Abstract
Reliable online co-estimation of state of health (SOH) and state of charge (SOC) of Li-ion batteries were of paramount importance for the realistic battery management system (BMS). This work aimed at bridging laboratory test and real-life battery operation data with a comprehensive analysis to provide a coherent and non-invasive approach based on probability density function (PDF), for building a pragmatic model to co-evaluate SOH and SOC of Li-ion batteries for smart grid applications. PDF results based on practical applications revealed that there was a prominent regularity of the voltage probabilities with regards to the SOH, which were exploited for setting up an online SOH evaluation scale gauge. Utilization of the load current of realistic smart grid further improve the practical generality of the proposed algorithm. The battery online SOC was determined afterwards based on the online OCV variation and the extracted SOH values. Commercial Li-ion batteries at arbitrary SOH and SOC level were tested to validate the effectiveness and robustness of the proposed algorithm, and the test results showed high accuracy and reliability of the proposed algorithm for co-evaluating SOH and SOC.
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