Abstract
States estimation of lithium-ion batteries is an essential element of Battery Management Systems (BMS) to meet the safety and performance requirements of electric and hybrid vehicles. Accurate estimations of the battery’s State of Charge (SoC), State of Health (SoH), and State of Power (SoP) are essential for safe and effective operation of the vehicle. They need to remain accurate despite the changing characteristics of the battery as it ages. This paper proposes an online adaptive strategy for high accuracy estimation of SoC, SoH and SoP to be implemented onboard of a BMS. A third-order equivalent circuit model structure is considered with its state vector augmented with two more variables for estimation including the internal resistance and SoC bias. An Interacting Multiple Model (IMM) strategy with a Smooth Variable Structure Filter (SVSF) is then employed to determine the SoC, internal resistance, and SoC bias of a battery. The IMM strategy results in the generation of a mode probability that is related to battery aging. This mode probability is then combined with an estimation of the battery’s internal resistance to determine the SoH. The estimated internal resistance and the SoC are then used to determine the battery SoP which provides a complete estimation of the battery states of operation and condition. The efficacy of the proposed condition-monitoring strategy is tested and validated using experimental data obtained from accelerated aging tests conducted on Lithium Polymer automotive battery cells.
Highlights
Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are creating a disruptive change in the automotive industry as they present a sustainable alternative to their fossil-fuel based counterparts
The battery management system (BMS) must provide an accurate estimation of the State of Charge (SoC), State of Health (SoH), and the State of Power (SoP) of a battery pack [1], [4]
Μr where 0 < SOHi < 1, i = 1, ..., r presents the SoH for mode i and can be defined based on the models described for the Interacting Multiple Model (IMM)-Smooth Variable Structure Filter (SVSF)-VBL method
Summary
Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are creating a disruptive change in the automotive industry as they present a sustainable alternative to their fossil-fuel based counterparts. This approach requires regular calibration due to measurement errors and noise. These models can be optimized to capture the changing dynamics of the battery while aging or operating at different regions This method of model selection guarantees stability, and provides information on the SoH concurrently with SoC. This paper includes the following contributions: 1) A modified equivalent circuit model formulation is presented that considers observability in the context of estimating the states and parameters related to the SoH of the battery including the internal resistance, the SoC, and its bias.
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