Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Energy Storage Systems for Electric Vehicles
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Energy Storage Systems for Electric Vehicles

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

Deadline for manuscript submissions: closed (15 March 2020) | Viewed by 97374

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Dr. Erik Schaltz

E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, DK-9220 Aalborg, Denmark
Interests: application of power electronics, electric machines, fuel cells, batteries, ultracapacitors, etc. in electric and hybrid electric vehicles; battery state-estimation, management (electric and thermal), and modelling (electric, thermal, and lifetime) of battery cells and packs
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global electric car fleet has now exceeded 5 million and will continue to increase in the future, as electrification is an important means of decreasing the greenhouse gas emissions of the transportation sector.

The energy storage system is a very central component of the electric vehicle. The storage system needs to be cost-competitive, light, efficient, safe, and reliable, and to occupy little space and last for a long time. It should also be produced and disposed of in an environmentally friendly manner. This leaves many research challenges, and the purpose of this Special Issue is therefore to provide a platform for sharing the latest findings on energy storage systems for electric vehicles (electric cars, buses, aircraft, ships, etc.).

Research in energy storage systems requires several sciences working together, and we therefore welcome contributions from many different disciplines. Topics of interest include but are not limited to the following:

  • Battery-management systems;
  • State-of-charge and state-of-health estimation;
  • Lifetime studies;
  • Thermal-battery-management systems;
  • Thermal energy storage for battery and/or cabin heating;
  • Packaging of battery cells;
  • Emerging battery technologies;
  • Alternative energy storage systems, e.g., hybrid supercapacitor, supercapacitor/battery combinations, etc;
  • Power electronics for energy storage devices;
  • Life cycle analysis;
  • Recycling of batteries.

Assoc. Prof. Erik Schaltz
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. Energies is an international peer-reviewed open access semimonthly 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 2600 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 systems
  • thermal-battery-management systems
  • state-of-charge
  • state-of-health
  • emerging batteries
  • thermal energy storage
  • life cycle analysis
  • recycling

Published Papers (25 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

31 pages, 5180 KiB  
Article
An Energy Consumption Model for Designing an AGV Energy Storage System with a PEMFC Stack
by Roman Niestrój, Tomasz Rogala and Wojciech Skarka
Energies 2020, 13(13), 3435; https://0-doi-org.brum.beds.ac.uk/10.3390/en13133435 - 03 Jul 2020
Cited by 27 | Viewed by 4201
Abstract
This article presents a methodology for building an AGV (automated guided vehicle) power supply system simulation model with a polymer electrolyte membrane fuel cell stack (PEMFC). The model focuses on selecting the correct parameters for the hybrid energy buffering system to ensure proper [...] Read more.
This article presents a methodology for building an AGV (automated guided vehicle) power supply system simulation model with a polymer electrolyte membrane fuel cell stack (PEMFC). The model focuses on selecting the correct parameters for the hybrid energy buffering system to ensure proper operating parameters of the vehicle, i.e., minimizing vehicle downtime. The AGV uses 2 × 1.18 kW electric motors and is a development version of a battery-powered vehicle in which the battery has been replaced with a hybrid power system using a 300 W PEMFC. The research and development of the new power system were initiated by the AGV manufacturer. The model-based design (MBD) methodology is used in the design and construction of a complete simulation model for the system, which consists of the fuel cell system, energy processing, a storage system, and an energy demand models. The energy demand model has been developed based on measurements from the existing AGV, and the remaining parts of the model are based on simulation models tuned to the characteristics obtained for the individual subsystems or from commonly available data. A parametric model is created with the possibility for development and determination by simulation of either the final system or from the parameters of the individual models’ elements (components of the designed system). The presented methodology can be used to develop alternative versions of the system, in particular the selection of the correct size of supercapacitors and batteries which depend on the energy demand profile and the development of the DC/DC converter and controllers. Additionally, the varying topology of the whole system was also analyzed. Minimization of downtime has been presented as one of many possible uses of the presented model. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

45 pages, 21441 KiB  
Article
Real Time Design and Implementation of State of Charge Estimators for a Rechargeable Lithium-Ion Cobalt Battery with Applicability in HEVs/EVs—A Comparative Study
by Nicolae Tudoroiu, Mohammed Zaheeruddin and Roxana-Elena Tudoroiu
Energies 2020, 13(11), 2749; https://0-doi-org.brum.beds.ac.uk/10.3390/en13112749 - 31 May 2020
Cited by 10 | Viewed by 2508
Abstract
Estimating the state of charge (SOC) of Li-ion batteries is an essential task of battery management systems for hybrid and electric vehicles. Encouraged by some preliminary results from the control systems field, the goal of this work is to design and implement in [...] Read more.
Estimating the state of charge (SOC) of Li-ion batteries is an essential task of battery management systems for hybrid and electric vehicles. Encouraged by some preliminary results from the control systems field, the goal of this work is to design and implement in a friendly real-time MATLAB simulation environment two Li-ion battery SOC estimators, using as a case study a rechargeable battery of 5.4 Ah cobalt lithium-ion type. The choice of cobalt Li-ion battery model is motivated by its promising potential for future developments in the HEV/EVs applications. The model validation is performed using the software package ADVISOR 3.2, widely spread in the automotive industry. Rigorous performance analysis of both SOC estimators is done in terms of speed convergence, estimation accuracy and robustness, based on the MATLAB simulation results. The particularity of this research work is given by the results of its comprehensive and exciting comparative study that successfully achieves all the goals proposed by the research objectives. In this scientific research study, a practical MATLAB/Simscape battery model is adopted and validated based on the results obtained from three different driving cycles tests and is in accordance with the required specifications. In the new modelling version, it is a simple and accurate model, easy to implement in real-time and offers beneficial support for the design and MATLAB implementation of both SOC estimators. Also, the adaptive extended Kalman filter SOC estimation performance is excellent and comparable to those presented in the state-of-the-art SOC estimation methods analysis. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

