utilization and vehicle-following performance considering the effect of package dropout. To deal with the disturbance caused by the event-triggered scheme, the parameter space approach is adopted to derive the feasible region from which cooperative adaptive cruise control controller satisfies internal stability, distance accuracy, and string stability. Subsequently, the Bernoulli random distribution process is employed to depict the phenomenon of package dropout, and the hybrid coefficient is proposed to realize the allocation between the adaptive trigger threshold strategy and the adaptive headway strategy. The simulation of a six-vehicle platoon is carried out to verify the effectiveness of the designed control strategy. Results show that about 78.76% of communication resources have been saved by applying the event-triggered scheme, while guaranteeing the desired vehicle-following performance. And in the non-ideal communication environment with frequent package dropouts, the hybrid adaptive strategy achieves the coordination among communication resource utilization, string stability margin, distance accuracy, and traffic efficiency.

Lithium-ion batteries have become the mainstream power source for electric vehicles because of their excellent performance. However, lithium-ion batteries still experience aging and capacity attenuation during usage. It is therefore critical to accurately predict battery remaining capacity for increasing battery safety and prolonging battery life. This paper first adopts the metabolism grey algorithm and a simplified electrochemical model to predict battery capacity under different operating conditions. To improve the prediction performance where the capacity changes nonlinearly, a decoupling analysis of battery capacity loss is then conducted based on the simplified electrochemical model. Finally, an adaptive fitting method is developed for capacity prediction, aiming at improving the prediction accuracy at the inflection point of battery capacity diving. The prediction results indicate that the developed adaptive fitting method can achieve high prediction accuracy under battery capacity attenuation at different discharge stages with errors lower than 2.2%. And the battery capacity decay shows linear variation, and the proposed method effectively forecast the inflection point of battery capacity diving.

The type of road surface condition (RSC) will directly affect the driving performance of vehicles. Monitoring the type of RSC is essential for both transportation agencies and individual drivers. However, most existing methods are solely based on a dynamics-based method or an image-based method, which is susceptible to road excitation limitations and interference from the external environment. Therefore, this paper proposes a decision-level fusion identification framework of the RSC based on ego-vehicle trajectory reckoning to accurately obtain the type of RSC that the front wheels of the vehicle will experience. First, a road feature extraction model based on multi-task learning is conducted, which can simultaneously segment the drivable area and road cast shadow. Second, the optimized candidate regions of interest are classified with confidence levels by ShuffleNet. Considering environmental interference, candidate regions of interest regarded as virtual sensors are fused by improved Dempster-Shafer evidence theory to obtain the fusion results. Finally, the ego-vehicle trajectory reckoning module based on the kinematic bicycle model is added to the proposed fusion method to extract the RSC experienced by the front wheels. The performance of the entire framework is verified on a specific dataset with shadow and split curve roads. The results reveal that the proposed method can identify the RSC with accurate predictions in real time.

Transmission losses in battery electric vehicles have compared to internal combustion engine powertrains a larger share in the total energy consumption and play therefore a major role. Furthermore, the power flows not only during propulsion through the transmissions, but also during recuperation, whereby efficiency improvements have a double effect. The investigation of transmission losses of electric vehicles thus plays a major role. In this paper, three simulation models of the Institute of Automotive Engineering (the lossmap-based simulation model, the modular simulation model, and the 3D simulation model) are presented. The lossmap-based simulation model calculates transmission losses for electric and hybrid transmissions, where three spur gear transmission concepts for battery electric vehicles are investigated. The transmission concepts include a single-speed transmission as a reference and two two-speed transmissions. Then, the transmission lossmaps are integrated into the modular simulation model (backward simulation) and in the 3D simulation model (forward simulation), which improves the simulation results. The modular simulation model calculates the optimal operation of the transmission concepts and the 3D simulation model represents the more realistic behavior of the transmission concepts. The different transmission concepts are investigated in Worldwide Harmonized Light Vehicle Test Cycle and evaluated in terms of transmission losses as well as the total energy demand. The map-based simulation model allows the transmission losses to be broken down into the individual component losses, thus allowing transmission concepts to be examined and evaluated in terms of their efficiency in the early development stage to develop optimum powertrains for electric axle drives. By considering transmission losses in detail with a high degree of accuracy, less efficient concepts can be eliminated at an early development stage. As a result, only relevant concepts are built as prototypes, which reduces development costs.

Fault diagnosis is key to enhancing the performance and safety of battery storage systems. However, it is challenging to realize efficient fault diagnosis for lithium-ion batteries because the accuracy diagnostic algorithm is limited and the features of the different faults are similar. The model-based method has been widely used for degradation mechanism analysis, state estimation, and life prediction of lithium-ion battery systems due to the fast speed and high development efficiency. This paper reviews the mainstream modeling approaches used for battery diagnosis. First, a review of the battery’s degradation mechanisms and the external factors affecting the aging rate is presented. Second, the different modeling approaches are summarized, from microscopic to macroscopic scales, including density functional theory, molecular dynamics, X-ray computed tomography technology, electrochemical model, equivalent circuit model, distributed model and neural network algorithm. Subsequently, the advantages and disadvantages of these model approaches are discussed for fault detection and diagnosis of batteries in different application scenarios. Finally, the remaining challenges of model-based battery diagnosis and the future perspective of using cloud control and battery intelligent networking to enhance diagnostic performance are discussed.

