1,720,961 research outputs found
Innovative models and algorithms for the optimization of layout and control strategy of complex diesel HEVs
No full textThis study is focused on the design, optimization and analysis of non plug-in parallel and complex vehicles and on the evaluation of their potential to reduce fuel consumption and NOx emissions, in comparison with a reference vehicle. The simulated vehicles are equipped with compression ignition engines; two different engines were considered for the layout optimization process, and the related data were provided by GMPT-E (General Motors PowerTrain-Europe). A tool has been developed and employed to identify the optimal layout of each vehicle on the basis of the minimization of the overall powertrain costs during the whole vehicle life. These costs include the initial investment due to the production of the components as well as the operating costs related to fuel consumption and to battery depletion. The control strategy has been defined as the algorithm that selects the transmission gear and that manages the power to be provided by the engine and the electric machines of the power-train. In this framework, the transmission gear and the power management are the two control variables. Identification of the optimal control strategy is necessary in order to fully exploit the potential of the hybrid architecture to reduce fuel consumption and pollutant emissions. It is therefore carried out by the so-called optimizer in terms of a specific objective function. This function aims at maximizing the fuel economy with some constraints to the pollutant emissions and to the battery energy and life consumption, according to the application. To this end, two global optimizers, one of a deterministic nature and another of a stochastic type, have been developed, applied and compared. These methods are fundamental for the definition of the vehicle optimal control strategy. They are indeed referred to as benchmark optimizers. A zero-dimensional kinematic model of the vehicle has been developed in the Matlab environment in order to evaluate the evolution of the system variables, as a function of the vehicle velocity and of the control variables. A new mathematical technique has been developed and applied to the vehicle simulation model in order to decrease the computational time of the optimizers. First, the vehicle model equations were written in order to allow a coarse time grid to be used, then, the control variables were discretized, and the values of the system variables were evaluated and stored in a matrix, for all the possible combinations of control variables and for each time node, before the optimization process. However, since the benchmark optimizers are not suitable for on-board applications, one static optimizer and two different rule-based optimizers, which are referred to as real-time optimizers, have been also developed, compared to the benchmark tools and implemented in the vehicle control unit, in order to perform an on-board optimization. Usually this kind of optimization is based on heuristic techniques that may lack of performance in a broad range of applications. In this thesis, machine-learning techniques have been introduced to train the real-time tools. The training procedure that is applied to the rule-based optimizers consists of two parts: the input variable clustering and the rule definition. The vehicle velocity and power, as well as the battery state of charge, have been selected as the input variables. A clustering algorithm has been coded to discretize the input domain of the rule itself into a mesh, i.e., each combination of input variables is associated to an unique cluster. The rule connects every cluster to one of the discrete values of each control variable. Two different approaches have been followed in this study to develop the rule-based optimizers. A clustering algorithm has been developed for the first tool to generate the mesh that is associated to the rule, while genetic algorithms are applied to generate the action to take for each cluster of the mesh itself. In fact, the tool is referred to as Cluster Extracted Rule Optimized (CERO). Genetic algorithms have been instead applied for the second tool to generate the optimal mesh that is associated to the rule, while the most frequent action, in the set of actions that have been suggested by the benchmark optimizer in a cluster of the mesh, is correlated to that cluster. The tool is referred to as Cluster Optimized Rule Extracted (CORE). The vehicle control unit is required to receive the data about the instantaneous vehicle velocity, power and battery state of charge during the trip. These data are processed to identify the cluster they belong to, which is used to index the rule to extract the discrete values of the control variables. The control unit is therefore able to actuate the power-train components to drive the vehicle. The performance of the hybrid vehicles has been evaluated over several driving missions for different oriented optimizations and a detailed energetic analysis has been carried out in order to clearly identify the key operating modes that contribute most to the fuel consumption and NOx mission savings of the different hybrid architecture
Cost-optimized design of a dual-mode diesel parallel hybrid electric vehicle for several driving missions and market scenarios
