Vehicle Power Optimization via Cloud Route Prediction
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Solution Overview
Problem
Existing methods for optimizing vehicle power consumption, particularly in hybrid and plug-in hybrid electric vehicles, rely on expensive and time-consuming data collection from probe vehicles and focus on energy optimization, which may neglect parameters like state of charge, leading to inefficiencies.
Innovation Solution
A method that utilizes an electronic control unit (ECU), GPS receiver, and communication means to send vehicle data to a network cloud for calculating a probable final destination and optimized route, then determines optimized power utilization of propulsion sources, allowing for automatic control of drive train modes to minimize power consumption, considering factors like charging station availability and emission optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optimization is based on energy maps and probe vehicle data collection, then route optimization can be achieved, but the data collection becomes expensive and time consuming
Solution Approach 1:
The system pre-calculates and stores energy maps for different routes, propulsion sources, and driving conditions before they are needed. These pre-computed maps allow the optimization algorithm to quickly determine optimal routes without performing expensive real-time calculations or requiring extensive probe vehicle data collection during operation.
Solution Approach 2:
Instead of using expensive probe vehicles to collect real-world energy consumption data, the system creates simplified computational models and energy maps that replicate the essential energy consumption characteristics. These copied representations allow for fast optimization calculations without the overhead of actual vehicle instrumentation and data collection campaigns.
2Use of energy by moving object
If optimization focuses on energy consumption, then route planning can be performed, but parameters such as state of charge go unused leading to less efficient optimization
Solution Approach 1:
The system transitions from optimizing based solely on energy consumption to optimizing based on power consumption. This parameter change allows the optimization to incorporate state of charge and other time-critical parameters, enabling more efficient utilization of hybrid propulsion systems and battery energy while maintaining route optimization capabilities.
3Device complexity
If energy optimization is performed over larger time intervals, then computational complexity is reduced, but the optimization accuracy decreases
Solution Approach 1:
Energy maps and power consumption data are pre-calculated for various routes, conditions, and time intervals before runtime. This preliminary computation stores the results in lookup tables that can be quickly queried during optimization, avoiding the need for complex real-time calculations while maintaining high accuracy through the use of pre-computed power-based optimizations.
Data Source
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AI summary
The invention relates to a method for optimizing the power consumption of a vehicle. The vehicle comprises an electronic control unit (ECU), a GPS receiver and communication means for remotely accessing a network cloud and/or server, the method comprising: - sending vehicle data from the GPS receiver and the ECU to the network cloud and/or server upon starting the vehicle; - calculating a most probable final destination and a most probable optimized route of the vehicle in the network cloud and/or server based on the sent vehicle data; - calculating a most probable driving mode map in the network cloud and/or server based on the most probable final destination, the most probable optimized route and the sent vehicle data; - calculating an optimized power utilization of propulsion sources for the vehicle in the network cloud and/or server based on said most probable driving mode map, most probable final destination and most probable optimized route; - returning the optimized power utilization of propulsion sources to the vehicle from the network cloud and/or server, the most probable power utilization of propulsion sources being dependent on the most probable driving mode, most probable final destination and most probable optimized route of the vehicle; - using the optimized power utilization of propulsion sources to control the drive train modes and/or peripheral equipment of the vehicle during driving in order to optimize the power consumption of the vehicle.