24 pages, 4910 KiB  
Article
Leveraging Cell Expansion Sensing in State of Charge Estimation: Practical Considerations
by Miriam A. Figueroa-Santos, Jason B. Siegel and Anna G. Stefanopoulou
Energies 2020, 13(10), 2653; https://0-doi-org.brum.beds.ac.uk/10.3390/en13102653 - 22 May 2020
Cited by 25 | Viewed by 3253
Abstract
Measurements such as current and terminal voltage that are typically used to determine the battery’s state of charge (SOC) are augmented with measured force associated with electrode expansion as the lithium intercalates in its structure. The combination of the sensed behavior is shown [...] Read more.
Measurements such as current and terminal voltage that are typically used to determine the battery’s state of charge (SOC) are augmented with measured force associated with electrode expansion as the lithium intercalates in its structure. The combination of the sensed behavior is shown to improve SOC estimation even for the lithium ion iron phosphate (LFP) chemistry, where the voltage–SOC relation is flat (low slope) making SOC estimation using measured voltage difficult. For the LFP cells, the measured force has a non-monotonic F–SOC relationship. This presents a challenge for estimation as multiple force values can correspond to the same SOC. The traditional linear quadratic estimator can be driven to an incorrect SOC value. To address these difficulties, a novel switching estimation gain is used based on determining the operating region that corresponds to the actual SOC. Moreover, a drift in the measured force associated with a shift of the cell SOC–expansion behavior over time is addressed with a bias estimator for the force signal. The performance of Voltage-based (V) and Voltage and Force-based (V&F) SOC estimation algorithms are then compared and evaluated against a desired ± 5 % absolute error bound of the SOC using a dynamic stress test current protocol that tests the proposed estimation scheme across wide range of SOC and current rates. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

13 pages, 3999 KiB  
Article
A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks
by Angelo Bonfitto
Energies 2020, 13(10), 2548; https://0-doi-org.brum.beds.ac.uk/10.3390/en13102548 - 18 May 2020
Cited by 36 | Viewed by 2603
Abstract
This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used [...] Read more.
This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the automotive sector. The promising results in terms of accuracy encourage to deepen the experimental validation with a deployment on a vehicle battery management system. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

16 pages, 2922 KiB  
Article
An Optimal Fast-Charging Strategy for Lithium-Ion Batteries via an Electrochemical–Thermal Model with Intercalation-Induced Stresses and Film Growth
by Guangwei Chen, Zhitao Liu and Hongye Su
Energies 2020, 13(9), 2388; https://0-doi-org.brum.beds.ac.uk/10.3390/en13092388 - 11 May 2020
Cited by 14 | Viewed by 2770
Abstract
Optimal fast charging is an important factor in battery management systems (BMS). Traditional charging strategies for lithium-ion batteries, such as the constant current–constant voltage (CC–CV) pattern, do not take capacity aging mechanisms into account, which are not only disadvantageous in the life-time usage [...] Read more.
Optimal fast charging is an important factor in battery management systems (BMS). Traditional charging strategies for lithium-ion batteries, such as the constant current–constant voltage (CC–CV) pattern, do not take capacity aging mechanisms into account, which are not only disadvantageous in the life-time usage of the batteries, but also unsafe. In this paper, we employ the dynamic optimization (DP) method to achieve the optimal charging current curve for a lithium-ion battery by introducing limits on the intercalation-induced stresses and the solid–liquid interface film growth based on an electrochemical–thermal model. Furthermore, the backstepping technique is utilized to control the temperature to avoid overheating. This paper concentrates on solving the issue of minimizing charging time in a given target State of Charge (SoC), while limiting the capacity loss caused by intercalation-induced stresses and film formation. The results indicate that the proposed optimal charging method in this paper offers a good compromise between the charging time and battery aging. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

18 pages, 1288 KiB  
Article
Battery-Aware Electric Truck Delivery Route Exploration
by Donkyu Baek, Yukai Chen, Naehyuck Chang, Enrico Macii and Massimo Poncino
Energies 2020, 13(8), 2096; https://0-doi-org.brum.beds.ac.uk/10.3390/en13082096 - 24 Apr 2020
Cited by 7 | Viewed by 2405
Abstract
The energy-optimal routing of Electric Vehicles (EVs) in the context of parcel delivery is more complicated than for conventional Internal Combustion Engine (ICE) vehicles, in which the total travel distance is the most critical metric. The total energy consumption of EV delivery strongly [...] Read more.
The energy-optimal routing of Electric Vehicles (EVs) in the context of parcel delivery is more complicated than for conventional Internal Combustion Engine (ICE) vehicles, in which the total travel distance is the most critical metric. The total energy consumption of EV delivery strongly depends on the order of delivery because of transported parcel weight changing over time, which directly affects the battery efficiency. Therefore, it is not suitable to find an optimal routing solution with traditional routing algorithms such as the Traveling Salesman Problem (TSP), which use a static quantity (e.g., distance) as a metric. In this paper, we explore appropriate metrics considering the varying transported parcel total weight and achieve a solution for the least-energy delivery problem using EVs. We implement an electric truck simulator based on EV powertrain model and nonlinear battery model. We evaluate different metrics to assess their quality on small size instances for which the optimal solution can be computed exhaustively. A greedy algorithm using the empirically best metric (namely, distance × residual weight) provides significant reductions (up to 33%) with respect to a common-sense heaviest first package delivery route determined using a metric suggested by the battery properties. This algorithm also outperforms the state-of-the-art TSP heuristic algorithms, which consumes up to 12.46% more energy and 8.6 times more runtime. We also estimate how the proposed algorithms work well on real roads interconnecting cities located at different altitudes as a case study. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

