Electric Vehicle Energy Management System for Charging Reliability
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Solution Overview
Problem
Current energy management systems for electric trams and buses lack robustness in anticipating and avoiding critical energy depletion situations, particularly due to unforeseen operational hazards like traffic slowdowns and untimely stops, leading to potential failures before reaching charging stations.
Innovation Solution
A method and system for automatic energy management that calculates a total forecast energy estimate based on current position, speed, and auxiliary power consumption, adjusting speed and auxiliary power to ensure the vehicle reaches the next charging station quickly and comfortably, by comparing predicted energy needs with available onboard energy and reserving energy for potential charging system malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predetermined energy management strategies are implemented to preserve range when energy falls below a critical threshold, then energy depletion risk is reduced, but the system lacks robustness against unforeseen operational hazards such as traffic slowdowns and untimely stops
Solution Approach 1:
The patent implements dynamic energy management by continuously monitoring real-time operational parameters (speed, position, energy consumption rate) and adjusting the energy threshold and management strategies accordingly. This replaces static predetermined thresholds with adaptive thresholds that respond to actual vehicle conditions and external factors like traffic patterns and route characteristics, thereby improving robustness against unforeseen hazards while maintaining reliability
Solution Approach 2:
The system incorporates feedback mechanisms by continuously measuring actual energy consumption, comparing it with predicted consumption, and using this information to adjust future energy management decisions. The feedback loop includes monitoring operational hazards and modifying the energy threshold dynamically, allowing the system to adapt to changing conditions and maintain reliable operation even when facing unexpected situations
2Reliability
If energy management strategies are defined on worst-case scenarios, then energy depletion is avoided, but the strategies are implemented in absence of real need when operating conditions are not penalizing
Solution Approach 1:
The patent dynamically changes the energy threshold parameter based on real-time assessment of operational conditions. Instead of using a fixed worst-case threshold, the system adjusts the threshold according to actual factors such as passenger load, route profile, weather conditions, and traffic patterns. This allows the system to maintain high reliability when needed while avoiding unnecessary energy conservation measures during normal operating conditions
Solution Approach 2:
The energy management strategy transitions from static worst-case planning to dynamic condition-based adjustment. The system continuously evaluates current operational conditions and modifies the energy threshold and management approach accordingly, enabling the vehicle to operate efficiently under normal conditions while automatically becoming more conservative when actual conditions approach worst-case scenarios
3Use of energy by moving object
If the vehicle reduces speed and auxiliary power to conserve energy, then energy autonomy is extended, but passenger comfort and travel time are degraded
Solution Approach 1:
The system uses feedback to continuously monitor energy consumption rates and adjust speed and auxiliary power settings dynamically. Rather than applying fixed reduction measures, the system responds to real-time conditions, reducing power only when necessary to maintain energy autonomy while minimizing impact on comfort. The feedback loop allows the system to recover comfort features when energy conditions improve
Solution Approach 2:
The patent implements dynamic adjustment of speed and auxiliary power based on real-time energy status and operational conditions. The system continuously optimizes the balance between energy conservation and passenger comfort by adapting power distribution to current needs, rather than applying static restrictions. This allows the vehicle to maintain comfort during normal operation while extending energy autonomy when needed
Data Source
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AI summary
This method of automatically managing the energy carried by an electric vehicle for a mission on an interstation between departure and arrival stations consists of: providing mission characteristics, which include a reference speed profile on segments subdividing the interstation; evaluating a current position and speed of the vehicle; estimating a cruising speed of the vehicle on the remaining segments, based on the reference speed profile, current speed and position; calculating a total predicted energy (Emis-prev) as an estimate of the energy to be consumed to reach the arrival station, based on the current position, estimated cruising speeds and auxiliary power supplied to passenger comfort devices; determining an available on-board energy (Eemb-dis) as the energy stored by the vehicle at the current position;and display the total predicted energy and the available onboard energy.