System integration and power-flow control of on-board power sources are critical to the performance and cost competitiveness of hybrid electric vehicles (HEVs). The existing methods mostly focus on ...fuel minimization in hybrid powertrains, while disregarding many other concerns. This article presents an innovative multicriteria optimization approach and showcases its validity and usefulness in a case study of a fuel-cell hybrid bus. Three key technical contributions are made. First, a convex multicriteria optimization framework is devised for quickly and efficiently evaluating the optimal tradeoffs between the fuel-cell durability and hydrogen economy in the bus, as well as the corresponding fuel-cell dimension. Second, the impact of driving pattern on both the optimal fuel-cell size and Pareto optimality is investigated by considering discrepant driving schedules. Finally, a preliminary but useful economic assessment in both current and future scenarios is performed to explore the most cost-effective tradeoff.
This paper focuses on the real-time trajectory planning problem for autonomous vehicles driving in realistic urban environments. To solve the complex navigation problem, we adopt a hierarchical ...motion planning framework. First, a rough reference path is extracted from the digital map using commands from the high-level behavioral planner. The conjugate gradient nonlinear optimization algorithm and the cubic B-spline curve are employed to smoothen and interpolate the reference path sequentially. To follow the refined reference path as well as handle both static and moving objects, the trajectory planning task is decoupled into lateral and longitudinal planning problems within the curvilinear coordinate framework. A rich set of kinematically feasible path candidates are generated to deal with the dynamic traffic both deliberatively and reactively. In the meanwhile, the velocity profile generation is performed to improve driving safety and comfort. After that, the generated trajectories are carefully evaluated by an objective function, which combines behavioral decisions by reasoning about the traffic situations. The optimal collision-free, smooth, and dynamically feasible trajectory is selected and transformed into commands executed by the low-level lateral and longitudinal controllers. Field experiments have been carried out with our test autonomous vehicle on the realistic inner-city roads. The experimental results demonstrated capabilities and effectiveness of the proposed trajectory planning framework and algorithms to safely handle a variety of typical driving scenarios, such as static and moving objects avoidance, lane keeping, and vehicle following, while respecting the traffic rules.
This brief presents an integrated optimization framework for battery sizing, charging, and on-road power management in plug-in hybrid electric vehicles. This framework utilizes convex programming to ...assess interactions between the three optimal design/control tasks. The objective is to minimize carbon dioxide (CO 2 ) emissions, from the on-board internal combustion engine and grid generation plants providing electrical recharge power. The impacts of varying daily grid CO 2 trajectories on both the optimal battery size and charging/power management algorithms are analyzed. We find that the level of grid CO 2 emissions can significantly impact the nature of emission-optimal on-road power management. We also observe that the on-road power management strategy is the most important design task for minimizing emissions, through a variety of comparative studies.
This paper studies the codesign optimization approach to determine how to optimally adapt automatic control of an intelligent electric vehicle to driving styles. A cyber-physical system (CPS)-based ...framework is proposed for codesign optimization of the plant and controller parameters for an automated electric vehicle, in view of vehicle's dynamic performance, drivability, and energy along with different driving styles. System description, requirements, constraints, optimization objectives, and methodology are investigated. Driving style recognition algorithm is developed using unsupervised machine learning and validated via vehicle experiments. Adaptive control algorithms are designed for three driving styles with different protocol selections. Performance exploration method is presented. Parameter optimizations are implemented based on the defined objective functions. Test results show that an automated vehicle with optimized plant and controller can perform its tasks well under aggressive, moderate, and conservative driving styles, further improving the overall performance. The results validate the feasibility and effectiveness of the proposed CPS-based codesign optimization approach.
A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this ...paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.
Driver decisions and behaviors are essential factors that can affect the driving safety. To understand the driver behaviors, a driver activities recognition system is designed based on the deep ...convolutional neural networks (CNN) in this paper. Specifically, seven common driving activities are identified, which are the normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle radio device, texting, and answering the mobile phone, respectively. Among these activities, the first four are regarded as normal driving tasks, while the rest three are classified into the distraction group. The experimental images are collected using a low-cost camera, and ten drivers are involved in the naturalistic data collection. The raw images are segmented using the Gaussian mixture model to extract the driver body from the background before training the behavior recognition CNN model. To reduce the training cost, transfer learning method is applied to fine tune the pre-trained CNN models. Three different pre-trained CNN models, namely, AlexNet, GoogLeNet, and ResNet50 are adopted and evaluated. The detection results for the seven tasks achieved an average of 81.6% accuracy using the AlexNet, 78.6% and 74.9% accuracy using the GoogLeNet and ResNet50, respectively. Then, the CNN models are trained for the binary classification task and identify whether the driver is being distracted or not. The binary detection rate achieved 91.4% accuracy, which shows the advantages of using the proposed deep learning approach. Finally, the real-world application are analyzed and discussed.
Increasing concerns on human driver comfort/health and emerging demands on suspension systems for off-road vehicles call for an effective and efficient off-road vehicle ride dynamics model. This ...study devotes both analytical and experimental efforts in developing a comprehensive off-road vehicle ride dynamics model. A three-dimensional tire model is formulated to characterize tire–terrain interactions along all the three translational axes. The random roughness properties of the two parallel tracks of terrain profiles are further synthesized considering equivalent undeformable terrain and a coherence function between the two tracks. The terrain roughness model, derived from the field-measured responses of a conventional forestry skidder, was considered for the synthesis. The simulation results of the suspended and unsuspended vehicle models are derived in terms of acceleration PSD, and weighted and unweighted rms acceleration along the different axes at the driver seat location. Comparisons of the model responses with the measured data revealed that the proposed model can yield reasonably good predictions of the ride responses along the translational as well as rotational axes for both the conventional and suspended vehicles. The developed off-road vehicle ride dynamics model could serve as an effective and efficient tool for predicting vehicle ride vibrations, to seek designs of primary and secondary suspensions, and to evaluate the roles of various operating conditions.
► A comprehensive off-road vehicle ride dynamics model is developed. ► 3-dimensional tire model is formulated to characterize tire–terrain interactions. ► Random roughness properties of two parallel tracks of the terrain are synthesized. ► Model is validated by comparing the simulation results with the measured data. ► The model effectiveness for evaluating suspension design is demonstrated.
Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context, as well as the driver status since ADAS share the vehicle control authorities ...with the human driver. This paper provides an overview of the ego-vehicle driver intention inference (DII), which mainly focuses on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consist of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.
As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great ...importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg-Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.
Precision control of clutch pressure is critical in heavy-duty automatic transmission applications in which the fast response of the clutch actuator is required. A conventional clutch actuator system ...with a pressure-reducing valve (PRV) is not applicable in this kind of application due to the fact that a large transient flow and high output power for power-on shift are necessary. In this paper, a pilot-operated PRV is developed for heavy-duty automatic transmission systems. The developed PRV can make the clutch actuator system have a fast response and a high flow capacity simultaneously. The PRV utilizes a three-stage structure with a high-speed proportional solenoid valve (PSV) as the pilot stage to do the tradeoff between the valve response and the flow capacity. First, a linearized input-output dynamic analytical model for the clutch pressure control system is developed based on fluid dynamics. Then, the parameters are identified, and the model is validated by using experimental data. For the validated input-output model, both open- and closed-loop (feedback) pressure control strategies are designed and implemented in a test setup. It infers from the experimental results that the feedback control can lead to excellent control precision. The developed clutch actuator system is applicable for heavy-duty automatic transmissions.
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