Online Energy Management System (EMS) Including Engine and Catalyst Temperatures for a Parallel HEV

Maamria, Djamaleddine; Sciarretta, Antonio; Chaplais, François; Petit, Nicolas

doi:10.1016/j.ifacol.2017.08.1291

Cited by 27 publications

(12 citation statements)

References 18 publications

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“…In [20], an ECMS-like approach was used to control the battery equivalence factor and the tailpipe NO x equivalence factor, and a heuristic method was used to determine the ATS temperature equivalence factor. In [21], the ATS temperature equivalence factor was found as the dual variable of an offline optimization, and a look-up table was developed that returns its value depending on the current ATS temperature and the tailpipe NO x equivalence factor. In [22][23][24], MPC approaches were presented that optimize performance through online optimization.…”

Section: Literature Review: Optimal Powertrain Controlmentioning

confidence: 99%

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Optimal Integrated Emission Management through Variable Engine Calibration

Ritzmann

Chinellato²,

Hütter³

et al. 2021

Energies

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In this work, the potential for improving the trade-off between fuel consumption and tailpipe NOx emissions through variable engine calibration (VEC) is demonstrated for both conventional and hybrid electric vehicles (HEV). First, a preoptimization procedure for the engine operation is proposed to address the challenge posed by the large number of engine control inputs. By excluding infeasible and suboptimal operation offline, an engine model is developed that can be evaluated efficiently during online optimization. Next, dynamic programming is used to find the optimal trade-off between fuel consumption and tailpipe NOx emissions for various vehicle configurations and driving missions. Simulation results show that for a conventional vehicle equipped with VEC and gear optimization run on the worldwide harmonized light vehicles test cycle (WLTC), the fuel consumption can be reduced by 5.4% at equivalent NOx emissions. At equivalent fuel consumption, the NOx emissions can be reduced by 80%. For an HEV, the introduction of VEC, in addition to the optimization of the torque split and the gear selection, drastically extended the achievable trade-off between fuel consumption and tailpipe NOx emissions in simulations. Most notably, the region with very low NOx emissions could only be reached with VEC.

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Section: Literature Review: Optimal Powertrain Controlmentioning

confidence: 99%

“…For all of the examples above, the engine calibration was considered to be fixed. While only the torque split was optimized in [18,[20][21][22][23][24], the gear selection and the engine on/off decisions were further considered in [17,19].…”

Section: Literature Review: Optimal Powertrain Controlmentioning

confidence: 99%

Optimal Integrated Emission Management through Variable Engine Calibration

Ritzmann

Chinellato²,

Hütter³

et al. 2021

Energies

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“…The Optimum Power Point Tracking strategy is primarily used for controlled output; based on slow tracking and diminished consumption; some of the methods have restricted operation. 27 Due to the vibration around maximum power point (MPP), the functional point on perturb and observe approach outcomes at loss of energy. It is not appropriate for periodically changing cases.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of distributed generation and electric vehicle charging stations‐based SPOA ² B approach

Thangaraju¹

2021

Int J of Intelligent Sys

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Demand response acts as an effectual tool to better balance electricity demand and supply in the smart grid. The response to prices and incentives is described as "changes on electricity usage from conventional consumption patterns to end-use customers." In this paper, a hybrid approach is proposed for Electric Vehicle Based Grid connected to the distribution generation (DG). The proposed system is joined performance of student psychology optimization algorithm (SPOA) and AdaBoost algorithm, thus it known as SPOA 2 B approach. The major aspire of this study is "to minimize the peak power cutoff, voltage regulation, spin reserve for making the optimization mode ideally convex, and accurate second-order conic relaxations." Additionally, the SPOA 2 B method is a proficient deal by standard business solvers with allocation models, and then acquires least annualized costs. In this study, the electric vehicle charging station (EVCS) is linked to DG, and then it gives charging service to electric vehicles (EVs). When the EV appears at the charging station, it informs the EVCS operator of their own energy requirement with anticipated departure time. Every EVCS can capture the information of all EVs using an SPOA 2 B technique. By then, the proposed system is performed on MATLAB/ Simulink site and the efficiency is compared with other processes. The number of iteration, like, 100, 250, 500,

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“…Furthermore, the cold engine and ambient temperatures affect the aftertreatment system leading to an interaction between fuel economy and emission reduction at low ambient temperatures [19]. Despite the considerable impact of engine, battery, and aftertreatment thermal management on the HEV fuel economy and efficiency, only a few references [20][21][22][23][24] have addressed integrated power and thermal management (iPTM) of HEVs.…”

Section: Introductionmentioning

confidence: 99%

Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction

Hu¹,

Amini²,

Feng³

et al. 2020

Preprint

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Connected vehicles (CVs) have situational awareness that can be exploited for control and optimization of the powertrain system. While extensive studies have been carried out for energy efficiency improvement of CVs via eco-driving and planning, the implication of such technologies on the thermal responses of CVs (including those of the engine and aftertreatment systems) has not been fully investigated. One of the key challenges in leveraging connectivity for optimization-based thermal management of CVs is the relatively slow thermal dynamics, which necessitate the use of a long prediction horizon to achieve the best performance. Long-term prediction of the CV speed, unlike the short-range prediction based on vehicle-to-infrastructure (V2I) and vehicle-tovehicle (V2V) communications-based information, is difficult and error-prone.The multiple timescales inherent to power and thermal systems call for a variable timescale optimization framework with access to short-and long-term vehicle speed preview. To this end, a model predictive controller (MPC) with a multi-range speed preview for integrated power and thermal management (iPTM) of connected hybrid electric vehicles (HEVs) is presented in this paper. The MPC is formulated to manage the power-split between the engine and the battery while enforcing the power and thermal (engine coolant and catalytic converter temperatures) constraints. The MPC exploits prediction and optimization over a shorter receding horizon and longer shrinking horizon. The vehicle speed is predicted (or planned in case of eco-driving) based on V2I communications over the shorter receding horizon. Over the longer shrinking horizon, the vehicle speed estimation is based on the data collected from the connected vehicles traveling on the same route as the ego-vehicle. Simulation results of applying the MPC over real-world urban driving cycles in Ann Arbor, MI are presented to demonstrate the effectiveness and fuel-saving potentials of the proposed iPTM strategy under the uncertainty associated with long-term predictions of the CV's speed.

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Online Energy Management System (EMS) Including Engine and Catalyst Temperatures for a Parallel HEV

Cited by 27 publications

References 18 publications

Optimal Integrated Emission Management through Variable Engine Calibration

Optimal Integrated Emission Management through Variable Engine Calibration

Optimal allocation of distributed generation and electric vehicle charging stations‐based SPOA ² B approach

Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction

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Online Energy Management System (EMS) Including Engine and Catalyst Temperatures for a Parallel HEV

Cited by 27 publications

References 18 publications

Optimal Integrated Emission Management through Variable Engine Calibration

Optimal Integrated Emission Management through Variable Engine Calibration

Optimal allocation of distributed generation and electric vehicle charging stations‐based SPOA 2 B approach

Engine and Aftertreatment Co-Optimization of Connected HEVs via Multi-Range Vehicle Speed Planning and Prediction

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Optimal allocation of distributed generation and electric vehicle charging stations‐based SPOA ² B approach