19 pages, 6313 KiB  
Article
Torque Coordination Control of an Electro-Hydraulic Composite Brake System During Mode Switching Based on Braking Intention
by Yang Yang, Yundong He, Zhong Yang, Chunyun Fu and Zhipeng Cong
Energies 2020, 13(8), 2031; https://0-doi-org.brum.beds.ac.uk/10.3390/en13082031 - 19 Apr 2020
Cited by 20 | Viewed by 2830
Abstract
The electro-hydraulic composite braking system of a pure electric vehicle can select different braking modes according to braking conditions. However, the differences in dynamic response characteristics between the motor braking system (MBS) and hydraulic braking system (HBS) cause total braking torque to fluctuate [...] Read more.
The electro-hydraulic composite braking system of a pure electric vehicle can select different braking modes according to braking conditions. However, the differences in dynamic response characteristics between the motor braking system (MBS) and hydraulic braking system (HBS) cause total braking torque to fluctuate significantly during mode switching, resulting in jerking of the vehicle and affecting ride comfort. In this paper, torque coordination control during mode switching is studied for a four-wheel-drive pure electric vehicle with a dual motor. After the dynamic analysis of braking, a braking force distribution control strategy is developed based on the I-curve, and the boundary conditions of mode switching are determined. A novel combined pressure control algorithm, which contains a PID (proportional-integral-derivative) and fuzzy controller, is used to control the brake pressure of each wheel cylinder, to realize precise control of the hydraulic brake torque. Then, a novel torque coordination control strategy is proposed based on brake pedal stroke and its change rate, to modify the target hydraulic braking torque and reflect the driver’s braking intention. Meanwhile, motor braking torque is used to compensate for the insufficient braking torque caused by HBS, so as to realize a smooth transition between the braking modes. Simulation results show that the proposed coordination control strategy can effectively reduce torque fluctuation and vehicle jerk during mode switching. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

19 pages, 5322 KiB  
Article
Research on the Critical Issues for Power Battery Reusing of New Energy Vehicles in China
by Zongwei Liu, Xinglong Liu, Han Hao, Fuquan Zhao, Amer Ahmad Amer and Hassan Babiker
Energies 2020, 13(8), 1932; https://0-doi-org.brum.beds.ac.uk/10.3390/en13081932 - 14 Apr 2020
Cited by 25 | Viewed by 4864
Abstract
With the rapid development of new energy vehicles (NEVs) industry in China, the reusing of retired power batteries is becoming increasingly urgent. In this paper, the critical issues for power batteries reusing in China are systematically studied. First, the strategic value of power [...] Read more.
With the rapid development of new energy vehicles (NEVs) industry in China, the reusing of retired power batteries is becoming increasingly urgent. In this paper, the critical issues for power batteries reusing in China are systematically studied. First, the strategic value of power batteries reusing, and the main modes of battery reusing are analyzed. Second, the economic benefit models of power batteries echelon utilization and recycling are constructed. Finally, the economic benefits of lithium iron phosphate (LIP) battery and ternary lithium (TL) battery under different reusing modes are analyzed based on the economic benefit models. The results show that when the industrial chain is fully coordinated, LIP battery echelon utilization is profitable based on a reasonable scenario scheme. However, the multi-level echelon utilization is only practical under an ideal scenario, and more attention should be paid to the first level echelon utilization. Besides, the performance matching of different types of batteries has a great impact on the echelon utilization income. Thus, considering the huge potentials of China’s energy storage market, the design of automobile power batteries in the future should give due consideration to the performance requirements of energy storage batteries. Moreover, the TL battery could only be recycled directly, while the LIP has the feasibility of echelon utilization at present. At the same time, it will strengthen the cost advantage of the LIP battery, which deserves special attention. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

11 pages, 3677 KiB  
Article
Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage
by Alejandro Gismero, Erik Schaltz and Daniel-Ioan Stroe
Energies 2020, 13(7), 1811; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071811 - 09 Apr 2020
Cited by 59 | Viewed by 4381
Abstract
The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be [...] Read more.
The state of charge (SOC) and state of health (SOH) are two crucial indicators needed for a proper and safe operation of the battery. Coulomb counting is one of the most adopted and straightforward methods to calculate the SOC. Although it can be implemented for all kinds of applications, its accuracy is strongly dependent on the operation conditions. In this work, the behavior of the batteries at different current and temperature conditions is analyzed in order to adjust the charge measurement according to the battery efficiency at the specific operating conditions. The open-circuit voltage (OCV) is used to reset the SOC estimation and prevent the error accumulation. Furthermore, the SOH is estimated by evaluating the accumulated charge between two different SOC using a recursive least squares (RLS) method. The SOC and SOH estimations are verified through an extensive test in which the battery is subjected to a dynamic load profile at different temperatures. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

