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Energy Management Strategies for Battery and Hybrid Electric Vehicles

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 11275

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Special Issue Editors

Dr. Minghui Hu

E-Mail Website
Guest Editor

College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Interests: battery electric vehicles; hybrid electric vehicles

Dr. Chunyun Fu

E-Mail Website
Co-Guest Editor

College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Interests: intelligent and connected electric vehicles

Dr. Changzhao Liu

E-Mail Website
Co-Guest Editor

College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Interests: electromechanical transmission systems

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed a rapid growth in both the sales and demand for battery and hybrid electric vehicles worldwide. On the one hand, the energy management problem for electric vehicles remains a challenging task, especially when dealing with complex powertrain configurations. On the other hand, the advent of cutting-edge technologies, such as V2X, brings about new possibilities for further efficiency enhancement. This Special Issue aims to provide a forum for interested academics and engineers to discuss related topics, such as energy-oriented design and control methods for hybrid powertrains, as well as management and control strategies for power batteries and electric motors.

Dr. Minghui Hu
Dr. Chunyun Fu
Dr. Changzhao Liu
Guest Editors

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Keywords

energy management strategy
efficiency optimization
battery electric vehicles
hybrid electric vehicles
powertrain

Published Papers (7 papers)

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Research

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21 pages, 16055 KiB

Open AccessArticle

Optimization of Power-System Parameters and Energy-Management Strategy Research on Hybrid Heavy-Duty Trucks

by Yongjian Zhou, Rong Yang, Song Zhang, Kejun Lan and Wei Huang

Energies 2023, 16(17), 6217; https://doi.org/10.3390/en16176217 - 27 Aug 2023

Viewed by 966

Abstract

Hybrid heavy-duty trucks have attracted wide attention due to their excellent fuel economy and high mileage. For power-split hybrid heavy-duty trucks, the optimization of powertrain parameters is closely related to the control strategies of hybrid vehicles. In particular, the parameters of the powertrain system will directly affect the control of the vehicles’ power performance and economy. However, currently, research on hybrid heavy-duty trucks employing power-split configurations is lacking. Furthermore, few studies consider both the optimization of powertrain parameters and the control strategy at the same time to carry out comprehensive optimization research. In order to address these issues, this paper focuses on the fuel economy of hybrid heavy-duty trucks with power-split configurations. Improved particle swarm optimization (IPSO) and dynamic programming (DP) algorithms are introduced to optimize powertrain parameters. With these methods being applied, hybrid heavy-duty trucks show a 2.15% improvement in fuel consumption compared to that of the previous optimization. Moreover, based on the optimal powertrain parameters, a DP-based rule-control strategy (DP-RCS) and optimal DP-RCS scheme are presented and used in this paper to conduct our research. Simulation results show that the optimal DP-RCS reduces fuel consumption per hundred kilometers by 11.35% compared to the rule-based control strategy (RCS), demonstrating that the combination of powertrain parameter optimization and DP-RCS effectively improves the fuel economy of hybrid heavy-duty trucks. Full article

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

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26 pages, 9217 KiB

Open AccessArticle

An ECMS Based on Model Prediction Control for Series Hybrid Electric Mine Trucks

by Jichao Liu, Yanyan Liang, Zheng Chen and Hai Yang

Energies 2023, 16(9), 3942; https://doi.org/10.3390/en16093942 - 07 May 2023

Viewed by 1180

Abstract

This paper presents an equivalent consumption minimization strategy (ECMS) based on model predictive control for series hybrid electric mine trucks (SHE-MTs), the objective of which is to minimize fuel consumption. Two critical works are presented to achieve the goal. Firstly, to gain the real-time speed trajectory on-line, a speed prediction model is established by utilizing a recurrent neural network (RNN). Specifically, a hybrid optimization algorithm based on the genetic algorithm (GA) and the particle swarm optimization algorithm (PSOA) is used to enhance the prediction precision of the speed prediction model. Then, on this basis, an ECMS based on MPC (ECMS-MPC) is proposed. In this process, to improve the real-time and working condition adaptability of the ECMS-MPC, the power-optimal fuel consumption mapping model of the range extender is established, and the equivalent factor (EF) is real-time adjusted by means of the PSOA. Finally, taking a cement mining road as the research object, the proposed strategy is simulated with the collected actual vehicle data. The experimental results indicate that the prediction precision of the proposed speed prediction model is over 98%, realizing on-line speed prediction effectively. Furthermore, compared to the existing real-time EMSs, its fuel-saving rate had an increase of more than 13%. This indicates that the designed ECMS-MPC is able to offer a novel and effective method for the on-line energy management of the SHE-MTs. Full article

