Energies

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22 pages, 2464 KiB

Open AccessArticle

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

by Fabrizio Donatantonio, Alessandro Ferrara, Pierpaolo Polverino, Ivan Arsie and Cesare Pianese

Energies 2022, 15(6), 1972; https://doi.org/10.3390/en15061972 - 8 Mar 2022

Cited by 16 | Viewed by 2549

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Modeling and Control of Hybrid Vehicles)

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15 pages, 7287 KiB

Open AccessArticle

Optimal Modulation of Regenerative Braking in Through-The-Road Hybridized Vehicles

by Gianfranco Rizzo, Francesco Antonio Tiano, Valerio Mariani and Matteo Marino

Energies 2021, 14(20), 6835; https://doi.org/10.3390/en14206835 - 19 Oct 2021

Cited by 10 | Viewed by 3128

Abstract

Regenerative braking can significantly improve the energy efficiency of hybrid and electric vehicles, and many studies have been carried out in order to improve and optimize the energy recovery of the braking energy. In the paper, the optimization of regenerative braking by means [...] Read more.

Regenerative braking can significantly improve the energy efficiency of hybrid and electric vehicles, and many studies have been carried out in order to improve and optimize the energy recovery of the braking energy. In the paper, the optimization of regenerative braking by means of braking force modulation is analysed, with specific application to the case of cars converted into Through-the-road (TTR) hybrid vehicles, and an optimal modulation strategy is also proposed. Car hybridization is an emerging topic since it may be a feasible, low-cost, intermediate step toward the green transition of the transport system with a potential positive impact in third-world countries. In this case, the presence of two in-wheel-motors installed on the rear axle and of the original mechanical braking system mounted on the vehicle can result in limited braking energy recovery in the absence of proper braking management strategies. A vehicle longitudinal model has been integrated with an algorithm of non-linear constrained optimization to maximize the energy recovery for various starting speed and stopping time, also considering the efficiency map and power limitations of the electric components. In the best conditions, the recovery can reach about 40% of the vehicle energy, selecting the best deceleration at each speed and proper modulation, and with a realistic estimate of the grip coefficient. Full article

(This article belongs to the Special Issue Modeling and Control of Hybrid Vehicles)

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15 pages, 2044 KiB

Open AccessArticle

Modeling Electric Vehicle Charging Demand with the Effect of Increasing EVSEs: A Discrete Event Simulation-Based Model

by Neil Stephen Lopez, Adrian Allana and Jose Bienvenido Manuel Biona

Energies 2021, 14(13), 3734; https://doi.org/10.3390/en14133734 - 22 Jun 2021

Cited by 19 | Viewed by 3755

Abstract

Electric vehicle (EV) use is growing at a steady rate globally. Many countries are planning to ban internal combustion engines by 2030. One of the key issues needed to be addressed before the full-scale deployment of EVs is ensuring energy security. Various studies [...] Read more.

Electric vehicle (EV) use is growing at a steady rate globally. Many countries are planning to ban internal combustion engines by 2030. One of the key issues needed to be addressed before the full-scale deployment of EVs is ensuring energy security. Various studies have developed models to simulate and study hourly electricity demand from EV charging. In this study, we present an improved model based on discrete event simulation, which allows for modeling characteristics of individual EV users, including the availability of electric vehicle supply equipment (EVSE) outside homes and the charging threshold of each EV user. The model is illustrated by simulating 1000 random electric vehicles generated using data from an actual survey. The results agree with previous studies that daily charging demands do not significantly vary. However, the results show a significant shift in charging schedule during weekends. Moreover, the simulation demonstrated that the charging peak demand can be reduced by as much as 11% if EVSEs are made more available outside homes. Interestingly, a behavioral solution, such as requiring users to fully utilize their EV’s battery capacity, is more effective in reducing the peak demand (14–17%). Finally, the study concludes by discussing a few potential implications on electric vehicle charging policy. Full article

(This article belongs to the Special Issue Modeling and Control of Hybrid Vehicles)

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11 pages, 3022 KiB

Open AccessArticle

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

by Pierpaolo Polverino, Ivan Arsie and Cesare Pianese

Energies 2021, 14(12), 3502; https://doi.org/10.3390/en14123502 - 12 Jun 2021

Cited by 29 | Viewed by 3392

Abstract

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 [...] Read more.

