Patent Description:
A variety of hybrid electric vehicles include an electric motor and an internal combustion engine. Inclusion of the electric motor (and associated power source such as a battery) permits improved fuel economy and reduced exhaust emissions. Electric vehicles do not include an internal combustion engine, and instead are entirely powered by the electric motor and associated power source. While a lot of research has gone into minimizing fuel consumption, it will be appreciated that battery performance can significantly affect the long-term performance of the hybrid electric vehicle in terms of monetary savings and desired energy efficiency. Accordingly, there is a desire for methods and systems for improving the life expectancy of the battery within electric and hybrid electric vehicles, and thus improve the long-term cost effectiveness of the electric and hybrid electric vehicles, by estimating the health of the battery within electric and hybrid electric vehicles.

The present inventors have recognized, among other things, that a problem to be solved is the need for new and/or alternative approaches for improving the life expectancy of a battery within an electric or hybrid electric vehicle, and thus improve the long-term cost effectiveness of the electric or hybrid electric vehicle, by estimating the health of the electric or hybrid electric vehicle battery, including the RUL (remaining useful life).

In embodiments encompassed by the wording of the claims, a method of diagnosing the health of a battery within an electric or hybrid electric vehicle is provided. The battery is configured to provide power for operation of the electric or hybrid electric vehicle, the electric or hybrid electric vehicle including a battery monitoring system. The method includes receiving battery condition signals from the battery monitoring system and using the received battery condition signals to estimate an SOH (state of health) of the battery and an SOC (state of charge) of the battery. The estimated SOH and the estimated SOC are used in combination with a degradation model to estimate one or more of a capacity loss-related parameter and an internal resistance-related parameter. The estimated capacity loss-related parameter and/or the internal resistance-related parameter are used to estimate a RUL (remaining useful life) value. A closed loop feedback is used to update the degradation model and the estimated RUL value over time are stored. The estimated RUL value are monitored for sudden changes. Using the estimated RUL value, an altered demanded torque profile from electric motors of the electric or hybrid electric vehicle is determined, the altered demanded torque profile being determined for increasing the life of the battery. The step of determining the altered demanded torque profile comprises using a model predictive control, MPC, analysis to minimize a cost function associated with the RUL value over a time horizon, wherein weighting terms in the MPC analysis are changed in response to sudden changes in the estimated RUL value so as to penalize actions that caused monitored sudden changes in the estimated RUL value.

Additionally, estimating the RUL value may further include using a time of battery usage value.

Additionally, estimating the RUL value may further include using a charge throughput value.

Additionally, the SOH of the battery may include a capacity value for the battery.

Additionally, the SOH of the battery may include an internal resistance value for the battery.

Additionally, receiving battery condition signals from the battery monitoring system may include receiving battery condition signals representing one or more of a battery current of the battery, a terminal voltage of the battery, a surface temperature of the battery, and a core temperature of the battery.

In embodiments encompassed by the wording of the claims, a system for providing power within an electric or hybrid electric vehicle is provided. The system includes a battery and a battery monitoring system that is configured to output signals representative of conditions within the battery. A battery diagnostics system is configured to receive the signals outputted by the battery monitoring system. The battery diagnostics system includes a state and health estimation block that is configured to output signals representing a current health state of the battery. The battery diagnostics system includes a health prognostics block configured to receive the signals outputted by the state and health estimation block. The health prognostics block includes a degradation model that is configured to output signals representing a loss of capacity within the battery and/or an internal resistance within the battery, and a lifetime prediction block that is configured to receive the outputted signals from the degradation model and to estimate an RUL (remaining useful life) value for the battery, to use a closed-loop feedback to update the degradation model and to store the estimated RUL value over time and to monitor the estimated RUL value for sudden changes. An engine management system is configured to determine, using the estimated RUL value, an altered demanded torque profile from electric motors of the electric or hybrid electric vehicle, the altered demanded torque profile for increasing the life of the battery, wherein the altered demanded torque profile is determined using a model predictive control, MPC, analysis adapted to minimize a cost function associated with the RUL value over a time horizon, wherein weighting terms in the MPC analysis are configured to be changed in response to sudden changes in the estimated RUL value so as to penalize actions that caused monitored sudden changes in the estimated RUL value.

Additionally, the lifetime prediction block may be configured to estimate the RUL value for the battery based on the signals outputted by the degradation model.

Additionally, the lifetime prediction block may be configured to estimate the RUL value for the battery based also on a time of battery usage value and/or a charge throughput value for the battery.

