Optimization-Driven Powertrain-Oriented Adaptive Cruise Control to Improve Energy Saving and Passenger Comfort

Assessing the potential of advanced driver assistance systems requires developing dedicated control algorithms for controlling the longitudinal speed of automated vehicles over time. In this paper, a multiobjective off-line optimal control approach for planning the speed of the following vehicle in adaptive cruise control (ACC) driving is proposed. The implemented method relies on the principle of global optimality fostered by dynamic programming (DP) and aims to minimize propelling energy consumption and enhance passenger comfort. The powertrain model and onboard control system are integrated within the proposed car-following optimization framework. The retained ACC approach ensures that the distance between the following vehicle and the preceding vehicle is always maintained within allowed limits. The flexibility of the proposed method is demonstrated here through ease of implementation on a wide range of powertrain categories, including a conventional vehicle propelled by an internal combustion engine solely, a pure electric vehicle, a parallel P2 hybrid electric vehicle (HEV) and a power-split HEV. Moreover, different driving conditions are considered to prove the effectiveness of the proposed optimization-driven ACC approach. Obtained simulation results suggest that up to 22% energy-saving and 48% passenger comfort improvement might be achieved for the ACC-enabled vehicle compared with the preceding vehicle by implementing the proposed optimization-driven ACC approach. Engineers may adopt the proposed workflow to evaluate corresponding real-time ACC approaches and assess optimal powertrain design solutions for ACC driving

Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health

Dynamic programming (DP) is currently the reference optimal energy management approach for hybrid electric vehicles (HEVs). However, several research concerns arise regarding the effective application of DP for optimal HEV control problems which involve a significant number of control variables, state variables and optimization constraints. This paper deals with an optimal control problem for a full parallel P2 HEV with constraints on battery state-of-charge (SOC), battery lifetime in terms of state-of-health (SOH), and smooth driving in terms of the frequencies of internal combustion engine (ICE) activations and gear shifts over time. The DP formulation for the considered HEV control problem is outlined, yet its practical application is demonstrated as unfeasible due to a lack of computational power and memory in current desktop computers. To overcome this drawback, a computationally efficient version of DP is proposed which is named Slope-weighted Rapid Dynamic Programming (SRDP). Computational advantage is achieved by SRDP in considering only the most efficient HEV powertrain operating points rather than the full set of control variable values at each time instant of the drive cycle. A benchmark study simulating various drive cycles demonstrates that the introduced SRDP can achieve compliance with imposed control constraints on battery SOC, battery SOH and smooth driving. At the same time, SRDP can achieve up to 78% computational time saving compared with a baseline DP approach considering the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). On the other hand, the increase in the fuel consumption estimated by SRDP is limited within 3.3% compared with the baseline DP approach if the US06 Supplemental Federal Test Procedure is considered. SRDP could thus be exploited to efficiently explore the large design space associated to HEV powertrains

Planning The Velocity of a Parallel Hybrid Electric in Vehicle-to-vehicle Autonomous Driving: an Optimization-based Approach

Improved numerical tools are required to foster flexible and effective advancement of innovative electrified and highly automated road vehicles. This paper proposes an optimization-based approach to off-line plan the longitudinal velocity of a hybrid electric vehicle (HEV) when travelling as Ego vehicle in a vehicle-to-vehicle (V2V) autonomous driving scenario. A parallel P2 hybrid powertrain layout is retained along with the corresponding on-board supervisory controller. A mathematical formulation for the optimal V2V autonomous driving control problem is provided and consequently solved with an optimization method based on dynamic programming (DP). The implemented DP formulation particularly exploits information about the overall longitudinal speed profile of a Lead vehicle in a predefined driving mission to determine the velocity profile of the Ego vehicle. Optimization constraints involve maintaining the inter-vehicular distance value within allowed limits while aiming at minimizing both the magnitude of Ego vehicle acceleration events and the overall Ego vehicle fuel consumption as predicted according to the on-board hybrid supervisory control logic. Simulation results for different driving missions demonstrate that, using the proposed DP formulation, the Ego vehicle can achieve both smoother speed profiles and improved fuel economy by some percentage points in V2V autonomous driving compared to the retained Lead vehicle embedding the same HEV powertrain layout

Next Generation HEV Powertrain Design Tools: Roadmap and Challenges

Hybrid electric vehicles (HEVs) represent a fundamental step in the global evolution towards transportation electrification. Nevertheless, they exhibit a remarkably complex design environment with respect to both traditional internal combustion engine vehicles and battery electric vehicles. Innovative and advanced design tools are therefore crucially required to effectively handle the increased complexity of HEV development processes. This paper aims at providing a comprehensive overview of past and current advancements in HEV powertrain design methodologies. Subsequently, major simplifications and limits of current HEV design methodologies are detailed. The final part of this paper defines research challenges that need accomplishment to develop the next generation HEV architecture design tools. These particularly include the application of multi-fidelity modeling approaches, the embedded design of powertrain architecture and on-board control logic and the endorsement of multi-disciplinary optimization procedures. Resolving these issues may indeed remarkably foster the widespread adoption of HEVs in the global vehicle market

Hydraulic Brake Systems for Electrified Road Vehicles: A Down-sizing Approach

Down-sizing hydraulic brake systems can is made possible in electrified road vehicles thanks to the braking torque contribution provided by electric machines. Benefits in terms of weight and cost of the system can be ensured in this way. Nevertheless, appropriate care should be taken not to excessively deteriorate the overall electrical energy recovery capability of the electrified vehicle during braking maneuvers. For this reason, a multi-target optimization framework is developed in this paper to down-size hydraulic brake systems for electrified road vehicles while simultaneously maximizing the braking energy recovery capability of the electrified powertrain. Firstly, hydraulic brake system, electrified powertrain and vehicle chassis are modeled in a dedicated simulation platform. Subsequently, particle-swarm optimization is employed as search algorithm to identify optimal sizing parameters for the hydraulic brake system. Sizing variables particularly include diameter and stroke of the master cylinder, electrically assisted booster diameter, front brake piston diameter and rear brake piston diameter. The simulation of homologation tests for safety standards ensures that retained combinations of sizing parameters complies with regulatory requirements. A case study proves that the developed methodology is flexible and effective at rapidly producing several sub-optimal sizing options for both front-wheel drive and rear-wheel drive layouts for a retained battery electric vehicle

