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Batteries are one of the most compact and reliable sources of sustainable energy. Lead-Acid batteries are the battery-powered sort of batteries concocted during the 1980s. The significant utilization of lead-acid battery is in beginning, lighting and start frameworks of vehicles.To guarantee the health and to dodge potential disappointments of a battery it is important to examine its Territory of health precisely. This examination expects to give efficiently evaluating the accessible writing on the condition of health estimation techniques. This study focuses on many factors and provides a suggestion for the defended battery manufacturing process. This study provides increasing efforts toward the advancement of battery interms of specific power, energy density, durability, invulnerability, economics, and performance in various applications.

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A Battery Management System (BMS) can prolong the life of the battery but it depends on the accuracy of the adopted scheme. Different techniques have been developed to enhance the BMS by monitoring the State of Health (SOH) of the battery. In this paper, the detection of battery voltage is analyzed by using the cycle counting method, which is a conventional technique and compared with Artificial Neural Network (ANN), a heuristic method. The advantage of the proposed ANN method is that SOH can be monitored without disconnecting the battery from the load. Also, the sampling data to the ANN are derived from various techniques including Open Circuit Voltage (OCV) method, Ambient temperature measurement, and valley point detection. A feed-forward backpropagation algorithm is used to achieve the purpose of real-time monitoring of the LAB. The results show that the precise estimation of SOH can be obtained by Feed-Forward Neural Network (FFNN) when trained with more sampling data.