Hybrid EV On/Off Line Setting via Climbing Angle and Creep Power
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Solution Overview
Problem
Current methods for setting the electric vehicle (EV) on/off line in hybrid vehicles are manually performed without considering correlations between factors, leading to excessive mapping time, human error, and increased memory requirements, resulting in inefficient engine control and potential performance optimization issues.
Innovation Solution
A method for setting the EV on/off line based on a vehicle's state of charge (SOC), climbing angle, and creep power, which includes operations to determine optimal engine operating lines, weight factors, and power settings to automate the mapping process, reducing human intervention and memory needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual mapping is performed by experience, then flexibility in adjustment is maintained, but mapping time becomes excessively long and human error increases
Solution Approach 1:
The system performs self-mapping by automatically calculating EV on/off lines based on engine power maps, vehicle resistance, and driving conditions without requiring manual intervention. The control unit autonomously determines climbing angles and sets EV lines according to predefined algorithms, eliminating the need for experienced personnel to perform manual mapping while significantly reducing mapping time.
2Reliability
If manual mapping is performed, then human judgment can be applied, but the possibility of human error increases and consistency varies between persons
Solution Approach 1:
The control unit autonomously calculates EV on/off lines using consistent algorithms based on engine power characteristics, vehicle resistance, and driving conditions. This self-service approach eliminates variability between different persons performing mapping and ensures consistent results across all vehicles, while the automated calculation based on fundamental parameters ensures reliability.
3Device complexity
If manual mapping is performed without considering correlations, then simplicity of process is maintained, but logic size becomes excessive and memory capacity increases
Solution Approach 1:
The system changes the approach from storing extensive manual mapping data to calculating EV lines dynamically based on fundamental parameters such as engine power maps, vehicle resistance coefficients, and driving conditions. By transforming the problem from data storage to algorithmic calculation, the system reduces memory requirements while maintaining mapping accuracy through physics-based calculations.
4Adaptability or versatility
If extensive mapping data is stored to cover all conditions, then completeness of coverage is improved, but memory capacity and cost increase
Solution Approach 1:
Instead of storing pre-calculated mapping data for all possible conditions, the control unit calculates EV on/off lines dynamically based on real-time driving conditions, vehicle parameters, and engine characteristics. This approach provides complete condition coverage through algorithmic adaptation rather than exhaustive data storage, significantly reducing memory capacity requirements.
Data Source
AI summary
A method for setting an electric vehicle (EV) on/off line of a hybrid vehicle considers a driving load of the vehicle by setting the EV on/off line based on a climbing angle and creep power. The method for setting the EV on/off line of the hybrid vehicle includes an operation of setting a region according to a state of charge (SOC), an EV online setting operation based on a climbing angle of the vehicle, and an EV offline setting operation based on creep power of the vehicle. The method provides a simple and intuitive EV line setting method to reduce a mapping time and substantially eliminate the possibility of human error, thus increasing logic reliability, so as to reduce use of a hybrid control unit (HCU) memory and provide cost-saving effects.