20 pages, 1888 KiB  
Article
Analytical Solution for Coupled Diffusion Induced Stress Model for Lithium-Ion Battery
by Davide Clerici, Francesco Mocera and Aurelio Somà
Energies 2020, 13(7), 1717; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071717 - 04 Apr 2020
Cited by 29 | Viewed by 4949
Abstract
Electric cycling is one of the major damage sources in lithium-ion batteries and extensive work has been produced to understand and to slow down this phenomenon. The damage is related to the insertion and extraction of lithium ions in the active material. These [...] Read more.
Electric cycling is one of the major damage sources in lithium-ion batteries and extensive work has been produced to understand and to slow down this phenomenon. The damage is related to the insertion and extraction of lithium ions in the active material. These processes cause mechanical stresses which in turn generate crack propagation, material loss and pulverization of the active material. In this work, the principles of diffusion induced stress theory are applied to predict concentration and stress field in the active material particles. Coupled and uncoupled models are derived, depending on whether the effect of hydrostatic stress on concentration is considered or neglected. The analytical solution of the coupled model is proposed in this work, in addition to the analytical solution of the uncoupled model already described in the literature. The analytical solution is a faster and simpler way to deal with the problem which otherwise should be solved in a numerical way with finite difference method or a finite element model. The results of the coupled and uncoupled models for three different state of charge levels are compared assuming the physical parameters of anode and cathode active material. Finally, the effects of tensile and compressive stress are analysed. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

25 pages, 45159 KiB  
Article
High Reynold’s Number Turbulent Model for Micro-Channel Cold Plate Using Reverse Engineering Approach for Water-Cooled Battery in Electric Vehicles
by Satyam Panchal, Krishna Gudlanarva, Manh-Kien Tran, Roydon Fraser and Michael Fowler
Energies 2020, 13(7), 1638; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071638 - 02 Apr 2020
Cited by 70 | Viewed by 5558
Abstract
The investigation and improvement of the cooling process of lithium-ion batteries (LIBs) used in battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs) are required in order to achieve better performance and longer lifespan. In this manuscript, the temperature and velocity profiles of [...] Read more.
The investigation and improvement of the cooling process of lithium-ion batteries (LIBs) used in battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs) are required in order to achieve better performance and longer lifespan. In this manuscript, the temperature and velocity profiles of cooling plates used to cool down the large prismatic Graphite/LiFePO4 battery are presented using both laboratory testing and modeling techniques. Computed tomography (CT) scanning was utilized for the cooling plate, Detroit Engineering Products (DEP) MeshWorks 8.0 was used for meshing of the cooling plate, and STAR CCM+ was used for simulation. The numerical investigation was conducted for higher C-rates of 3C and 4C with different ambient temperatures. For the experimental work, three heat flux sensors were attached to the battery surface. Water was used as a coolant inside the cooling plate to cool down the battery. The mass flow rate at each channel was 0.000277677 kg/s. The k-ε model was then utilized to simulate the turbulent behaviour of the fluid in the cooling plate, and the thermal behaviour under constant current (CC) discharge was studied and validated with the experimental data. This study provides insight into thermal and flow characteristics of the coolant inside a cooing plate, which can be used for designing more efficient cooling plates. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

19 pages, 6677 KiB  
Article
Li-Ion Battery Performance Degradation Modeling for the Optimal Design and Energy Management of Electrified Propulsion Systems
by Li Chen, Yuqi Tong and Zuomin Dong
Energies 2020, 13(7), 1629; https://0-doi-org.brum.beds.ac.uk/10.3390/en13071629 - 02 Apr 2020
Cited by 28 | Viewed by 4337
Abstract
Heavy-duty hybrid electric vehicles and marine vessels need a sizeable electric energy storage system (ESS). The size and energy management strategy (EMS) of the ESS affects the system performance, cost, emissions, and safety. Traditional power-demand-based and fuel-economy-driven ESS sizing and energy management has [...] Read more.
Heavy-duty hybrid electric vehicles and marine vessels need a sizeable electric energy storage system (ESS). The size and energy management strategy (EMS) of the ESS affects the system performance, cost, emissions, and safety. Traditional power-demand-based and fuel-economy-driven ESS sizing and energy management has often led to shortened battery cycle life and higher replacement costs. To consider minimizing the total lifecycle cost (LCC) of hybrid electric propulsion systems, the battery performance degradation and the life prediction model is a critical element in the optimal design process. In this work, a new Li-ion battery (LIB) performance degradation model is introduced based on a large set of cycling experiment data on LiFePO4 (LFP) batteries to predict their capacity decay, resistance increase and the remaining cycle life under various use patterns. Critical parameters of the semi-empirical, amended equivalent circuit model were identified using least-square fitting. The model is used to calculate the investment, operation, replacement and recycling costs of the battery ESS over its lifetime. Validation of the model is made using battery cycling experimental data. The new LFP battery performance degradation model is used in optimizing the sizes of the key hybrid electric powertrain component of an electrified ferry ship with the minimum overall LCC. The optimization result presents a 12 percent improvement over the traditional power demand-driven hybrid powertrain design method. The research supports optimal sizing and EMS development of hybrid electric vehicles and vessels to achieve minimum lifecycle costs. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

