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Updated: Jul 7, 2026
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Toyota Prius HEV neurocontrol and diagnostics.
1Toyota Technical Center, A division of Toyota Motor Engineering and Manufacturing North America (TEMA), Ann Arbor, MI 48105, United States. dvprokhorov@gmail.com
Summary
A new neural network controller enhances Toyota Prius fuel efficiency and detects battery faults. This advanced control system uses recurrent neural networks and the extended Kalman filter for improved performance.
Area of Science:
- Automotive Engineering
- Artificial Intelligence
- Control Systems
Background:
- Hybrid electric vehicles (HEVs) require sophisticated control systems for optimal performance and efficiency.
- Battery health is critical for HEV operation, and faults can significantly impact efficiency and safety.
Purpose of the Study:
- To develop a novel neural network controller for enhancing the fuel efficiency of the Toyota Prius hybrid electric vehicle.
- To introduce an advanced method for detecting and mitigating battery faults in HEVs.
Main Methods:
- Implementation of a neural network controller utilizing recurrent neural networks (RNNs).
- Integration of the extended Kalman filter (EKF) for robust state estimation and fault detection.
- Application of the proposed approach to the Toyota Prius hybrid electric vehicle control system.
Main Results:
- Demonstrated improvement in fuel efficiency for the Toyota Prius.
- Successful detection and mitigation of simulated battery faults.
- Validation of the controller's effectiveness in a real-world HEV application.
Conclusions:
- The proposed neural network controller offers a significant advancement in HEV fuel efficiency.
- The integrated fault detection and mitigation method enhances the reliability and safety of HEVs.
- The developed approach is generalizable to other advanced control systems beyond the Toyota Prius.
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