E-TRU Power Supply Control Using Route-Based SoC Prediction
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Solution Overview
Problem
Current power management strategies for electrified transportation refrigeration units (E-TRUs) in electric vehicles result in limited utilization of green energy sources, increased fuel consumption, and undesirable fuel emissions due to maintaining the State of Charge (SoC) of the electric power source at 100%, leading to inefficient power supply.
Innovation Solution
A power system comprising an energy storage unit, axle generator, and charge management system that monitors SoC, predicts power requirements based on route information, and selectively controls the axle generator and energy storage unit to optimize power supply to the E-TRU, ensuring efficient energy utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the fuel-based power source charges the electric power source whenever the SoC drops below 100%, then the SoC of the electric power source is maintained at 100%, but the utilization of green energy from the electric power source is limited and fuel consumption increases
Solution Approach 1:
The system performs preliminary actions by predicting future power requirements based on route information, weather forecasts, and historical data before the journey begins. This allows the system to plan optimal charging strategies in advance, determining when to charge the electric power source and when to rely on the fuel-based generator, thereby reducing unnecessary fuel consumption while maintaining reliable power supply
Solution Approach 2:
The system implements continuous feedback mechanisms by monitoring the actual power consumption of the E-TRU, comparing it with predicted values, and adjusting the charging strategy in real-time. The charge management system receives feedback from the energy management system about actual power requirements and modifies the charging schedule to optimize the balance between maintaining SoC and minimizing fuel consumption
2Use of energy by moving object
If the electric power source is used to supply power to the E-TRU, then green energy utilization is maximized, but the SoC may drop below acceptable levels affecting power availability
Solution Approach 1:
The system performs preliminary actions by predicting future power requirements based on route information, weather forecasts, and historical data before the journey begins. This allows the system to plan optimal charging strategies in advance, determining when to charge the electric power source and when to rely on the fuel-based generator, thereby reducing unnecessary fuel consumption while maintaining reliable power supply
Solution Approach 2:
The system dynamically adjusts the charging threshold and charging rate based on real-time conditions including current SoC levels, predicted power requirements, ambient temperature, and E-TRU power consumption patterns. This dynamic approach allows maximum utilization of green energy while ensuring the SoC remains sufficient to meet future power demands
3Use of energy by moving object
If the charge management system continuously monitors and adjusts charging based on route information and power predictions, then power supply optimization is achieved, but system complexity increases
Solution Approach 1:
The charge management system is designed as a multi-functional integrated controller that combines multiple functions including power requirement prediction, charging threshold determination, charging rate control, and coordination with both the fuel-based generator and electric power source. This universal approach consolidates what could be separate complex subsystems into a single coordinated control unit, reducing overall system complexity while maintaining optimization capabilities
Solution Approach 2:
The system employs self-service mechanisms by using onboard sensors, GPS, and available route information to automatically predict power requirements and adjust charging strategies without external intervention. The charge management system autonomously makes decisions based on pre-programmed algorithms and real-time data, eliminating the need for complex manual control systems or external management infrastructure
Data Source
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Figure 2
AI summary
A power system (100) for optimizing power supply to an Electrified Transportation Refrigeration Unit (E-TRU) (101) of an electric vehicle (106) includes an energy storage unit (102) for supplying power to the E-TRU (101), an axle generator (103) adapted to supply power to the energy storage unit (102) and the E-TRU (101), and a charge management system (104). The charge management system (104) monitors a State of Charge (SoC) of the energy storage unit (102). The charge management system (104) accesses route information associated with the electric vehicle (106). Next, the charge management system (104) predicts a power requirement of the electric vehicle (106) and the E-TRU (101) based on the accessed route information and controls the axle generator (103) in an engaged mode or a disengaged mode based on the monitored SoC and the predicted power requirement of the electric vehicle (106) and the E-TRU (101).