15 pages, 6623 KiB  
Article
Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan
by Zheng Chen, Jiapeng Xiao, Xing Shu, Shiquan Shen, Jiangwei Shen and Yonggang Liu
Energies 2020, 13(6), 1410; https://0-doi-org.brum.beds.ac.uk/10.3390/en13061410 - 18 Mar 2020
Cited by 10 | Viewed by 2127
Abstract
In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium–ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available [...] Read more.
In this paper, a co-estimation scheme of the state of charge (SOC) and available capacity is proposed for lithium–ion batteries based on the adaptive model-based algorithm. A three-dimensional response surface (TDRS) in terms of the open circuit voltage, the SOC and the available capacity in the scope of whole lifespan, is constructed to describe the capacity attenuation, and the battery available capacity is identified by a genetic algorithm (GA), together with the parameters related to SOC. The square root cubature Kalman filter (SRCKF) is employed to estimate the SOC with the consideration of capacity degradation. The experimental results demonstrate the effectiveness and feasibility of the co-estimation scheme. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

15 pages, 2928 KiB  
Article
A Layered Bidirectional Active Equalization Method for Retired Power Lithium-Ion Batteries for Energy Storage Applications
by Yang Yang, Wenchao Zhu, Changjun Xie, Ying Shi, Furong Liu, Weibo Li and Zebo Tang
Energies 2020, 13(4), 832; https://0-doi-org.brum.beds.ac.uk/10.3390/en13040832 - 14 Feb 2020
Cited by 8 | Viewed by 2097
Abstract
The power from lithium-ion batteries can be retired from electric vehicles (EVs) and can be used for energy storage applications when the residual capacity is up to 70% of their initial capacity. The retired batteries have characteristics of serious inconsistency. In order to [...] Read more.
The power from lithium-ion batteries can be retired from electric vehicles (EVs) and can be used for energy storage applications when the residual capacity is up to 70% of their initial capacity. The retired batteries have characteristics of serious inconsistency. In order to solve this problem, a layered bidirectional active equalization topology is proposed in this paper. Specifically, a bridge-type equalization topology based on an inductor is adopted in the bottom layer, and the distributed equalization topological structure based on the bidirectional BUCK-BOOST circuit is adopted in the top layer. State of charge (SOC) is used as the equalization target variable, and the bottom layer equalization algorithm based on a “partition” idea and route optimization is proposed. The static equalization experiments and charge equalization experiments are performed by the 12 retired batteries selected from an electric sanitation vehicle. The results show that the proposed equalization method can reduce the SOC difference between retired batteries and can effectively improve the inconsistency of the retired battery pack with a faster equalization speed. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

23 pages, 8102 KiB  
Article
Real-time Energy Management Strategy for Oil-Electric-Liquid Hybrid System based on Lowest Instantaneous Energy Consumption Cost
by Yang Yang, Zhen Zhong, Fei Wang, Chunyun Fu and Junzhang Liao
Energies 2020, 13(4), 784; https://0-doi-org.brum.beds.ac.uk/10.3390/en13040784 - 11 Feb 2020
Cited by 6 | Viewed by 2180
Abstract
For the oil–electric–hydraulic hybrid power system, a logic threshold energy management strategy based on the optimal working curve is proposed, and the optimal working curve in each mode is determined. A genetic algorithm is used to determine the optimal parameters. For driving conditions, [...] Read more.
For the oil–electric–hydraulic hybrid power system, a logic threshold energy management strategy based on the optimal working curve is proposed, and the optimal working curve in each mode is determined. A genetic algorithm is used to determine the optimal parameters. For driving conditions, a real-time energy management strategy based on the lowest instantaneous energy cost is proposed. For braking conditions and subject to the European Commission for Europe (ECE) regulations, a braking force distribution strategy based on hydraulic pumps/motors and supplemented by motors is proposed. A global optimization energy management strategy is used to evaluate the strategy. Simulation results show that the strategy can achieve the expected control target and save about 32.14% compared with the fuel consumption cost of the original model 100 km 8 L. Under the New European Driving Cycle (NEDC) working conditions, the energy-saving effect of this strategy is close to that of the global optimization energy management strategy and has obvious cost advantages. The system design and control strategy are validated. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

21 pages, 7368 KiB  
Article
Efficiency Optimization and Control Strategy of Regenerative Braking System with Dual Motor
by Yang Yang, Qiang He, Yongzheng Chen and Chunyun Fu
Energies 2020, 13(3), 711; https://0-doi-org.brum.beds.ac.uk/10.3390/en13030711 - 06 Feb 2020
Cited by 19 | Viewed by 4950
Abstract
The regenerative braking system of electric vehicles can not only achieve the task of braking but also recover the braking energy. However, due to the lack of in-depth analysis of the energy loss mechanism in electric braking, the energy cannot be fully recovered. [...] Read more.
The regenerative braking system of electric vehicles can not only achieve the task of braking but also recover the braking energy. However, due to the lack of in-depth analysis of the energy loss mechanism in electric braking, the energy cannot be fully recovered. In this study, the energy recovery problem of regenerative braking using the independent front axle and rear axle motor drive system is investigated. The accurate motor model is established, and various losses are analyzed. Based on the principle of minimum losses, the motor control strategy is designed. Furthermore, the power flow characteristics in electric braking are analyzed, and the optimal continuously variable transmission (CVT) speed ratio under different working conditions is obtained through optimization. To understand the potential of dual-motor energy recovery, a regenerative braking control strategy is proposed by optimizing the dynamic distribution coefficient of the dual-electric mechanism and considering the restrictions of regulations and the I curve. The simulation results under typical operating conditions and the New York City Cycle (NYCC) proposed conditions indicate that the improved strategy has higher joint efficiency. The energy recovery rate of the proposed strategy is increased by 1.18% in comparison with the typical braking strategy. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

