Hybrid Vehicle Energy Management With Route-Based SOC Planning
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Solution Overview
Problem
Existing energy management strategies in hybrid electric vehicles focus solely on vehicle operating conditions, leading to increased fuel consumption.
Innovation Solution
An intelligent energy management system that integrates multi-domain data fusion, including cockpit and power domain information, to predict route-specific energy consumption and plan target state of charge (SOC) for each road section, optimizing engine operation to minimize fuel consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional energy management strategies focus solely on vehicle operating conditions, then the control system is simple, but fuel consumption increases
Solution Approach 1:
The patent merges multiple data domains (cockpit domain including user behavior and road condition information, and power domain including vehicle state information) into a unified multi-domain data fusion system. This integration allows the energy management system to comprehensively consider various factors affecting fuel consumption, thereby reducing energy loss while maintaining manageable system complexity through centralized control architecture.
Solution Approach 2:
The system performs preliminary action by predicting route-specific energy consumption before the vehicle actually travels the route. By using multi-domain data fusion to forecast energy requirements for different road sections in advance, the system can proactively optimize engine operation and power distribution, reducing fuel consumption without requiring complex real-time adjustments during driving.
2Use of energy by moving object
If the system optimizes engine operation to minimize fuel consumption, then fuel efficiency improves, but the complexity of data processing and control increases
Solution Approach 1:
The patent segments the travel route into multiple road sections and divides the energy consumption prediction and control into section-specific tasks. By processing data for each road section independently and planning target SOC for each segment, the system reduces the complexity of overall data processing while achieving comprehensive fuel efficiency optimization across the entire route.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual vehicle operation and comparing it with predicted energy consumption. The control device adjusts engine operation and power distribution based on feedback from multi-domain sensors and system performance data, improving fuel efficiency through iterative optimization without requiring excessively complex upfront processing.
3Loss of energy
If the system plans target SOC for each road section, then fuel consumption is minimized, but the computational requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the overall route into multiple road sections and calculating target SOC for each section separately. This approach reduces computational requirements compared to optimizing the entire route as a single problem, as each section can be processed independently with smaller data sets and simpler calculations, while still achieving minimum fuel consumption through cumulative optimization.
4Loss of energy
If the engine operates in efficient intervals continuously, then fuel consumption reduces, but the adaptability to varying driving conditions decreases
Solution Approach 1:
The patent implements dynamics by making the engine operating strategy adaptive rather than fixed. The control device dynamically adjusts engine operation to maintain efficient intervals while responding to varying driving conditions, road sections, and vehicle states. This dynamic control allows the system to keep the engine in efficient operating ranges while adapting to different scenarios, thereby reducing fuel consumption without sacrificing adaptability.
Solution Approach 2:
The system utilizes parameter changes by adjusting engine operating parameters (such as speed, torque, and load) based on predicted road section characteristics and actual vehicle conditions. By changing these parameters dynamically, the engine can operate in efficient intervals across diverse driving conditions, achieving fuel savings while maintaining versatility and adaptability to varying requirements.
Data Source
AI summary
An intelligent energy management system, includes: a drive device including an engine configured to output power to a wheel of the vehicle, a drive motor configured to output power to the wheel, and an electric generator connected to the engine and driven by the engine to generate electricity; a power battery configured to supply electricity to the drive motor and charged with an alternating current outputted from the electric generator or the drive motor; and a control device configured to acquire multi-domain data fusion information, predict, according to the multi-domain data fusion information, a route-specific vehicle energy consumption corresponding to a preset travel route, plan, according to a road section-specific vehicle energy consumption corresponding to each road section, a target SOC corresponding to each road section, and control, according to the target SOC and an actual vehicle demand corresponding to each road section, the drive device.