Soc and soh co-estimation systems and methods for electric vehicles

Abstract

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.

Description

technical field [0001] The present disclosure relates generally to methods and systems for diagnosing power management systems used in electric vehicles, and more particularly, to estimating the internal state of an energy storage power source of the power management system. Background technique [0002] The power management system can be used in pure electric vehicles (EV) and / or hybrid electric vehicles (HEV) with an electric motor and an internal combustion engine (ICE). As used herein, the term "electric vehicle" refers to hybrid electric vehicles and / or pure electric vehicles that provide an alternative to conventional fuel engine systems to supplement or completely replace engine systems such as ICEs. In one example, the electric vehicle is an extended-range electric vehicle (EREV). In EREV, primary electric drive is achieved using a battery or associated rechargeable energy storage system (RESS) that acts as a direct current (DC) voltage source for the electric motor...

AI Technical Summary

Problems solved by technology

[0005] However, this dual estimation method of the Kalman filter is prone to bias after a predetermined period

For example, for a newly assembled and validated battery pack, SOC and SOH estimates may initially be accurate, but become inaccurate due to increased sensor bias and noise, hardware and / or software failures in the power management system, or due to aging components of the electric vehicle. As a result of battery degradation, deviations in SOC and SOH estimates may occur after a certain period of time

In addition, large SOC and SOH estimation deviations may cause unnecessary damage to the power management system as well as other components of the electric vehicle

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