Battery Management Systems of Electric and Hybrid Electric Vehicles II
A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Modelling, Simulation, Management and Application".
Deadline for manuscript submissions: closed (30 March 2022) | Viewed by 5130
Special Issue Editor
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 cell 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, extremely useful in improving the battery performance and optimizing 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, such that the longevity of the battery is extended. In this Special Issue, we are looking for contributions helping to address concerns around 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 BMS design and development considerations;
- Major challenges in BMS design of EVs/HEVs and in supercharger infrastructure of EVs;
- Main battery types integrated in EV/HEV 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;
- MATLAB batteries/Simulink models with extension to SIMSCAPE block modeling;
- Cell voltage sampling, battery temperature sampling, battery voltage, and current sampling methods;
- Battery temperature and cell balancing using special integrated 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 the 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. Nicolae Tudoroiu
Guest Editor
Manuscript Submission Information
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Keywords
- battery management system (BMS)
- state estimation
- state of charge (SOC) of the battery
- state of health estimation of the ESS
- cell balancing of the ESS
- aging modeling of ESS
- Kalman filters techniques
- particle filters 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
