Hybrid Vehicle Route Optimization Using Extended Adjacency Graphs
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Solution Overview
Problem
Existing eco-navigation strategies fail to efficiently determine routes that minimize energy consumption for hybrid vehicles, as they do not adequately account for the complex energy management involving both internal combustion engines and electric machines, and the constraints on battery state of charge, leading to impractical computation times and reduced precision.
Innovation Solution
A method that constructs an energy consumption model for hybrid vehicles, incorporating both internal combustion engine and electric machine consumption, and uses an energy management law to determine optimal control strategies, along with a shortest path algorithm to minimize fuel consumption while respecting battery state of charge constraints, utilizing an extended assistant graph to represent the road network and account for variations in energy storage state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Dijkstra's shortest path algorithm is used to determine the route minimizing energy expenditure, then the route optimization is simple, but it cannot handle negative energy expenditure values (energy recovery) and is not adaptable to hybrid vehicles
Solution Approach 1:
The patent segments the energy consumption calculation by creating separate consumption models for the internal combustion engine and the electric machine. This allows the algorithm to handle different energy sources independently, accommodating both positive energy consumption and negative energy recovery values from regenerative braking, thus resolving the limitation of Dijkstra's algorithm while maintaining computational feasibility.
Solution Approach 2:
The patent introduces a new dimension to the routing problem by incorporating state of charge constraints as a additional parameter space. Instead of simply finding the shortest path in terms of energy, the algorithm searches for paths that satisfy both energy minimization and battery state constraints, transforming the problem from a single-objective to a multi-dimensional optimization that is suitable for hybrid vehicles.
2Measurement precision
If eco-navigation strategies are developed for hybrid vehicles considering both engine types and battery constraints, then the energy consumption minimization is improved, but the computation time becomes impractically long
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing energy consumption values for each road segment under different driving conditions and battery states. During route optimization, these pre-computed values are retrieved and combined rather than calculating from scratch, significantly reducing computation time while maintaining precise energy consumption modeling for hybrid vehicles.
Solution Approach 2:
The patent applies partial action by focusing the detailed energy consumption modeling only on critical segments of the journey where battery state constraints are most likely to be binding. For other segments, simplified consumption models are used, reducing overall computational complexity while still achieving accurate energy minimization where it matters most.
3Ease of operation
If existing eco-navigation methods are applied to hybrid vehicles, then route determination is simplified, but the precision of energy consumption calculation is reduced due to inadequate modeling of dual-engine systems
Solution Approach 1:
The patent creates a universal energy consumption model that can handle multiple vehicle operating modes (electric-only, hybrid, combustion-only) through a single unified framework. The model automatically selects and combines the appropriate consumption calculations based on real-time vehicle state, maintaining ease of operation while achieving precise energy consumption calculations across all hybrid vehicle operating conditions.
Data Source
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AI summary
The invention relates to a method for determining a route for a hybrid vehicle. The method minimizes energy consumption while verifying constraints, particularly concerning the battery's state of charge and/or its variations. It is based on an energy consumption model (DCM) comprising a thermal model and an electrical model, as well as an energy management law (EML). It also incorporates the construction of an extended adjoint graph (EGG), for which a value representing the variation in the battery's state of charge is assigned to each arc, enabling the calculation of fuel consumption (QCONSO) on each arc of the extended adjoint graph. The method also includes a route search step (ITI) using a shortest path algorithm and a post-constraint verification.The invention also relates to a computer program product comprising program code instructions for implementing the route determination method.