We organized this Feature Topic and expect it will promote the development of environmentally benign automotive lightweighting technologies and contribute to the “Carbon Net Zero” process. We would also like to take this opportunity to commemorate the Honorary and Founding Executive Editor-in-Chief Professor Fangwu (Mike) Ma for his contributions to eco-driving and for his dedicated support in the publication of the previous Special Issue on Automotive Lightweight in 2020 (https://link.springer.com/journal/42154/volumes-and-issues/3-3).

This Feature Topic comprises eight papers that showcase the latest advances in the pursuit of lightweighting in automotive applications. The core contributions of these articles are summarized below.

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.

Additive manufacturing (AM) is widely used in the automotive industry and has been expanded to include aerospace, marine, and rail. High flexibility and the possibility of manufacturing complex parts in AM motivate the integration of additive manufacturing with classical forming technologies, which can improve tooling concepts and reduce costs. This study presents three applications of this integration. First, the possibility of successful utilization of selective laser melting for manufacturing extrusion tools with complex cooling channels and paths for thermocouples is reported, leading to significantly reduced inner die temperatures during the extrusion process. Second, sheet lamination is integrated with laser metal deposition (LMD) to manufacture deep-drawing dies. Promising results are achieved in reducing the stair step effect, which is the main challenge in sheet lamination, by LMD and following post-processing such as milling, ball burnishing, and laser polishing. The new manufacturing route shows that LMD can economically and efficiently reduce the stair step effect and omit the hardening step from the conventional manufacturing process route. Finally, LMD is used to manufacture a hot stamping punch with improved surface roughness by ball burnishing and near-surface complex cooling channels. The experimental results show that the manufactured punch has lower temperatures during hot stamping compared with the conventionally manufactured punch. This study shows the successful integration of AM processes with classical forming processes.

Hot stamping steels have become a crucial strategy for achieving lightweighting and enhancing crash safety in the automotive industry over the past two decades. However, the carbon emissions of the materials and their related stamping processes have been frequently overlooked. It is essential to consider these emissions during the design stage. Emerging materials and technologies in hot stamping pose challenges to the automotive industry's future development in carbon emission reduction. This review discusses the promising materials for future application and their special features, as well as the emerging manufacturing and part design processes that have extended the limit of application for new materials. Advanced heating processes and corresponding equipment have been proven to improve heating efficiency and control temperature uniformity. The material utilization and the overall performance of the components are improved by tailored blanks and an integrated part design approach. To achieve low-carbon-emission (LCE) hot stamping, it is necessary to systematically consider the steel grade, heating process, and part design, rather than solely focusing on reducing carbon emissions during the manufacturing process stage. This review aims to present the latest progress in steel grade, heating process, and part design of hot stamping in the automotive industry, providing solutions for LCE from a holistic perspective.

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.

In multi-stage press hardening, the product properties are determined by the thermo-mechanical history during the sequence of heat treatment and forming steps. To measure these properties and finally to control them by feedback, two soft sensors are developed in this work. The press hardening of 22MnB5 sheet material in a progressive die, where the material is first rapidly austenitized, then pre-cooled, stretch-formed, and finally die bent, serves as the framework for the development of these sensors. To provide feedback on the temporal and spatial temperature distribution, a soft sensor based on a model derived from the Dynamic mode decomposition (DMD) is presented. The model is extended to a parametric DMD and combined with a Kalman filter to estimate the temperature (-distribution) as a function of all process-relevant control variables. The soft sensor can estimate the temperature distribution based on local thermocouple measurements with an error of less than 10 °C during the process-relevant time steps. For the online prediction of the final microstructure, an artificial neural network (ANN)-based microstructure soft sensor is developed. As part of this, a transferable framework for deriving input parameters for the ANN based on the process route in multi-stage press hardening is presented, along with a method for developing a training database using a 1-element model implemented with LS-Dyna and utilizing the material model Mat248 (PHS_BMW). The developed ANN-based microstructure soft sensor can predict the final microstructure for specific regions of the formed and hardened sheet in a time span of far less than 1 s with a maximum deviation of a phase fraction of 1.8 % to a reference simulation.

Lightweight sheet metals are highly desirable for automotive applications due to their exceptional strength-to-density ratio. An accurate description of the pronounced plastic anisotropy exhibited by these materials in finite element analysis requires advanced plasticity models. In recent years, significant efforts have been devoted to developing plasticity models and numerical analysis methods based on the non-associated flow rule (non-AFR). In this work, a newly proposed coupled quadratic and non-quadratic model under non-AFR is utilized to comprehensively investigate the non-associated and non-quadratic characteristics during the yielding of three lightweight sheet metals, i.e., dual-phase steel DP980, TRIP-assisted steel QP980, and aluminum alloy AA5754-O. These materials are subjected to various proportional loading paths, including uniaxial tensile tests with a 15° increment, uniaxial compressive tests with a 45° increment, in-plane torsion tests, and biaxial tensile tests using laser-deposited arm-strengthened cruciform specimens. Results show that the non-AFR approach provides an effective means for accurately modeling the yield behavior, including yield stresses and the direction of plastic strain rates, simultaneously, utilizing two separate functions and a simple calibration procedure. The introduction of the non-quadratic plastic potential reduces the average errors in angle when predicting plastic strain directions by the quadratic plastic potential function. Specifically, for DP980, the average error is reduced from 3.1° to 0.9°, for QP980 it is reduced from 6.1° to 3.9°, and for AA5754-O it is reduced from 7.0° to 0.2°. This highlights the importance of considering the non-quadratic characteristic in plasticity modeling, especially for aluminum alloys such as AA5754-O.

