Traction Battery Power Capability Estimation for Variable Drive Time
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Solution Overview
Problem
Existing vehicle systems struggle to accurately estimate and manage the power capability of traction batteries, which is influenced by factors such as battery temperature, voltage, state of charge, and age, leading to inefficiencies in power management and vehicle performance.
Innovation Solution
A system and method that utilizes a battery energy control module (BECM) to monitor and estimate the power capability of a traction battery using an equivalent circuit model (ECM) and adaptive estimation methods like the extended Kalman filter, allowing precise control of power discharge and charge based on specified time periods and vehicle operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the controller uses a fixed default time period for power capability estimation, then the system complexity is reduced, but the accuracy of power capability management deteriorates when vehicle operation duration varies
Solution Approach 1:
The controller dynamically adjusts the time period for power capability estimation based on the expected duration of vehicle operation. Instead of using a fixed default time period, the system adapts the estimation window to match actual operational needs, thereby improving accuracy without requiring overly complex fixed-structure controllers.
Solution Approach 2:
The controller modifies the time period parameter for power capability estimation according to vehicle operation conditions. By changing this key parameter based on expected operation duration, the system achieves accurate power management across varying operational scenarios while maintaining reasonable controller complexity.
2Reliability
If the controller adjusts power discharge based on specified time periods, then the power capability management accuracy is improved, but the control system complexity increases
Solution Approach 1:
The controller implements feedback by continuously monitoring vehicle operation duration and adjusting power discharge decisions based on estimated power capability for the specified time period. This feedback mechanism improves reliability of power management while keeping control system complexity manageable through algorithmic rather than hardware complexity.
3Measurement precision
If the system uses adaptive estimation methods like extended Kalman filter, then the power capability estimation accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system replaces complex hardware-based measurement systems with computational estimation methods like the extended Kalman filter. This substitution achieves high estimation accuracy through software algorithms, reducing the need for additional physical sensors while managing computational complexity through efficient implementation.
Data Source
AI summary
A controller of a vehicle discharges power from a traction battery to an electric machine to propel the vehicle and charges the traction battery with power from the electric machine according to a value of a power capability parameter for the traction battery that corresponds to a time period selected by the controller.