This overview is intended to provide an introduction to the subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation. The detailed description is included to provide further information about the disclosure.

An electric vehicle may include an electric motor and a battery that is configured to provide electrical power to the electric motor in order to propel the electric vehicle. An electric vehicle does not include a secondary power source such as an internal combustion engine and accompanying fuel tank. A hybrid electric vehicle may include an electric motor and a battery that is configured to provide electrical power to the electric motor in order to propel the hybrid electric vehicle. A hybrid electric vehicle is known as a "hybrid" electrical vehicle because at least one other or "secondary" power source is available. Some hybrid electric vehicles include an internal combustion engine and a fuel tank holding fuel, such as gasoline or diesel, for the internal combustion engine. Some hybrid electric vehicles may use other power sources, such as a fuel cell, rather than an internal combustion engine. In some examples, a hybrid electric vehicle may be additionally configured to obtain electrical grid power, where such vehicles are known as plug-in hybrid vehicles, capable of using electrical power obtained from a grid as well as electrical power obtained from an internal combustion engine. Hybrid electrical vehicles may operate in different modes (full electrical, parallel electrical/secondary source, and full secondary source), and those modes may be limited in some vehicles and/or may change depending on driving conditions.

In some cases, for an electric vehicle, there may be some flexibility in how the electric motor is operated, including placing limitations on how much power can be withdrawn from the battery at a particular time, for example, or how much power can be withdrawn from the battery based at least in part upon how much charge remains within the battery. The vehicle management system may manage how the electric motor is operated. Knowing how the battery is performing, and having an idea of how long the battery will last, can be beneficial in optimizing the life of the battery.

For a hybrid electric vehicle having both an electric motor and an internal combustion engine, it will be appreciated that there can be some flexibility in how the vehicle is managed by its vehicle management system. Depending on a variety of conditions and parameters, it may be advantageous to run the internal combustion engine (or other secondary power source) a little more than strictly necessary in order to improve the life expectancy of the battery, for example. Knowing how the battery is performing, and having an idea of how long the battery will last, can be beneficial in optimizing the life of the battery. It will be appreciated that the batteries used to power the electric motor in electric and hybrid electric vehicles can represent a substantial investment for the vehicle's owner.

<FIG> is a schematic block diagram of an illustrative system <NUM> for providing electrical power within an electric or hybrid electric vehicle. The system <NUM> includes a battery <NUM> and a battery monitoring system <NUM> that is configured to monitor the performance of the battery <NUM> and to output signals that are representative of conditions within the battery <NUM>. In some cases, the battery monitoring system <NUM> may be part of an engine management system or even a vehicle management system. The battery monitoring system <NUM> may even be part of the battery <NUM>, for example, taking the form of a microprocessor, microcontroller, application specific integrated circuit (ASIC), or other electrical logic, sensing and memory circuitry integrated with the battery itself. The battery monitoring system <NUM> may output signals that are used to monitor battery performance, such as but not limited to battery current, internal resistance, terminal voltage and/or surface temperature. In some cases, core temperature may be used instead of surface temperature.

The battery monitoring system <NUM> is operably coupled with a battery diagnostics system <NUM> that is configured to receive the signals that are outputted by the battery monitoring system <NUM>. In some cases, the battery monitoring system <NUM> may be operably coupled with the battery diagnostics system <NUM> via a vehicle network, for example. The battery diagnostics system <NUM> includes a state and health estimation block <NUM> that is configured to output signals representing a current health state of the battery.

The battery diagnostics system <NUM> also includes a health prognostics block <NUM> that is configured to receive the signals outputted by the state and health estimation block <NUM>. The health prognostics block <NUM> includes a degradation model <NUM> that is configured to output signals representing a loss of capacity within the battery <NUM> and/or changes to an internal resistance within the battery <NUM> and a lifetime prediction block <NUM> that is configured to receive the outputted signals from the degradation model <NUM> and to estimate an RUL (remaining useful life) value for the battery <NUM> and/or a CBW (cumulative battery wear cost) value for the battery <NUM>.

In some instances, the lifetime prediction block <NUM> may be configured to estimate the RUL value and/or the CBW value for the battery based on the signals outputted by the degradation model. The lifetime prediction block <NUM> may be configured to estimate the RUL value and/or the CBW value for the battery <NUM> based also on a time of battery usage value and/or a charge throughput value for the battery <NUM>.

