智能电池管理系统

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.

Key Investigators: Prof. Yangsheng Xu, Jingyu Yan

相关内容

• 智能电池管理系统
• 全方位转向汽车
• 混合动力能量管理
• 智能全方向混合动力车
• 混合动力车能源管理技术
• 智能电子安全
• 四轮驱动及四轮转向

　　电池作为一种储能设备，是电动汽车和混合动力汽车的核心部件。对其运行过程的监测、控制与保护是提高电动车和混合动力车性能的关键技术之一。电池管理系统不仅能够通过监测计算电池荷电状态、功能状态以及健康状态等参数来延长电池寿命、保证电池功能；同时在意外情况发生时，提供对车载人员的安全保护，避免触电危险。

　　电池管理系统硬件结构分为检测和控制两部分：

(1) 高压大电流信号检测板，将被测高压大电流信号转换为0至5伏之间的小信号，隔离大信号对控制板的影响。
(2) 微处理器控制板，监测计算各实时参数，实施控制策略，并与整车控制器通信。

　　电池荷电状态的估计是电池管理系统设计的重点与难点，其定义为电池剩余电量占满载电量的百分比。获取精确的荷电状态，能够有效的避免过充电、过放电等对电池的破坏性操作，延长电池寿命。然而，荷电状态为不可直接测量的参数，只能通过其它可获取信息对其进行估计。除常规的电流积分法、开路电压法、卡尔曼滤波法外，还提出两种途径：

(1) 基于数据融合的估计方法，以期综合常用方法的结果来获取一个更优的估计。
(2) 鲁棒卡尔曼滤波方法，以期在存在建模误差及有色噪声的情况下，加强卡尔曼滤波器的鲁棒性。