Battery State of Charge Control with Route Preview Classification
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Solution Overview
Problem
Current energy management systems in hybrid-electric vehicles lack an efficient method to predictively manage the traction battery's state of charge based on route information, leading to suboptimal fuel economy and battery performance.
Innovation Solution
A controller is programmed to classify route segments using fuzzy rules applied to vehicle acceleration and road grade, determining a target battery power that guides the operation of the traction battery to achieve a specific state of charge by the end of each segment, incorporating a virtual segment for remaining route data to optimize battery power usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If present operating inputs (accelerator pedal demand, brake pedal demand) are used to manage battery state of charge, then the control system is simple to operate, but fuel economy and battery performance are suboptimal
Solution Approach 1:
The system performs preliminary classification of route segments using fuzzy rules based on preview information (road grade, vehicle acceleration) before executing battery control strategies. This allows the control system to prepare optimal state of charge targets in advance, improving fuel economy without requiring complex real-time calculations during vehicle operation
Solution Approach 2:
The route is divided into multiple segments that are classified based on characteristics such as road grade and vehicle acceleration. Each segment receives tailored battery control strategies, allowing the system to manage battery state of charge more effectively across different driving conditions while maintaining manageable control complexity through modular segment-based processing
2Productivity
If route preview information is used to predictively manage battery state of charge, then fuel economy and battery performance are improved, but the control system complexity increases
Solution Approach 1:
The system changes the parameter representation by classifying route segments into discrete categories based on fuzzy logic evaluation of continuous parameters (road grade, vehicle acceleration). This transformation allows complex continuous data to be processed through rule-based classification, improving battery performance predictions while keeping the control system architecture relatively simple
Solution Approach 2:
Fuzzy logic rules serve as an intermediary layer between raw route preview information and battery control decisions. The fuzzy rules process and interpret multiple input parameters (road grade, acceleration) to generate classification results that guide battery state of charge management, simplifying the overall control architecture while enabling sophisticated predictive control
3Measurement precision
If fuzzy rules are applied to classify route segments, then precise target state of charge can be achieved, but computational complexity increases
Solution Approach 1:
The system transforms continuous physical parameters (road grade, vehicle acceleration) into discrete classification categories through fuzzy logic. This parameter transformation enables precise target state of charge determination by mapping complex continuous inputs to manageable discrete classes, achieving precision without proportional increases in computational complexity
Data Source
AI summary
In a vehicle, a controller divides a route into route segments. The controller operates a traction battery over the route segments to achieve a target state of charge upon completion of one of the route segments. The target state of charge is based on a target battery power defined by a classification for each of the route segments including the one of the route segments according to a set of fuzzy rules applied to vehicle acceleration and road grade associated with the route segments. Fuzzy rules may be applied to the classification for each of the route segments.