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

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23 pages, 21239 KiB

Open AccessArticle

Electromechanical Coupling Dynamic Characteristics of the Dual-Motor Electric Drive System of Hybrid Electric Vehicles

by Shuaishuai Ge, Shuang Hou and Mingyao Yao

Energies 2023, 16(7), 3190; https://doi.org/10.3390/en16073190 - 31 Mar 2023

Cited by 7 | Viewed by 1823

Abstract

The electric mode is the main operational mode of dual-motor hybrid electric vehicles (HEVs), so the reliability of the dual-motor electric drive system (DEDS) is particularly important. To research the electromechanical coupling mechanism of the DEDS of HEVs, firstly, considering the time-varying mesh stiffness of gears and the nonlinear characteristics of inverters, an electromechanical coupling dynamics model of the DEDS was established, including the permanent magnet synchronous motor (PMSM) and the gear transmission system. Then, the electromechanical coupled dynamic characteristics of the DEDS in the single-motor and dual-motor drive modes were analyzed under steady-state and impact load conditions, respectively. The results show that the motor stator current frequency is modulated by the complicated gear meshing frequency, and the operation state of the gear transmission system can thus be monitored in the stator current. Impact load causes the instantaneous torsional vibration of the transmission system dominated by the first-order natural frequency, and the vibration characteristic frequency appears in the form of a side frequency in the stator current signal; moreover, compared with the single-motor drive mode, the speed synchronization error in the dual-motor drive mode will aggravate torsional vibration in the gear system. The impact energy of the gear system caused by external impact load can be suppressed by reducing the speed synchronization error. Full article

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

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21 pages, 3061 KiB

Open AccessArticle

Equivalent Consumption Minimization Strategy Based on Belt Drive System Characteristic Maps for P0 Hybrid Electric Vehicles

by Shailesh Hegde, Angelo Bonfitto, Renato Galluzzi, Luis M. Castellanos Molina, Nicola Amati and Andrea Tonoli

Energies 2023, 16(1), 487; https://doi.org/10.3390/en16010487 - 02 Jan 2023

Cited by 4 | Viewed by 1523

Abstract

A P0 system is used in hybrid automobiles to improve engine economy and performance. An essential element of the P0 system for effectively transmitting power to the drive train is the belt drive system (BDS). The features of electric machine (EM) and internal combustion engines (ICE) are taken into account by standard energy management systems, such as the equivalent consumption minimization strategy (ECMS). In order to maximize the effectiveness of the P0 system, this work provides a novel formulation of the ECMS, which considers the power loss map of the BDS in addition to the characteristic maps of EM and ICE. A test bench is built up to characterize the BDS, and it is verified using an open-loop Hardware in the Loop (HIL) in the WLTP driving cycle. To find the most appropriate equivalence factors for the ECMS, which would ordinarily be tuned through trial and error, a genetic algorithm (GA) is used. With regard to the standard ECMS, the proposed methodology intends to reduce fuel usage and CO₂ emissions. Two belts in BDS were tested in the WLTP to achieve CO₂ savings of 1.1 and 0.9 [g/km], indicating the enhancement of system performance by using the BDS power loss maps in ECMS. Full article

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

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18 pages, 4768 KiB

Open AccessArticle

Comprehensive Control Strategy of Fuel Consumption and Emissions Incorporating the Catalyst Temperature for PHEVs Based on DRL

by Guangli Zhou, Fei Huang, Wenbing Liu, Chunling Zhao, Yangkai Xiang and Hanbing Wei

