Hybrid Vehicle Energy Management via Predictive State of Charge
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Solution Overview
Problem
Hybrid and electric vehicles face challenges in maximizing energy efficiency due to the driver's inability to effectively utilize sailing or recuperation phases, as the necessary information is not readily available for efficient load management.
Innovation Solution
A method involving the detection and classification of multiple consumption parameters using trainable class boundaries, which are then used to determine a future state of charge for the traction battery, allowing for the adjustment of operating parameters such as the activation state of the electric traction machine and charging rate to optimize energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If driver control is used for energy management, then the system remains simple and easy to operate, but the energy efficiency cannot be maximized because the driver lacks access to necessary information about sailing and recuperation phases
Solution Approach 1:
A control device is introduced as an intermediary between the driver and the vehicle's powertrain systems. This control device receives consumption parameters from various sensors, determines future state of charge using a trained mapping model, and automatically adjusts operating parameters of the traction power component. The intermediary handles the complex information processing and decision-making, while the driver simply operates the vehicle normally.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where consumption parameters are continuously monitored, the future state of charge is predicted based on these parameters and upcoming route conditions, and the operating parameters are adjusted accordingly. The mapping model is also trained using feedback from actual consumption data and measured state of charge values, continuously improving the system's energy management capabilities.
2Loss of energy
If multiple consumption parameters are detected and processed to determine future state of charge, then energy efficiency is improved through optimized operating parameters, but the complexity of detection and measurement increases
Solution Approach 1:
The mapping model serves multiple functions: it classifies consumption parameters, predicts future state of charge, and provides the basis for operating parameter optimization. The same trained mapping is used for both prediction and control decisions, reducing the need for separate complex analysis systems while achieving comprehensive energy management.
3Measurement precision
If the mapping is trained using consumption parameters and measured state of charge, then the prediction accuracy of future state of charge is improved, but the training process and system adaptation complexity increases
Solution Approach 1:
The mapping model performs self-training by automatically adapting to the specific vehicle and driving conditions using data collected during normal operation. The system collects consumption parameters and measured state of charge values, uses these to train the mapping model, and continuously improves its predictions without requiring manual intervention or complex external training procedures. The system serves itself by learning from its own operational data.
Data Source
AI summary
The present disclosure is related to hybrid vehicles. The teachings thereof may be embodied in vehicles as well as operation schemes meant to increase energy efficiency, such as a method comprising: detecting multiple consumption parameters of the hybrid vehicle; determining a future state of charge of a traction battery of the vehicle by mapping the consumption parameters onto a state-of-charge value, wherein the mapping includes classifying the multiple consumption parameters according to trainable class boundaries; training the class boundaries based at least in part on the detected consumption parameters and an associated measured state of charge; and adjusting an operating parameter of a traction power component of the hybrid vehicle according to the determined future state of charge.