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Energy Management Control and Optimization for Hybrid Electric Vehicles

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 37595

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Prof. Dr. Juan P. Torreglosa

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Department of Electrical Engineering, Escuela Técnica Superior de Ingeniería, Universidad de Huelva, Campus El Carmen, Avda. de las Fuerzas Armadas, s/n, 21007 Huelva, Spain
Interests: energy management systems; control systems; microgrids; transportation electrification; charging infrastructures
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Dear Colleagues,

According to the International Energy Agency, about 55% of the crude oil demand is for transportation. Concerns over energy security from petroleum reserves and the effect of greenhouse gas emissions on global climate are driving interest in alternatives. Hybrid electric vehicles have thrived as a lucrative solution to the aforementioned problems, with their intermediate approach to achieving superior mileage and low tailpipe emission compared to conventional internal combustion engine vehicles. To achieve these advantages, it is crucial to have a real-time energy management strategy capable of coordinating the on-board power sources in order to maximize fuel economy. This Special Issue aims to address the challenges posed by energy management control and optimization in vehicle hybridization. Papers are invited that propose novel power management methods capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications to improve the powertrain efficiency at different operating conditions. Topics may include the improvement of rule-based control strategies by optimizing the design of their rules and the suitability of optimization-based methods to real-time application as well as the proposal of novel control strategies. Experimental results describing real-life applications of novel technologies are also very welcome.

Prof. Dr. Juan P. Torreglosa
Guest Editor

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Keywords

hybrid electric vehicles
energy management
control
optimization

Published Papers (13 papers)

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Editorial

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4 pages, 183 KiB

Open AccessEditorial

Energy Management Control and Optimization for Hybrid Electric Vehicles

by Juan P. Torreglosa

Appl. Sci. 2022, 12(18), 9263; https://0-doi-org.brum.beds.ac.uk/10.3390/app12189263 - 15 Sep 2022

Viewed by 975

Abstract

This Issue, Energy Management Control and Optimization for Hybrid Electric Vehicles, was set in motion over three years ago with the objective of addressing the challenges posed by energy management control and optimization in vehicle hybridization [...] Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

Research

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15 pages, 757 KiB

Open AccessArticle

Data Preparation and Training Methodology for Modeling Lithium-Ion Batteries Using a Long Short-Term Memory Neural Network for Mild-Hybrid Vehicle Applications

by Daniel Jerouschek, Ömer Tan, Ralph Kennel and Ahmet Taskiran

Appl. Sci. 2020, 10(21), 7880; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217880 - 06 Nov 2020

Cited by 7 | Viewed by 1946

Abstract

Voltage models of lithium-ion batteries (LIB) are used to estimate their future voltages, based on the assumption of a specific current profile, in order to ensure that the LIB remains in a safe operation mode. Data of measurable physical features—current, voltage and temperature—are processed using both over- and undersampling methods, in order to obtain evenly distributed and, therefore, appropriate data to train the model. The trained recurrent neural network (RNN) consists of two long short-term memory (LSTM) layers and one dense layer. Validation measurements over a wide power and temperature range are carried out on a test bench, resulting in a mean absolute error (MAE) of 0.43 V and a mean squared error (MSE) of 0.40 V². The raw data and modeling process can be carried out without any prior knowledge of LIBs or the tested battery. Due to the challenges involved in modeling the state-of-charge (SOC), measurements are used directly to model the behavior without taking the SOC estimation as an input feature or calculating it in an intermediate step. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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23 pages, 2861 KiB

Open AccessArticle

Dynamic Time-Of-Use Pricing Strategy for Electric Vehicle Charging Considering User Satisfaction Degree

by Qian Zhang, Yue Hu, Weiyu Tan, Chunyan Li and Zhuwei Ding

Appl. Sci. 2020, 10(9), 3247; https://0-doi-org.brum.beds.ac.uk/10.3390/app10093247 - 07 May 2020