15 pages, 5161 KiB  
Article
Lithium-Ion Polymer Battery for 12-Voltage Applications: Experiment, Modelling, and Validation
by Yiqun Liu, Y. Gene Liao and Ming-Chia Lai
Energies 2020, 13(3), 638; https://0-doi-org.brum.beds.ac.uk/10.3390/en13030638 - 03 Feb 2020
Cited by 11 | Viewed by 5221
Abstract
Modelling, simulation, and validation of the 12-volt battery pack using a 20 Ah lithium–nickel–manganese–cobalt–oxide cell is presented in this paper. The cell characteristics influenced by thermal effects are also considered in the modelling. The parameters normalized directly from a single cell experiment are [...] Read more.
Modelling, simulation, and validation of the 12-volt battery pack using a 20 Ah lithium–nickel–manganese–cobalt–oxide cell is presented in this paper. The cell characteristics influenced by thermal effects are also considered in the modelling. The parameters normalized directly from a single cell experiment are foundations of the model. This approach provides a systematic integration of actual cell monitoring with a module model that contains four cells connected in series. The validated battery module model then is utilized to form a high fidelity 80 Ah 12-volt battery pack with 14.4 V nominal voltage. The battery cell thermal effectiveness and battery module management system functions are constructed in the MATLAB/Simulink platform. The experimental tests are carried out in an industry-scale setup with cycler unit, temperature control chamber, and computer-controlled software for battery testing. As the 12-volt lithium-ion battery packs might be ready for mainstream adoption in automotive starting–lighting–ignition (SLI), stop–start engine idling elimination, and stationary energy storage applications, this paper investigates the influence of ambient temperature and charging/discharging currents on the battery performance in terms of discharging voltage and usable capacity. The proposed simulation model provides design guidelines for lithium-ion polymer batteries in electrified vehicles and stationary electric energy storage applications. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

16 pages, 5304 KiB  
Article
Dual Nonlinear Kalman Filter-Based SoC and Remaining Capacity Estimation for an Electric Scooter Li-NMC Battery Pack
by Filip Maletić, Mario Hrgetić and Joško Deur
Energies 2020, 13(3), 540; https://0-doi-org.brum.beds.ac.uk/10.3390/en13030540 - 22 Jan 2020
Cited by 14 | Viewed by 3700
Abstract
Accurate, real-time estimation of battery state-of-charge (SoC) and state-of-health represents a crucial task of modern battery management systems. Due to nonlinear and battery degradation-dependent behavior of output voltage, the design of these estimation algorithms should be based on nonlinear parameter-varying models. The paper [...] Read more.
Accurate, real-time estimation of battery state-of-charge (SoC) and state-of-health represents a crucial task of modern battery management systems. Due to nonlinear and battery degradation-dependent behavior of output voltage, the design of these estimation algorithms should be based on nonlinear parameter-varying models. The paper first describes the experimental setup that consists of commercially available electric scooter equipped with telemetry measurement equipment. Next, dual extended Kalman filter-based (DEKF) estimator of battery SoC, internal resistances, and parameters of open-circuit voltage (OCV) vs. SoC characteristic is presented under the assumption of fixed polarization time constant vs. SoC characteristic. The DEKF is upgraded with an adaptation mechanism to capture the battery OCV hysteresis without explicitly modelling it. Parameterization of an explicit hysteresis model and its inclusion in the DEKF is also considered. Finally, a slow time scale, sigma-point Kalman filter-based capacity estimator is designed and inter-coupled with the DEKF. A convergence detection algorithm is proposed to ensure that the two estimators are coupled automatically only after the capacity estimate has converged. The overall estimator performance is experimentally validated for real electric scooter driving cycles. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

18 pages, 30134 KiB  
Article
Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine
by Yongliang Zheng, Feng He, Xinze Shen and Xuesheng Jiang
Energies 2020, 13(2), 426; https://0-doi-org.brum.beds.ac.uk/10.3390/en13020426 - 15 Jan 2020
Cited by 24 | Viewed by 3266
Abstract
Aimed at the limitation of traditional fuzzy control strategy in distributing power and improving the economy of a fuel cell hybrid electric vehicle (FCHEV), an energy management strategy combined with working conditions identification is proposed. Feature parameters extraction and sample divisions were carried [...] Read more.
Aimed at the limitation of traditional fuzzy control strategy in distributing power and improving the economy of a fuel cell hybrid electric vehicle (FCHEV), an energy management strategy combined with working conditions identification is proposed. Feature parameters extraction and sample divisions were carried out for typical working conditions, and working conditions were identified by the least square support vector machine (LSSVM) optimized by grid search and cross validation (CV). The corresponding fuzzy control strategies were formulated under different typical working conditions, in addition, the fuzzy control strategy was optimized with total equivalent energy consumption as the goal by particle swarm optimization (PSO). The adaptive switching of fuzzy control strategies under different working conditions were realized through the identification of driving conditions. Results showed that the fuzzy control strategy with the function of driving conditions identification had a more efficient power distribution and better economy. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

