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Generalized Adaptation of an Electric-Hydraulic hybrid drive system
Principal Investigator:
James Long, Oregon Institute of Technology
Co-Investigators:
- Steve Edgeman, Oregon Institute of Technology
- Xin Wang, Oregon Institute of Technology
Summary:
KersTech Vehicle Systems is in the process of creating a hybrid drive system combining a flat format hydraulic motor with an electric motor for energy recapture of vehicle momentum. The initial drive system is targeted at a specific vehicle application. A main component of the drive system being developed is a controller and the associated algorithm. The controller algorithm is a new adaptation of...
KersTech Vehicle Systems is in the process of creating a hybrid drive system combining a flat format hydraulic motor with an electric motor for energy recapture of vehicle momentum. The initial drive system is targeted at a specific vehicle application. A main component of the drive system being developed is a controller and the associated algorithm. The controller algorithm is a new adaptation of an optimally robust algorithm for model uncertainties and resilience against control feedback gain perturbations. This achieves general performance criteria to secure quadratic optimality with inherent asymptotic stability property together with quadratic dissipative type of disturbance reduction. The control system will be generalized for support of standard vehicle systems deriving a specification for sizing of algorithm parameters, behavior, and basic drive components. The combination of control algorithm, control algorithm implementation, electric-hydraulic hybrid drive will be tested to produce plans for system scaling and generalized application. The end result will be an electric-hydraulic hybrid drive prototype targeted at a specific test platform yet capable of adaptation to any vehicle of a similar weight. Early simulations indicate a 20 - 30% increase in efficiency over typical electric vehicles in frequent start-stop drive cycles. This translates into an increase in EV range and runtime and potential decrease in battery bank size and cost.
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Project Details
| Project Type: | Research |
|---|---|
| Project Status: | Completed |
| End Date: | December 31, 2016 |
| UTC Funding: | $90,180 |
Downloadable Products
- Field Oriented Control of Induction Motors Using Symmetrical Optimum Method with Applications in Hybrid Electric Vehicles (PUBLICATION)
- The Generalized State Dependent Riccati Equation Control of Continuous Time Nonlinear Systems (PUBLICATION)
- Robust and resilient state dependent control of discrete-time non-linear systems with general performance criteria (FINAL_REPORT)