ADAPTIVE CONTROL STRATEGY AND METHOD FOR OPTIMIZING HYBRID ELECTRIC VEHICLES

• US 20090198396A1
• Filed: 02/04/2008
• Published: 08/06/2009
• Est. Priority Date: 02/04/2008
• Status: Active Grant

• Current Assignee: Turntide Technologies, Inc.
• Original Assignee: Illinois Institute of Technology
• Inventors: LUKIC, Srdjan M., RODRIGUEZ, Fernando, EMADI, Ali
• Granted Patent: US 7,954,579 B2
• Time in Patent Office: Days
• US Class Current: 701/22
• CPC Class Codes: B60W 10/06 including control of combustion engines
• CPC Class Codes: B60W 10/08 including control of electric propulsion units, e.g. motors or generators
• CPC Class Codes: B60W 10/26 for electrical energy, e.g. batteries or capacitors
• CPC Class Codes: B60W 20/00 Control systems specially adapted for hybrid vehicles hybrid vehicle design, B60K6/00; electric vehicles B60L
• CPC Class Codes: B60W 20/11 using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance utilising navigation and traffic information in the control strategy B60W20/12
• CPC Class Codes: B60W 2050/0013 Optimal controllers
• CPC Class Codes: B60W 2050/0014 Adaptive controllers
• CPC Class Codes: B60W 2050/0016 State machine analysis
• CPC Class Codes: B60W 2510/244 Charge state
• CPC Class Codes: B60W 30/1882 characterised by the working point of the engine, e.g. by using engine output chart
• CPC Class Codes: Y02T 10/40 Engine management systems
• CPC Class Codes: Y02T 10/62 Hybrid vehicles
• CPC Class Codes: Y02T 10/84 Data processing systems or methods, management, administration
• CPC Class Codes: Y10S 903/93 Conjoint control of different elements

Abstract

This invention relates a control strategy for a hybrid electric vehicle having an electric motor, a battery and an internal combustion engine. The control strategy improves fuel economy and reduces emissions while providing sufficient acceleration over a varying set of driving conditions through an adaptive control unit with an artificial neural network. The artificial neural network is trained on a pre-processed training set based on the highest fuel economies of multiple control strategies and multiple driving profiles. Training the artificial neural network includes a training algorithm and a learning algorithm. The invention also includes a method of operating a hybrid electric vehicle with an adaptive control strategy using an artificial neural network.

Claims

1. A hybrid electric vehicle including an electric motor, a battery and an internal combustion engine, the vehicle comprising:

a power control module having an adaptive control unit for interfacing with sensors and actuators of the electric motor, the battery and the internal combustion engine; and

the adaptive control unit including an artificial neural network that is adapted to changing driving conditions or patterns.

2. The vehicle of claim 1 wherein the hybrid electric vehicle has a configuration of at least one of the group consisting of parallel hybrids, series hybrids and parallel/series hybrids.

3. The vehicle of claim 2 wherein the parallel/series hybrids include a torque coupler.

4. The vehicle of claim 1 wherein the adaptive control unit includes at least one of the group consisting of a digital signal processor, a micro-processor, a field programmable gate array and combinations thereof.

5. The vehicle of claim 1 wherein the vehicle includes at least one of the group consisting of a car, a bus, a truck, a sport utility vehicle and combinations thereof.

6. The vehicle of claim 1 wherein the adaptive control unit seeks to maximize fuel economy, minimize emissions, meet peak power demands and provide acceptable transient response during changing driving conditions.

7. The vehicle of claim 6 wherein the changing driving conditions include urban profiles, highway profiles and combinations thereof.

8. The vehicle of claim 1 wherein the artificial neural network includes an input layer having about 20 neurons, a hidden layer having about 20 neurons and an output layer having about 1 neuron.

9. The vehicle of claim 1 wherein the fuel economy improves by at least between about 1 percent and about 7 percent above a constant causal based control scheme.

10. The vehicle of claim 1 further comprising a training set based on changing driving conditions or profiles for training the artificial neural network.

11. The vehicle of claim 10 further comprising a pre-processing algorithm to operate on the training set and reduce repetitive row vectors.

12. The vehicle of claim 10 wherein the training set includes input and output values based on highest fuel economies for various driving conditions and control schemes.

13. The vehicle of claim 1 further comprising a plug for plug-in hybrid functionality.

14. A method of operating a hybrid electric vehicle comprising:

providing a hybrid electric vehicle having an adaptive control unit, wherein the adaptive control unit includes an artificial neural network;

receiving input signals from sensors of the hybrid electric vehicle;

calculating output signals in the adaptive control unit based on maximizing fuel economy, minimizing emissions, meeting peak power demands and providing acceptable transient response during changing driving conditions; and

sending output signals to an internal combustion engine or an electric motor of the hybrid electric vehicle.

15. The method of claim 14 further comprising training the artificial neural network with a training set having various driving conditions or profiles for a sufficient number of epochs.

16. The method of claim 15 wherein the step of training includes a Levenberg-Marquardt training algorithm and a gradient descent learning algorithm with momentum weight/bias.

17. The method of claim 14 further comprising applying a pre-processing algorithm to the training set for reducing repetitive vectors.

18. The method of claim 14 further comprising periodically updating the step of calculating based on age or wear of drivertrain components.

19. The method of claim 14 wherein the artificial neural network seeks to maximize fuel economy, minimize emissions, meet peak power demands and provide acceptable transient response during changing driving conditions or profiles including urban and highway use.

20. A control scheme for a hybrid electric vehicle including an electric motor and an internal combustion engine, the control scheme comprising:

an adaptive control strategy executing in an artificial neural network to receive input signals, optimize the hybrid electric vehicle and produce output signals to at least one of the electric motor or the internal combustion engine.