22 pages, 2933 KiB  
Article
Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging
by Lu Han, Xiaohong Jiao and Zhao Zhang
Energies 2020, 13(1), 202; https://0-doi-org.brum.beds.ac.uk/10.3390/en13010202 - 01 Jan 2020
Cited by 49 | Viewed by 3580
Abstract
A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research [...] Read more.
A hybrid electric vehicle (HEV) is a product that can greatly alleviate problems related to the energy crisis and environmental pollution. However, replacing such a battery will increase the cost of usage before the end of the life of a HEV. Thus, research on the multi-objective energy management control problem, which aims to not only minimize the gasoline consumption and consumed electricity but also prolong battery life, is necessary and challenging for HEV. This paper presents an adaptive equivalent consumption minimization strategy based on a recurrent neural network (RNN-A-ECMS) to solve the multi-objective optimal control problem for a plug-in HEV (PHEV). The two objectives of energy consumption and battery loss are balanced in the cost function by a weighting factor that changes in real time with the operating mode and current state of the vehicle. The near-global optimality of the energy management control is guaranteed by the equivalent factor (EF) in the designed A-ECMS. As the determined EF is dependent on the optimal co-state of the Pontryagin’s minimum principle (PMP), which results in the online ECMS being regarded as a realization of PMP-based global optimization during the whole driving cycle. The time-varying weight factor and the co-state of the PMP are map tables on the state of charge (SOC) of the battery and power demand, which are established offline by the particle swarm optimization (PSO) algorithm and real historical traffic data. In addition to the mappings of the weight factor and the major component of the EF linked to the optimal co-state of the PMP, the real-time performance of the energy management control is also guaranteed by the tuning component of the EF of A-ECMS resulting from the Proportional plus Integral (PI) control on the deviation between the battery SOC and the optimal trajectory of the SOC obtained by the Recurrent Neural Network (RNN). The RNN is trained offline by the SOC trajectory optimized by dynamic programming (DP) utilizing the historical traffic data. Finally, the effectiveness and the adaptability of the proposed RNN-A-ECMS are demonstrated on the test platform of plug-in hybrid electric vehicles based on GT-SUITE (a professional integrated simulation platform for engine/vehicle systems developed by Gamma Technologies of US company) compared with the existing strategy. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

23 pages, 8839 KiB  
Article
Analysis of the Electric Bus Autonomy Depending on the Atmospheric Conditions
by Călin Iclodean, Nicolae Cordoș and Adrian Todoruț
Energies 2019, 12(23), 4535; https://0-doi-org.brum.beds.ac.uk/10.3390/en12234535 - 28 Nov 2019
Cited by 9 | Viewed by 5811
Abstract
The public-transport sector represents, on a global level, a major ecological and economic concern. Improving air quality and reducing greenhouse gas (GHG) production in the urban environment can be achieved by using electric buses instead of those operating with internal combustion engines (ICE). [...] Read more.
The public-transport sector represents, on a global level, a major ecological and economic concern. Improving air quality and reducing greenhouse gas (GHG) production in the urban environment can be achieved by using electric buses instead of those operating with internal combustion engines (ICE). In this paper, the energy consumption for a fleet of electric buses Solaris Urbino 12e type is analyzed, based on the experimental data taken from a number of 22 buses, which operate on a number of eight urban lines, on a route of approximately 100 km from the city of Cluj-Napoca, Romania; consumption was monitored for 12 consecutive months (July 2018–June 2019). The energy efficiency of the model for the studied electric buses depends largely on the management of the energy stored on the electric bus battery system, in relation to the characteristics of the route traveled, respectively to the atmospheric conditions during the monitored period. Based on the collected experimental data and on the technical characteristics of the electric buses, the influence of the atmospheric conditions on their energy balance was highlighted, considering the interdependence relations between the considered atmospheric conditions. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

15 pages, 4510 KiB  
Article
SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm
by Miaomiao Zeng, Peng Zhang, Yang Yang, Changjun Xie and Ying Shi
Energies 2019, 12(16), 3122; https://0-doi-org.brum.beds.ac.uk/10.3390/en12163122 - 14 Aug 2019
Cited by 48 | Viewed by 3954
Abstract
In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor [...] Read more.
In order to improve the convergence time and stabilization accuracy of the real-time state estimation of the power batteries for electric vehicles, a fuzzy unscented Kalman filtering algorithm (F-UKF) of a new type is proposed in this paper, with an improved second-order resistor-capacitor (RC) equivalent circuit model established and an online parameter identification used by Bayes. Ohmic resistance is treated as a battery state of health (SOH) characteristic parameter, F-UKF algorithms are used for the joint estimation of battery state of charge (SOC) and SOH. The experimental data obtained from the ITS5300-based battery test platform are adopted for the simulation verification under discharge conditions with constant-current pulses and urban dynamometer driving schedule (UDDS) conditions in the MATLAB environment. The experimental results show that the F-UKF algorithm is insensitive to the initial value of the SOC under discharge conditions with constant-current pulses, and the SOC and SOH estimation accuracy under UDDS conditions reaches 1.76% and 1.61%, respectively, with the corresponding convergence time of 120 and 140 s, which proves the superiority of the joint estimation algorithm. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Graphical abstract

