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Batteries
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Battery Management Systems of Electric and Hybrid Electric Vehicles
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Battery Management Systems of Electric and Hybrid Electric Vehicles

A special issue of Batteries (ISSN 2313-0105).

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 37868

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

Special Issue Editor

Prof. Dr. Nicolae Tudoroiu

E-Mail Website
Guest Editor
Engineering Technologies Department, John Abbott College, Sainte-Anne-de-Bellevue, QC, Canada
Interests: systems theory (linear, nonlinear, optimal, stochastic, and adaptive); artificial intelligence; neural networks; control systems; automation (PLC); signal processing; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The battery management system (BMS) is a key component of electric and hybrid electric vehicles (EVs/HEVs) that integrates energy storage systems (ESS) such as batteries of different chemistries, supercapacitors or hybrid components, sensors, controllers, serial communication, and computation hardware with software algorithms on-board implemented to assess the maximum charging/discharging cycles current and the duration from the estimation of state of charge (SOC) and state of health ( SOH) of the battery pack. The BMS performs the tasks by integrating one or more of the functions, such as sampling the voltages of the battery cells and the temperatures in the battery module, sampling the voltage of the battery, sampling the current of the battery, as well as cells balancing and determining the state of charge (SOC) of the battery. Thus, a BMS is an essential interface between the battery and the EV/HEV, very useful to improve battery performance and to optimize vehicle operation in a safe and reliable manner. A comprehensive and mature BMS contains hardware and software components, cell balancing, and safety circuitry that play an important role in monitoring, controlling, computing and continually showing the safety state, SOC, and SOH, so as to extend the longevity of the battery. In this Special Issue, we are looking for contributions helping to address concerns for current BMSs, mainly the state of charge, state of health, and state of life, considered as a critical task for a BMS. Reviewing the latest methodologies for the state evaluation of batteries and presenting some future challenges for BMSs and possible innovative solutions will be also well appreciated.

Topics of interest include but are not limited to:

  • Comprehensive and mature BMSs design and development considerations;
  • Major challenges in BMS design of EVs/HEVs and in supercharger infrastructure of EVs;
  • Main batteries types integrated in EVs/HEVs modeling and temperature considerations:
    • lead acid battery type charge and discharge models
    • Lithium-ion battery type (Li–Ion)—charge and discharge models
    • Nickel–cadmium battery type (Ni–Cd)—charge and discharge models
    • Nickel–metal–hydride type (Ni–MH)—charge and discharge models
  • Commercial batteries characterization, diagnosis, prognosis, and performance optimization, from experimental testing, statistical analysis, thermal modeling, to BMS algorithms;
  • Batteries MATLAB/Simulink models with extension to SIMSCAPE blocks modeling;
  • Cells voltage sampling, battery temperature sampling, battery voltage, and current sampling methods;
  • Battery temperature and cells balancing using special integrate circuits (ICs);
  • Battery aging mechanisms and modeling;
  • Battery state of charge (SOC) estimation—estimation algorithms;
  • Battery state-of-health (SOH) estimation—estimation algorithms;
  • Balancing circuits with consideration of the lifetime of battery;
  • Influence of aging on cost and environmental analyses of batteries of different chemistries;
  • Optimal sizing and design of batteries of different chemistries.

Prof. Dr. Eng. Nicolae Tudoroiu
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. Batteries is an international peer-reviewed open access monthly 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 2700 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 (BMS)
  • State estimation
  • State of charge (SOC) of the battery
  • State of health estimation of the ESS
  • Cells balancing of the ESS
  • Aging modeling of ESS
  • Kalman filter techniques
  • Particle filter estimation
  • Linear and nonlinear observers
  • State of life estimation of the ESS
  • Genetic algorithms
  • Fault detection, diagnosis, and isolation (FDDI) in the ESS
  • PID control
  • Fuzzy logic control

Published Papers (6 papers)

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Research

38 pages, 21849 KiB  
Article
SOC Estimation of a Rechargeable Li-Ion Battery Used in Fuel-Cell Hybrid Electric Vehicles—Comparative Study of Accuracy and Robustness Performance Based on Statistical Criteria. Part I: Equivalent Models
by Roxana-Elena Tudoroiu, Mohammed Zaheeruddin, Nicolae Tudoroiu and Sorin-Mihai Radu
Batteries 2020, 6(3), 42; https://doi.org/10.3390/batteries6030042 - 14 Aug 2020
Cited by 10 | Viewed by 7216
Abstract
Battery state of charge (SOC) accuracy plays a vital role in a hybrid electric vehicle (HEV), as it ensures battery safety in a harsh operating environment, prolongs life, lowers the cost of energy consumption, and improves driving mileage. Therefore, accurate SOC battery estimation [...] Read more.
Battery state of charge (SOC) accuracy plays a vital role in a hybrid electric vehicle (HEV), as it ensures battery safety in a harsh operating environment, prolongs life, lowers the cost of energy consumption, and improves driving mileage. Therefore, accurate SOC battery estimation is the central idea of the approach in this research, which is of great interest to readers and increases the value of its application. Moreover, an accurate SOC battery estimate relies on the accuracy of the battery model parameters and its capacity. Thus, the purpose of this paper is to design, implement and analyze the SOC estimation accuracy of two battery models, which capture the dynamics of a rechargeable SAFT Li-ion battery. The first is a resistor capacitor (RC) equivalent circuit model, and the second is a generic Simscape model. The model validation is based on the generation and evaluation of the SOC residual error. The SOC reference value required for the calculation of residual errors is the value estimated by an ADVISOR 3.2 simulator, one of the software tools most used in automotive applications. Both battery models are of real interest as a valuable support for SOC battery estimation by using three model based Kalman state estimators developed in Part 2. MATLAB simulations results prove the effectiveness of both models and reveal an excellent accuracy. Full article
(This article belongs to the Special Issue Battery Management Systems of Electric and Hybrid Electric Vehicles)
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36 pages, 21639 KiB  
Article
SOC Estimation of a Rechargeable Li-Ion Battery Used in Fuel Cell Hybrid Electric Vehicles—Comparative Study of Accuracy and Robustness Performance Based on Statistical Criteria. Part II: SOC Estimators
by Roxana-Elena Tudoroiu, Mohammed Zaheeruddin, Nicolae Tudoroiu and Sorin-Mihai Radu
Batteries 2020, 6(3), 41; https://doi.org/10.3390/batteries6030041 - 14 Aug 2020
Cited by 6 | Viewed by 4675
Abstract
The purpose of this paper is to analyze the accuracy of three state of charge (SOC) estimators of a rechargeable Li-ion SAFT battery based on two accurate Li-ion battery models, namely a linear RC equivalent electrical circuit (ECM) and a nonlinear Simscape generic [...] Read more.
The purpose of this paper is to analyze the accuracy of three state of charge (SOC) estimators of a rechargeable Li-ion SAFT battery based on two accurate Li-ion battery models, namely a linear RC equivalent electrical circuit (ECM) and a nonlinear Simscape generic model, developed in Part 1. The battery SOC of both Li-ion battery models is estimated using a linearized adaptive extended Kalman filter (AEKF), a nonlinear adaptive unscented Kalman filter (AUKF) and a nonlinear and non-Gaussian particle filter estimator (PFE). The result of MATLAB simulations shows the efficiency of all three SOC estimators, especially AEKF, followed in order of decreasing performance by AUKF and PFE. Besides, this result reveals a slight superiority of the SOC estimation accuracy when using the Simscape model for SOC estimator design. Overall, the performance of all three SOC estimators in terms of accuracy, convergence of response speed and robustness is excellent and is comparable to state of the art SOC estimation methods. Full article
(This article belongs to the Special Issue Battery Management Systems of Electric and Hybrid Electric Vehicles)
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16 pages, 4511 KiB  
Article
Experimental Data Comparison of an Electric Minibus Equipped with Different Energy Storage Systems
by Fabio Cignini, Antonino Genovese, Fernando Ortenzi, Adriano Alessandrini, Lorenzo Berzi, Luca Pugi and Riccardo Barbieri
Batteries 2020, 6(2), 26; https://doi.org/10.3390/batteries6020026 - 28 Apr 2020
Cited by 18 | Viewed by 4711
Abstract
As electric mobility becomes more important every day, scientific research brings us new solutions that increase performance, reduce financial and economic impacts and increase the market share of electric vehicles. Therefore, there is a necessity to compare technical and economic aspects of different [...] Read more.
As electric mobility becomes more important every day, scientific research brings us new solutions that increase performance, reduce financial and economic impacts and increase the market share of electric vehicles. Therefore, there is a necessity to compare technical and economic aspects of different technologies for each transport application. This article presents a comparison of three bus prototypes in terms of dynamic performance. The analysis is based on the collection of real data (acceleration, maximum speed and energy consumption) under different settings. Each developed prototype uses the same bus chassis but relies on different energy storage systems. Results show that the dynamic bus performance is independent on the three energy storage technologies, whereas technologies affect the management costs, charging time and available range. An extensive experimental analysis reveals that the bus equipped with a hybrid storage (lithium-ion batteries and supercapacitors) had the most favorable net present value, in comparison with storage composed of only lead–acid or lithium-ion batteries. This result is due to the greater life of lithium-ion batteries and to the capability of supercapacitors, which reduce both batteries depth of discharge and discharge rate. Full article
(This article belongs to the Special Issue Battery Management Systems of Electric and Hybrid Electric Vehicles)
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16 pages, 7920 KiB  
Article
Measuring Test Bench with Adjustable Thermal Connection of Cells to Their Neighbors and a New Model Approach for Parallel-Connected Cells
by Alexander Fill, Tobias Mader, Tobias Schmidt, Raphael Llorente and Kai Peter Birke
Batteries 2020, 6(1), 2; https://doi.org/10.3390/batteries6010002 - 26 Dec 2019
Cited by 13 | Viewed by 5665
Abstract
This article presents a test bench with variable temperature control of the individual cells connected in parallel. This allows to reconstruct arising temperature gradients in a battery module and to investigate their effects on the current distribution. The influence of additional contact resistances [...] Read more.
This article presents a test bench with variable temperature control of the individual cells connected in parallel. This allows to reconstruct arising temperature gradients in a battery module and to investigate their effects on the current distribution. The influence of additional contact resistances induced by the test bench is determined and minimized. The contact resistances are reduced from R Tab + = 81.18 μ Ω to R Tab + = 55.15 μ Ω at the positive respectively from R Tab − = 35.59 μ Ω to R Tab − = 28.2 μ Ω at the negative tab by mechanical and chemical treating. An increase of the contact resistance at the positive tab is prevented by air seal of the contact. The resistance of the load cable must not be arbitrarily small, as the cable is used as a shunt for current measurement. In order to investigate their impacts, measurements with two parallel-connected cells and different load cables with a resistance of R Cab + = 0.3 m Ω , R Cab + = 1.6 m Ω and R Cab + = 4.35 m Ω are conducted. A shift to lower current differences with decreasing cable resistance but qualitatively the same dynamic of the current distribution is found. An extended dual polarization model is introduced, considering the current distribution within the cells as well as the additional resistances induced by the test bench. The model shows a high correspondence to measurements with two parallel-connected cells, with a Root Mean Square Deviation (RMSD) of ξ RMSD = 0.083 A. Full article
(This article belongs to the Special Issue Battery Management Systems of Electric and Hybrid Electric Vehicles)
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16 pages, 2760 KiB  
Article
Sensor Fault Detection and Isolation for Degrading Lithium-Ion Batteries in Electric Vehicles Using Parameter Estimation with Recursive Least Squares
by Manh-Kien Tran and Michael Fowler
Batteries 2020, 6(1), 1; https://doi.org/10.3390/batteries6010001 - 20 Dec 2019
Cited by 51 | Viewed by 8833
Abstract
With the increase in usage of electric vehicles (EVs), the demand for Lithium-ion (Li-ion) batteries is also on the rise. The battery management system (BMS) plays an important role in ensuring the safe and reliable operation of the battery in EVs. Sensor faults [...] Read more.
With the increase in usage of electric vehicles (EVs), the demand for Lithium-ion (Li-ion) batteries is also on the rise. The battery management system (BMS) plays an important role in ensuring the safe and reliable operation of the battery in EVs. Sensor faults in the BMS can have significant negative effects on the system, hence it is important to diagnose these faults in real-time. Existing sensor fault detection and isolation (FDI) methods have not considered battery degradation. Degradation can affect the long-term performance of the battery and cause false fault detection. This paper presents a model-based sensor FDI scheme for a Li-ion cell undergoing degradation. The proposed scheme uses the recursive least squares (RLS) method to estimate the equivalent circuit model (ECM) parameters in real time. The estimated ECM parameters are put through weighted moving average (WMA) filters, and then cumulative sum control charts (CUSUM) are implemented to detect any significant deviation between unfiltered and filtered data, which would indicate a fault. The current and voltage faults are isolated based on the responsiveness of the parameters when each fault occurs. The proposed FDI scheme is then validated through conducting a series of experiments and simulations. Full article
(This article belongs to the Special Issue Battery Management Systems of Electric and Hybrid Electric Vehicles)
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14 pages, 5966 KiB  
Article
Development of a Polymeric Arrayed Waveguide Grating Interrogator for Fast and Precise Lithium-Ion Battery Status Monitoring
by Jan Meyer, Antonio Nedjalkov, Elke Pichler, Christian Kelb and Wolfgang Schade
Batteries 2019, 5(4), 66; https://doi.org/10.3390/batteries5040066 - 18 Oct 2019
Cited by 9 | Viewed by 5731
Abstract
We present the manufacturing and utilization of an all-polymer arrayed waveguide grating (AWG) interacting with a fiber Bragg grating (FBG) for battery status monitoring on the example of a 40 Ah lithium-ion battery. The AWG is the main component of a novel low-cost [...] Read more.
We present the manufacturing and utilization of an all-polymer arrayed waveguide grating (AWG) interacting with a fiber Bragg grating (FBG) for battery status monitoring on the example of a 40 Ah lithium-ion battery. The AWG is the main component of a novel low-cost approach for an optical interrogation unit to track the FBG peak wavelength by means of intensity changes monitored by a CMOS linear image sensor, read out by a Teensy 3.2 microcontroller. The AWG was manufactured using laser direct lithography as an all-polymer-system, whereas the FBG was produced by point-by-point femtosecond laser writing. Using this system, we continuously monitored the strain variation of a battery cell during low rate charge and discharge cycles over one month under constant climate conditions and compared the results to parallel readings of an optical spectrum analyzer with special attention to the influence of the relative air humidity. We found our low-cost interrogation unit is capable of precisely and reliably capturing the typical strain variation of a high energy pouch cell during cycling with a resolution of 1 pm and shows a humidity sensitivity of −12.8 pm per %RH. Full article
(This article belongs to the Special Issue Battery Management Systems of Electric and Hybrid Electric Vehicles)
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