Special Issue "Battery Management System for Future Electric Vehicles"

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

Deadline for manuscript submissions: closed (31 August 2019).

Special Issue Editors

Guest Editor
Prof. Dirk Söffker Website E-Mail
Chair of Dynamics and Control, University of Duisburg-Essen, Forsthausweg 2, 47057 Duisburg, Germany
Interests: Control of energy flows – hybrid powertrains and wind turbine control; Diagnostics and prognostics of technical systems; Modeling, diagnosis, and control of elastic mechanical structures; Control theory: robust observers and nonlinear control; Cognitive technical systems: automata and assistance.
Guest Editor
Assist. Prof. Bedatri Moulik Website E-Mail
Amity School of Engineering and Technology, Amity University, Noida, Sector 125, Noida, Uttar Pradesh 201313, India
Interests: power management, control, and optimization of electric and hybrid vehicles; battery management; advanced driver assistance systems

Special Issue Information

Dear Colleagues,

Considering the threat of polluting emissions and energy dependence, the electrification of road transport has become a global focus. The main performance parameters of electric vehicles (EVs) include size, cost, charging time, energy consumption, and efficiency. Batteries being a crucial component in EVs, evaluating the influence of the charging–discharging pattern on battery usage, performance, safety, and life is vital. The primary tasks of battery management systems (BMS) include ensuring safety and reliability by accurate state estimation and monitoring, extending end of life (EoL) by minimizing aging, fault detection and alarm, thermal management, information storage and networking between the modules.

For future EV-generations, additional control features are required to optimize charging–discharging patterns to extend battery life, decrease battery cost, while also providing maximum usability. It can be assumed that detailed real and virtual cell level monitoring and control will be relevant.

Current BMS are based on standard cycle tests. From the results it is difficult to predict the remaining useful life when subjected to unknown drive patterns and cycles; thermal management is another issue particularly during fast charging.

This Special Issue aims to address the recent developments in battery modeling, parameter estimation, prediction of remaining useful life, and related control algorithms for power, lifetime, and thermal management. Contributions related to charging approaches and their effects on battery performance are also welcome. Innovative hybridization concepts to assist, protect, and/or extend the battery life and/or performance will also be encouraged.

To perfect the Special Issue “Battery Management System for Future Electric Vehicles”, contributions should be clearly focused on the addressed research areas. Contributions should not be focused on technological state-of-the-art systems, pure numerical simulations studies using know formulas, application reports, known battery charging/discharging strategies, and should not only repeat known results (from previous works or the work of others). Prospective authors should provide original work with significant and novel contributions, providing new facts, ideas, insights, and results.

Prof. Dirk Söffker
Assist. Prof. Bedatri Moulik
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 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
  • Battery modeling
  • Battery state estimation
  • Battery monitoring
  • Thermal management
  • Hybrid electric vehicles, hybrid electric powertrains
  • Complete battery system modeling
  • Generic battery models
  • Cycle and calendar life, modeling and control
  • Lifetime modeling, remaining useful lifetime models and evaluations
  • Charging approaches: models, experiments
  • Filters-based prognosis of battery health
  • Observer-based state estimation for complex nonlinear battery models
  • Optimal charging-discharging cycles related to battery type
  • Optimal component sizing for battery management
  • Optimal hybridization schemes (in light of increasing capacities of SuperCaps and FCs) for better battery management.

Published Papers (2 papers)

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Research

Open AccessFeature PaperArticle
Cooperative Optimization of Electric Vehicles and Renewable Energy Resources in a Regional Multi-Microgrid System
Appl. Sci. 2019, 9(11), 2267; https://doi.org/10.3390/app9112267 - 31 May 2019
Abstract
By integrating renewable energy sources (RESs) with electric vehicles (EVs) in microgrids, we are able to reduce carbon emissions as well as alleviate the dependence on fossil fuels. In order to improve the economy of an integrated system and fully exploit the potentiality [...] Read more.
By integrating renewable energy sources (RESs) with electric vehicles (EVs) in microgrids, we are able to reduce carbon emissions as well as alleviate the dependence on fossil fuels. In order to improve the economy of an integrated system and fully exploit the potentiality of EVs’ mobile energy storage while achieving a reasonable configuration of RESs, a cooperative optimization method is proposed to cooperatively optimize the economic dispatching and capacity allocation of both RESs and EVs in the context of a regional multi-microgrid system. An across-time-and-space energy transmission (ATSET) of the EVs was considered, and the impact of ATSET of EVs on economic dispatching and capacity allocation of multi-microgrid system was analyzed. In order to overcome the difficulty of finding the global optimum of the non-smooth total cost function, an improved particle swarm optimization (IPSO) algorithm was used to solve the cooperative optimization problem. Case studies were performed, and the simulation results show that the proposed cooperative optimization method can significantly decrease the total cost of a multi-microgrid system. Full article
(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)
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Open AccessArticle
Adaptive Dual Extended Kalman Filter Based on Variational Bayesian Approximation for Joint Estimation of Lithium-Ion Battery State of Charge and Model Parameters
Appl. Sci. 2019, 9(9), 1726; https://doi.org/10.3390/app9091726 - 26 Apr 2019
Cited by 2
Abstract
An accurate state of charge (SOC) estimation is vital for the safe operation and efficient management of lithium-ion batteries. At present, the extended Kalman filter (EKF) can accurately estimate the SOC under the condition of a precise battery model and deterministic noise statistics. [...] Read more.
An accurate state of charge (SOC) estimation is vital for the safe operation and efficient management of lithium-ion batteries. At present, the extended Kalman filter (EKF) can accurately estimate the SOC under the condition of a precise battery model and deterministic noise statistics. However, in practical applications, the battery characteristics change with different operating conditions and the measurement noise statistics may vary with time, resulting in nonoptimal and even unreliable estimation of SOC by EKF. To improve the SOC estimation accuracy under uncertain measurement noise statistics, a variational Bayesian approximation-based adaptive dual extended Kalman filter (VB-ADEKF) is proposed in this paper. The variational Bayesian inference is integrated with the dual EKF (DEKF) to jointly estimate the lithium-ion battery parameters and SOC. Meanwhile, the measurement noise variances are simultaneously estimated in the SOC estimation process to compensate for the model uncertainties, so that the adaptability of the proposed algorithm to dynamic changes in battery characteristics is greatly improved. A constant current discharge test, a pulse current discharge test, and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the DEKF algorithm. The experimental results show that the proposed VB-ADEKF algorithm outperforms the traditional DEKF algorithm in terms of SOC estimation accuracy, convergence rate, and robustness. Full article
(This article belongs to the Special Issue Battery Management System for Future Electric Vehicles)
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