Automotive Innovation最新文献

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 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.

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.

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.

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.

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.

Parameter effects of the potential-field-driven model predictive control (PF-MPC) method on performances of shared control systems during obstacles avoidance are investigated. The PF-MPC controllers of autonomous driving and shared control systems are designed based on the constructed potential fields and model predictive control method, and the driver-vehicle dynamics and the driver-related costs are also considered in the design of the shared controller. To explore a potential approach of alleviating driver-automation conflicts of the shared control systems, different motion planning results generated by the PF-MPC controller are explored by adjusting effects of potential fields’ parameters, which provides possibilities to decrease driver-automation conflicts between the planned trajectory and driver’s target path. Moreover, two case studies are designed to discuss different frameworks and parameters effects on shared control systems. Results show that the proposed shared control frameworks considering driver-vehicle dynamics and the driver-related cost show better performances regarding driver-automation conflicts management and driving safety than the decentralized control framework. And the longitudinal normalized constant of potential fields parameters shows influences on the driver-automation conflicts management and driving safety performances of shared control.

The dynamic response of fuel cell vehicle is greatly affected by the pressure of reactants. Besides, the pressure difference between anode and cathode will also cause mechanical damage to proton exchange membrane. For maintaining the relative stability of anode pressure, this study proposes a decentralized model predictive controller (DMPC) to control the anodic supply system composed of a feeding and returning ejector assembly. Considering the important influence of load current on the system, the piecewise linearization approach and state space with current-induced disturbance compensation are comparatively analyzed. Then, an innovative switching strategy is proposed to prevent frequent switching of the sub-model-based controllers and to ensure the most appropriate predictive model is applied. Finally, simulation results demonstrate the better stability and robustness of the proposed control schemes compared with the traditional proportion integration differentiation controller under the step load current, variable target and purge disturbance conditions. In particular, in the case of the DC bus load current of a fuel cell hybrid vehicle, the DMPC controller with current-induced disturbance compensation has better stability and target tracking performance with an average error of 0.15 kPa and root mean square error of 1.07 kPa.

Updating high-definition maps is imperative for the safety of autonomous vehicles. However, positional changes in lane lines are hard to be detected in a timely manner due to a limited number of expensive surveying vehicles over a large geographic area. Herein, a novel method is proposed to detect the geometric changes of lane lines using low-cost sensors, such as consumer-grade global navigation satellite system (GNSS) hardware receivers and cameras. The proposed framework geometric change detection using low-cost sensors (GCD-L) and algorithm change segment compare (CSC), which are based on the lane width between the curb line and the adjacent leftmost lane line, can perceive the positional changes of the leftmost lane line on highway and expressway roads. The effectiveness of the proposed method is verified by evaluating it on a real-world typical urban ring road dataset. The experimental results show that 71% detected change segments are valid with only two round crowdsourced maps.