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.

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Lithium-ion battery State of Charge (SoC) estimation for Electric Vehicle (EV) applications must be robust and as accurate as possible to maximize battery utilization and ensure safe operation over a wide range of operating conditions. SoC estimation commonly utilizes filters such as the Extended Kalman Filter (EKF) which rely on battery models, usually in the form of Equivalent Circuit Models (ECM). At low temperatures the battery response to current draw becomes increasingly non-linear, resulting in amplified SoC estimation errors. In this study, current dependent SoC estimation at low temperature is proposed using an Interacting Multiple Model (IMM) filter with three ECMs covering a range of C-rates. The IMM is combined with the Smooth Variable Structure Filter (SVSF) to obtain robust SoC estimates within a SoC estimation error of 2%.

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Battery Management Systems (BMSs) are used to manage the utilization of batteries and their operation in Electric and Hybrid Vehicles. It is imperative for efficient and safe operation of batteries to be able to accurately estimate the State of Charge (SoC), State of Health (SoH) and State of Power (SoP). The SoC and SoH estimation must remain robust and accurate despite aging and in presence of noise, uncertainties and sensor biases. This paper introduces a robust adaptive filter referred to as the Adaptive Smooth Variable Structure Filter with a time-varying Boundary Layer (ASVSF-VBL) for the estimation of the SoC and SoH in electrified vehicles. The internal model of the filter is a third-order equivalent circuit model (ECM) and its state vector is augmented to enable estimation of the internal resistance and current bias. It is shown that system and measurement noise covariance adaptation for the SVSF-VBL approach improves the performance in state estimation of a battery. The estimated internal resistance is then utilized to improve determination of the battery’s SoH. The effectiveness of the proposed method is validated using experimental data from tests on Lithium Polymer automotive batteries. The results indicate that the SoC estimation error can remain within less than 2% over the full operating range of SoC along with an accurate estimation of SoH.

Bearing condition monitoring is a widely studied field, but applications to the automotive industry have received little attention as the bearing failure rates are typically low in traditional internal combustion engine vehicles with 200 – 300k mile lifespans. The rapid advancement of electric and autonomous vehicles enables vehicles with million-mile lifespans. This implies that the reliable life of existing bearing designs is exceeded throughout the vehicle life, which can potentially lead to vehicle failure. To enable the development of a bearing fault detection and prognostics system, healthy and faulty bearing data must be collected, and the ground-truth states of the health of bearings need to be determined for algorithm refinement and validation. This work explores the fault injecting options, and ground-truthing together with their limitations. Two methods based on precision machining and seeded spalling are developed and used to inject inner race faults in a ball bearing. A non-invasive ground-truthing method is proposed to quantify the state of health of the fault injected bearings in which bench test data is collected under various speed and load conditions. The vibration signals from the bench tests are used to calculate the root-square of the area under the acceleration Power Spectral Density curve (known as GRMS) for each speed and load condition. To remove the dependency of the results on load and speed conditions, a speed-load-GRMS plot is generated, and a plane is fitted to the data for each fault level. Next, the volume under the plot is calculated, yielding a single cumulative GRMS value for each fault level. This value is used as the ground-truth health of bearing for each fault level. For the bearing with the faults injected using precision machining fault injection, the obtained ground-truth values are 1.56, 3.68, and 4.36 times larger than the same figure for the healthy bearing for the faults with the widths of 0.1 mm, 0.5 mm, and 2 mm, respectively. The observed correlation between the fault sizes and the calculated ground-truth values validates the proposed method which can provide a good separation among different health states of a bearing.