<FIG> is a schematic block diagram of an illustrative diagnostic system <NUM>. The diagnostic system <NUM> may be considered as being an example of the system <NUM> shown in <FIG>. Features ascribed to the system <NUM> may be considered as being applicable to the system <NUM>. Similarly, features ascribed to the system <NUM> may be considered as being applicable to the system <NUM>.

In the system <NUM>, a predicted lifetime of the battery <NUM> may be estimated by the health prognostics filter <NUM>. The health prognostics filter <NUM> receives inputs from a state and health estimation block <NUM>. The state and health estimation block <NUM> provides an estimated state of charge (SOC) for the battery <NUM> and an estimated core battery temperature for the battery <NUM>. The state and health estimation block <NUM> also provides up to date state of health information such as estimated latest capacity of the battery <NUM> and internal resistance within the battery <NUM>.

The health prognostics filter <NUM> includes a degradation model <NUM> that processes an SOH (state of health) capacity error. This error is a difference between the capacity estimated by the state and health estimation block <NUM> and the battery capacity estimated by the degradation model <NUM>. The degradation model <NUM> may utilize one or more different models for modeling battery degradation. For example, one such model describes the rate of capacity change as a function of current and state of charge for different cell/battery core temperatures. Another model may parametrize the rate of capacity change as a function of temperature and state of charge where the current is first mapped into the cell temperature.

A model update estimation block <NUM> provides closed-loop updating for the degradation model <NUM>. The health prognostics filter <NUM> also includes a lifetime prediction block <NUM> that processes inputs such as time of battery usage, charge throughput, capacity loss and capacity loss rate in order to compute the RUL (remaining useful life) and CBW (cumulative battery wear) cost values.

<FIG> is a flow diagram showing an illustrative method of diagnosing the health of a battery (such as the battery <NUM>) within an electric or hybrid electric vehicle, the battery configured to provide power for operation of the electric or hybrid electric vehicle, the electric or hybrid electric vehicle including a battery monitoring system (such as the battery monitoring system <NUM>). The method <NUM> includes receiving battery condition signals from the battery monitoring system, as indicated at block <NUM>. The battery condition signals may include battery condition signals that are commonly available within the vehicle management system of an electric or hybrid electric vehicle, for example. The battery condition signals may represent one or more of a battery current of the battery, a terminal voltage of the battery, a surface temperature of the battery, and a core temperature of the battery.

The received battery condition signals may be used to estimate an SOH (state of health) of the battery and an SOC (state of charge) of the battery, as indicated at block <NUM>. The SOH of the battery may include a capacity value for the battery, and/or an internal resistance value for the battery.

The estimated SOH and the estimated SOC may be used in combination with a degradation model to estimate one or more of a capacity loss-related parameter and a internal resistance-related parameter, as indicated at block <NUM>. The estimated capacity loss-related parameter and/or the internal resistance-related parameter may be used to estimate a RUL (remaining useful life) value and/or a CBW (cumulative battery wear cost) value, as indicated at block <NUM>. In some cases, estimating the RUL value and/or the CBW value may further include using a time of battery usage value. Estimating the RUL value and/or the CBW value may additionally or alternatively include using a charge throughput value.

In some cases, and as indicated at block <NUM>, the method <NUM> may further include using a closed loop feedback to update the degradation model. The method <NUM> may further include storing the estimated RUL value over time and monitoring the estimated RUL value for sudden changes, as indicated at block <NUM>.

<FIG> is a flow diagram showing an illustrative method <NUM> of optimizing battery life for a battery (such as the battery <NUM>) within an electric or hybrid electric vehicle. The method <NUM> includes periodically capturing standard signals from a battery monitoring system, the standard signals providing information regarding a current condition of the battery, as indicated at block <NUM>. The standard signals may include signals representing one or more of battery current, terminal voltage of the battery, surface temperature of the battery, and/or core temperature of the battery.

The captured standard signals are used to periodically estimate an RUL (remaining useful life) of the battery, as indicated at block <NUM>. The periodically captured standard signals may be provided to a state and health estimation block that is configured to output information describing a state of health of the battery.

The captured standard signals are used to periodically estimate a CBW (cumulative battery wear) cost, as indicated at block <NUM>. The periodically estimated RUL and/or the periodically estimated CBW are used to extend the lifetime of the battery within the hybrid vehicle, as indicated at block <NUM>. Using the estimated RUL and the estimated CBW to extend the lifetime of the battery within the hybrid vehicle may include changing a control algorithm based on the estimated RUL and/or the estimated CBW.

