EV Range Prediction Using Confidence-Based Route Control
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Solution Overview
Problem
Current range prediction algorithms for electric vehicles are inaccurate due to dynamic and uncertain factors such as traffic congestion and driver behavior, leading to inefficient energy utilization and route planning.
Innovation Solution
A computer system that estimates energy consumption and variance for electric vehicle routes based on reference data from secondary vehicles, calculates a confidence interval, and adjusts vehicle control modes to optimize energy use and route planning, including charging stops, considering factors like weather and time constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If range prediction algorithms base estimated range on past information such as most recently travelled range and respective power used, then the prediction method is simple to implement, but the prediction accuracy deteriorates due to highly dynamic and uncertain factors such as traffic congestion and driver behavior
Solution Approach 1:
The system performs preliminary actions by collecting and storing reference energy consumption vehicle data from multiple secondary vehicles before making range predictions. This pre-collected data serves as a foundation for more accurate predictions, allowing the system to account for dynamic factors like traffic congestion and driver behavior patterns that vary across different routes and conditions.
Solution Approach 2:
The system implements feedback by continuously collecting reference energy consumption data from multiple secondary vehicles and using this aggregated information to refine and update range predictions. The prediction algorithm incorporates feedback from actual energy consumption data, variance measurements, and confidence interval calculations to improve accuracy over time while adapting to changing driving conditions.
2Measurement precision
If the system collects reference energy consumption vehicle data from multiple secondary vehicles and calculates confidence intervals, then the range prediction accuracy is improved, but the system complexity increases
Solution Approach 1:
The system applies universality by using a multi-functional computer system that performs data collection, statistical analysis, confidence interval calculation, and route optimization all through a single integrated platform. The same processing circuitry that controls vehicle operations also handles the complex statistical computations and data aggregation from multiple secondary vehicles, eliminating the need for separate dedicated hardware systems.
Solution Approach 2:
The system implements self-service by automatically collecting reference energy consumption data from multiple secondary vehicles, performing statistical computations, calculating confidence intervals, and generating optimized route recommendations without requiring external intervention. The vehicle's own computer system processes and analyzes the data, making the complex calculations and predictions autonomously.
3Use of energy by moving object
If the system determines present energy level and compares it with confidence interval upper limit to control vehicle mode, then the energy utilization is optimized, but the control system complexity increases
Solution Approach 1:
The system applies dynamics by implementing adaptive control modes that dynamically adjust based on real-time comparisons between present energy level and confidence interval upper limits. The vehicle can switch between different control modes (such as economy mode, standard mode, or charging mode) depending on the energy situation, allowing flexible optimization of energy utilization while responding to changing conditions during vehicle operation.
Solution Approach 2:
The system implements parameter changes by modifying vehicle control parameters based on the comparison between present energy level and confidence interval upper limits. When the present energy level falls below the upper limit, the system adjusts control parameters to conserve energy or plan charging stops, thereby optimizing energy utilization through dynamic parameter modification rather than fixed control logic.
4Measurement precision
If the system estimates energy consumption and variance for multiple routes and calculates confidence intervals, then the route planning accuracy is improved, but the computation time increases
Solution Approach 1:
The system applies partial action by calculating confidence intervals and energy consumption estimates for a limited set of candidate routes rather than exhaustively analyzing all possible routes. The system identifies and evaluates only the most promising routes based on initial assessments, computing detailed confidence intervals for these selected options, thereby achieving sufficient route planning accuracy without excessive computation time.
Data Source
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AI summary
A computer system (100) comprising processing circuitry (102) configured to: receive a start position and a planned destination position for an electric vehicle of a vehicle type, the vehicle comprising an electrical energy storage system, estimate energy consumption and variance for at least one route between the start position and the destination position for the vehicle type, based on reference energy consumption vehicle data collected from secondary vehicles, calculate a confidence interval for the estimated energy consumption and variance at a predetermined confidence level, determine a present energy level in the electrical energy storage system, in response to the present energy level being below the confidence interval upper limit, control the vehicle according to a first mode, the first mode including a first route between the start position and the destination position, and in response to the present energy level exceeding or being equal to the confidence interval upper limit, control the vehicle according to a second mode, the second mode including a second route between the start position and the destination position.