Cited by 20 | Viewed by 2773

Abstract

In order to solve the problem that the static peak-valley price for electric vehicles cannot truly reflect the relationship between electricity supply and demand, as well as the fact that the low utilization rate of renewable energy in the micro-grid, a dynamic time-of-use pricing strategy for electric vehicle charging considering user satisfaction degree is proposed, to achieve the goal of friendly charging for the micro-grid. Firstly, this paper researches the travel patterns of electric vehicles to establish the grid connection scenes and predict the controllable capacity of electric vehicles. Secondly, the charging preferences of different types of users are studied, and a comprehensive satisfaction degree model is set up to obtain different users’ charging strategies. Furthermore, the paper raises a pricing strategy on account of the dispatching requirements of the micro-grid, and realizes the effective dispatch of electric vehicle charging load based on price signals. Finally, we gain the dynamic time-of-use charging price, using the strategy proposed above, and the economic benefits brought to the micro-grid and electric vehicle users are analyzed, which validates the rationality and effectiveness of the pricing strategy. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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16 pages, 5241 KiB

Open AccessArticle

Characteristics of Battery SOC According to Drive Output and Battery Capacity of Parallel Hybrid Electric Vehicle

by Insu Cho and Jinwook Lee

Appl. Sci. 2020, 10(8), 2833; https://0-doi-org.brum.beds.ac.uk/10.3390/app10082833 - 19 Apr 2020

Cited by 4 | Viewed by 3231

Abstract

To mitigate global warming caused by vehicles, emission regulations have been implemented for all automobiles. Hybrid electric vehicles (HEVs) are being designed to meet consumer demand for eco-friendly vehicles that offer increased power and improved fuel efficiency. HEVs are powered by an internal combustion engine (ICE) in combination with one or more electric motors that use electrical energy stored in a secondary battery, which is typically a lithium-based battery. With the use of such a hybrid drivetrain system, the fuel efficiency can be improved over that of conventional ICE vehicles. In this study, we conducted a vehicle-driving experiment to evaluate a transmission-mounted electric device (TMED) type parallel HEV using a chassis dynamometer and on-board diagnostics (OBD) signal-measuring equipment. In addition, we performed a numerical analysis using the CRUISE vehicle simulation code with experimental data. In our analysis, the engine output, which affects the torque of the drive motor, and the capacity (energy density) of the lithium-ion polymer battery were set as variables that affect the fuel-economy performance. As a result of this numerical analysis, a hybrid power-drivetrain model based on CRUISE was developed, and the current balance was evaluated according to the change in the battery capacity. We found that the battery state of charge (SOC) dropped because of a decrease in battery capacity. Thus, we predicted that the lithium-ion battery capacity would be reduced. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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20 pages, 6851 KiB

Open AccessArticle

Multi-Objective Optimization Study of Regenerative Braking Control Strategy for Range-Extended Electric Vehicle

by Hanwu Liu, Yulong Lei, Yao Fu and Xingzhong Li

Appl. Sci. 2020, 10(5), 1789; https://0-doi-org.brum.beds.ac.uk/10.3390/app10051789 - 05 Mar 2020

Cited by 19 | Viewed by 3685

Abstract

Currently, the researches on the regenerative braking system (RBS) of the range-extended electric vehicle (R-EEV) are inadequate, especially on the comparison and analysis of the multi-objective optimization (MOO) problem. Actually, the results of the MOO problem should be mutually independent and balanced. With the aim of guaranteeing comprehensive regenerative braking performance (CRBP), a revised regenerative braking control strategy (RRBCS) is introduced, and a method of the MOO algorithm for RRBCS is proposed to balance the braking performance (BP), regenerative braking loss efficiency (RBLE), and battery capacity loss rate (BCLR). Firstly, the models of the main components related to the RBS of the R-EEV for the calculation of optimization objectives are built in MATLAB/Simulink and AVL/Cruise. The BP, RBLE, and BCLR are selected as the optimization objectives. The non-dominated sorting genetic algorithm (NSGA-II) is applied in RRBCS to solve the MOO problem, and a group of the non-inferior Pareto solution sets are obtained. The simulation results show a clear conflict that three optimization objectives cannot be optimal at the same time. Then, we evaluate the performance of the proposed method by taking the individual with the optimal CRBP as the final optimal solution. The comparation among BP, RBLE, BCLR, and CRBP before and after optimization are analyzed and discussed. The results illustrate that characteristic parameters of RRBCS is crucial to optimization objectives. After parameters optimization, regenerative braking torque works early to increase braking energy recovery on low tire-road adhesion condition, and to reduce the battery capacity loss rate at the expense of small braking energy recovery on the medium tire-road adhesion condition. In addition, the results of the sensitivity analysis show that after parameter optimization, RRBCS is proved to perform better road adaptability regarding the distribution of solutions. These results thoroughly validate the proposed approach for multi-objective optimization of RRBCS and have a strong directive to optimize the control strategy parameters of RBS. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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25 pages, 23420 KiB