In some instances, using the captured standard signals to periodically estimate the RUL and/or the CBW may include utilizing a degradation model of capacity loss and/or internal resistance growth. In some instances, using the captured standard signals to periodically estimate the RUL and/or the CBW may include utilizing a lifetime prediction filter block that receives as inputs one or more of time of battery usage, charge throughput, capacity loss, capacity loss rate, internal resistance growth, and internal resistance growth rate.

In some cases, and as indicated at block <NUM>, the method <NUM> may further include storing the estimated RUL over time and monitoring the estimated RUL for sudden changes. The estimated RUL may be used for planning system maintenance, for example, as indicated at block <NUM>. In some instances, the method <NUM> may additionally or alternatively include communicating the estimated RUL via an HMI (human machine interface) within the electric or hybrid electric vehicle. As an example, the estimated RUL may be communicated to a touch screen display viewable by the driver of the electric or hybrid electric vehicle.

There are mathematical models that may be used in estimating battery life. Equation (<NUM>) below provides an estimate for battery life in years: <MAT> where Qlifetime is the lifetime throughput and Qthrpt is the annual throughput. It will be appreciated that Qlifetime depends on the battery degradation rate (and fitted degradation model) being a function of the SOC (state of charge), applied current and temperature. <FIG> provides a graphical representation <NUM> of Lbatt plotted versus time. This illustrates how the remaining useful life (RUL) changes until reaching <NUM> percent of nominal capacity.

Equation (<NUM>) below provides a definition for Qthrpt, which is the integrated current I divided by the battery-in-usage time Tu: <MAT>.

Equation (<NUM>) below defines the battery wear, in which Cnewbatt is the cost of a battery replacement and nrt is the round trip efficiency:
<MAT>.

Equation (<NUM>) provides the cumulative battery wear cost (CBW):
<MAT>.

Equation (<NUM>) provides the instantaneous time of degradation: <MAT> where Q<NUM> is the <NUM>% charge of nominal (new) battery capacity and Qloss is the amount of degraded capacity.

Equation (<NUM>) provides the amount of lifetime charge, where TEOL provides useful information during charging where the degradation model has significant magnitude: <MAT> The computation of lifetime throughout Qlifetime is triggered during charging and is kept constant during discharging because during discharging, a calendar aging degradation rate is expected. Qlifetime is assumed to be an average value, rather than an instantaneously driven one by the TEOL (see equation (<NUM>) and equation (<NUM>)). For this reason, Qlifetime is filtered by either a running average filter, a weighted average filter or a mean average with forgetting factor.

<FIG> provides a flow diagram showing an illustrative method <NUM> for battery lifetime prognosis. The method <NUM> begins at an initialization block <NUM>. At a decision block <NUM>, a determination is made as to whether the battery is being charged. If not, control passes to block <NUM>, where Q̂lifetime,k+<NUM> is set equal to Q̂lifetime,k+<NUM>. Control then passes to block <NUM>, where equation (<NUM>), equation (<NUM>) and equation (<NUM>) are relied upon.

Reverting momentarily to the decision block <NUM>, if the determination is made that the battery is being charged, control passes to block <NUM>, where the recursive mean average with forgetting factor is defined as shown in equation (<NUM>): <MAT> It will be appreciated that this technique incorporates all past inputs into the filter's output, not just the last n+<NUM>. The input measurement that is mΔt seconds old is discounted by a factor of λ and if λ is close to <NUM>, the older inputs are forgotten quickly or immediately leading to less filtering. If λ is close to <NUM>, however, the older inputs are forgotten relatively slowly leading to more filtering. While other averaging filters may be used, a recursive version with a forgetting factor formula is the most computationally efficient of all averaging filters. In some cases, control also passes from initialization block <NUM> to a block <NUM>, where equation (<NUM>) is applied. From there, control passes to block <NUM>. From block <NUM>, control passes to an end block <NUM>.

<FIG> is a graphical representation showing how the overall cost of consuming the battery within an electric or hybrid electric vehicle may be altered by changing the control algorithm or changing parameters within the control algorithm in order to minimize. Cost is plotted versus time. A graph <NUM> represents a cumulative battery wear cost, which is an indication of how close the vehicle is to having to replace the battery, for a first control algorithm or scenario. A graph <NUM> represents the cumulative battery wear cost for a second control algorithm or scenario in which the control algorithm attempts to minimize the cumulative battery wear cost and thus maximize the expected life of the battery. It will be appreciated that the second control algorithm, represented by the graph <NUM>, provides a lower cumulative battery wear cost, relative to that represented by the graph <NUM>.

