EV Battery SOC and SOH Estimation with Divergence Bounds
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Solution Overview
Problem
Existing power management systems in electric vehicles face divergence issues in estimating the state-of-charge (SOC) and state-of-health (SOH) of energy storage supplies, leading to potential damage due to increasing sensor bias, noise, and hardware/software malfunctions over time.
Innovation Solution
A controller-based system that estimates SOC and SOH values using current, voltage, and temperature data, along with time-based information, and calculates bounded values to prevent divergence by applying upper and lower bounds, allowing for controlled electrification processes such as modifying charge/discharge limits and cooling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dual estimation methods (Kalman filter) are used to estimate SOC and SOH, then initial accuracy is achieved for newly assembled battery packs, but estimation divergence occurs after a certain time period due to sensor bias, noise, and hardware/software malfunctions
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring sensor readings and comparing estimated SOC/SOH values against actual measurements. When divergence is detected through residual analysis or consistency checks, the system adjusts estimation parameters or triggers recalibration, creating a closed-loop control that maintains accuracy over time despite sensor drift or malfunctions
Solution Approach 2:
The estimation system dynamically adapts its parameters based on operating conditions and battery state. The Kalman filter gains, process noise covariance, and measurement noise covariance are adjusted in real-time based on battery temperature, charge/discharge rates, and estimated SOC/SOH values, allowing the system to maintain precision across varying conditions while preventing divergence
2Measurement precision
If estimation algorithms run continuously to maintain accurate SOC and SOH values, then real-time monitoring capability is achieved, but computational complexity and processing time increase
Solution Approach 1:
The estimation algorithm is segmented into modular components: state prediction module, measurement update module, parameter adaptation module, and divergence detection module. Each module performs a specific function with optimized computational requirements, allowing the system to achieve real-time monitoring while managing complexity through functional decomposition and parallel processing where applicable
Data Source
AI summary
A system is provided for performing a power estimation process for an electric vehicle using a controller. The controller estimates an inner state of an energy storage supply of the electric vehicle. The inner state represents a state-of-charge (SOC) and/or a state-of-health (SOH) of the energy storage supply. The controller also estimates an SOC value and/or an SOH value of the energy storage supply based on at least one of: a present current level, a present voltage level, a present temperature, and time-based information. The controller further estimates a bounded SOC value based on the SOC value, a first upper bound, a the first lower bound, and/or estimates a bounded SOH value based on the SOH value, a second upper bound, and a second lower bound. The controller then controls an electrification process of the electric vehicle based on the bounded SOC and/or SOH values.