Open AccessArticle

Design and Optimization of a Dual-Input Coupling Powertrain System: A Case Study for Electric Tractors

by Tonghui Li, Bin Xie, Zhen Li and Jiakun Li

Appl. Sci. 2020, 10(5), 1608; https://0-doi-org.brum.beds.ac.uk/10.3390/app10051608 - 28 Feb 2020

Cited by 25 | Viewed by 3257

Abstract

In this study, a dual-input coupling powertrain system (DICPS) was proposed to improve the energy utilization efficiency of pure electric tractors (PETs). The working principles of the DICPS under different modes were analyzed and dynamic models were established. To study the influence of changing key parameters in the DICPS on the economic performance of PETs, a parameter-matching design method was proposed and the feasible region of the design parameters was determined according to the tractor’s dynamic performance. In addition, we put forward an energy management strategy (EMS) based on the optimal system efficiency and a dual-motor-driven electric tractor (DMET) model was built in MATLAB/Simulink. The simulation results revealed that different parameter configurations of DICPS will lead to significant changes in overall efficiency, with a maximum difference of 6.6% (under a rotary tillage cycle). We found that the optimal parameter configuration for the DMET under two typical working conditions was PDR = 0.5, k = 1.6. Compared with the single-motor powertrain system (SMPS), the DICPS with the optimal configuration of key parameters can significantly improve overall efficiency by about 9.8% (under a plowing cycle). Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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20 pages, 2780 KiB

Open AccessArticle

A New Cascaded Framework for Lithium-Ion Battery State and Parameter Estimation

by Jianwen Meng, Moussa Boukhnifer, Demba Diallo and Tianzhen Wang

Appl. Sci. 2020, 10(3), 1009; https://0-doi-org.brum.beds.ac.uk/10.3390/app10031009 - 04 Feb 2020

Cited by 14 | Viewed by 2588

Abstract

Lithium-ion battery on-line monitoring is challenging due to the unmeasurable characteristic of its internal states. Up to now, the most effective approach for battery monitoring is to apply advanced estimation algorithms based on equivalent circuit models. Besides, a usual method for estimating slowly varying unmeasurable parameters is to include them in the state vector with the zero-time derivative condition, which constitutes the so-called extended equivalent circuit model and has been widely used for the battery state and parameter estimation. Although various advanced estimation algorithms are applied to the joint estimation and dual estimation frameworks, the essence of these estimation frameworks has not been changed. Thus, the improvement of the battery monitoring result is limited. Therefore, a new battery monitoring structure is proposed in this paper. Firstly, thanks to the superposition principle, two sub-models are extracted. For the nonlinear one, an observability analysis is conducted. It shows that the necessary conditions for local observability depend on the battery current, the initial value of the battery capacity, and the square of the derivative of the open circuit voltage with respect to the state of charge. Then, the obtained observability analysis result becomes an important theoretical support to propose a new monitoring structure. Commonly used estimation algorithms, namely the Kalman filter, extended Kalman filter, and unscented Kalman filter, are selected and employed for it. Apart from providing a simultaneous estimation of battery open circuit voltage, more rapid and less fluctuating battery capacity estimation are the main advantages of the new proposed monitoring structure. Numerical studies using synthetic data have proven the effectiveness of the proposed framework. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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18 pages, 4056 KiB

