Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries

作者：Andre Dave^*; Appel Christian; Soczka Guth Thomas; Sauer Dirk Uwe

来源：Journal of Power Sources, 2013, 224: 20-27.

DOI：10.1016/j.jpowsour.2012.10.001

• State of health
• Lithium-ion battery
• State of charge
• Battery management system
• Unscented Kalman filter
• Support vector regression

Two novel methods to estimate the state of charge (SOC) and state of health (SOH) of a lithium-ion battery are presented. Based on a detailed deduction, a dual filter consisting of an interaction of a standard Kalman filter and an Unscented Kalman filter is proposed in order to predict internal battery states. In addition, a support vector machine (SVM) algorithm is implemented and coupled with the dual filter. Both methods are verified and validated by cell measurements in form of cycle profiles as well as storage and cycle ageing tests. A SOC estimation error below 1% and accurate resistance determination are presented.

• 出版日期2013-2-15