The application of lightweight structures with excellent energy absorption performance is crucial for enhancing vehicle safety and energy efficiency. Cellular structures, inspired by the characteristics observed in natural organisms, have exhibited exceptional structural utilization in terms of energy absorption compared with traditional structures. In recent years, various innovative cellular structures have been proposed to meet different engineering needs, resulting in significant performance improvements. This paper provides a comprehensive overview of novel cellular structures for energy absorption applications. In particular, it outlines the application forms and design concepts of cellular structures under typical loading conditions in vehicle collisions, including axial loading, oblique loading, bending loading, and blast loading. Cellular structures have evolved to meet the demands of complex loading conditions and diverse research methods, focusing on achieving high-performance characteristics across multiple load cases. Moreover, this review discusses manufacturing techniques and strategies for enhancing the manufacturing performance of cellular structures. Finally, current key challenges and future research directions for cellular structures are discussed. The aim of this study is to provide valuable guidelines for researchers and engineers in the development of next-generation lightweight cellular structures.

Roll forming has been widely used to manufacture long channels with complex cross-sections. End flare, one of the typical shape errors, seriously affects the forming accuracy of roll-formed parts, especially using advanced high-strength steel. In this paper, the mechanism of end flare during the roll forming process of a high-strength automobile seat rail is analyzed. The roll forming process of an actual seat rail is designed. The finite element models of the roll forming process and cut-off springback are established to predict the deformation process and occurrence of end flare. Simulation results indicate that the uneven distribution of longitudinal and shear residual stress along the length of the part is the main reason for the end flare. Based on the simulation, two strategies are proposed to mitigate the end flare. Employing multiple bending processes in the transverse direction effectively balances the longitudinal and shear residual stress. Additionally, the longitudinal bending process can make the longitudinal residual stress in the roll-formed parts more homogenised. Finally, verification experiments are carried out, and the forming accuracy of the seat rail is significantly improved.

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.

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.

Uncertain environment on multi-lane highway, e.g., the stochastic lane-change maneuver of surrounding vehicles, is a big challenge for achieving safe automated highway driving. To improve the driving safety, a heuristic reinforcement learning decision-making framework with integrated risk assessment is proposed. First, the framework includes a long short-term memory model to predict the trajectory of surrounding vehicles and a future integrated risk assessment model to estimate the possible driving risk. Second, a heuristic decaying state entropy deep reinforcement learning algorithm is introduced to address the exploration and exploitation dilemma of reinforcement learning. Finally, the framework also includes a rule-based vehicle decision model for interaction decision problems with surrounding vehicles. The proposed framework is validated in both low-density and high-density traffic scenarios. The results show that the traffic efficiency and vehicle safety are both improved compared to the common dueling double deep Q-Network method and rule-based method.

This paper proposes an improved decision-making method based on deep reinforcement learning to address on-ramp merging challenges in highway autonomous driving. A novel safety indicator, time difference to merging (TDTM), is introduced, which is used in conjunction with the classic time to collision (TTC) indicator to evaluate driving safety and assist the merging vehicle in finding a suitable gap in traffic, thereby enhancing driving safety. The training of an autonomous driving agent is performed using the Deep Deterministic Policy Gradient (DDPG) algorithm. An action-masking mechanism is deployed to prevent unsafe actions during the policy exploration phase. The proposed DDPG?+?TDTM?+?TTC solution is tested in on-ramp merging scenarios with different driving speeds in SUMO and achieves a success rate of 99.96% without significantly impacting traffic efficiency on the main road. The results demonstrate that DDPG?+?TDTM?+?TTC achieved a higher on-ramp merging success rate of 99.96% compared to DDPG?+?TTC and DDPG.

Traditional research on automatic lane change has primarily focused on high-speed scenarios and has not considered the dynamic state changes of surrounding vehicles. This paper addresses this problem by proposing a trajectory planning method to enable automatic lane change at medium and low speeds. The method is based on a dynamic safety domain model, which takes into account the actual state change of surrounding vehicles, as well as the upper boundary of the safety domain for collision avoidance and the lower boundary of comfort for vehicle stability. The proposed method involves the quantification of the safety and comfort boundaries through parametric modeling of the vehicle. A quintic polynomial trajectory planning method is proposed and evaluated through simulation and testing, resulting in improved safety and comfort for automatic lane change.

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 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.