<FIG> provides a graphical representation of a battery degradation model in which capacity is plotted versus current. In this example, it is assumed that the battery degradation model has a pure current dependency, and dependency on SOC and temperature have been omitted. A point <NUM> represents nominal while a line <NUM> indicates charging the battery and a line <NUM> indicates discharging the battery. In this particular example, the nominal battery capacity Q = <NUM> Ah, the nominal charging current I = -30A, the nominal SOC (state of charge) is <NUM> percent and the nominal degradation rate for the nominal current and nominal SOC Qr (<NUM>, -<NUM>) = <NUM>. 4858x10-<NUM> Ah/s. These specific parameters may be adjusted for other examples using different battery types, sizes, etc..

The nominal degradation time at the beginning of life (Qloss =<NUM>) may be given by: <MAT> where Csy is a second to year conversion constant. The nominal Qlifetime is given by: <MAT>.

<FIG> is a graphical representation of a demonstration example of a nominal scenario. In <FIG>, lifetime charge throughput is plotted against time. A line <NUM> shows the nominal (constant) value for the lifetime charge throughput while a line <NUM> shows the lifetime charge throughput in accordance with a model. <FIG> shows battery current plotted against time in seconds, <FIG> shows charge throughput plotted against time in seconds, <FIG> shows battery lifetime plotted against time and <FIG> shows cumulative battery wear cost plotted versus time. In <FIG>, a line <NUM> shows the nominal battery lifetime while a line <NUM> shows the battery lifetime in accordance with the model. In <FIG>, a line <NUM> shows the nominal cumulative battery wear cost while a line <NUM> shows the cumulative battery wear cost in accordance with the model.

These plots demonstrate the lifetime and cumulative battery wear for the nominal/constant lifetime charge throughput and model/filter case of lifetime charge throughput. The nominal case provides the RUL (here lifetime) that is based on the nominal lifetime charge throughput that is based on the nominal current reflecting typical load and nominal time of degradation. The model/filter, on the other hand, processes the lifetime charge based on evolving signals having more accurate and updated inputs.

<FIG> is a graphical representation of a demo example of a sudden capacity change scenario. A sudden capacity loss of <NUM> Ah (<NUM> percent of nominal) occurs around time equals <NUM> seconds. In <FIG>, lifetime charge throughput is plotted against time. A line <NUM> shows the nominal (constant) value for the lifetime charge throughput while a line <NUM> shows the lifetime charge throughput in accordance with a model. <FIG> shows battery current plotted against time, <FIG> shows charge throughput plotted against time, <FIG> shows battery lifetime plotted against time and <FIG> shows cumulative battery wear cost plotted versus time. In <FIG>, a line <NUM> shows the nominal battery lifetime while a line <NUM> shows the battery lifetime in accordance with the model. The line <NUM> starts to diverge from the line <NUM> after the sudden capacity change. In <FIG>, a line <NUM> shows the nominal cumulative battery wear cost while a line <NUM> shows the cumulative battery wear cost in accordance with the model. The line <NUM> starts to diverge from the line <NUM> after the sudden capacity change.

<FIG> are graphical representations of various battery parameters showing changes as a result of using different control algorithms or strategies, demonstrating the usefulness of RUL and CBW. <FIG> shows battery current plotted against time, <FIG> shows charge throughput plotted against time, <FIG> shows battery lifetime plotted against time and <FIG> shows cumulative battery wear cost plotted versus time.

In <FIG>, a line <NUM> represents a first control strategy while a line <NUM> represents a second control strategy. In <FIG>, a line <NUM> represents the first control strategy while a line <NUM> represents the second control strategy. In <FIG>, a line <NUM> represents the first control strategy while a line <NUM> represents the second control strategy. In <FIG>, a line <NUM> represents the first control strategy and a line <NUM> represents the second control strategy.

As can be seen, two different control strategies can have a different effect on battery lifetime. It can be seen that the first control strategy generates a higher annual change and therefore wears the battery more than the second control strategy. The health-prediction filter based on the online processed battery current information predicts a higher RUL (or lifetime) and lower cumulative battery wear cost for the second control strategy. In some cases, the CBW indicator can enter the EPSC (energy power split controller) cost function and thus penalize the uneconomical usage of the battery <NUM>.