Full text linkThe present study has focused on the refinement of a previously developed tool for the optimization of the layout of hybrid electric vehicles and on its application to a newly proposed non-plug in parallel hybrid vehicle, which has been equipped with a planetary gear set and a single-speed gearbox positioned between a compression ignition engine and a permanent magnet electric machine. This vehicle is capable of torque-coupling and speed-coupling between the engine and the electric machine, and for this reason has been referred to as a ‘‘dual-mode vehicle". The tool performs a bi-level (nested) coupling of design and control strategy optimization, and is able to identify the optimal design of each hybrid vehicle by minimizing the powertrain costs over a 10-year time span. The vehicle design determines the size of battery, engine and electric machine, as well as the values of the speed ratio of each power coupling device. Different powertrain cost definitions, which account for the production costs of the components and the operating costs related to fuel consumption and battery depletion over the lifetime of the vehicle, have been proposed. The latter cost contribution depends directly on the control strategy adopted to manage the power flow between the electric machine and the engine, as well as on the selection of the transmission gear. The optimal control strategy has been identified using a specifically developed fast running dynamic programming-based optimizer, which minimizes an objective function over a given training driving mission. The performance of the dual-mode vehicle with the optimal layout has been investigated in detail over several driving missions and compared with that of more traditional hybrid vehicles equipped with either a speed coupling device or with a torque coupling device, as well as with a conventional reference vehicle. Moreover, several sensitivity analyses have been carried out in order to investigate the impact of the cost definition, of the objective function and of the training driving mission on the powertrain design and on its performance (fuel economy, pollutant emissions, battery management). Finally, different market scenarios have been explored, in terms of fuel price, battery life and battery cost, and their effects on the identification of the optimal design, as well as on the performance of the resulting vehicles, have been analyze
An unsupervised machine-learning technique for the definition of a rule-based control strategy in a complex HEV
No full textAn unsupervised machine-learning technique, aimed at the identification of the optimal rule-based control strategy, has been developed for parallel hybrid electric vehicles that feature a torque-coupling (TC) device, a speed-coupling (SC) device or a dual-mode system, which is able to realize both actions. The approach is based on the preliminary identification of the optimal control strategy, which is carried out by means of a benchmark optimizer, based on the deterministic dynamic programming technique, for different driving scenarios. The optimization is carried out by selecting the optimal values of the control variables (i.e., transmission gear and power flow) in order to minimize fuel consumption, while taking into account several constraints in terms of NOx emissions, battery state of charge and battery life consumption. The results of the benchmark optimizer are then processed with the aim of extracting a set of optimal rule-based control strategies, which can be implemented onboard in real-time. The input variables of the rule-based strategy are the vehicle power demand, the vehicle speed and the state of charge of the battery. The method for the rule extraction can be summarized as follows. A clustering algorithm discretizes the input domain (in terms of vehicle power demand, vehicle speed and state of charge of the battery) into a mesh of clusters. The generic rule associated to a specific cluster (i.e., the combination of gear and power flow that has to be actuated) is identified by searching for the control strategy most frequently adopted by the benchmark optimizer within the considered cluster. The optimal mesh of clusters is generated using a genetic algorithm technique. Optimal sets of rules are identified for different driving scenarios. These strategies can then be implemented on-board, provided the mission features are known at the beginning of the trip. The main advantage of the proposed technique is that the definition of the rule-based strategy is derived from a machine learning method and is not based on heuristic techniques
Layout design and energetic analysis of a complex diesel parallel hybrid electric vehicle
No full textThe present paper is focused on the design, optimization and analysis of a complex parallel hybrid electric vehicle, equipped with two electric machines on both the front and rear axles, and on the evaluation of its potential to reduce fuel consumption and NOx emissions over several driving missions. The vehicle has been compared with two conventional parallel hybrid vehicles, equipped with a single electric machine on the front axle or on the rear axle, as well as with a conventional vehicle. All the vehicles have been equipped with compression ignition engines.
The optimal layout of each vehicle was identified on the basis of the minimization of the overall powertrain costs during the whole vehicle life. These costs include the initial investment due to the production of the components as well as the operating costs related to fuel consumption and to battery depletion.
Identification of the optimal powertrain control strategy, in terms of the management of the power flows of the engine and electric machines, and of gear selection, is necessary in order to be able to fully exploit the potential of the hybrid architecture.
To this end, two global optimizers, one of a deterministic nature and another of a stochastic type, and two real-time optimizers have been developed, applied and compared.