Energies 2022, 15(20), 7523; https://doi.org/10.3390/en15207523 - 12 Oct 2022

Cited by 1 | Viewed by 1259

Abstract

PHEVs (plug-in hybrid electric vehicles) equipped with diesel engines have multiple model transitions in the driving cycle for their particular structure. The high frequency of start–stop of a diesel engine will increase fuel consumption and reduce the catalytic efficiency of SCR (Selective Catalyst Reduction) catalysts, which will increase cold start emissions. For comprehensive optimization of fuel consumption and emissions, an optimal control strategy of PHEVs that originated from the PER-TD3 algorithm based on DRL (deep reinforcement learning) is proposed in this paper. The priority of samples is assigned with greater sampling weight for high learning efficiency. Experimental results are compared with those of the DP (dynamic programming)-based strategy in HIL (hardware in loop) equipment. The engine fuel consumption and NO_X emissions were 2.477 L/100 km and 0.2008 g/km, nearly 94.1% and 90.1% of those of the DP-based control strategy. By contrast, the fuel consumption and NOx of DDPG (Deep Deterministic Policy Gradient)-based and TD3(Twin Delayed Deep Deterministic Policy Gradient) -based control strategy were 2.557, 0.2078, 2.509, and 0.2023, respectively. By comparative results, we can see that the comprehensive control strategy of PHEVs based on the PER-TD3 algorithm we proposed can achieve better performance with comparison to TD3-based and DDPG-based, which is the state-of-the-art strategy in DRL. The HIL-based experimental results prove the effectiveness and real-time potential of the proposed control strategy. Full article

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

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13 pages, 4549 KiB

Open AccessArticle

A Novel Intelligent Fan Clutch for Large Hybrid Vehicles

by Ruizhi Shu, Hang Gong, Guanghui Hu and Jin Huang

Energies 2022, 15(12), 4308; https://doi.org/10.3390/en15124308 - 12 Jun 2022

Cited by 2 | Viewed by 1977

Abstract

To solve the problems of complex structure, poor reliability, and low intelligence of existing fan clutches for large hybrid vehicles, this paper proposes a new adaptive shape memory alloy intelligent fan clutch for large hybrid vehicle motor cooling. Based on the pure shear shape memory alloy thermodynamic effects, the relationship between shape memory alloy spring recovery force and temperature has been established; based on the shape memory alloy spring thermal drive characteristics and clutch construction dimensions, clutch torque transmission equations have been established. The shape memory alloy fan clutch transmission characteristics were quantitatively analyzed in terms of temperature, torque, rotational speed, and slip rate. The results show that the shape memory alloy fan clutch model based on the finite element method (FEM) and the established transmission model can accurately describe the mechanical characteristics of the shape memory alloy phase change process and the clutch torque transmission characteristics. When the clutch input speed is 3000 rad/min and the temperature is 100 °C, the output torque is 19.04 N·m, the speed is 2877.2 rad/min, and the slip rate is 4.3%. Due to the shape memory effect of shape memory alloy, the clutch can intelligently adjust the fan speed by sensing the ambient temperature. A fan clutch can satisfy the heat dissipation requirement of a large hybrid vehicle’s transmission system under complicated road conditions. Full article

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

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Review

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23 pages, 5497 KiB

Open AccessReview

Energy Management Strategies for Hybrid Loaders: Classification, Comparison and Prospect

by Jichao Liu, Yanyan Liang, Zheng Chen and Wenpeng Chen

Energies 2023, 16(7), 3018; https://doi.org/10.3390/en16073018 - 25 Mar 2023

Cited by 2 | Viewed by 1458

Abstract

As one of the effective and crucial ways to achieve the energy saving and emission reduction of loaders, hybrid technology has attracted the attention of many scholars and manufacturers. Selecting an appropriate energy management strategy (EMS) is essential to reduce fuel consumption and emissions for hybrid loaders (HLs). In this paper, firstly, the application status of drivetrain configuration of HLs is presented. Then, the working condition characteristics of loaders are analyzed. On the basis of this, the configurations of HLs are classified, and the features and research status of each configuration are described. Next, taking the energy consumption optimization of HLs as the object, the implementation principle and research progress of the proposed rule strategy and optimization strategy are compared and analyzed, and the differences of existing EMSs and future development challenges are summarized. Finally, combining the advantages of intelligent control and optimal control, the future prospective development direction of EMSs for HLs is considered. The conclusions of the paper can be identified in two points: firstly, although the existing EMSs can effectively optimize the energy consumption of HLs, the dependence of the strategy on the mechanism model and the vehicle parameters can reduce the applicability of the strategy to heterogeneous HLs and the robustness to a complex working condition. Secondly, combining the advantages of intelligent control and optimal control, designing an intelligent EMS not depending on the vehicle analytical model will provide a new method for solving the energy consumption optimization problem of HL. Full article

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

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