Sophisticated estimation of hardly measurable conditions of lithium-ion batteries

The major tasks of Battery Management Systems (BMS) are to guaranty safe operation conditions and to maintain every single cell of the hole battery pack. These tasks require measurements and balancing processes for each cell. Modern BMS take advantage of the wealth of information to estimate hardly measurable conditions like State of charge (SoC) or State of Health (SoH). This paper describes a method to estimate the State of Charge for every single cell in a battery pack using an Unscented Kalman Filter (UKF) running on an electronic control unit of the BMS. The verification of the developed algorithms on the control unit takes a long time on a real battery system. For that purpose a real-time Hardware-in-the-Loop (HiL) test bench is developed. In this test bench a LiFePO4 cell model was implemented by Matlab/Simulink®. So the developed and embedded algorithm can be verified by means of various test cases. In this paper the results are presented on signal level. Future work will include a HiL test bench on power level together with an cell emulator. Beside this the test bench offers the opportunity to develop models and sophisticated algorithms for further beneficial state variables like the SoH or the inner temperature of each single cell of the battery pack.