In some instances, the RUL (remaining useful life) and the CBW (cumulative battery wear cost) that have been calculated for the battery <NUM> by the battery diagnostics system <NUM> may be used by an engine management system to alter the demanded torque profiles in order to maximize the life of the battery <NUM>. A demanded torque profile defines, in part, how much power (torque) is demanded from the electric motor(s) that at least partially propel the vehicle, and thus defines in part when electrical power is demanded from the battery <NUM>, and at what rate electrical power is demanded from the battery <NUM>. The engine management system may rely upon other battery condition parameters as well, such as but not limited to the current charge state of the battery <NUM>. In some cases, the engine management system may include an EPSC (energy power-split controller) that is configured to utilize the RUL and/or the CBW parameters to determine a demanded torque profile in order to maximize the lifetime of the battery, for example. In some examples, power split control (for a hybrid), or battery utilization (for an all-electric vehicle) may use an optimization problem to determine control parameters, such as by incorporating a model predictive control (MPC) analysis. In MPC, a cost function is minimized over a time horizon; the RUL and/or CBW may be used within the cost function of an MPC, or changes or impact to RUL and/or CBW may be accounted for within the cost function to penalize such changes. In some examples, a weighting term in an MPC or other optimization analysis may change in response to changes in RUL and/or CBW to penalize certain actions (such as penalizing actions requiring large torques) to extend battery life.

It will be appreciated that in a hybrid electric vehicle that includes both one or more electric motors as well as an internal combustion engine or fuel cell, for example, has the capability to adjust how much power is being demanded from the internal combustion engine or fuel cell at a particular time, relative to how much power is being demanded from the one or more electric motors. In some instances, demanding relatively less power from the one or more electric motors, and temporarily relying more on the internal combustion engine or fuel cell, may temporarily decrease effective fuel economy while increasing the expected life expectancy of the battery <NUM>.

Additionally or alternatively, the EPSC may be configured to utilize the RUL and/or the CBW parameters in order to minimize wear of the battery <NUM>. It will be appreciated that the life expectancy of the battery <NUM> and wear of the battery <NUM> are inversely related, i.e., decreasing battery wear increases battery life expectancy, and vice versa. The EPSC may be configured to solve an optimization problem on the prediction horizon by including RUL and/or CBW parameters as terms within the optimization problem directly, or by having one or more terms, weighting values, etc. in the optimization problem derive from the RUL and/or CBW.

In some instances, particularly for all electric vehicles that do not have a separate internal combustion engine or fuel cell, along with accompanying fuel source, the engine management system may include a vehicle cruise controller that generates a power profile for the vehicle that maximizes the lifetime of the battery <NUM> and/or minimizes battery wear. For example, the cruise controller may allow reduction in vehicle speed while going up a hill, and may make up for such a reduction in a subsequent downhill component of a travel route. In some instances, providing a little less power in response to the user stepping on the accelerator may help to extend the life expectancy of the battery <NUM>, trading a small loss in total power for improving the life expectancy of the battery <NUM>. In some cases, the vehicle cruise controller may solve an optimization problem on the prediction horizon with the CBW indicator embedded in the cost function.

Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic or optical disks, magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description.

The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

Claim 1:
A method of diagnosing the health of a battery within an electric or hybrid electric vehicle, the battery configured to provide power for operation of the electric or hybrid electric vehicle, the electric or hybrid electric vehicle including a battery monitoring system, the method comprising:
receiving (<NUM>) battery condition signals from the battery monitoring system;
using (<NUM>) the received battery condition signals to estimate an SOH, state of health, of the battery and an SOC, state of charge, of the battery;
using (<NUM>) the estimated SOH and the estimated SOC in combination with a degradation model to estimate one or more of a capacity loss-related parameter and an internal resistance-related parameter;
using (<NUM>) the estimated capacity loss-related parameter and/or the internal resistance-related parameter to estimate a RUL, remaining useful life, value ;
using (<NUM>) a closed loop feedback to update the degradation model; and
storing (<NUM>) the estimated RUL value over time and characterised by
monitoring the estimated RUL value for sudden changes,
wherein the method further comprises:
determining, using the estimated RUL value, an altered demanded torque profile from electric motors of the electric or hybrid electric vehicle, the altered demanded torque profile for increasing the life of the battery, wherein the step of determining the altered demanded torque profile comprises using a model predictive control, MPC, analysis to minimize a cost function associated with the RUL value over a time horizon, wherein weighting terms in the MPC analysis are changed in response to sudden changes in the estimated RUL value so as to penalize actions that caused monitored sudden changes in the estimated RUL value.