Interacting Multiple Model Strategy for Electric Vehicle Batteries State of Charge/Health/ Power Estimation

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.

Adaptive Smooth Variable Structure Filter Strategy for State Estimation of Electric Vehicle Batteries

“…Updates of the model parameters or possibly its structure are needed for determining the battery SoH. The model can be modified using two approaches including model switching or parameter updating or estimation [15,46]. Parameter estimation can be employed if observability condition is satisfied.…”

“…and there exists a k ∈ Z such that ∂V k ocv ∂SOC k = 0 [15]. Therefore, estimation strategies can be used here for state and parameter estimation.…”

“…Online parameter estimation is commonly considered to improve the performance by adapting the model for different conditions [14]. Multiple model strategies can also increase the adaptation of a battery by considering a range of scenarios [15,16]. Once a model is chosen to determine the dynamics of the battery, a robust filter is needed to estimate the states of the battery [17][18][19].…”

“…MM methods consider switching between a finite number of models to provide adaptability against changes and uncertainties. Different forms of multiple model methods have been proposed for state and parameter estimation of batteries [15,16,35]. Filter tuning methods, on the other hands, are used to adjust filter and model parameters as the system changes.…”

“…where k+1 = z k+1 − z k+1|k is the innovation sequence, C v k+1 is the innovation sequence covariance matrix, m is the number of measurements, and α is the adaptive parameter. The z k+1|k is obtained from Equation (15). The logarithmic form of the above equation is…”