Open AccessArticle

A Neural Network Fuzzy Energy Management Strategy for Hybrid Electric Vehicles Based on Driving Cycle Recognition

by Qi Zhang and Xiaoling Fu

Appl. Sci. 2020, 10(2), 696; https://0-doi-org.brum.beds.ac.uk/10.3390/app10020696 - 19 Jan 2020

Cited by 20 | Viewed by 3820

Abstract

Aiming at the problems inherent in the traditional fuzzy energy management strategy (F-EMS), such as poor adaptive ability and lack of self-learning, a neural network fuzzy energy management strategy (NNF-EMS) for hybrid electric vehicles (HEVs) based on driving cycle recognition (DCR) is designed. The DCR was realized by the method of neural network sample learning and characteristic parameter analysis, and the recognition results were considered as the reference input of the fuzzy controller with further optimization of the membership function, resulting in improvement in the poor pertinence of F-EMS driving cycles. The research results show that the proposed NNF-EMS can realize the adaptive optimization of fuzzy membership function and fuzzy rules under different driving cycles. Therefore, the proposed NNF-EMS has strong robustness and practicability under different driving cycles. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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19 pages, 5523 KiB

Open AccessArticle

Control Strategy to Regulate Voltage and Share Reactive Power Using Variable Virtual Impedance for a Microgrid

by Eder Molina, John E. Candelo-Becerra and Fredy E. Hoyos

Appl. Sci. 2019, 9(22), 4876; https://0-doi-org.brum.beds.ac.uk/10.3390/app9224876 - 14 Nov 2019

Cited by 9 | Viewed by 2047

Abstract

This paper presents a control strategy to regulate voltage and share reactive power from distributed generators in a microgrid when electric vehicles (EVs) are connected and disconnected at different nodes and times. The control strategy considers fixed and variable virtual impedances created in the microgrid (MG) when loads change (EVs are connected or disconnected). Fixed virtual impedance is related to the distance from each house to the power inverter and is used as an input of the primary control. Variable virtual impedance is associated with the distance from each EV to the power inverter and is also used as an input of the primary control. Thus, the control of the inverter seeks to regulate the voltage where the EVs create variations in the network. The results show that the control strategy regulates well the voltage of different nodes, and the reactive power is distributed to renewable generators based on the distance from the loads to the inverters. By considering the fixed and variable virtual impedances in the primary control, voltage can be regulated, assuming various consumptions of EVs in the network. This result is promising for reactive power control and sharing for each distributed generator (DG) in a microgrid where a great number of EVs affect the operation. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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23 pages, 2660 KiB

Open AccessArticle

Optimal Day-Ahead Scheduling of a Smart Micro-Grid via a Probabilistic Model for Considering the Uncertainty of Electric Vehicles’ Load

by Behnam Rasouli, Mohammad Javad Salehpour, Jin Wang and Gwang-jun Kim

Appl. Sci. 2019, 9(22), 4872; https://0-doi-org.brum.beds.ac.uk/10.3390/app9224872 - 14 Nov 2019

Cited by 20 | Viewed by 2273

Abstract

This paper presents a new model based on the Monte Carlo simulation method for considering the uncertainty of electric vehicles’ charging station’s load in a day-ahead operation optimization of a smart micro-grid. In the proposed model, some uncertain effective factors on the electric vehicles’ charging station’s load including battery capacity, type of electric vehicles, state of charge, charging power level and response to energy price changes are considered. In addition, other uncertainties of operating parameters such as market price, photovoltaic generation and loads are also considered. Therefore, various stochastic scenarios are generated and involved in a cost minimization problem, which is formulated in the form of mixed-integer linear programming. Finally, the proposed model is simulated on a typical micro-grid with two 60 kW micro-turbines, a 60 kW photovoltaic unit and some loads. The results showed that by applying the proposed model for estimation of charging station load, the total operation cost decreased. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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16 pages, 3059 KiB

