INTRODUCTION

The present disclosure relates to the real-time estimation of modeled battery parameters and battery states of a multi-cell battery pack. Accurate estimation allows an associated controller to effectively and efficiently control a myriad of different power usage and utilization decisions during battery charging, steady-state, and discharging operating modes. The present disclosure thus lends itself to the real-time control of electrified powertrains, powerplants, robots, mobile platforms, and other types of electrical systems in which improved battery parameter and state estimation accuracy is desirable.

Ongoing measurements of the various responses to a given input are not always possible or practicable in a deterministic system, which in turn often necessitates the use of system models and response estimation based on such models. In a typical high-energy battery pack, for instance, such as a lithium-ion traction battery pack of an electric or hybrid electric motor vehicle, voltage and temperature are periodically measured and estimated as responses to electrical current. Different voltage states may be modeled, including equilibrium potential, hysteresis effects-based voltage responses, voltage drops due to ohmic resistance, voltage drops due to battery pack dynamics, e.g., double-layer and/or diffusion voltage, etc. Each of the exemplary voltage responses may be described in a model using an algebraic or differential function, or by using a convolution integral. The above-noted voltage responses in particular influence key battery state estimates such as state of charge (SOC) and state of power (SOP)/power capability. As a result, equivalent circuit models are typically used in conjunction with adaptive battery state estimation (BSE) logic in order to estimate voltage responses and other battery parameters.

As will be appreciated by those of ordinary skill in the art, a battery cell resting under open-circuit conditions, given sufficient time, will eventually settle at an equilibrium voltage referred to in the art as the cell's open-circuit voltage (OCV). Ideally, the OCV of a given battery cell is unique for each SOC independently of whether the battery cell was charging or discharging immediately prior to switching to an open-circuit condition, and independently of the magnitude of the battery current. While OCV is accurately ascertained in a battery pack in an off state for an extended duration, a key challenge presents itself when attempting to perform battery state estimations of a battery pack that is actively charging or discharging, particularly in dynamically changing operating environments.

In lithium-ion batteries in particular, a non-linear relationship exists between OCV and SOC. In hybrid electric and battery electric vehicles, for instance, BSE logic in the form of a programmed algorithm may reference an available OCV curve to help estimate SOC in real-time. Alternatively, SOC may be tracked over time from an initial SOC value using a procedure referred to in the art as Coulomb counting. Other BSE logic variations seek to balance voltage-based estimates with available Coulomb counting-based estimates in order to produce a composite estimate.

SUMMARY

A method and an associated system are disclosed herein that are intended to improve upon available battery parameter and state estimation accuracy in an electrical system having a multi-cell battery pack. As part of the disclosed solution, a controller is programmed to execute instructions embodying the present method, with the controller doing so using battery state estimation (BSE) logic and current control logic as described herein. The controller uses an application-specific equivalent circuit model to accurately estimate and regress one or more relevant battery parameters. Representative non-limiting regressed battery parameters within the scope of the present disclosure include open-circuit voltage (OCV), ohmic resistance (R-ohmic), and impedance of the battery pack, with SOC and SOP being representative battery states that may be estimated from such battery parameters using the disclosed approach.

As understood in the art, certain battery parameters enjoy greater predictive value than other battery parameters during higher-frequency current inputs, particularly when estimating SOC and SOP/power capability of a battery pack. As a result, it is desirable to optimize estimation accuracy for such battery parameters. Ohmic resistance is one such parameter. Ohmic resistance, which is generally defined as the apparent internal resistance of a battery pack and the resistance of the various electrical conductors used in the battery pack's construction. Ohmic resistance tends to manifest as an instantaneous cell voltage response to changes in battery current, is particularly significant to SOP/power capability estimations.

It is recognized herein as a basis for the present solution that battery state estimators configured to regress battery parameters, which may include Extended Kalman Filters, Sigma-Point Kalman Filters, recursive least-squares regression techniques, and the like, may experience insufficient levels of input signal variation/excitation under certain operating conditions. Insufficient excitation in turn may lead to inaccurate estimation results. Noise present in a signal measurement environment, such as measured electrical current, voltage, and temperature, may result in a low signal-to-noise ratio. When insufficient frequency content is present in the input signals being furnished to the resident BSE logic, the predicted battery parameters may tend to drift, with the battery parameters possibly rising or falling in a monotonic manner as a result. The present solution is therefore intended to address this problem by selectively modifying a constant baseline current of the battery pack, i.e., a charging or discharging current, by purposefully injecting time-variant frequency content in the form of current oscillations into the baseline current.

