<![CDATA[Electric cars are currently being developed by many people because of low pollution and many countries used them in their daily activity. One of the important and main component is a battery, especially the Battery Management System (BMS) which can optimize the implementation of electric cars. BMS can protect and maintain the battery performance efficiently and at the same time can be a fault detection. Basically, It has three important parameters, there are current, voltage, and temperature that must be maintained and there is no overcurrent, overcharging, and discharging for too long because it can cause a fire. The protection of the BMS on electric cars need battery testing and done by taking current and voltage data, which prioritizes discharging and overdischarging test with a capacity of 2,2 Ah or a maximum capacity of 4,2 Volt. This research optimizes the work of BMS when experiencing faults/errors in order to work properly. The battery is modelled with a simple battery model (Rint) which previously identified parameters and formed a state space that aims to make fault detection. The results showed that fault detection using the Kalman Filter algorithm is very efficient and reliable in improving readings of overcurrent and overdischarge data so as to maintain security and extend/lifetime battery so that it can be implemented safely by the public]]>
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