1,720,980 research outputs found
Driveability Constrained Models for Optimal Control of Hybrid Electric Vehicles
Full text linkThis work investigates the effect of three different driveability constraints on the optimal energy management strategy for a p2 parallel hybrid. Two of these constraints are used to prevent frequent gear shifting and engine start/stops, while the third is used to increase the sportiness of the vehicle by maximizing the available torque reserve at all times. The constraints are imposed by reformulating them as penalty terms to be added to the base running cost of the control strategy, which is fuel consumption. Dynamic programming, a popular optimal control technique, is then used to design the energy management strategy that minimizes the total cost. A case study is developed for a p2 parallel hybrid and simulated on a combination of the Artemis driving cycles. The impact of each driveability constraint is analyzed with respect to a set of relevant features of the control strategy, such as the choice of engine operating points and the gear shift pattern. The resulting discussion provides some useful insight for the design of real-time, rule-based control strategies
Dynamic Programming Algorithms for Optimal Control Problems in Hybrid Electric Vehicles
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Robust Modeling for Optimal Control of Parallel Hybrids With Dynamic Programming
Full text linkThe aim of this work is to provide insight and guidelines for engineers and researchers when developing hybrid
powertrain models to be employed in a dynamic programming optimal control algorithm. In particular, we focus on the advantages and disadvantages of the various control sets that can be used to characterize the power flow (e.g. the engine torque or a
torque-split coefficient).
Dynamic programming is the reference optimal control technique for hybrid electric vehicles. However, its practical implementation is not exempt from numerical issues which may hamper its accuracy. Amongst these, some are directly related to the different modeling choices that can be made when defining the system dynamics of the powertrain.
To treat these issues, we first define four relevant evaluation criteria: control bounds definition, numerical efficiency, model complexity and interpretability. Then, we introduce eight different control sets and we discuss and compare them in light of these criteria. This discussion is supported by an extensive set of numerical experiments on a p2 parallel hybrid. Finally, we revisit our analysis and simulation results to draw modeling recommendations
MPC-Based Cooperative Longitudinal Control for Vehicle Strings in a Realistic Driving Environment
No full textThis 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
DynaProg: Deterministic Dynamic Programming solver for finite horizon multi-stage decision problems
Full text linkDynaProg is an open-source MATLAB toolbox for solving multi-stage deterministic optimal decision
problems using Dynamic Programming. This class of optimal control problems can be solved with
Dynamic Programming (DP), which is a well-established optimal control technique suited for highly
non-linear dynamic systems. Unfortunately, the numerical implementation of Dynamic Programming
can be challenging and time consuming, which may discourage researchers from adopting it. The
toolbox addresses these issues by providing a numerically fast DP optimization engine wrapped in
a simple interface that allows the user to set up an optimal control problem in a straightforward yet
flexible environment, with no restrictions on the controlled system’s simulation model. Therefore, it
enables researchers to easily explore the usage of Dynamic Programming in their fields of expertise.
Thorough documentation and a set of step-by-step examples complete the toolbox, thus allowing for
easy deployment and providing insight of the optimization engine. Finally, the source code’s classoriented
design allows researchers experienced in Dynamic Programming to extend the toolbox if
needed
Improving the Feasibility of Electrified Heavy-Duty Truck Fleets with Dynamic Wireless Power Transfer
No full textThis study assesses the capabilities of dynamic wireless power transfer with respect to range extension and payload capacity of heavy-duty trucks. Currently, a strong push towards tailpipe CO2 emissions abatement in the heavy-duty transport sector by policymakers is driving the development of battery electric trucks. Yet, battery-electric heavy-duty trucks require large battery packs which may reduce the payload capacity and increase dwell time at charging stations, negatively affecting their acceptance among fleet operators. By investigating various levels of development of wireless charging technology and exploring various deployment scenarios for an electrified highway lane, the potential for a more efficient and environmentally friendly battery sizing was explored. Furthermore, the additional energy provided by the eRoad can be beneficially exploited by commercial fleet operators to extend the range of electric trucks, reduce the purchase cost by adopting rightsized battery packs and reduce dwell time. This could lead to reducing both economic and environmental costs of the fleet. In particular, a use case was developed to be representative of a long-haul delivery mission. To this end, the long-haul mission defined by the European heavy-duty CO2 regulation was modified to include the eRoad segment. The study considers a 6x2 rigid truck belonging to group 9 as defined by the EU regulation. Different payloads were also considered to reflect the inherent variability in freight activity. A parametric study was conducted by changing the travel speed during dynamic charging events and the eRoad segment’s length, which reflects current and future technology development and costs. The study was carried out by first investigating the effect of each parameter individually and then simultaneously, with the aim of optimizing the range extension or battery right-sizing as well as the delivery time of goods
