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Batteries used as energy storage devices will play an important role in future power systems due to the growing popularity of micro grids (MG) and plug-in hybrid electric vehicles (PEVs). The performance of the MGs and PEVs strongly relies on their battery bank management systems, which always consists of multiple cells connected together in series...
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... circuit may perfectly fit the experimental data but require tremendous amount of computational time and memory to solve detailed partial differential equations of the battery model. Complex models are limited in their application for embedded control and real-time applications. The equivalent circuit model used in this design is given in Fig. 1, which is a commonly used representation of a lithium ion cell that simplifies their numerical analysis while sufficiently accounting for all the dynamic characteristics of the cell. The series resistance simulates the short term instant transient response while the parallel resistance and capacitance simulates the long term ...
Citations
... The proposed method improved the battery performance by controlling the temperature and consequently achieved overshoot and undershoot of 0.497% and 0.975% during the heating and cooling subroutine, respectively. Ma et al. (2014) designed a novel TMS based on FLC to handle the inconsistencies of series-connected lithium-ion batteries. The developed model increased the safety of the battery system effectively by regulating the temperature and balancing SOC during the charging and discharging processes. ...
Globally, the research on battery technology in electric vehicle applications is advancing tremendously to address the carbon emissions and global warming issues. The effectiveness of electric vehicles depends on the accurate assessment of key parameters as well as proper functionality and diagnosis of the battery storage system. However, poor monitoring and safety strategies of the battery storage system can lead to critical issues such as battery overcharging, over-discharging, overheating, cell unbalancing, thermal runaway, and fire hazards. To address these concerns, an effective battery management system plays a crucial role in enhancing battery performance including precise monitoring, charging-discharging control, heat management, battery safety, and protection. The goal of this paper is to deliver a comprehensive review of different intelligent approaches and control schemes of the battery management system in electric vehicle applications. In line with that, the review evaluates the intelligent algorithms in battery state estimation concerning their features, structure, configuration, accuracy, advantages, and disadvantages. Moreover, the review explores the various controllers in battery heating, cooling, equalization, and protection highlighting categories, characteristics, targets, achievements, benefits, and shortcomings. The key issues and challenges in terms of computation complexity, execution problems along with various internal and external factors are identified. Finally, future opportunities and directions are delivered to design an efficient intelligent algorithm and controller toward the development of an advanced battery management system for future sustainable electric vehicle applications.
... The design of a novel fuzzy logic based battery bank management system is presented [126]. The battery bank management system has self-healing capabilities to protect itself from SoC and thermal imbalances. ...
Load shedding and blackout are common features of poorly maintained electric power systems. In many developing and undeveloped countries load shedding is common for hours. In such cases, a low power backup supply for emergency loads are extensively used in millions of household. Normally, lead-acid batteries are used for energy storage but the capacity of these batteries are very low (few kWh only depending upon number and capacity of the battery). The capacity of battery which is commonly used in a conventional Indian home is 12 V and 180 AH. As the actual capacity of the lead-acid battery is about 70%. Thus the effective energy which can be retrieved is 1.512 kWh only hence, the backup period is very less. However, an electric vehicle (EV) has enormous energy storage capacity (normally above 10 kWh). Except Toyota Prius PHEV, the middle-sized electric vehicles have 22–32kWh battery package. The luxury sedan like Tesla S, has a range of 60–90 kWh to provide long driving range. Thus the energy storage capacity of any type of EV can be easily utilized, during the idle period of vehicle, as a standby power supply for home.
By introducing Vehicle-to-Home (V2H), Home-to-Vehicle (H2V) electric power system and solar PV system, a distributed generation system based nanogrid has been developed. For this purpose, the power electronic controllers are developed to implement the control scheme effectively. Here, batteries of plug-in electric vehicle (PEV) are charged by the grid as well as by solar PV which supply power during the peak-load-period as well as during the load shedding and blackout.
The PEV can supply power to the residential loads at the peak-demand-period when the vehicle reaches home. The charging of the battery is shifted to the night during the off-peak-load period. Thus, the batteries of PEV work as an energy storage system for microgrid. Moreover, to reduce dependency on the grid, solar energy is also utilized.
