Battery Management Systems of Electric and Hybrid Electric Vehicles

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

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