Automotive Innovation最新文献

The mass reduction of passenger vehicles has been a great focus of academic research and federal policy initiatives of the United States with coordinated funding efforts and even a focus of a Manufacturing USA Institute. The potential benefit of these programs can be described as modest from a societal point of view, for example reducing vehicle mass by up to 25% with modest cost implications (under $5 per pound saved) and the ability to implement with existing manufacturing methods. Much more aggressive reductions in greenhouse gas production are necessary and possible, while delivering the same service. This is demonstrated with a higher-level design thinking exercise on an environmentally responsible lightweight vehicle, leading to the following criteria: lightweight, low aerodynamic drag, long-lived (over 30 years and 2 million miles), adaptable, electric, and used in a shared manner on average over 8 h per day. With these specifications, passenger-mile demand may be met with around 1/10 of the current fleet. Such vehicles would likely have significantly different designs and construction than incumbent automobiles. It is likely future automotive production will be more analogous to current aircraft production with higher costs per pound and lower volumes, but with dramatically reduced financial and environmental cost per passenger mile, with less material per vehicle, and far less material required in the national or worldwide fleets. Subsidiary benefits of this vision include far fewer parking lots, greater accessibility to personal transportation, and improved pedestrian safety, while maintaining a vibrant and engaging economy. The systemic changes to the business models and research and development directions (including lightweight design and manufacturing) are discussed, which could bring forth far more sustainable personal transportation.

The state-of-the-art technology in the field of vehicle automation will lead to a mixed traffic environment in the coming years, where connected and automated vehicles have to interact with human-driven vehicles. In this context, it is necessary to have intention prediction models with the capability of forecasting how the traffic scenario is going to evolve with respect to the physical state of vehicles, the possible maneuvers and the interactions between traffic participants within the seconds to come. This article presents a Bayesian approach for vehicle intention forecasting, utilizing a game-theoretic framework in the form of a Mixed Strategy Nash Equilibrium (MSNE) as a prior estimate to model the reciprocal influence between traffic participants. The likelihood is then computed based on the Kullback-Leibler divergence. The game is modeled as a static nonzero-sum polymatrix game with individual preferences, a well known strategic game. Finding the MSNE for these games is in the PPAD (cap) PLS complexity class, with polynomial-time tractability. The approach shows good results in simulations in the long term horizon (10s), with its computational complexity allowing for online applications.

The clinch–adhesive process, which combines mechanical clinching and adhesive bonding, is one of the most applied processes for joining aluminum alloy and steel in the manufacturing of vehicle bodies. In this hybrid process, the clinching joints and adhesive bonds are coupled and influenced by each other, posing challenges to the process design and joining strength evaluation. To understand the influence of the clinching process on the performance of the adhesive layer, this study analyzes the mechanical behavior of clinch–adhesive joints between high-strength steel JSC780 and aluminum alloy A5052-H34 with different stack-up orientations and varying numbers of clinching points. The results reveal that, under the steel-on-top condition, the clinching process causes a discontinuous distribution of the adhesive layer, which significantly decreased the bonding strength. In contrast, under the aluminum-on-top condition, the clinching process has a lesser impact on the distribution of the adhesive layer, resulting in much higher strength than the steel-on-top condition. Simulation models are constructed to quantify the effect of clinching points on the performance of the adhesive layer. The results highlight the need to consider diverse cohesive zone model parameters for the different stack orientations and clinching points in the design of clinch–adhesive aluminum alloy/steel structures.

LiFePO₄ (LFP) lithium-ion batteries have gained widespread use in electric vehicles due to their safety and longevity, but thermal runaway (TR) incidents still have been reported. This paper explores the TR characteristics and modeling of LFP batteries at different states of charge (SOC). Adiabatic tests reveal that TR severity increases with SOC, and five stages are identified based on battery temperature evolution. Reaction kinetics parameters of exothermic reactions in each TR stage are extracted, and TR models for LFP batteries are established. The models accurately simulate TR behaviors at different SOCs, and the simulated TR characteristic temperatures also agree well with the experimental results, with errors of TR characteristic temperatures less than 3%. The prediction errors of TR characteristic temperatures under oven test conditions are also less than 1%. The results provide a comprehensive understanding of TR in LFP batteries, which is useful for battery safety design and optimization.

An online trajectory planning method for collision avoidance is proposed to improve vehicle driving safety and comfort simultaneously. The collision-free trajectory for autonomous driving is formulated as a nonlinear optimization problem. A novel approximate convex optimization approach is developed for the online optimal trajectory in both longitudinal and lateral directions. First, a dual variable is used to model the non-convex collision-free constraint for driving safety and is calculated by solving a dual problem of the relative distance between vehicles. Second, the trajectory is further optimized in a model predictive control framework considering the safety. It realizes continuous-time and dynamic feasible motion with collision avoidance. The geometry of object vehicles is described by polygons instead of circles or ellipses in traditional methods. In order to avoid aggressive maneuver in the longitudinal and lateral directions for driving comfort, rates of the acceleration and the steering angle are restricted. The final formulated optimization problem is convex, which can be solved by using quadratic programming solvers and is computationally efficient for online application. Simulation results show that this approach can obtain similar driving performance compared to a state-of-the-art nonlinear optimization method. Furthermore, various driving scenarios are tested to evaluate the robustness and the ability for handling complex driving tasks.

The detection and mitigation of cyber-attacks in connected vehicle systems (CVSs) are critical for ensuring the security of intelligent connected vehicles. This paper presents a solution to estimate sensor and actuator cyber-attacks in CVSs. A novel method is proposed that utilizes an augmented system representation technique and a nonlinear unknown input observer (UIO) to achieve asymptotic estimation of both CVS dynamics and cyber-attacks. The nonlinear CVS dynamics is represented in a Takagi–Sugeno (TS) fuzzy form with nonlinear consequents, which allows for the effective use of the differential mean value theorem to handle unmeasured premise variables. Furthermore, via Lyapunov stability theory sufficient conditions are proposed, expressed in terms of linear matrix inequalities, to design TS fuzzy UIO. Several test scenarios are performed with high-fidelity Simulink-CarSim co-simulations to show the effectiveness of the proposed cyber-attack estimation method.

This study aims at developing an optimization framework for electric vehicle charging by considering different trade-offs between battery degradation and charging time. For the first time, the application of practical limitations on charging and cooling power is considered along with more detailed health models. Lithium iron phosphate battery is used as a case study to demonstrate the effectiveness of the proposed optimization framework. A coupled electro-thermal equivalent circuit model is used along with two battery health models to mathematically obtain optimal charging current profiles by considering stress factors of state-of-charge, charging rate, temperature and time. The optimization results demonstrate an improvement over the benchmark constant current–constant voltage (CCCV) charging protocol when considering both the charging time and battery health. A main difference between the optimal and the CCCV charging protocols is found to be an additional ability to apply constraints and adapt to initial conditions in the proposed optimal charging protocol. In a case study, for example, the ‘optimal time’ charging is found to take 12 min while the ‘optimal health’ charging profile suggests around 100 min for charging the battery from 25 to 75% state-of-charge. Any other trade-off between those two extreme cases is achievable using the proposed charging protocol as well.

The study of series–parallel plug-in hybrid electric vehicles (PHEVs) has become a research hotspot in new energy vehicles. The global optimal Pareto solutions of energy management strategy (EMS) play a crucial role in the development of PHEVs. This paper presents a multi-objective global optimization algorithm for the EMS of PHEVs. The algorithm combines the Radau Pseudospectral Knotting Method (RPKM) and the Nondominated Sorting Genetic Algorithm (NSGA)-II to optimize both energy conservation and battery lifespan under the suburban driving conditions of the New European Driving Cycle. The driving conditions are divided into stages at evident mode switching points and the optimal objectives are computed using RPKM. The RPKM results serve as the fitness values in iteration through the NSGA-II method. The results of the algorithm applied to a PHEV simulation show a 26.74%–53.87% improvement in both objectives after 20 iterations compared to the solutions obtained using only RPKM. The proposed algorithm is evaluated against the weighting dynamic programming and is found to be close to the global optimality, with the added benefits of faster and more uniform solutions.

Safe and smooth interaction between other vehicles is one of the ultimate goals of driving automation. However, recent reports of demonstrative deployments of automated vehicles (AVs) indicate that AVs are still difficult to meet the expectation of other interacting drivers, which leads to several AV accidents involving human-driven vehicles (HVs) without the understanding about the dynamic interaction process. By investigating 4300 video clips of traffic accidents, it is found that the limited dynamic visual field of drivers is one leading factor in inter-vehicle interaction accidents. A game-theoretic decision algorithm considering social compatibility is proposed to handle the interaction with a human-driven truck at an unsignalized intersection. Starting from a probabilistic model for the visual field characteristics of truck drivers, social fitness and reciprocal altruism in the decision are incorporated in the game payoff design. Human-in-the-loop experiments are carried out, in which 24 subjects are invited to drive and interact with AVs deployed with the proposed algorithm and two comparison algorithms. Totally, 207 cases of intersection interactions are obtained and analyzed, which shows that the proposed decision-making algorithm can improve both safety and time efficiency, and make AV decisions more in line with the expectation of interacting human drivers. These findings can help inform the design of automated driving decision algorithms, to ensure that AVs can be safely and efficiently integrated into the human-dominated traffic.