A Comprehensive Hybrid Vehicle Model for Energetic Analyses on Different Powertrain Architectures

Cervone, Davide; Sessa, Bernardo; Arsie, Ivan; Pianese, Cesare; Polverino, Pierpaolo

doi:10.4271/2019-24-0064

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…With this architecture, pure electric drive is possible. Such configuration has been chosen instead of a P1 configuration (with the electric motor located nearby the thermal engine) since it shows a lower fuel consumption during urban driving due to higher mechanical efficiency and allows regenerative braking in any driving condition (also when the clutch is not engaged) [6]. Nevertheless, it can be stated that the algorithms proposed in this work can be applied to any architecture without loss of generality.…”

Section: Model Of the Hybrid Electric Vehiclementioning

confidence: 99%

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

et al. 2022

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Well-designed energy management strategies are essential for the good operation of Hybrid Electric Vehicles (HEVs) in terms of fuel economy and pollutant emissions reduction, regardless of the specific powertrain architecture. The goal of this paper is to propose two innovative supervisory control strategies for HEVs derived from different optimization algorithms and to assess HEVs’ fuel consumption reduction (compared to conventional vehicles). These approaches are derived from the literature and modified by the authors to present novel algorithms for the optimization problem. One is based on Dynamic Programming (DP), here referred to as the Forward Approach to Dynamic Programming (FADP) and introduces a different implementation of the DP to achieve computational and accuracy benefits. The other is based on the Equivalent Consumption Minimization Strategy (ECMS) approach, and it adapts to the latest driving conditions using information gathered in a finite-length backward-looking horizon. These techniques are used to achieve the optimal power share between the thermal engine and the battery of a parallel HEV. Their performances are compared and analysed in terms of achieved fuel economy and computational time with respect to conventional DP and Pontryagin’s Minimum Principle (PMP) approaches.

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Section: Model Of the Hybrid Electric Vehiclementioning

confidence: 99%

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

et al. 2022

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“…The equation that describes the longitudinal dynamics of the vehicle is expressed in the following [2,12]:…”

Section: Vehicle Longitudinal Dynamics Modelmentioning

confidence: 99%

“…The battery pack is made of 30 parallel branches of 24 cells in series, each with a maximum capacity of 33 Ah and an overall mass of 30 kg. The single cells are modeled accounting for an equivalent circuit representation [12], in which the output voltage is related to the ideal voltage through the internal resistance as follows:…”

Section: Battery Modelmentioning

confidence: 99%

“…Among the different procedures to solve an optimal control problem, dynamic programming (DP) allows the finding of the optimal global solution respecting multiple constraints. The main disadvantage is its heavy computational load, which increases exponentially with the number of system state and control variables [12]. DP was firstly introduced in 1957 as a solution for multi-stage decision problems in which time plays a significant role, and the order of operations may be crucial [17].…”

Section: Receding Horizon (Rh)-based Energy Management Strategy (Ems)mentioning

confidence: 99%

“…Therefore, the aforementioned techniques (i.e., DP and PMP) are implemented in offline global optimization research to achieve benchmark solutions for supporting the onboard EMS design or the powertrain system configuration/sizing [9]. Nevertheless, other optimization-based techniques, such as equivalent consumption minimization strategy (ECMS) and model predictive control (MPC), are extensively used for real-time EMS, but only a sub-optimal solution can be achieved in these cases [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon

2021

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Fuel consumption and emissions in parallel hybrid electric vehicles (HEVs) are directly linked to the way the load request to the wheels is managed between the internal combustion engine and the electric motor powered by the battery. A significant reduction in both consumption and emissions can be achieved by optimally controlling the power split on an entire driving mission (full horizon—FH). However, the entire driving path is often not predictable in real applications, hindering the fulfillment of the advantages gained through such an approach. An improvement can be achieved by exploiting more information available onboard, such as those derived from Advanced Driver Assistance Systems (ADAS) and vehicle connectivity (V2X). With this aim, the present work presents the design and verification, in a simulated environment, of an optimized controller for HEVs energy management, based on dynamic programming (DP) and receding horizon (RH) approaches. The control algorithm entails the partial knowledge of the driving mission, and its performance is assessed by evaluating fuel consumption related to a Worldwide harmonized Light vehicles Test Cycle (WLTC) under different control features (i.e., horizon length and update distance). The obtained results show a fuel consumption reduction comparable to that of the FH, with maximum drift from optimal consumption of less than 10%.

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Comprehensive model for energetic analyses of a series hybrid-electric vehicle powered by a passive Turbulent Jet Ignition engine

Frasci

Cervone²,

Nacci³

et al. 2022

Energy Conversion and Management

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A Comprehensive Hybrid Vehicle Model for Energetic Analyses on Different Powertrain Architectures

Cited by 8 publications

References 14 publications

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon

Comprehensive model for energetic analyses of a series hybrid-electric vehicle powered by a passive Turbulent Jet Ignition engine

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