A new mathematical technique has been developed and applied to the vehicle simulation model in order to decrease the computational time of the optimizers. First, the vehicle model equations were written in order to allow a coarse time grid to be used, then, the control variables (i.e., power flow and gear number) were discretized, and the values of the main model variables were evaluated and stored in a matrix (referred to as configuration matrix), for all the possible combinations of control variables and for each time node, before the optimization process. In this way, the optimizers can read the actual values of the relevant variables from the pre-processed data, instead of calculating them iteratively during the optimization stage. The performance of the hybrid vehicles has been evaluated over several driving missions, including the NEDC, the FTP, the AUDC, the ARDR and the AMDC, and a detailed energetic analysis has been carried out in order to clearly identify the key operating modes that contribute most to the fuel consumption and NOx emission savings of the different hybrid architectures
Robust equivalent consumption-based controllers for a dual-mode diesel parallel HEV
No full textNew equivalent consumption minimization strategy (ECMS) tools have been developed and applied to
identify the optimal control strategy of a dual-mode parallel hybrid electric vehicle equipped with a
compression-ignition engine. In this architecture, the electric machine is coupled to the engine through
either a single-speed gearbox (torque-coupling) or a planetary gear set (speed-coupling).
One of the main novelties of the present study concerns the definition of the instantaneous equivalent
consumption (EC) function, which takes into account not only fuel consumption (FC) and the energy flow
through the electric components, but also NO
x
emissions, battery aging, and the battery SOC.
The EC function has been trained using a cross-validation machine-learning technique, based on a
genetic algorithm, where the training data set has been selected in order to maximize performances over
a testing data set. The adoption of this technique, in conjunction with the new definition of EC, have led to
the identification of very robust controllers, which provide an accurate control for different driving sce-
narios, even when the EC function is not specifically trained on the same missions over which it is tested.
To this aim, a data set of fifty driving cycles and six user-defined missions, which cover a total distance of
70–100 km, has been considered as a training driving set.
The ECMS controllers can be implemented in a vehicle control unit, and their performance has resulted
to be close to that of a dynamic programming tool, which has here been used as benchmark, over a large
set of different missions, without need for feedback control on the battery SOC or driving pattern
prediction
Offline and real-time optimization of EGR rate and injection timing in diesel engines
No full textNew methodologies have been developed to optimize EGR rate and injection timing in diesel engines, with the aim of minimizing fuel
consumption (FC) and NOx engine-out emissions.
The approach entails the application of a recently developed control-oriented engine model, which includes the simulation of the heat
release rate, of the in-cylinder pressure and brake torque, as well as of the NOx emission levels. The engine model was coupled with a
C-class vehicle model, in order to derive the engine speed and torque demand for several driving cycles, including the NEDC, FTP,
AUDC, ARDC and AMDC.
The optimization process was based on the minimization of a target function, which takes into account FC and NOx emission levels.
The selected control variables of the problem are the injection timing of the main pulse and the position of the EGR valve, which have
been considered as the most influential engine parameters on both fuel consumption and NOx emissions. The gear number has also
been selected for optimization.
One benchmark tool, which is based on the dynamic programming technique, and one real-time tool, which implements a static
optimization method, have been developed for the optimization process. A new mathematical technique has been introduced and
applied in order to decrease the computational time of the optimizers to a great extent.
It was verified that the real-time method has the potential of being implemented in the engine control unit (ECU), in order to realize an
onboard optimization of the selected engine and vehicle parameters
Optimization of the layout and control strategy for parallel through-the-road hybrid electric vehicles
No full textThis paper describes the optimization of the layout and of the control strategy of through-the-road (TTR) parallel hybrid electric vehicles equipped with two compression-ignition engines that feature different values of maximum output power.
First, a tool has been developed to define the optimal layout of each TTR vehicle. This is based on the minimization of the powertrain and fuel cost over a 10-year time span, taking into account the fuel consumption. Several performance requirements are guaranteed during the optimization, namely maximum vehicle velocity, 0-100 km/h acceleration time, gradeability and the all-electric range.
A benchmark optimizer that is based on the dynamic programming theory has been developed to identify the optimal working mode and the gear number, which are the control variables of the problem. A mathematical technique, based on the pre-processing of a configuration matrix, has been developed in order to speed up the calculation time.
After the layout optimization, the potential of the two identified hybrid vehicles in improving the fuel economy, compared with the conventional vehicle, has been analyzed and discussed over several driving missions, i.e., the New European Driving Cycle, the Artemis Urban Driving Cycle, the Artemis Rural Driving Cycle, the Artemis Motorway Driving Cycle and the Federal Test Procedure. The contributions related to the vehicle electrification and to the control strategy were identified separately.
Finally, a real-time optimizer has also been developed, which is based on the instantaneous maximization of an equivalent powertrain efficiency
Optimization of the operating strategy of a BAS hybrid diesel powertrain on type-approval and real-world representative driving cycles
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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