HEV Controller Trip Distance and Thermal Demand Management
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Solution Overview
Problem
Hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) face inefficiencies in managing electric only and combustion engine drive modes, leading to reduced electric drive ranges and increased fuel consumption due to suboptimal control systems that fail to adapt to changing environmental, vehicle, and component conditions.
Innovation Solution
The implementation of advanced controller systems that estimate trip distances, detect thermal demands, and adjust drive modes based on real-time data from navigation systems, sensors, and historical probabilities to optimize the engagement of electric drive modes and combustion engine modes, ensuring efficient energy use and minimizing fuel consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual or semi-automatic drive mode control systems are used, then driver flexibility is maintained, but drive mode efficiency and electric drive range are reduced
Solution Approach 1:
The control system dynamically adjusts drive mode selection based on real-time conditions including trip distance, battery state of charge, ambient temperature, and component thermal demands. The system transitions from static manual/semi-automatic control to dynamic automated control that optimizes energy usage while maintaining driver flexibility through selectable operation modes.
Solution Approach 2:
The system incorporates multiple sensors to detect real-time vehicle conditions, battery state of charge, ambient environment, and component thermal states. This feedback is continuously processed by the controller to optimize drive mode selection, creating a closed-loop control system that improves electric drive range by preventing inefficient energy consumption.
2Use of energy by moving object
If electric only drive mode is extended to maximize electric drive range, then fuel consumption is reduced, but vehicle accessibility and component reliability may deteriorate due to thermal management issues
Solution Approach 1:
The controller predicts thermal demands of various components (transaxle, battery, intercooler, emissions control system) before engaging electric drive mode. By performing preliminary thermal assessment and planning, the system ensures components will remain within safe operating temperature ranges, preventing reliability issues while maximizing electric drive range.
Solution Approach 2:
The system monitors and adjusts operating parameters including battery state of charge, ambient temperature, and component temperatures. By dynamically changing these parameters and comparing them against thresholds, the system determines optimal moments to engage electric drive mode, ensuring both fuel efficiency and component reliability.
3Productivity
If advanced controller systems with multiple sensors and real-time data processing are implemented, then drive mode efficiency is improved, but device complexity increases
Solution Approach 1:
The controller system is designed to perform multiple functions: detecting trip distance via navigation system, monitoring battery state of charge, sensing ambient temperature, predicting component thermal demands, and selecting optimal drive modes. By consolidating these diverse functions into a single multi-functional controller, the system improves drive mode efficiency while minimizing the increase in device complexity.
Data Source
AI summary
A hybrid electric vehicle (HEV) that includes one or more controller(s) configured to manage electric only and combustion engine drive modes, and at start-up, to generate a trip distance, and to detect an electric drive range, and battery, cabin, and powertrain thermal demands, among other conditions. The controller(s) engage a combustion engine drive mode if the distance exceeds the range, and the thermal demands exceed respective thresholds. The controller(s) also engage an electric drive mode if the range exceeds the distance, and the threshold exceed the thermal demands. Further variations include the controller(s) responsive to receiving a destination, and communicating the destination to a navigation system, and detecting if a charge event is likely to occur at the destination. In other arrangements, the controller(s) also adjust the trip distance upon detecting an historical probability of a charge event at the destination and whether the destination is a final destination.