The lithium-ion battery has high energy and power density, long life cycle, low toxicity, low discharge rate, more reliability, and better efficiency compared to other batteries. On the other hand, the issue of a reduction in charging time of the lithium-ion battery is still a bottleneck for the commercialization of electric vehicles (EVs). Therefore, an approach to charge lithium-ion batteries at a faster rate is needed. This paper proposes an efficient, real-time, fast-charging methodology of lithium-ion batteries. Fuzzy logic was adopted to drive the charging current trajectory. A temperature control unit was also implemented to evade the effects of fast charging on the aging mechanism. The proposed method of charging also protects the battery from overvoltage and overheating. Extensive testing and comprehensive analysis were conducted to examine the proposed charging technique. The results show that the proposed charging strategy favors a full battery recharging in 9.76% less time than the conventional constant-current–constant-voltage (CC/CV) method. The strategy charges the battery at a 99.26% state of charge (SOC) without significant degradation. The entire scheme was implemented in real time, using Arduino interfaced with MATLAB^TM Simulink. This decrease in charging time assists in the fast charging of cell phones and notebooks and in the large-scale deployment of EVs.

The increasing world human population has given rise to the current energy crisis and impending global warming. To meet the international environmental obligations, alternative technological advances have been made to harvest clean and renewable energy. The solar photovoltaics (PV) system is a relatively new concept of clean technology that can be employed as an autonomous power source for a range of off-grid applications. In this study, the dual battery storage system is coupled with a solar PV system and a low voltage grid, benefitting from the feed-in tariff (FIT) policy. The main outcomes of this study are: (I) A novel dual battery storage system for the optimal use of the PV system/energy is proposed; (II) The problem is formulated in the form of a mathematical model, and a cost function is devised for effective cost calculation; (III) An optimal cost analysis is presented for the effective use of PV energy; (IV) real-time data of a solar PV taken from the owner and the demand profile collected from the user is applied to the proposed approach, with United Kingdom (UK) tariff incentives. This system works in a loop by charging one system from the solar PV for one day, and discharging the other system. This model gives certainty that power is exported to the grid when the solar PV generates an excess amount; batteries are utilized during the peak hours, and power is purchased when the demand is not met by the batteries, or when the demand is higher than the generation. This study examined the economic knowledge of solar PV and battery storage systems by considering the FIT incentives.

Reliable and accurate state of charge (SOC) monitoring is the most crucial part in the design of an electric vehicle (EV) battery management system (BMS). The lithium ion battery (LIB) is a highly complex electrochemical system, which performance changes with age. Therefore, measuring the SOC of a battery is a very complex and tedious process. This paper presents an online data-driven battery model identification method, where the battery parameters are updated using the Lagrange multiplier method. A battery model with unknown battery parameters was formulated in such a way that the terminal voltage at an instant time step is a linear combination of the voltages and load current. A cost function was defined to determine the optimal values of the unknown parameters with different data points measured experimentally. The constraints were added in the modified cost function using Lagrange multiplier method and the optimal value of update vector was determined using the gradient approach. An adaptive open circuit voltage (OCV) and SOC estimator was designed for the LIB. The experimental results showed that the proposed estimator is quite accurate and robust. The proposed method effectively tracks the time-varying parameters of a battery with high accuracy. During the SOC estimation, the maximum noted error was 1.28%. The convergence speed of the proposed method was only 81 s with a deliberate 100% initial error. Owing to the high accuracy and robustness, the proposed method can be used in the design of a BMS for real time applications.

The increasing world human population has given rise to the current energy crisis and impending global warming. To meet the international environmental obligations, alternative technological advances have been made to harvest clean and renewable energy. Solar photovoltaic (PV) systems are a relatively new concept of clean technology that can be employed as an autonomous power source for a range of off-grid applications. In this study, two battery storage systems were coupled with a solar PV system and low voltage grid, benefitting from the feed-in tariff (FIT) policy. This system works in a loop by charging one system from the solar PV for one day and discharging the other system. A mathematical model was formulated, which considered the FIT policies on the revenue system. This model provides certainty that power is exported to the grid when solar PV generates an excess amount; batteries are utilized during the peak hours and power is purchased when the demand is not met by the batteries or when the demand is higher than generation. Real time data of a solar PV was taken from the owner, and the demand profile was collected from the user to determine the significance of two batteries under the time-of-use tariff. This study examined the economic knowledge of solar PV and battery storage systems by considering the FIT incentives.