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%. Full article

(This article belongs to the Special Issue Modeling and Control of Hybrid Vehicles)

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24 pages, 58253 KiB

Open AccessArticle

Online Synthesis of an Optimal Battery State-of-Charge Reference Trajectory for a Plug-in Hybrid Electric City Bus

by Jure Soldo, Branimir Škugor and Joško Deur

Energies 2021, 14(11), 3168; https://doi.org/10.3390/en14113168 - 28 May 2021

Cited by 3 | Viewed by 2193

Abstract

The powertrain efficiency of plug-in hybrid electric vehicles (PHEV) can be increased by effectively using the engine along the electric motor to gradually discharge the battery throughout a driving cycle. This sets the requirement of the optimal shaping of the battery state-of-charge (SoC) [...] Read more.

The powertrain efficiency of plug-in hybrid electric vehicles (PHEV) can be increased by effectively using the engine along the electric motor to gradually discharge the battery throughout a driving cycle. This sets the requirement of the optimal shaping of the battery state-of-charge (SoC) reference trajectory. The paper deals with the online synthesis of the optimal SoC reference trajectory, which inherently includes adaptive features in relation to the prediction of upcoming driving cycle features such as the trip distance, the road grade profile, the mean vehicle velocity and the mean demanded power. The method performs iteratively, starting from an offline-synthesized SoC reference trajectory obtained based on dynamic programming (DP) control variable optimization results. The overall PHEV control strategy incorporating the proposed online SoC reference trajectory synthesis method is verified against the DP benchmark and different offline synthesis methods. For this purpose, a model of a PHEV-type city bus is used and simulated over a wide range of driving cycles and conditions including varying road grade and low-emission zones (LEZ). Full article

(This article belongs to the Special Issue Modeling and Control of Hybrid Vehicles)

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30 pages, 2627 KiB

Open AccessArticle

Time-Optimal Low-Level Control and Gearshift Strategies for the Formula 1 Hybrid Electric Powertrain

by Camillo Balerna, Marc-Philippe Neumann, Nicolò Robuschi, Pol Duhr, Alberto Cerofolini, Vittorio Ravaglioli and Christopher Onder

Energies 2021, 14(1), 171; https://doi.org/10.3390/en14010171 - 31 Dec 2020

Cited by 9 | Viewed by 7792

Abstract

Today, Formula 1 race cars are equipped with complex hybrid electric powertrains that display significant cross-couplings between the internal combustion engine and the electrical energy recovery system. Given that a large number of these phenomena are strongly engine-speed dependent, not only the energy [...] Read more.

Today, Formula 1 race cars are equipped with complex hybrid electric powertrains that display significant cross-couplings between the internal combustion engine and the electrical energy recovery system. Given that a large number of these phenomena are strongly engine-speed dependent, not only the energy management but also the gearshift strategy significantly influence the achievable lap time for a given fuel and battery budget. Therefore, in this paper we propose a detailed low-level mathematical model of the Formula 1 powertrain suited for numerical optimization, and solve the time-optimal control problem in a computationally efficient way. First, we describe the powertrain dynamics by means of first principle modeling approaches and neural network techniques, with a strong focus on the low-level actuation of the internal combustion engine and its coupling with the energy recovery system. Next, we relax the integer decision variable related to the gearbox by applying outer convexification and solve the resulting optimization problem. Our results show that the energy consumption budgets not only influence the fuel mass flow and electric boosting operation, but also the gearshift strategy and the low-level engine operation, e.g., the intake manifold pressure evolution, the air-to-fuel ratio or the turbine waste-gate position. Full article

(This article belongs to the Special Issue Modeling and Control of Hybrid Vehicles)

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21 pages, 12393 KiB

Open AccessArticle

Fast and Robust Hybrid Starter and Generator Speed Control for Improving Drivability of Parallel Hybrid Electric Vehicles

by ByungHoon Yang, KyoungJoo Kim and HyungSoo Mok

Energies 2020, 13(19), 5055; https://doi.org/10.3390/en13195055 - 25 Sep 2020

Cited by 1 | Viewed by 2399

Abstract

Speed control algorithms were studied to improve vehicle fuel economy and driving performance by rapidly combining two power sources—the engine and the driving motor. A hybrid starter and generator (HSG) was used in parallel hybrid vehicles, improving vehicle drive system efficiency by eliminating [...] Read more.

