Battery, as an energy storage device, is the key component of electric vehicle (EV) and hybrid electric vehicle (HEV). The inspection, control and protection of its running process are critical technologies to enhance the performance of EV and HEV. Battery management system (BMS) can not only prolong battery life-circle and assure battery functions by inspecting and estimating battery state-of-charge (SOC), state-of-function (SOF) and state-of-health (SOH), but also protect drivers and passengers from electric shock in accidents.
The hardware structure of BMS is divided into inspection board and MCU control board:
(1) High voltage / high current signals inspection board, convert the high voltage or high current signals to the small signals from 0 to 5 volt and eliminate the disturbance to the MCU control board.
(2) MCU control board, inspect and calculate real-time parameters, apply control strategies and communication with vehicle management system.
The estimation of SOC, defined as the percentage ratio of the reserved charge to the full charge, is the one of most important and difficult issue in BMS design. The accurate estimation of SOC will prolong the battery life-circle by exempting the destroy operations such as over charge and over discharge. However, SOC can not be measured directly but estimated from other acquirable information. Besides the conventional estimation methods, such as current integral, open circuit voltage, Kalman filter, we propose two ways to estimate SOC:
(1) Estimation method based on data fusion theory, aimed to obtain the better estimation by combining the results of conventional methods.
(2) Robust Kalman filter, aimed to strengthen the robust property of Kalman filter in the unperfected model and colored noise situation.
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