Precise state and parameter estimations are essential for identification, analysis and control of vehicle engineering problems, especially under significant model and measurement uncertainties. The widely used filtering/estimation algorithms, such as Kalman series like Kalman filter, extended Kalman filter, unscented Kalman filter, and particle filter, generally aim to approach the true state/parameter distribution via iteratively updating the filter gain at each time step. However, the optimality of these filters would be deteriorated by unrealistic initial condition or significant model error. Alternatively, this paper proposes to approximate the optimal filter gain by considering the effect factors within infinite time horizon, on the basis of estimation-control duality. The proposed approximate optimal filter (AOF) problem is designed and subsequently solved by actor-critic reinforcement learning (RL) method. The AOF design transforms the traditional optimal filtering problem with the minimum expected mean square error into an optimal control problem with the minimum accumulated estimation error, in which the estimation error is used as the surrogate system state and the infinite-horizon filter gain is the control input. The estimation-control duality is proved to hold when certain conditions about initial vehicle state distributions and policy structure are maintained. In order to evaluate of the effectiveness of AOF, a vehicle state estimation problem is then demonstrated and compared with the steady-state Kalman filter. The results showed that the obtained filter policy via RL with different discount factors can converge to theoretical optimal gain with an error within 5%, and the average estimation errors of vehicle slip angle and yaw rate are less than 1.5?×?10–4.

The service life of an electric vehicle is, to some extent, determined by the life of the traction battery. A good charging strategy has an important impact on improving the cycle life of the lithium-ion battery. Here, this paper presents a comparative study on the cycle life and material structure stability of lithium-ion batteries, based on typical charging strategies currently applied in the market, such as constant current charging, constant current and constant voltage charging, multi-stage constant current charging, variable current intermittent charging, and pulse charging. Compared with the reference charging strategy, the charging capacity of multi-stage constant current charging reaches 88%. Moreover, the charging time is reduced by 69%, and the capacity retention rate after 500 cycles is 93.3%. Through CT, XRD, SEM, and Raman spectroscopy analysis, it is confirmed that the smaller the damage caused by this charging strategy to the overall structure of the battery and the layered structure and particle size of the positive electrode material, the higher the capacity retention rate is. This work facilitates the development of a better charging strategy for a lithium-ion battery from the perspective of material structure.

The full-lifespan management concept provides a new pathway to seeking solutions from macro-application scenarios to micro-mechanism levels. This paper presents a cyber hierarchy multiscale optimal control method for multiple intelligent hybrid vehicles to fully release the potentials of vehicle components while guaranteeing driving safety and stability. It can be generally divided into the cyber intelligent driving system on the cyber-end and the intelligent vehicle system on the vehicle-end. On the cyber-end, the state information of the surrounding vehicles is transmitted via the Vehicle-to-Everything structure and further processed in the cloud platform to generate future driving behaviors based on a car-following theory. On the vehicle-end, an optimized control sequence for vehicle components at micro-levels is derived by incorporating a physics-informed neural network model for battery health prediction. The results show that global optimization needs high coupling between the macro- and micro-physical processes. By introducing the genetic algorithm for time smoothing, the improved driving strategy is capable of macro- and micro-coupling, and thus improves the controllable performance in time series. Moreover, this method spans the complexity of space, time, and chemistry, enhances the interpretation performance of machine learning, and slows down the battery aging in the process of multiscale optimization.

A novel real-time predictive control strategy is proposed for path following (PF) and vehicle stability of autonomous electric vehicles under extreme drive conditions. The investigated vehicle configuration is a distributed drive electric vehicle, which allows to independently control the torques of each in-wheel motor (IWM) for superior stability, but bringing control complexities. The control-oriented model is established by the Magic Formula tire function and the single-track vehicle model. For PF and direct yaw moment control, the nonlinear model predictive control (NMPC) strategy is developed to minimize PF tracking error and stabilize vehicle, outputting front tires’ lateral force and external yaw moment. To mitigate the calculation burdens, the continuation/general minimal residual algorithm is proposed for real-time optimization in NMPC. The relaxation function method is adopted to handle the inequality constraints. To prevent vehicle instability and improve steering capacity, the lateral velocity differential of the vehicle is considered in phase plane analysis, and the novel stable bounds of lateral forces are developed and online applied in the proposed NMPC controller. Additionally, the Lyapunov-based constraint is proposed to guarantee the closed-loop stability for the PF issue, and sufficient conditions regarding recursive feasibility and closed-loop stability are provided analytically. The target lateral force is transformed as front steering angle command by the inversive tire model, and the external yaw moment and total traction torque are distributed as the torque commands of IWMs by optimization. The validations prove the effectiveness of the proposed strategy in improved steering capacity, desirable PF effects, vehicle stabilization, and real-time applicability.