Speed control algorithms were studied to improve vehicle fuel economy and driving performance by rapidly combining two power sources—the engine and the driving motor. A hybrid starter and generator (HSG) was used in parallel hybrid vehicles, improving vehicle drive system efficiency by eliminating torque converters. The proposed zero-overshoot and zero-phase-error speed controller with active damping has the following three characteristics. First, it has an active damping structure resistant to load fluctuations (e.g., cranking torque fluctuation during engine starting). Second, there is no speed overshoot for the step command corresponding to the minimum engine running speed. Finally, it has no steady-state error for the ramp command generated by the moving vehicle. These control features reduce the time required to match the speeds of the two power sources, reducing delay when the vehicle starts and reducing energy consumption by minimizing unnecessary engine rotation. Simulation and vehicle test results proved that the proposed algorithm produced faster response characteristics and smaller steady-state errors than conventional control algorithms such as proportional-integral, integral-proportional, and two-degree-of-freedom algorithms. In this study, the fuel efficiency and driving performance of the hybrid vehicle could be improved by improving the performance of the speed control alone without any additional hardware changes. Full article

(This article belongs to the Special Issue Modeling and Control of Hybrid Vehicles)

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21 pages, 5381 KiB

Open AccessArticle

On the Hybridization of Microcars with Hybrid UltraCapacitors and Li-Ion Batteries Storage Systems

by Fernando Ortenzi, Natascia Andrenacci, Manlio Pasquali and Carlo Villante

Energies 2020, 13(12), 3230; https://doi.org/10.3390/en13123230 - 22 Jun 2020

Cited by 8 | Viewed by 2357

Abstract

The objective proposed by the EU to drastically reduce vehicular CO₂ emission for the years up to 2030 requires an increase of propulsion systems’ efficiency, and accordingly, the improvement their technology. Hybrid electric vehicles could have a chance of achieving this, by [...] Read more.

The objective proposed by the EU to drastically reduce vehicular CO₂ emission for the years up to 2030 requires an increase of propulsion systems’ efficiency, and accordingly, the improvement their technology. Hybrid electric vehicles could have a chance of achieving this, by recovering energy during braking phases, running in pure electric mode and allowing the internal combustion engine to operate under better efficiency conditions, while maintaining traditionally expected vehicle performances (mileage, weight, available on-board volume, etc.). The energy storage systems for hybrid electric vehicles (HEVs) have different requirements than those designed for Battery Electric Vehicles (BEVs); high specific power is normally the most critical issue. Using Li-ion Batteries (LiBs) in the designing of on-board Energy Storage Systems (ESS) based only on power specifications gives an ESS with an energy capacity which is sufficient for vehicle requirements. The highest specific power LiBs are therefore chosen among those technologically available. All this leads to an ESS design that is strongly stressed over time, because current output is very high and very rapidly varies, during both traction and regeneration phases. The resulting efficiency of the ESS is correspondingly lowered, and LiBs lifetime can be relevantly affected. Such a problem can be overcome by adopting hybrid storage systems, coupling LiBs and UltraCapacitors (UCs); by properly dimensioning and controlling the ESS’ components, in fact, the current output of the batteries can be reduced and smoothed, using UCs during transients. In this paper, a simulation model, calibrated and validated on an engine testbed, has been used to evaluate the performances of a hybrid storage HEV microcar under different operative conditions (driving cycles, environment temperature and ESS State of Charge). Results show that the hybridization of the powertrain may reduce fuel consumption by up to 27%, while LiBs lifetime may be more than doubled. Full article

(This article belongs to the Special Issue Modeling and Control of Hybrid Vehicles)

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