Parallel Kalman SOC Estimation for Multi-String EV Batteries

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Solution Overview

Problem

Conventional state of charge (SOC) estimation systems in electrified vehicles using a single Kalman filter are slow and result in reduced accuracy, particularly for larger battery systems with multiple battery cells.

Innovation Solution

Implementing parallel separate Kalman-type filters for each battery string in electrified vehicles to estimate the state of charge, allowing for more accurate and faster SOC estimation, especially in vehicles with two parallel battery packs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a single Kalman filter is used for SOC estimation in electrified vehicles, then the system complexity is low, but the SOC estimation accuracy is reduced and the estimation speed is slow

Engineering Contradiction:

ImproveSOC estimation accuracyVSAvoidfilter system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent divides the battery system into multiple parallel battery packs (first battery pack and second battery pack), and implements separate Kalman filters for each pack. This segmentation allows independent SOC estimation for each battery pack, improving overall estimation accuracy while managing system complexity through modular architecture

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent combines multiple individual Kalman filter estimations into a unified final SOC value through a control system that processes outputs from both the first and second Kalman filters. This merging approach integrates information from parallel battery packs to achieve accurate overall SOC estimation

Inventive Principle:

Principle #5Merging (Combining)

2Productivity

If a single Kalman filter is used for SOC estimation, then the computational resources required are minimal, but the SOC estimation speed is slow

Engineering Contradiction:

ImproveSOC estimation speedVSAvoidcomputational energy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

By segmenting the SOC estimation task into parallel Kalman filters operating on individual battery packs simultaneously, the system achieves faster overall estimation speed. Each filter processes data independently and concurrently, reducing total computation time compared to sequential processing

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements separate Kalman filters for each battery pack even though a single filter could technically handle the entire system. This excessive action of using multiple filters provides redundant estimation pathways that improve speed and reliability, with the trade-off of increased computational energy being justified by the performance gains

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250370047A1Parallel kalman filter techniques for electrified vehicle battery systems

Publication Date: 2025.12.04 FCA US LLC

AI summary

A state of charge (SOC) estimation method for a battery system of an electrified vehicle includes providing a first SOC estimator comprising a first Kalman filter and configured to estimate a first SOC of a first string of battery cells of the battery system having a first current flowing therethrough and providing a second SOC estimator comprising a second Kalman filter and configured to estimate a second SOC of a second string of battery cells of the battery system having a second current flowing therethrough, determining, in parallel, the first and second estimated SOCs using the first and second SOC estimators, respectively, determining a final SOC for the battery system based on the first and second estimated SOCs, and generating an output based on the determined final SOC for the electrified vehicle.