18 pages, 2917 KiB  
Article
Butyronitrile-Based Electrolytes for Fast Charging of Lithium-Ion Batteries
by Peter Hilbig, Lukas Ibing, Martin Winter and Isidora Cekic-Laskovic
Energies 2019, 12(15), 2869; https://0-doi-org.brum.beds.ac.uk/10.3390/en12152869 - 25 Jul 2019
Cited by 19 | Viewed by 4771
Abstract
After determining the optimum composition of the butyronitrile: ethylene carbonate: fluoroethylene carbonate (BN:EC:FEC) solvent/co-solvent/additive mixture, the resulting electrolyte formulation (1M LiPF6 in BN:EC (9:1) + 3% FEC) was evaluated in terms of ionic conductivity and the electrochemical stability window, as well as [...] Read more.
After determining the optimum composition of the butyronitrile: ethylene carbonate: fluoroethylene carbonate (BN:EC:FEC) solvent/co-solvent/additive mixture, the resulting electrolyte formulation (1M LiPF6 in BN:EC (9:1) + 3% FEC) was evaluated in terms of ionic conductivity and the electrochemical stability window, as well as galvanostatic cycling performance in NMC/graphite cells. This cell chemistry results in remarkable fast charging, required, for instance, for automotive applications. In addition, a good long-term cycling behavior lasts for 1000 charge/discharge cycles and improved ionic conductivity compared to the benchmark counterpart was achieved. XPS sputter depth profiling analysis proved the beneficial behavior of the tuned BN-based electrolyte on the graphite surface, by confirming the formation of an effective solid electrolyte interphase (SEI). Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

Review

Jump to: Research

21 pages, 4178 KiB  
Review
Thermal Storage Using Metallic Phase Change Materials for Bus Heating—State of the Art of Electric Buses and Requirements for the Storage System
by Werner Kraft, Veronika Stahl and Peter Vetter
Energies 2020, 13(11), 3023; https://0-doi-org.brum.beds.ac.uk/10.3390/en13113023 - 11 Jun 2020
Cited by 13 | Viewed by 4465
Abstract
Battery-powered electric buses currently face the challenges of high cost and limited range, especially in winter conditions, where interior heating is required. To face both challenges, the use of thermal energy storage based on metallic phase change materials for interior heating, also called [...] Read more.
Battery-powered electric buses currently face the challenges of high cost and limited range, especially in winter conditions, where interior heating is required. To face both challenges, the use of thermal energy storage based on metallic phase change materials for interior heating, also called thermal high-performance storage, is considered. By replacing the battery capacity through such an energy storage system, which is potentially lighter, smaller, and cheaper than the batteries used in buses, an overall reduction in cost and an increase of range in winter conditions could be reached. Since the use of thermal high-performance storage as a heating system in a battery-powered electric bus is a new approach, the requirements for such a system first need to be known to be able to proceed with further steps. To find these requirements, a review of the relevant state of the art of battery-powered electric buses, with a focus on heating systems, was done. Other relevant aspects were vehicle types, electric architecture, battery systems, and charging strategies. With the help of this review, requirements for thermal high-performance storage as a heating system for a battery-powered electric bus were produced. Categories for these requirements were the thermal capacity and performance, long-term stability, mass and volume, cost, electric connection, thermal connection, efficiency, maintenance, safety, adjustment, and ecology. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

23 pages, 3533 KiB  
Review
Overview and Comparative Assessment of Single-Phase Power Converter Topologies of Inductive Wireless Charging Systems
by Phuoc Sang Huynh, Deepak Ronanki, Deepa Vincent and Sheldon S. Williamson
Energies 2020, 13(9), 2150; https://0-doi-org.brum.beds.ac.uk/10.3390/en13092150 - 01 May 2020
Cited by 18 | Viewed by 3553
Abstract
The acquisition of inductive power transfer (IPT) technology in commercial electric vehicles (EVs) alleviates the inherent burdens of high cost, limited driving range, and long charging time. In EV wireless charging systems using IPT, power electronic converters play a vital role to reduce [...] Read more.
The acquisition of inductive power transfer (IPT) technology in commercial electric vehicles (EVs) alleviates the inherent burdens of high cost, limited driving range, and long charging time. In EV wireless charging systems using IPT, power electronic converters play a vital role to reduce the size and cost, as well as to maximize the efficiency of the overall system. Over the past years, significant research studies have been conducted by researchers to improve the performance of power conversion systems including the power converter topologies and control schemes. This paper aims to provide an overview of the existing state-of-the-art of power converter topologies for IPT systems in EV charging applications. In this paper, the widely adopted power conversion topologies for IPT systems are selected and their performance is compared in terms of input power factor, input current distortion, current stress, voltage stress, power losses on the converter, and cost. The single-stage matrix converter based IPT systems advantageously adopt the sinusoidal ripple current (SRC) charging technique to remove the intermediate DC-link capacitors, which improves system efficiency, power density and reduces cost. Finally, technical considerations and future opportunities of power converters in EV wireless charging applications are discussed. Full article
(This article belongs to the Special Issue Energy Storage Systems for Electric Vehicles)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-25
Back to TopTop