EV Battery Range Prediction Using Historical Route Graphs
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Solution Overview
Problem
Current electric vehicle navigation systems struggle to accurately predict battery consumption due to limitations in computing power and inability to account for changes in driving conditions, leading to inadequate energy management and range estimation.
Innovation Solution
A method using a GPS receiver and electronic control unit to build a graph of historical driving paths, predicting energy consumption based on geographical position and battery data, allowing for efficient energy management strategies without requiring high computing capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a larger battery is installed to extend the range of an electric vehicle, then the energy storage capacity is improved, but the vehicle weight increases significantly
Solution Approach 1:
The system performs preliminary actions by predicting future battery consumption before the driving cycle completes. It builds historical path graphs in advance and uses them to forecast energy needs, allowing proactive energy management rather than reactive responses to low battery levels.
Solution Approach 2:
The system implements feedback by continuously monitoring actual battery consumption and comparing it with predicted consumption from the historical path graph. This feedback loop allows the system to learn from deviations and improve prediction accuracy over time, adapting to changing driving conditions and battery states.
2Measurement precision
If statistical methods are used to predict future driving conditions, then the prediction quality is improved, but the computing power requirement increases beyond what is available in commercial vehicles
Solution Approach 1:
The patent employs a simplified prediction model that uses basic statistical methods and historical path graphs stored in memory, rather than complex artificial intelligence algorithms. This approach uses computationally inexpensive methods that can run on standard commercial vehicle electronic control units without requiring specialized high-performance hardware.
Solution Approach 2:
The system segments the prediction problem by dividing future driving conditions into discrete historical paths from the path graph. Instead of attempting to predict continuous complex driving scenarios, the system selects from pre-defined path segments based on current driving conditions, reducing computational complexity while maintaining prediction quality.
3Measurement precision
If the residual capacity of the battery is monitored continuously, then the energy management accuracy is improved, but the system complexity increases
Solution Approach 1:
The electronic control unit performs multiple functions: it monitors battery state of charge, predicts future consumption using the historical path graph, compares actual versus predicted consumption, and provides guidance to the driver. By consolidating these functions into a single control unit that already exists in commercial vehicles, the system achieves high energy management accuracy without adding significant system complexity.
Data Source
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Figure 2
Figure 4A
AI summary
Method for assisting in the management of the electric energy of an electric or hybrid vehicle (10), comprising the steps of: providing a graph (1) including a plurality of nodes (N0-N9) belonging to one or more branches (la, lb) of the graph (l); iteratively performing the following steps: acquiring current geographical geolocation coordinates of the vehicle (10); identifying, on the graph (1), a node geographically closer to said current geographical coordinates; identifying, based on said node closer to the current geographical coordinates, which branch of the graph (1) the vehicle (10) is travelling; acquiring the energy consumption information associated with the branch that the vehicle (10) is travelling; checking whether a current electrical charge of the batteries of the vehicle (10) allows the vehicle (10) to reach the respective arrival charging station (N6, N9) of the branch the vehicle (10) is travelling; and generating a feedback signal as a function of said check.