Hybrid Vehicle Controller for Predictive ICE Torque and NVH Balance
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Solution Overview
Problem
Conventional intelligent power management systems in hybrid electric vehicles are reactive and do not effectively account for factors beyond driver demand, leading to inefficiencies in energy consumption and emissions.
Innovation Solution
An intelligent vehicle controller that predicts and adjusts the operation of the internal combustion engine based on power level shifting data, using efficiency maps to optimize torque and RPM for both fuel efficiency and noise, vibration, and harshness levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional reactive power management systems are used to meet driver demand, then vehicle speed control is achieved, but energy efficiency and emissions are not optimized
Solution Approach 1:
The controller performs preliminary actions by predicting future power demands based on current driving conditions, vehicle state, and historical data. This allows the system to proactively adjust engine operation and powertrain components before the actual demand occurs, optimizing energy efficiency while maintaining responsiveness to driver needs.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring vehicle state, driving conditions, and powertrain performance. This real-time feedback enables the controller to refine predictions and adjust control strategies dynamically, balancing energy optimization with responsive vehicle operation.
2Use of energy by moving object
If the controller optimizes for fuel efficiency, then energy consumption is reduced, but NVH levels may increase
Solution Approach 1:
The controller dynamically changes operating parameters such as engine torque, RPM, and power distribution between electric motor and ICE based on predicted driving conditions. By optimizing these parameters in advance, the system achieves fuel efficiency while maintaining acceptable NVH levels through smooth transitions and avoidance of harsh operating conditions.
Solution Approach 2:
The system employs dynamic control strategies that adapt to changing driving conditions in real-time. The controller continuously adjusts powertrain operation to balance fuel efficiency with NVH considerations, using predictive algorithms to smooth transitions and prevent abrupt changes that would increase vibration and harshness.
3Use of energy by moving object
If predictive control is implemented to optimize power management, then energy efficiency improves, but system complexity increases
Solution Approach 1:
The controller is designed as a multi-functional system that integrates predictive algorithms, real-time monitoring, and multiple control functions within a single unit. This universal approach consolidates complexity rather than distributing it across multiple separate systems, making the predictive power management feasible while achieving improved energy efficiency.
Data Source
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AI summary
A hybrid electric vehicle includes an intelligent vehicle controller, an electric motor, a battery, an internal combustion engine (ICE), and an electrical generator coupled to the ICE configured to provide electricity to the battery and the electric motor. The intelligent vehicle controller receives ICE power level shifting data from the electrical generator, ICE, battery, and electric motor. The intelligent vehicle controller determines a desirable torque and/or a desirable revolutions per minute (RPM) for the ICE based on the received ICE power level shifting data by utilizing an efficiency map that includes fuel efficiency contours and noise, vibration, and/or harshness (NVH) level lines for the hybrid electric vehicle. The intelligent vehicle controller may have first and second vehicle operation modes, and may derive first and a second desirable power levels for the ICE in the first and second operation modes, based on the ICE power level shifting data.