Energy storage system (ESS) technology is still the logjam for the electric vehicle (EV) industry. Lithium-ion (Li-ion) batteries have attracted considerable attention in the EV industry owing to their high energy density, lifespan, nominal voltage, power density, and cost. In EVs, a smart battery management system (BMS) is one of the essential components; it not only measures the states of battery accurately, but also ensures safe operation and prolongs the battery life. The accurate estimation of the state of charge (SOC) of a Li-ion battery is a very challenging task because the Li-ion battery is a highly time variant, non-linear, and complex electrochemical system. This paper explains the workings of a Li-ion battery, provides the main features of a smart BMS, and comprehensively reviews its SOC estimation methods. These SOC estimation methods have been classified into four main categories depending on their nature. A critical explanation, including their merits, limitations, and their estimation errors from other studies, is provided. Some recommendations depending on the development of technology are suggested to improve the online estimation.

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.

The energy storage system (ESS) is the main issue in traction applications, such as battery electric vehicles (BEVs). To alleviate the shortage of power density in BEVs, a hybrid energy storage system (HESS) can be used as an alternative ESS. HESS has the dynamic features of the battery and a supercapacitor (SC), and it requires an intelligent energy management system (EMS) to operate it effectively. In this study, a real-time EMS is proposed, which is comprised of a fuzzy logic controller-based low-pass filter and an adaptive proportional integrator-based charge controller. The proposed EMS intelligently distributes the required power from the battery and SC during acceleration. It allocates the braking energy to the SC on the basis of the state of charge. A simulation study was conducted for three standard drive cycles (New York City cycle, Artemis urban cycle, and New York composite cycle) using MATLAB Simulink. Comparative analysis of conventional and proposed EMSs was carried out. The results reveal that the proposed EMS reduced the stress, temperature, and power losses of the battery. The steady-state charging performance of the SC was 98%, 95%, and 96% for the mentioned drive cycles.

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.

Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.

Abstract: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 growing human population and the increasing energy needs have produced a serious energy crisis, which has stimulated researchers to look for alternative energy sources. The diffusion of small-scale renewable distributed generations (DG) with micro-grids can be a promising solution to meet the environmental obligations. The uncertainty and sporadic nature of renewable energy sources (RES) is the main obstacle to their use as autonomous energy sources. In order to overcome this, a storage system is required. This paper proposes an optimized strategy for a hybrid photovoltaic (PV) and battery storage system (BSS) connected to a low-voltage grid. In this study, a cost function is formulated to minimize the net cost of electricity purchased from the grid. The charging and discharging of the battery are operated optimally to minimize the defined cost function. Half-hourly electricity consumer load data and solar irradiance data collected from the United Kingdom (UK) for a whole year are utilized in the proposed methodology. Five cases are discussed for a comparative cost analysis of the electricity imported and exported. The proposed scheme provides a techno-economic analysis of the combination of a BSS with a low-voltage grid, benefitting from the feed-in tariff (FIT) scheme.

This paper presents a Light Electric Vehicle (LEV) fast charger with a Lithium-Ion Battery (LIB) and Super-Capacitor (SC). The LEV fast charger consists of an AC/DC rectifier and LLC (Inductor-Inductor-Capacitor) resonant Full bridge converter. The LLC resonant converter has high-efficiency and low switching loss because of Zero Voltage Switching (ZVS). So, it is used widely in the industry. In general, the fast charger algorithm uses the Constant Current (CC) mode and Constant Voltage (CV). The CC mode starts at first and then the CV mode finishes. However, there is a big control value gap between the CC mode and CV mode. Therefore, when changing from CC to CV, the transient state occurs. To compensate for the transient state, we propose a new control algorithm. By means of this algorithm, we can achieve a higher level of safety and stability. The fast charger with LIB of 800 Wh and SC of 50 Wh is analyzed and verified, and we obtain a maximum efficiency of 96.4%. The discussions are validated using the LLC resonant full bridge converter prototype at the laboratory level.