MPC Battery Charging Stabilization via Kalman Filter State Estimation
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Solution Overview
Problem
Existing battery control systems face instability due to unknown control loop limits, leading to nonlinearities that affect battery performance and lifespan, particularly in electric vehicles where quick charge conditions can exceed voltage and capacity limits, resulting in reduced battery life and increased costs.
Innovation Solution
A model predictive control (MPC) system using a Kalman filter and reduced-order battery models to estimate battery states and optimize charge/discharge processes, avoiding constraints by modifying error signals and adjusting charging characteristics based on shared bus voltage and cell parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the battery controller responds quickly to load changes and quick charge conditions, then the response speed improves, but the battery may exceed voltage and capacity limits causing instability
Solution Approach 1:
The control algorithm predicts future battery states and adjusts control actions in advance to prevent constraint violations. By looking ahead at predicted battery voltage and capacity trajectories, the system modifies charging/discharging rates before limits are reached, avoiding instability while maintaining quick response capability
Solution Approach 2:
The control system dynamically adjusts operating parameters based on real-time battery state and predicted future conditions. The controller continuously updates charge/discharge rates according to battery health, temperature, and load requirements, enabling adaptive response that maintains stability across varying operating conditions
2Device complexity
If conventional control loops are used with known limits, then the control simplicity is maintained, but the nonlinearities from constraints cause instability
Solution Approach 1:
The control system incorporates feedback from battery state estimates and constraint predictions to continuously adjust control actions. By monitoring predicted future states against voltage and capacity limits, the system modifies charging/discharging rates to prevent constraint violations, eliminating the nonlinear instability caused by hard constraints while maintaining computational efficiency
Data Source
AI summary
An apparatus for model predictive control (“MPC”) is disclosed. A method and system also perform the functions of the apparatus. The apparatus includes a measurement module that receives battery status information from one or more sensors receiving information from a battery cell, and a Kalman filter module that uses a Kalman filter and the battery status information to provide a state estimate vector. The apparatus includes a battery model module that inputs the state estimate vector and battery status information into a battery model and calculates a battery model output, the battery model representing the battery cell, and an MPC optimization module that inputs one or more battery model outputs and an error signal in a model predictive control algorithm to calculate an optimal response. The optimal response includes a modification of the error signal.