Development of an Adaptive Model Predictive Control for Platooning Safety in Battery Electric Vehicles

The recent and continuous improvement in the transportation field provides several different opportunities for enhancing safety and comfort in passenger vehicles. In this context, Adaptive Cruise Control (ACC) might provide additional benefits, including smoothness of the traffic flow and collision avoidance. In addition, Vehicle-to-Vehicle (V2V) communication may be exploited in the car-following model to obtain further improvements in safety and comfort by guaranteeing fast response to critical events. In this paper, firstly an Adaptive Model Predictive Control was developed for managing the Cooperative ACC scenario of two vehicles; as a second step, the safety analysis during a cut-in maneuver was performed, extending the platooning vehicles’ number to four. The effectiveness of the proposed methodology was assessed for in different driving scenarios such as diverse cruising speeds, steep accelerations, and aggressive decelerations. Moreover, the controller was validated by considering various speed profiles of the leader vehicle, including a real drive cycle obtained using a random drive cycle generator software. Results demonstrated that the proposed control strategy was capable of ensuring safety in virtually all test cases and quickly responding to unexpected cut-in maneuvers. Indeed, different scenarios have been tested, including acceleration and deceleration phases at high speeds where the control strategy successfully avoided any collision and stabilized the vehicle platoon approximately 20–30 s after the sudden cut-in. Concerning the comfort, it was demonstrated that improvements were possible in the aggressive drive cycle whereas different scenarios were found in the random cycle, depending on where the cut-in maneuver occurred

Effect of Temperature Distribution on the Predicted Cell Lifetimes for a Plug-In Hybrid Electric Vehicle Battery Pack

Monitoring and preserving state-of-health of high-voltage battery packs in electrified road vehicles currently represents an open and growing research topic. When predicting high-voltage battery lifetime, most current literature assumes a uniform temperature distribution among the different cells of the pack. Nevertheless, temperature has been demonstrated having a key impact on cell lifetime, and different cells of the same battery pack typically exhibit different temperature profiles over time, e.g. due to their position within the pack. Following these considerations, this paper aims at assessing the effect of temperature distribution on the predicted lifetime of cells belonging to the same battery pack. To this end, a throughput-based numerical cell ageing model is firstly selected due to its reasonable compromise between accuracy and computational efficiency. Subsequently, experimentally measured temperature and C-rate profiles over time in a driving mission are considered for three cells of the high-voltage battery pack of a commercially available plug-in hybrid electric vehicle. Obtained results suggest that due to temperature distribution in the high-voltage battery pack, the predicted lifetime for the hottest cell might be as low as 61% compared with the coldest cell of the pack. Importance and advancement of monitoring and managing the state-of-health of the single cells of an electrified vehicle battery pack might be fostered in this way thanks to the proposed study

Battery Electric Vehicles Platooning: Assessing Capability of Energy Saving and Passenger Comfort Improvement

Techniques exploiting the communication between vehicles, infrastructure or anything capable of, are being developed in the recent years due to their effectiveness in improving energy efficiency, comfort and safety. The scenario analyzed in this paper is of four vehicles platooning, in which the leader (i.e. the first in the platoon) is set to travel through different drive cycles and is followed by three other vehicles. An optimization-based algorithm based on Dynamic Programming (DP) is implemented to find the benchmark optimal control solution for the speed trajectory of the three following Battery Electric Vehicles (BEVs). Optimal control targets for planning the three automated vehicle velocity profiles involve both reducing aggressive changes in velocity, thus enhancing passenger comfort, and decreasing energy consumption. Results show a potential range of 1.8 – 7.6 % energy reduction when comparing the energy consumptions of the lead and first follower vehicle, whereas the implemented optimization-based velocity planner predicts enhanced energy economy for the second and third follower BEVs. In general, the highest advantages both in energy consumption and comfort are predicted in the urban scenarios due to the high number of acceleration/deceleration phases

Optimal Real-Time Velocity Planner of a Battery Electric Vehicle in V2V Driving

Autonomous driving systems are among the most interesting technologies in the transportation field nowadays, ensuring a high level of safety and comfort while also enhancing energy saving. For the following case study, a Battery Electric Vehicle (BEV) is considered able to communicate with other vehicles through vehicle-to-vehicle (V2V) technology by exchanging information on position and velocity. In this framework, an innovative real-time velocity planner has been developed aiming at maximizing the battery energy savings while improving the passenger comfort as well. This controller uses the principles of the equivalent consumption minimization strategy (ECMS) when the preceding vehicle is accelerating. Simulation results demonstrate improvements in comfort ranging from 26% to 42% ca. and in energy consumption (from 0.4% to 1.3%) when performing different drive cycles in V2V automated driving mode thanks to the proposed controller

Cooperative Adaptive Cruise Control: A Gated Recurrent Unit Approach

Embedded artificial intelligence solutions are promising controllers for future sustainable and automated road vehicles. This study presents a deep learning-based approach combined with vehicle communication technology for the design of a real-time cooperative adaptive cruise control (CACC). A particular type of recurrent neural network has been selected, namely a gated recurrent unit (GRU). GRU exhibits improved learning performance in control problems such as the CACC since it avoids the vanishing gradient problems that characterize long time series. A GRU has been trained using ad-hoc CACC datasets build-up according to an optimal control policy, i.e. dynamic programming (DP), for a battery electric vehicle. In particular, DP optimizes the longitudinal speed trajectory of the Ego (Following) vehicle in CACC so to achieve energy savings and passenger comfort improvement. Results demonstrate that the Ego vehicle controlled by the trained GRU can achieve an eco-friendly driving in CACC without compromising passenger comfort and safety requirements. Unlike DP, GRU holds strong real-time potential. The performance of the proposed GRU approach for CACC is verified by benchmarking with the optimal performance obtained off-line using DP in several driving missions

A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

MPC-Based Cooperative Longitudinal Control for Vehicle Strings in a Realistic Driving Environment

This paper deals with the energy efficiency of cooperative cruise control technologies when considering vehicle strings in a realistic driving environment. In particular, we design a cooperative longitudinal controller using a state-of-the-art model predictive control (MPC) implementation. Rather than testing our controller on a limited set of short maneuvers, we thoroughly assess its performance on a number of regulatory drive cycles and on a set of driving missions of similar length that were constructed based on real driving data. This allows us to focus our assessment on the energetic aspects in addition to testing the controller’s robustness. The analyzed controller, based on linear MPC, uses vehicle sensor data and information transmitted by the vehicle driving the string to adjust the longitudinal trajectory of the host vehicle to maintain a reduced inter-vehicular distance while simul- taneously optimizing energy efficiency. To keep our controller as close as possible to a real-life deployable technology, we also consider passenger comfort in our MPC design, which is a relevant aspect that is often a conflicting objective with respect to energy efficiency. Our simulation scenario is characterized by a homogeneous string of three battery electric vehicles and was modelled in a MATLAB/Simulink environment. An extensive set of simulation experiments forms the basis for our discussion on the energy-saving potential of cooperative driving automation systems