In a particular embodiment, a method is provided for estimating a state of a battery pack via a controller having BSE logic configured to regress a set of battery parameters. The method includes receiving or outputting a constant baseline current via the battery pack. The method also includes selectively requesting the injection/addition of time-variant frequency content in the form of current oscillations to the constant baseline current, with such a request occurring via the controller. This action is accomplished in response to a predetermined condition that is itself indicative of the above-noted insufficiency of frequency content. The constant baseline current and the current oscillations combine to form a final current.

The method in this particular embodiment includes estimating a battery parameter of the battery pack via the BSE logic to thereby provide an estimated battery parameter, and thereafter estimating the present state of the battery pack as an estimated battery state using the estimated battery parameter.

The BSE logic may include an extended Kalman filter or other Kalman filter formulation.

Selectively requesting the injection of the current oscillations into the constant baseline current may include requesting a constant charging current, via the controller, from an offboard charging station as the constant baseline current, and wherein controlling powerflow to or from the battery pack includes charging the battery pack using the final current. Alternatively, selectively requesting the injection of the current oscillations into the constant baseline current includes selectively controlling an ON/OFF state of an electrical load connected to the battery pack while receiving or delivering a constant baseline current to thereby create the current oscillations. Controlling the powerflow to or from the battery pack in this instance may include discharging the battery pack to the electrical load.

As another alternative, selectively requesting the injection of the current oscillations into the constant baseline current may include selectively requesting, from an offboard charging station, a series of constant charging currents each having a different frequency content to thereby create the current oscillations, and wherein controlling the powerflow to or from the battery pack using the estimated battery state includes charging the battery pack using the final current, or communicating a charging request from the controller to an offboard smart charger. Such a smart charger may be configured to detect a requirement of the battery pack for the final current, and that is configured to transmit the final current to the battery pack as a charging current.

The battery parameter may include an ohmic resistance, an impedance, and/or an open-circuit voltage of the battery pack in various embodiments.

In an exemplary embodiment, the frequency of the current oscillations may be less than about 1 Hz, and the constant baseline current may have a frequency of less than about 0.01 Hz. The current oscillations may include a pseudo-random binary signal having a time-variant frequency, or pulse width modulation or pulse density modulation signal having a time-variant frequency, or a sequence of chirp signals.

In a possible embodiment, the predetermined condition may include a threshold covariance or an estimated error value from the BSE logic, or a duration over which the constant baseline current remains constant prior to injection of the current oscillations.

An electrical system is also disclosed herein that, according to an exemplary embodiment, includes a rotary electric machine that is electrically connected to and driven by the battery pack, and a controller configured to estimate a present state of a battery pack using the BSE logic noted above. In an exemplary embodiment, the controller is configured to determine, via the BSE logic, a frequency content of a constant baseline current delivered to or from the battery pack, wherein the constant baseline current has a frequency of less than about 0.01 Hz. The controller is also configured to selectively request an injection of current oscillations into the constant baseline current in response to a predetermined condition, with the constant baseline current and the current oscillations combining to form a final current. In a non-limiting embodiment, the current oscillations have a frequency in a range of between about 0.1 Hz and 1 Hz, e.g., within ±5% or ±10% of the stated values or an otherwise reasonable tolerance thereof, and to estimate a battery parameter of the battery pack via the BSE logic concurrently with the current oscillations to thereby generate an estimated battery parameter. The estimated battery parameter in this embodiment is an ohmic resistance, an impedance, and/or an open-circuit voltage of the battery pack.

The controller is further configured to estimate the present state of the battery pack using the estimated battery parameter as an estimated battery state, and to thereafter control, using the estimated battery state, a powerflow from or to the electric machine respectively to or from the battery pack. One or more road wheels may be connected to the rotary electric machine.

The above summary is not intended to represent every possible embodiment or every aspect of the present disclosure. Rather, the foregoing summary is intended to exemplify some of the novel aspects and features disclosed herein. The above features and advantages, and other features and advantages of the present disclosure, will be readily apparent from the following detailed description of representative embodiments and modes for carrying out the present disclosure when taken in connection with the accompanying drawings and the appended claims.