Open AccessArticle

Economic Routing of Electric Vehicles using Dynamic Pricing in Consideration of System Voltage

by Hyung-Joo Lee, Hee-Jun Cha and Dongjun Won

Appl. Sci. 2019, 9(20), 4337; https://0-doi-org.brum.beds.ac.uk/10.3390/app9204337 - 15 Oct 2019

Cited by 5 | Viewed by 2359

Abstract

There is a growing market for electric vehicles (EVs) in recent years. Due to this, many studies on electric vehicles are in progress and research on charging operations for EVs are especially active. Recent research trends on electric vehicle routes rely on the stochastic modelling of various factors such as convenience of a user’s point of view, charging station (CS), location, destination, and so on. In this paper, a charging control scheme for electric vehicles is proposed from the point of view of the system operators rather than the user. From a user’s point of view, the EV route can be set up directly, but it is difficult for the system operator to directly participate in the route of the EV. In this paper, a method is proposed to indirectly change the route of the EV by changing the charging cost through real-time dynamic pricing, in order to prevent risks in the system operation due to voltage fluctuations in the system. With dynamic pricing, the voltage of the system is kept within a stable range, and the EV user sets the route with an economic benefit. The proposed scheme is verified through Dijkstra’s algorithm and a control strategy via a simulation model using MATLAB. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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Review

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25 pages, 2321 KiB

Open AccessReview

Analyzing the Improvements of Energy Management Systems for Hybrid Electric Vehicles Using a Systematic Literature Review: How Far Are These Controls from Rule-Based Controls Used in Commercial Vehicles?

by Juan P. Torreglosa, Pablo Garcia-Triviño, David Vera and Diego A. López-García

Appl. Sci. 2020, 10(23), 8744; https://0-doi-org.brum.beds.ac.uk/10.3390/app10238744 - 07 Dec 2020

Cited by 21 | Viewed by 3833

Abstract

The hybridization of vehicles is a viable step toward overcoming the challenge of the reduction of emissions related to road transport all over the world. To take advantage of the emission reduction potential of hybrid electric vehicles (HEVs), the appropriate design of their energy management systems (EMSs) to control the power flow between the engine and the battery is essential. This work presents a systematic literature review (SLR) of the more recent works that developed EMSs for HEVs. The review is carried out subject to the following idea: although the development of novel EMSs that seek the optimum performance of HEVs is booming, in the real world, HEVs continue to rely on well-known rule-based (RB) strategies. The contribution of this work is to present a quantitative comparison of the works selected. Since several studies do not provide results of their models against commercial RB strategies, it is proposed, as another contribution, to complete their results using simulations. From these results, it is concluded that the improvement of the analyzed EMSs ranges roughly between 5% and 10% with regard to commercial RB EMSs; in comparison to the optimum, the analyzed EMSs are nearer to the optimum than commercial RB EMSs. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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25 pages, 6271 KiB

Open AccessReview

Smart Power Electronics–Based Solutions to Interface Solar-Photovoltaics (PV), Smart Grid, and Electrified Transportation: State-of-the-Art and Future Prospects

by Sandra Aragon-Aviles, Ashutosh Trivedi and Sheldon S. Williamson

Appl. Sci. 2020, 10(14), 4988; https://0-doi-org.brum.beds.ac.uk/10.3390/app10144988 - 20 Jul 2020

Cited by 14 | Viewed by 3993

Abstract

The need to reduce the use of fossil fuels and greenhouse gas (GHG) emissions produced by the transport sector has generated a clear increasing trend in transportation electrification and the future of energy and mobility. This paper reviews the current research trends and future work for power electronics-based solutions that support the integration of photovoltaic (PV) energy sources and smart grid with charging systems for electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV). A compressive overview of isolated and non-isolated DC–DC converters and AC–DC converter topologies used to interface the PV-grid charging facilities is presented. Furthermore, this paper reviews the modes of operation of the system currently used. Finally, this paper explores the future roadmap of research for power electronics solutions related to photovoltaic (PV) systems, smart grid, and transportation electrification. Full article

(This article belongs to the Special Issue Energy Management Control and Optimization for Hybrid Electric Vehicles)

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