Impact of Predictive Battery Thermal Management for a 48V Hybrid Electric Vehicle
Full text linkOverheating 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
Online Temperature-aware Equivalent Consumption Minimization Strategy for Mild Hybrid Electric Powertrains
Full text linkIn this work, an online energy management strategy for mild hybrid electric vehicles is developed to minimize the fuel consumption while simultaneously preventing battery overheating. Since mild hybrids are typically equipped with a passively cooled battery pack, the energy management strategy design needs to keep the battery temperature below an upper limit, preventing accelerated aging and thermal runaway. To address this issue, the equivalent consumption minimization strategy (ECMS) approach is extended to develop a real-time capable controller, termed thermal ECMS (Th-ECMS), that is sensitive to the thermal dynamics of the battery and that can enforce constraints on its temperature. The rationale for our formulation is based on Pontryagin's minimum principle from optimal control theory. The online Th-ECMS is developed on the basis of the offline version of Th-ECMS, introduced in a previous work. Exploiting the a priori knowledge of the driving mission, the offline Th-ECMS calibrates the equivalence factors and obtains the optimal solution, which is compared with the globally optimal dynamic programming solution. This offline calibration method is run on a large number of driving missions and the collected data is used to train a feed-forward neural network that estimates optimal equivalence factors as functions of the battery state of charge, battery temperature, and distance yet to travel. The trained network is then used to populate two look-up tables mapping the equivalence factors, and implementable on the vehicle electronic control unit. Finally, the online Th-ECMS obtains the equivalence factors through the look-up tables in real-time. The online strategy was tested in four different driving missions, achieving a fuel economy remarkably similar to the optimal solution and successfully avoiding battery overheating
Battery Electric Vehicle Control Strategy for String Stability based on Deep Reinforcement Learning in V2V Driving
Full text linkThis works presents a Reinforcement Learning (RL) agent to implement a Cooperative Adaptive Cruise Control (CACC) system that simultaneously enhances energy efficiency and comfort, while also ensuring string stability. CACC systems are a new generation of ACC which systems rely on the communication of the so-called egovehicle with other vehicles and infrastructure using V2V and/ or V2X connectivity. This enables the availability of robust information about the environment thanks to the exchange of information, rather than their estimation or enabling some redundancy of data. CACC systems have the potential to overcome one typical issue that arises with regular ACC, that is the lack of string stability. String stability is the ability of the ACC of a vehicle to avoid unnecessary fluctuations in speed that can cause traffic jams, dampening these oscillations along the vehicle string rather than amplifying them. In this work, a real-time ACC for a Battery Electric Vehicle, based on a Deep Reinforcement Learning algorithm called Deep Deterministic Policy Gradient (DDPG), has been developed, aiming at maximizing energy savings, and improving comfort, thanks to the exchange of information on distance, speed and acceleration through the exploitation of vehicle-to-vehicle technology (V2V). The aforementioned DDPG algorithm is also designed in order to achieve the string stability. It relies on a multi-objective reward function that is adaptive to different driving cycles. The simulation results show how the agent can obtain energy savings up to 11% comparing the first following vehicle and the Lead on standard cycles and good adaptability to driving cycles different from the training one
Battery temperature aware equivalent consumption minimization strategy for mild hybrid electric vehicle powertrains
Full text linkAn energy management strategy for mild hybrids that prevents battery overheating is introduced in this digest. Energy management strategy design for mild hybrids requires particular care to prevent overheating of the battery pack as they typically do not have an active cooling system. To tackle this issue, we extend the well-known equivalent consumption minimization strategy approach to develop a real-time capable fuel-optimal controller that is sensitive to the battery’s thermal dynamics and that can enforce constraints on its temperature. The rationale for our formulation is developed using Pontryagin’s minimum principle from optimal control theory. The same principle is also used to design an off-line numerical procedure for the energy management strategy’s calibration. The effectiveness of the procedure is corroborated by numerical experiments on two different drive cycles, whose results are also compared with the solution obtained with a dynamic programming algorithm. Several peculiar aspects of our solution procedure, such as the method used to incorporate state constraints and the approximate boundary value problem solution method using a particle swarm optimization algorithm, are also detailed and discussed. The proposed controller is computationally light-weight and can be readily extended to on-line control provided that a suitable co-state selection procedure is employed, based on the data collected by using our calibration method on a large number of driving missions
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