This work emphasizes the utilization of plug-in electric vehicle as a backup power supply for residential loads in developing and underdeveloped countries as a part of the residential microgrid, without affecting its operation as an electric vehicle. The proposed V2H system also uses solar PV based charging of vehicle so that the whole system works as a nanogrid. The PEV is considered as a load of home when its batteries are charged by solar PV or grid. However, the main emphasis is given to utilize solar energy to reduce charging from the grid. Both slow DC charging and fast DC charging are applied besides constant voltage and constant current charging modes during the daytime to harness and utilize more solar energy.
The main objectives of the proposed work are to minimize the energy cost of a household, to reduce the dependency on the grid, to enhance the reliability of power supply to residential loads during load shedding and blackouts, and to maximize the utilization of power produced by solar PV array mounted on the rooftop. Moreover, both AC loads and DC loads are connected separately to AC bus and DC bus, respectively to avoid power losses in DC to AC and the AC to DC conversion.
A model of V2H system based on the fuzzy logic controller for managing the battery of the vehicle, rooftop solar PV array, emergency backup power, DC and AC residential loads and grid power is also proposed. An effective and reliable fuzzy logic controller based on Sugeno inference system has been designed and developed which is optimized by adaptive neuro-fuzzy inference system for better performance.
The main contributions of the work described and depicted in this thesis are:
• A power electronic controller based vehicle-to-home (V2H) power system has been developed:
i) to minimize the cost of energy consumption of the household
ii) to reduce dependency on the electric power grid
iii) to enhance the reliability of power supply to a household during load shedding/blackout, and
iv) to utilize rooftop solar PV based power generation optimally.
• Best utilization of enormous energy storage capacity of PEV with two different power sources (grid and solar PV).
• The proposed system worked as a residential nanogrid with the solar energy system.
• The proposed system also works at islanding condition.
• DC bus for DC loads has been used to avoid power loss due to the use of DC to AC and the AC to DC converters.
• The priority of utilizing electric power sources is also set which is as follows:
(i) solar photovoltaic array (SPV), (ii) plug-in electric vehicle (PEV), (iii) Grid, and (iv) emergency backup power supply.
• The fuzzy logic controller is developed for the proposed V2H system.
• Optimization of the fuzzy rule-based controller is developed for V2H system.
... The ESSs such as battery and supercapacitor (SC) storages are the essential interface to connect the PPLs to the grid. The battery energy storages are usually used in the DC microgrid with PPLs to compensate the requirements of the load in the critical conditions [7], [8]. Although the density of battery storages are relatively high, their volumetric power density is relatively low. ...
... The main ways to increase the power capacity of the batteries is to use multiple cells. However, the power-cost tradeoff and current sharing result in non-optimal battery storages [7]. Thus, battery storages are not capable to compensate the high power created by PPLs as primary power buffers. ...
... D. Dynamic modeling of the main supplier of the DC grid Fig. 4 illustrates the model and connection structure of the main supplier to the DC-link. Based on Fig. 4 7 . By considering the PPL as an unknown disturbance (i.e. ...
This paper proposes design of optimal and robust coordinated controller of hybrid energy storage system in a naval DC microgrid (MG) application. It is able to mitigate the negative effects of the pulsed power loads and meet the practical limitations of both converters input control and state variables signals based on IEEE standards. To do this, first, the dynamic model of the DC MG, which can represent in either all-electric aircraft or shipboard power systems is developed. Second, a novel model predictive controller (MPC) for energy storage converters is proposed such that all of the mentioned hard constraints are guaranteed. Third, a linear matrix inequality (LMI) approach is used to solve the MPC conditions. Finally, to evaluate the applicability and effectiveness of the proposed approach, some experimental tests are extracted. Obtained results verify better performance of the proposed approach over other state of the art control techniques.
... These estimators are often combined with a suitable model representation of the battery system in order to adapt to the nonlinearities inherent to the battery dynamics. Whereas Fuzzy Logic [3] and Artificial Neural Network [4] estimators require large model-training datasets, those observer-based estimators, such as sliding-mode observer [5], may suffer from lack of persistence of excitation of battery input/output signals [6]. ...
... Mohamed et al. [15][16][17] proposed some algorithms or schemes for mitigation of pulsed load. The impact of changes in the power of the inverter load on output characteristics of diesel generator set was analyzed in [18]. ...
Unlike traditional load, pulsed load typically features small average power and large peak power. In this paper, the mathematic models of microgrid consisting of synchronous generator and pulsed load are established. Average Magnitude Difference Compensate Function (AMDCF) is proposed to calculate the frequency of synchronous generator, and, based on AMDCF, relative deviation rate (RDR) which characterizes the impact of pulsed load on the AC side of grid is firstly defined and this paper describes calculation process in detail. Insulated Gate Bipolar Transistor (IGBT) is used as DC switch to control the on/off state of resistive load for simulating pulsed load, the period and duty-cycle of the pulsed load are simulated by setting the gate signal of IGBT, and the peak power of the pulsed load is simulated by setting the resistance. The system dynamic characteristics under pulsed load are analyzed in detail, and the influence of duty-cycle, period, peak power, and filter capacitance of the pulsed load on system dynamic indicators is studied and validated experimentally.
... Therefore, without properly planning the scale of the renewable energy farm, the utility grid may be badly influenced by the power generated by the renewable energy farm in the local micro grid (MG). This problem can be solved by utilizing energy storage devices such as batteries and ultracapacitors together with an energy management algorithm as well as high accuracy weather and load forecasting techniques [5]. A carefully designed hybrid power system with reasonable size of the renewable energy farm and energy storage system can greatly improve the power system performance and reduce the total cost. ...
This paper proposes a novel optimization method for sizing a renewable energy farm consisting of batteries and ultra-capacitors in a hybrid power system. The combination of ultra-capacitors with batteries is an emerging practice in advanced power electronic systems and a superior configuration scheme is crucial to deploy them effectively with the high penetration of renewable energy sources and critical loads in future power systems. The proposed sizing method fully utilizes the energy generated from the renewable energy farm and limits power fluctuation within the utility grid, improving grid stability and reducing construction and maintenance costs. This two-step optimization process appropriately sizes the renewable energy farm and the energy storage system by using a genetic algorithm (GA). Regional historical data of the solar irradiance, wind speed, and local load profile of Key West, Florida is used to establish the first cost function for optimizing the combination of PV and wind power based on the entire year's daily energy difference between the renewable energy farm and twenty percent of the local load. With the optimized renewable energy farm size, a second cost function is designed to get the optimal combination of battery and ultra-capacitor sizes to smooth the impact caused by the renewable energy farm. A case study of a hybrid power system located in Key West, Florida is presented to verify the advantages of the proposed optimal sizing method.
A rapid development is taking place in the field of renewable energy sources to increase the power generation because these sources are eco-friendly, non-polluting, freely available in nature sources like solar wind, biomass, hydro, and tidal. These renewable sources are mostly uncontrollable and all the same time different methods should be done to build a power plant to generate a continuous and constant power. The selection of the renewable energy source for the plant is one of the important roles for energy optimization. This is mainly focused toward the solar and wind power combination, whereas the solar system is the major renewable energy source for energy generation. In this work, a dynamic hardware model for an intelligent control-based effective utilization of hybrid renewable energy sources and Battery Management System. It also explains the implementation of fuzzy logic algorithm. The Battery Management System (BMS) is simulated in MATLAB software by using fuzzy logic controller (FLC). BMS explains the charging state and discharging state of battery. Then, it is implemented in hardware model for effective utilization of renewable energy sources. The identification of each subsystem has been made, and then, the proposed system is modeled and simulated using MATLAB—Simulink package. The proposed control strategy has been experimentally implanted, and practical results are compared to those obtained by simulation under the same metrological conditions, showing the effectiveness of the proposed system.
KeywordsBMSFLCMATLABFuzzy logic algorithmIntelligent control