Optimal Computer-aided Engineering of Propulsion and Brake Systems for Electrified and Automated Road Vehicles

L'abstract è presente nell'allegato / the abstract is in the attachmen

Predictive Control Framework for Thermal Management of Automotive Fuel Cell Systems at High Ambient Temperatures

Environmental conditions have a significant effect on the performance of fuel cell systems. This paper studies the vehicle hydrogen consumption, the thermal management system, and the thermal loads of an automotive fuel cell system. A predictive control framework for thermal management is investigated to minimize the overall hydrogen consumption. Initially, a numerical modeling approach for the automotive fuel cell system is presented from electrochemical and thermal perspectives. Then, the problem formulation related to the thermal management strategy is presented and solved with an optimization method based on dynamic programming (DP). The implemented DP exploits the a priori knowledge of the driving mission to appropriately control the fuel cell system gross power and the operation of the radiator fan, the coolant pump, and the compressor. Optimization constraints involve maintaining the fuel cell stack temperature below the operational limit and avoiding the thermal system from being activated when the vehicle is at rest. The fuel cell system is tested while the vehicle performs different numbers of repetitions of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) at high ambient temperature. Using the proposed predictive control framework for thermal management, results demonstrate that an average 62.5% to 63.0% efficiency can be attained by the fuel cell stack in extreme ambient conditions both in short distance and long distance driving missions

Impact of Predictive Battery Thermal Management for a 48V Hybrid Electric Vehicle

Overheating of battery packs in electrified vehicles is detrimental to their lifetime and performance. Unfortunately, designing a control strategy that ensures battery protection without jeopardizing fuel economy is not a straightforward task. In this paper, we investigate battery temperature-sensitive optimal energy management for a 48V mild-hybrid electric vehicle to prevent overheating with minimal fuel consumption increase. Indeed, this family of hybrid architectures is challenging due to the absence of an active cooling system.In particular, we modeled a p0 parallel-hybrid with a 48V battery pack and we employed dynamic programming to numerically investigate the fuel economy capability while tracking the battery pack temperature.First, we tuned a battery current-constrained powertrain control strategy in order to avoid battery overheating, which could be easily implemented on-board. Then, we implemented a predictive temperature-constrained strategy that exploits the a priori knowledge of driving conditions and temperature constraints to maximize fuel economy.Results show that both strategies are able to meet the battery temperature constraints, although the predictive temperature-constrained control strategy outperforms the current-constrained strategy in terms of fuel economy. This case study demonstrates the theoretical benefits of a predictive battery thermal management for 48V mild hybrids

Identifying Critical Use Cases for a Plug-in Hybrid Electric Vehicle Battery Pack from Thermal and Ageing Perspectives

The current trend towards an increasing electrification of road vehicles brings to life a whole series of unprecedent design issues. Among these, the ageing process that affects the lifetime of lithium-ion based energy storage systems is of particular importance since it turns out to be extremely sensitive to the variation of battery operating conditions normally occurring especially in hybrid electric vehicles (HEVs). This paper aims at analyzing the impact of operating conditions on the predicted lifetime of a parallel-through-the-road plug-in HEV battery both from thermal and ageing perspectives. The retained HEV powertrain architecture is presented first and modeled, and the related energy management system is implemented. Dedicated numerical models are also discussed for the high-voltage battery pack that allow predicting its thermal behavior and cyclic ageing. A wide variety of operating conditions is subsequently simulated including different driving scenarios, ambient temperatures, vehicle payloads, and battery state-of-charge (SOC) conditions. Obtained results highlight considerable impacts of the HEV operating conditions on the battery lifetime even in the advised operating temperature interval ranging from 15°C to 35°C. Moreover, charge-depleting HEV operation and high ambient temperature are identified as the most influencing conditions concerning the criticality of the use case. On the other hand, vehicle payload and specific driving scenario appear to have a reduced impact. Presented results might help engineers to improve the effectiveness of current high-voltage battery temperature control systems to extend the battery lifetime while ensuring improved energy economy

Optimal Energy Saving Adaptive Cruise Control in Overtaking Scenarios for a Hybrid Electric Vehicle

The overtaking planning problem plays a crucial role to foster the adaptive cruise control (ACC) technology. It reveals extremely challenging due to critical requirements on the real-time capability of the control system and on conflicting objectives for the longitudinal speed trajectory generated over time for the Following Vehicle (e.g. in terms of maneuver efficiency, passenger comfort, energy economy). In this paper, an approach to solve this problem is proposed by developing an optimal energy saving oriented ACC algorithm for overtaking scenarios considering a hybrid electric vehicle (HEV) as the Following Vehicle. An off-line optimization based on Dynamic Programming (DP) is implemented. The proposed DP formulation aims at controlling the Following Vehicle longitudinal jerk over time to minimize the overall HEV energy consumption throughout the overtaking maneuver. Optimization constraints are considered for the inter-vehicular distance between Leader Vehicle and Following vehicle over time, and for the operational limits of the HEV powertrain components. The developed ACC algorithm is demonstrated achieving up to 4.1% energy saving and significant improvements in terms of passenger comfort in different overtaking scenarios