Vehicle Energy Prediction Controller with Route Optimization
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Solution Overview
Problem
Current vehicle powertrain systems lack accurate prediction and optimization of energy consumption across varying routes and conditions, leading to inefficient energy use and potential range limitations, especially in hybrid, electric, and fuel cell vehicles.
Innovation Solution
A controller with a programmed energy/power prediction model that uses forward-looking data and real-time corrections to optimize energy consumption by predicting energy needs along a route, adjusting powertrain modes, and displaying accurate remaining range, regardless of the energy source, through a combination of onboard and offboard data and error feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a vehicle uses a conventional powertrain system without predictive modeling, then the system structure remains simple, but energy consumption prediction accuracy deteriorates leading to inefficient energy use and range limitations
Solution Approach 1:
The controller performs predictive energy consumption calculations before the vehicle actually travels the route, using map data, vehicle state data, and weather data to pre-compute energy requirements. This allows the system to plan energy usage in advance rather than reacting in real-time, improving prediction accuracy while managing complexity through proactive rather than reactive control
Solution Approach 2:
The system implements a feedback mechanism where actual energy consumption data from sensors is continuously compared with predicted values, and the model parameters are adjusted accordingly. This closed-loop approach maintains high prediction accuracy over time while the controller adapts to actual vehicle conditions and variations in the environment
2Use of energy by moving object
If the controller continuously monitors and adjusts powertrain operating modes based on real-time data, then energy efficiency improves, but computational load and processing time increase
Solution Approach 1:
The controller pre-calculates energy consumption for different powertrain operating modes along the predicted route before making adjustment decisions. By having energy consumption models and route analysis prepared in advance, the controller can quickly compare options and select optimal operating modes without intensive real-time computation, thus improving energy efficiency while minimizing processing delays
Solution Approach 2:
The system dynamically adjusts powertrain operating modes based on real-time vehicle state and environmental conditions, but uses pre-computed energy models to enable rapid decision-making. The controller can switch between different operating modes (e.g., electric-only, hybrid, charge-sustaining) adaptively while relying on预先 prepared energy consumption data to guide these transitions efficiently
3Adaptability or versatility
If the vehicle uses multiple energy sources (hybrid, electric, fuel cell), then versatility and performance improve, but determining optimal powertrain mode becomes more complex
Solution Approach 1:
The controller uses a unified energy consumption model that can handle multiple energy sources (battery electric, hybrid, fuel cell) through a common predictive framework. The same basic energy consumption calculations and route analysis methods apply regardless of the specific powertrain configuration, allowing the system to manage versatility while reducing the complexity of mode selection through a universal approach
Solution Approach 2:
The system adjusts energy consumption model parameters based on the specific powertrain configuration being used. Rather than having completely separate control systems for different vehicle types, the controller modifies key parameters (such as energy conversion efficiencies, power source characteristics) within a single flexible framework, enabling adaptable performance across multiple energy source configurations without proportionally increasing system complexity
Data Source
AI summary
A vehicle includes drive wheels, an energy source having an available energy, a torque-generating device powered by the energy source to provide an input torque, a transmission configured to receive the input torque and deliver an output torque to the set of drive wheels, and a controller. The controller, as part of a programmed method, predicts consumption of the available energy along a predetermined travel route using onboard data, offboard data, and a first logic block, and also corrects the predicted energy consumption using the onboard data, offboard data, and an error correction loop between a second logic block and the first logic block. The controller also executes a control action with respect to the vehicle using the corrected energy consumption, including changing a logic state of the vehicle.