Model-in-the-Loop Testing of SOC and SOH Estimation Algorithms in Battery Management Systems

With the increasing application of the lithium ion battery technology in automotive industry, development processes and validation methods for the battery management system (BMS) have drawn more and more attentions. One fundamental function of the BMS is to continuously estimate the battery’s state-of-charge (SOC) and state-of-health (SOH) to guarantee a safe and efficient operation of the battery system. For SOC as well as SOH estimations of a BMS, there are certain non-ideal situations in a real vehicle environment such as measurement inaccuracies, variation of cell characteristics over time, etc. which will influence the outcome of battery state estimation in a negative way. Quantifying such influence factors demands extensive measurements. Therefore, we have developed a model-in-the-loop (MIL) environment which is able to simulate the operating conditions that a BMS will encounter in a vehicle. Due to the high flexibility of this MIL environment, BMS developers are able to investigate quantitatively the influence from the individual or combined factors on their SOC and SOH estimation algorithms. In addition, exemplary test results are introduced to show how this MIL environment provides valuable data and insights to evaluate the accuracy and the robustness of one representative battery algorithm, and to reduce the function development time and costs.