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Vehicles
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Battery Management of Hybrid Electric Vehicles
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Battery Management of Hybrid Electric Vehicles

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: closed (10 October 2024) | Viewed by 4113

Special Issue Editor

Dr. Yiqun Liu

E-Mail Website
Guest Editor
College of Engineering Technology, Ferris State University, Big Rapids, MI, USA
Interests: lithium-ion battery; vehicle electrification; hybrid powertrain; internal combustion engine

Special Issue Information

Dear Colleagues,

Today, hybrid electric vehicles at all electrification levels (12V micro-hybrid with engine start/stop, 48V mild-hybrid, full-hybrid, plug-in hybrid, and pure-electric) are of great interest in the automotive industry, since they have better fuel economy, produces less emissions, and possess more refined drivability. As a major energy storage device in hybrid electric vehicles, the battery plays an important role in a vehicle’s energy consumption, performance, safety, and drivability. To ensure the onboard battery works under the optimal conditions, the battery management system continuously monitors, reports, and controls the voltage, current, temperature, state of charge, and state of cell balance, as well as protects the onboard battery from operating outside of its safe operating area. The battery management system improves the performance, prolongs the life cycle, and reduces the risk of failure of the onboard battery of hybrid electric vehicles.

For this Special Issue of Vehicles, entitled “Battery Management of Hybrid Electric Vehicles”, we are seeking original contributions within this research area. Topics include, but are not limited to, battery management system design for hybrid electric vehicles, the estimation of the state (state-of-charge, state-of-energy, state-of-power, state-of-function, state-of-health, remaining useful life, remaining discharge time, state-of-balance, and state-of-temperature) of the onboard battery pack of hybrid electric vehicles, battery modeling, and powertrain control optimization for hybrid electric vehicles.

Dr. Yiqun Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Vehicles is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • battery management system design
  • battery state estimation
  • hybrid electric powertrain optimization
  • onboard battery modeling

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Published Papers (3 papers)

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Research

25 pages, 4546 KiB  
Article
Improving the Efficiency of Electric Vehicles: Advancements in Hybrid Energy Storage Systems
by Mostafa Farrag, Chun Sing Lai, Mohamed Darwish and Gareth Taylor
Vehicles 2024, 6(3), 1089-1113; https://doi.org/10.3390/vehicles6030052 - 28 Jun 2024
Cited by 1 | Viewed by 983
Abstract
Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in [...] Read more.
Electric vehicles (EVs) encounter substantial obstacles in effectively managing energy, particularly when faced with varied driving circumstances and surrounding factors. This study aims to evaluate the performance of three different control systems in a fully operational hybrid energy storage system (HESS) installed in the Nissan Leaf. The objective is to improve the performance of EVs by focusing on optimising energy management in response to different global environmental and driving circumstances. This study utilises an analytical strategy by developing a distinct energy management system model using MATLAB/Simulink. This model is specifically designed for optimising the integration and control of batteries and supercapacitors (SCs) in a fully active HESS. This model mimics the performance of the controllers under three different driving cycles—Artemis rural, Artemis motorway, and US06. The findings demonstrate notable progress in managing the battery state of charge (SOC) and the system’s responsiveness, especially when employing the radial basis function (RBF) controller. This study emphasises the capacity of HESSs to enhance the effectiveness and durability of EVs, therefore promoting wider acceptance and progress in electric transportation technology. Full article
(This article belongs to the Special Issue Battery Management of Hybrid Electric Vehicles)
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18 pages, 3231 KiB  
Article
Battery Management for Improved Performance in Hybrid Electric Vehicles
by Carlos Armenta-Déu
Vehicles 2024, 6(2), 949-966; https://doi.org/10.3390/vehicles6020045 - 31 May 2024
Cited by 1 | Viewed by 1057
Abstract
This study aims to improve the battery performance in hybrid electric vehicles (HEVs) by reducing the vehicle speed. We developed a specific protocol for managing battery use and optimizing the energy consumption rate to achieve this goal. The protocol automatically controls the driving [...] Read more.
This study aims to improve the battery performance in hybrid electric vehicles (HEVs) by reducing the vehicle speed. We developed a specific protocol for managing battery use and optimizing the energy consumption rate to achieve this goal. The protocol automatically controls the driving operation, avoiding incompatible driving patterns with an energy-saving mode and performance improvement. This protocol was applied to a simulation process to predict energy rate lowering and battery performance enhancement. The proposed protocol applies to any hybrid electric vehicle type and any route conditions since it uses vehicle mass, drag and rolling coefficients, and road slope as variable parameters to determine the minimum energy consumption rate. We performed experimental tests to validate the simulation data and the proposed protocol. Furthermore, the protocol applies to variable starting vehicle speeds, from 10 to 50 km/h, corresponding to the current driving patterns, sport, normal, and eco, set up by car manufacturers. A reduction of 10% in vehicle speed in urban and peripheral routes achieves a minimum energy rate, enhancing battery management. Current vehicle speed shows a deviation from optimum management of 18% while applying vehicle speed reduction limits the deviation to 0.2%. Experimental results show a good agreement with simulation data, with 94% accuracy. We tested the protocol for urban and peripheral routes with maximum vehicle speed limits of 60 and 90 km/h. Full article
(This article belongs to the Special Issue Battery Management of Hybrid Electric Vehicles)
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15 pages, 3411 KiB  
Article
Random Forest-Based Grouping for Accurate SOH Estimation in Second-Life Batteries
by Joelton Deonei Gotz, José Rodolfo Galvão, Fernanda Cristina Corrêa, Alceu André Badin, Hugo Valadares Siqueira, Emilson Ribeiro Viana, Attilio Converti and Milton Borsato
Vehicles 2024, 6(2), 799-813; https://doi.org/10.3390/vehicles6020038 - 30 Apr 2024
Viewed by 1038
Abstract
Retired batteries pose a significant current and future challenge for electric mobility due to their high cost and the need for a state of health (SOH) above 80% to supply energy efficiently. Recycling and alternative applications are the primary options for these batteries, [...] Read more.
Retired batteries pose a significant current and future challenge for electric mobility due to their high cost and the need for a state of health (SOH) above 80% to supply energy efficiently. Recycling and alternative applications are the primary options for these batteries, with recycling still undergoing research as regards more efficient and cost-effective techniques. While advancements have been made, researchers are actively seeking improved methods. Repurposing retired batteries for lower-performance applications like stationary systems or low-speed vehicles is recommended. Second-life batteries (SLB) can be directly reused or reconstructed, with the latter involving the disassembly, measurement, and separation of cells based on their characteristics. The traditional measurement process, involving full charge and discharge cycles, is time-consuming. To address this, a Machine Learning (ML)-based SOH estimator is introduced in this work, offering the instant measurement and estimation of battery health without complete discharge. The results indicate that the model can accurately identify SOH within a nominal capacity range of 1400–2300 mAh, with a resolution near 45.70 mAh, in under five minutes of discharging. This innovative technique could be instrumental in selecting and assembling SLB packs. Full article
(This article belongs to the Special Issue Battery Management of Hybrid Electric Vehicles)
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