Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics.

Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics

Adaptive Estimation of Thermal Dynamics and Charge Imbalance in Battery Strings.

Effective battery management relies on accurate monitoring of battery states, including temperature, state of charge, and voltage among others. The large number of cells used in battery packs for vehicle applications require expensive monitoring hardware, which includes sensors, wiring, data acquisition and computation capacity. Due to the cost and complexity of the hardware, reduced sensing with limited and non-intrusive measurements is pursued by all manufacturers. In this dissertation, first, the monitoring of battery thermal dynamics based on only a limited number of sensors mounted on the surface of few cells is considered. Such scheme is augmented with model-based estimation techniques to capture the temperature gradient both across a single cell and among cells in the battery pack. Second, for lithium ion battery, the voltage of every single cell is currently measured to prevent overcharge and overdischarge. This dissertation develops nonlinear estimation techniques for reducing the individual cell voltage sensing requirement. Specifically, in the first part of this dissertation, a model-based estimator using surface temperature measurement and continuously identified parameters is designed for adaptive prediction of the cell core temperature. The model-based estimation is then extended for the thermal network of cells inside a pack. Based on the battery string thermal model, the number of sensors and their location required for full observability is investigated, followed by an optimal observer design under the frugal sensor allocation and cell-to-cell variability. In the second part of this dissertation, reduced voltage sensing, which relies on measuring the total voltage of multiple cells, is considered to replace the existing single-cell voltage sensing system. The feasibility of state of charge estimation under reduced voltage sensing is first investigated based on observability analysis. Nonlinear observers are then designed for SOC estimation and validated by experiments. The results are later extended to the case when both SOC and capacity imbalance exist in the battery string due to non-uniform cell self-discharge rates, cell degradation, and manufacturing variability. The developed estimation technique provides the potential of reducing the voltage sensing in battery packs by half.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108789/1/xflin_1.pd

An Electro-thermal Model for the A123 26650 LiFePO4 Battery

An electro-thermal model consisting of an equivalent-circuit electrical model and a two-state lumped thermal model is constructed for an A123 26650 LiFePO4 battery. The electrical and the thermal sub-models are coupled through heat generation and temperature dependency of the electrical parameters. The 5-state model captures the state of charge, voltage, surface temperature, and core temperature of a battery and is computationally efficient. The electrical and the thermal models are parameterized by pulse-relaxation and drive-cycle tests separately, where the electrical parameters are identified as dependent on temperature, SOC and current direction.Cooperative Agreement W56HZV-04-2-0001 U.S. Army Tank Automotive Research, Development and Engineering Center (TARDEC)http://deepblue.lib.umich.edu/bitstream/2027.42/97341/3/Battery Electrothermal Model-1.zip2

Parameterization and Observability Analysis of Scalable Battery Clusters for Onboard Thermal Management

Although the battery surface temperature is commonly measured, the core temperature of a cell may be much higher hence more critical than the surface temperature. The core temperature of a battery, though usually unmeasured in commercial applications, can be estimated by an observer, based on a lumped-parameter battery thermal model and the measurement of the current and the surface temperature. Even with a closed loop observer based on the measured surface temperature, the accuracy of the core temperature estimation depends on the model parameters. For such purpose, an online parameterization methodology and an adaptive observer are designed for a cylindrical battery. The single cell thermal model is then scaled up to create a battery cluster model to investigate the temperature pattern of the cluster. The modeled thermal interconnections between cells include cell to cell heat conduction and convection to the surrounding coolant flow. An observability analysis is performed on the cluster before designing a closed loop observer for the pack. Based on the analysis, guidelines for determining the minimum number of required sensors and their exact locations are derived that guarantee the observability of all temperature states

Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics.

Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics

Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics.

Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters were validated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics