METHOD FOR ESTIMATING AN OPERATING PARAMETER OF A BATTERY UNIT

Abstract

A method for estimating an operating parameter of a battery unit (202) in an energy storage system (200) of a vehicle (201) using a set of first estimators, each first estimator being configured to estimate a state residual size of the battery unit based on a given initial value of said operating parameter and using a prediction model, the operating parameter being indicative of one of capacity and impedance of the battery unit, the method comprising:—obtaining measurement data relating to operating conditions of the energy storage system at a number of points in time t.sub.k,—providing a set of initial values of said operating parameter, each initial value being associated with one of said first estimators,—using the set of first estimators, estimating (103) a set of state residual sizes of the battery unit at each point in time t.sub.k based on at least the measurement data and on the associated initial values,—determining a first operating parameter estimate of the battery unit based on the estimated set of state residual sizes,—estimating the operating parameter of the battery unit as a function of time based on at least said first operating parameter estimate, wherein estimating the set of state residual sizes comprises using a recursive algorithm for estimating the state residual size of each element of a set of state residuals, each estimated state residual size being determined based on the state residual size as estimated at a previous point in time t.sub.k−1 and a current magnitude of the state residual.

Claims

1. A method for estimating an operating parameter of a battery unit in an energy storage system of a vehicle using a set of first estimators, each first estimator being configured to: estimate a battery parameter or battery state of the battery unit based on a given initial value of said operating parameter by using a prediction model, the battery parameter or battery state relating to operating conditions of the battery unit, the prediction model being a model suitable for predicting a battery parameter or battery state value based on the given initial value of said operating parameter, and from the estimated battery parameter or battery state, estimating a state residual size of the battery unit, the state residual size being indicative of a magnitude of an error arising from estimating said battery parameter or battery state using the prediction model, the operating parameter being indicative of one of capacity and impedance of the battery unit, the method comprising: obtaining measurement data relating to operating conditions of the energy storage system at a number of points in time t.sub.k, providing to the set of first estimators a set of initial values of said operating parameter, each initial value being associated with one of said first estimators, using the set of first estimators, estimating a set of battery parameter or battery state values of the battery unit based on at least the associated initial values of said operating parameter, and from the estimated set of battery parameter or battery state values, using the measurement data relating to the battery parameter or battery state that was estimated and the set of first estimators, estimating a set of state residual sizes associated with the estimated set of battery parameter or battery state values at each point in time t.sub.k, wherein estimating the set of state residual sizes comprises using a recursive algorithm for estimating the state residual size of each element of a set of state residuals, each estimated state residual size being determined based on the state residual size as estimated at a previous point in time t.sub.k−1 and a current magnitude of the state residual, determining a first operating parameter estimate of the battery unit based on the estimated set of state residual sizes by finding the operating parameter estimate that minimizes the state residual size of the associated battery parameter or battery state, estimating the operating parameter of the battery unit as a function of time based on at least said first operating parameter estimate.

2. The method according to claim 1, wherein estimating the set of state residual sizes comprises: based on the associated initial values of the operating parameter, estimating the battery parameter using the prediction model, the estimated battery parameter corresponding to a measured battery parameter comprised in the measurement data, determining said state residual based on a difference between the estimated battery parameter and the measured battery parameter.

3. (canceled)

4. The method according to claim 1, further comprising: using an observer, filtering the first operating parameter estimate based on a maximum expected rate of change of said operating parameter.

5. The method according to claim 1, further comprising: upon fulfillment of a predefined condition, using at least the first operating parameter estimate to determine an adjusted set of initial values of said operating parameter to be used by the set of first estimators during an upcoming estimation episode, and feeding said adjusted set of initial values to the set of first estimators.

6. The method according to claim 5, wherein said predefined condition is considered to be fulfilled after a predetermined time period or after an end of a predefined estimation episode.

7. The method according to claim 1, further comprising: determining a battery state of the battery unit based on said measurement data, using a second estimator, determining a second operating parameter estimate based on said estimated battery state, said second estimator using a different type of algorithm than the set of first estimators, wherein the operating parameter of the battery unit as a function of time is estimated based on the first and the second operating parameter estimates.

8. The method according to claim 7, further comprising: determining a level of uncertainty of the first and/or the second operating parameter estimate, wherein estimating the operating parameter of the battery unit as a function of time based on the first and the second operating parameter estimates is performed based on at least a first predefined criterion relating to said level of uncertainty.

9. The method according to claim, wherein estimating the operating parameter of the battery unit as a function of time based on the first and the second operating parameter estimates is performed based on at least a second predefined criterion relating to an operating condition of the battery unit.

10. The method according to claim 9, wherein said second predefined criterion is related to at least an operational state of charge window of the battery unit as determined for a predetermined time period or estimation episode.

11. The method according to claim 9, wherein said second predefined criterion is related to at least a degree of relaxation of the battery unit.

12. The method according to claim 7, wherein the operating parameter is a capacity of the battery unit, and wherein enhanced Coulomb counting, compensating for noise in the measurement data relating to battery current and in the determined battery state, is used to determine said second operating parameter estimate.

13. The method according to claim 1, wherein the operating parameter is a capacity of the battery unit, the method further comprising: iteratively estimating an impedance of the battery unit based on the measurement data, wherein the estimated impedance is fed to the set of first estimators and used in the estimation of the set of state residual sizes of the battery unit.

14. The method according to claim 1, further comprising: mapping the estimated operating parameter of the battery unit under present operating conditions to that under standard operating conditions.

15. The method according to claim 1, further comprising: predicting expected operating conditions of the battery unit, wherein the estimation of the operating parameter is only carried out given that the expected operating conditions fulfill predetermined requirements relating to usage of the battery unit.

16. The method according to claim 1, wherein providing the set of initial values of said operating parameter comprises identifying an expected value of the operating parameter, and providing the set of initial values as a set of values distributed around the expected value.

17. A computer program comprising program code means for performing the method according to claim 1 when said computer program is run on a computer.

18. A computer readable medium carrying a computer program comprising program means for performing the method according to claim 1 when said program means is run on a computer.

19. A control unit configured to perform the method according to claim 1.

20. A battery management system for an energy storage system comprising the control unit according to claim 19.

21. A vehicle, such as a hybrid vehicle of a fully electrified vehicle, comprising an energy storage system and a control unit according to claim 19.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0090] With reference to the appended drawings, below follows a more detailed description of embodiments of the invention cited as examples.

[0091] In the drawings:

[0092] FIG. 1 shows a vehicle in which a method according to the invention may be implemented,

[0093] FIG. 2 schematically illustrates parts of a battery model describing a battery unit;

[0094] FIG. 3 is a flow-chart illustrating a method according to an embodiment of the invention,

[0095] FIG. 4 is a block diagram illustrating steps of a method according to an embodiment of the invention,

[0096] FIG. 5 is a flow-chart illustrating a method according to an embodiment of the invention, and

[0097] FIG. 6 is a block diagram illustrating steps of a method according to an embodiment of the invention.

[0098] The drawings are schematic and not necessarily drawn to scale.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE INVENTION

[0099] In the present detailed description, various embodiments of the method according to the present invention are mainly described with reference to an all-electric bus, comprising a propulsion system in the form of battery powered electric motors. However, it should be noted that various embodiments of the described invention are equally applicable for a wide range of hybrid and electric vehicles.

[0100] FIG. 1 shows a simplified perspective view of an all-electric vehicle in the form of a bus 201, which according to an embodiment is equipped with an electric machine (not shown) for operating the bus.

[0101] The bus 201 carries an electric energy storage system (ESS) 200 comprising a battery unit 202 in the form of a battery pack, the battery pack comprising a plurality of battery cells. The battery cells are connected in series to provide an output DC voltage having a desired voltage level. Suitably, the battery cells are of lithium-ion type, but other types may also be used. The number of battery cells per battery pack may be in the range of 50 to 500 cells. It is to be noted that the ESS may include a plurality of battery packs.

[0102] A sensor unit (not shown) may be arranged for collecting measurement data relating to operating conditions of the ESS, i.e. measuring temperature, voltage and current level of the associated battery pack 202. Measurement data from each sensor unit is transmitted to an associated battery management unit (BMU) 204, which is configured for managing the individual battery pack 202 during operation of the bus 201. The BMU 204 can also be configured for determining parameters indicating and controlling the condition or capacity of the battery pack 202, such as the state of charge (SOC), the state of health (SOH), the state of power (SOP) and the state of energy (SOE) of the battery pack 202.

[0103] The BMU 204 is connected to and configured to communicate with an ESS control unit 208, which controls the ESS. The ESS control unit 208 may include a microprocessor, a microcontroller, a programmable digital signal processor or another programmable device. Thus, the ESS control unit 208 comprises electronic circuits and connections (not shown) as well as processing circuitry (not shown) such that the ESS control unit 208 can communicate with different parts of the bus 201 or with different control units of the bus 201. The ESS control unit 208 may comprise modules in either hardware or software, or partially in hardware or software, and communicate using known transmission buses such a CAN-bus and/or wireless communication capabilities. The processing circuitry may be a general purpose processor or a specific processor. The ESS control unit comprises a non-transitory memory for storing computer program code and data. Thus, the skilled person realizes that the ESS control unit may be embodied by many different constructions. This is also applicable to the BMU 204.

[0104] Turning now to FIG. 2, there is depicted a battery model comprising an equivalent circuit of the battery unit 202, also known as a Thevenin battery model. The exemplary equivalent circuit model comprises a single RC circuit to model the battery unit, although more than one RC circuit may be used in the model, such as two RC circuits, depending on battery dynamics and application. The exemplary RC based equivalent circuit model is used for estimation of the state of charge and capacity of the battery unit, and is typically implemented by the above mentioned control unit. The exemplified (equivalent) circuit model illustrated in FIG. 2 is used for estimating the state of charge and capacity of the battery unit based on direct battery measurements. The battery unit state of charge estimation may for example be based on measured battery current inputs and a battery terminal voltage.

[0105] The equivalent circuit model described in relation to FIG. 2 consists of an active electrolyte resistance and conductive resistance of electrodes (or internal ohmic resistance) R.sub.0, in series with the parallel capacitance C.sub.1, and an active charge transfer resistance R.sub.1. V.sub.b refers to terminal voltage output, I.sub.b refers to the current in the circuit and V.sub.oc refers to the battery open circuit voltage. For given values of the terms V.sub.oc, R.sub.0, R.sub.1 and C.sub.1, the terminal voltage V.sub.b can be expressed as a function of the current I.sub.b. Normally R.sub.0 and R.sub.1 increases with age, while battery cell capacity (not illustrated in the figure) decreases with age.

[0106] By the equivalent circuit model of the battery unit, it becomes possible to determine a state of charge level of a battery unit. As such, it is possible to monitor the state of charge level of a battery unit of the battery system. In this context, it is to be noted that SOC estimation is normally based on voltage and current. One ingredient in a SOC estimator is thus to use the relation between SOC and open circuit voltage V.sub.oc. Therefore, the SOC of a battery unit is estimated and determined based on the battery unit open circuit voltage V.sub.oc. Determining battery unit SOC by open circuit voltage is commonly known in the art, and is typically performed by measuring the open circuit voltage of the battery unit. The open circuit voltage of a battery unit is determined by measuring the terminal voltage output V.sub.b of the battery unit when the unit is disconnected from any external load and no external electric current flows through the unit. In the presence of external load (i.e., under non-equilibrium conditions), the open circuit voltage V.sub.oc may also be estimated using terminal voltage measurements, current measurements, and estimate of internal dynamic polarization (i.e., voltage drop across the RC circuit(s)). The open circuit voltage V.sub.oc is in direct correlation with the SOC of the battery unit.

[0107] A method for estimating an operating parameter of a battery unit such as the one illustrated above according to an embodiment of the invention is schematically illustrated in FIG. 3. Optional steps are marked by dashed lines. In this case, the operating parameter is exemplified by the capacity Q of the battery unit. The steps are carried out repeatedly, such as at a certain time interval, but may not necessarily be carried out in the order shown in FIG. 3. The method steps may in this embodiment be carried out in the ESS control unit 208 or in the BMU 204. Reference is also made to FIG. 4, which is a block model illustrating computational blocks used by a method according to an embodiment of the invention.

[0108] In a first step 101, measurement data relating to operating conditions of the energy storage system are obtained, i.e. temperature T.sub.b, voltage V.sub.b and current level I.sub.b of the battery pack 202 of the ESS 200. Measurement data are obtained repeatedly at a number of points in time denoted by t.sub.k. Sensors may be used for this purpose.

[0109] In a second step 102, a set of initial capacity values Q.sub.in,i are provided to a set of first estimators. In the shown embodiment, three first estimators 301, 302, 303 are provided. It is however to be noted that a different number of first estimators may be used, such as five first estimators or more. The number of first estimators may be selected depending on application. Each first estimator herein comprises a non-linear observer, such as an extended Kalman filter, configured to estimate a SOC of the battery unit 202 based on an associated one of the initial values Q.sub.in,i=[Q.sub.in,1, Q.sub.in,2, Q.sub.in,3] of the capacity, using the equivalent circuit prediction model described above with reference to FIG. 2. Thus, each first estimator is provided with one of said initial values Q.sub.in,i.

[0110] In a third step 103, using the set of first estimators 301, 302, 303, a set of battery parameter or battery state values of the battery unit are estimated based on at least the associated initial values Q.sub.i of said operating parameter, and therefrom, a set of state residuals r.sub.i and state residual sizes φ.sub.i of the battery unit 202 at each point in time t.sub.k are estimated, based also on the measurement data relating to operating conditions of the ESS, i.e. relating to the battery parameter or battery state that was estimated. Herein, each first estimator 301, 302, 303 uses its given associated initial value Q.sub.i to estimate the SOC of the battery unit 202 and thereby also determine the state residual r.sub.i, i.e. the difference between the actual measured terminal voltage V.sub.b of the battery unit and a terminal voltage estimate V.sub.bi*, as estimated by the first estimator, r.sub.i=V.sub.b−V.sub.bi*. This difference may be referred to as the voltage error. The state residual is an indication of how precise the estimation of SOC is and thus how well the initial value Q.sub.in,i given to the respective first estimator 301, 302, 303 matches the actual capacity Q of the battery unit 202. The state residual size φ.sub.i, i.e. the size, i.e., norm, of each state residual, may be expressed as

[00002]${\phi }_i\stackrel{\Delta }{=}{.Math.r_i.Math.}_{\mathrm{ℒ2}}^2.$

[0111] The state residual size is here expressed as the L2 norm, but also other types of norms and root-mean-square may be used for sizing the state residuals.

[0112] A recursive algorithm is used for estimating the set of state residual sizes φ.sub.i, each estimated state residual size φ.sub.i(k) being determined based on, on one hand, the state residual size φ.sub.i(k−1) as estimated at a previous point in time t.sub.k−1, and, on the other hand, on a current magnitude of the state residual r.sub.i. The state residual sizes at the point in time t.sub.k+1 can, using the Euler method, be expressed as:

φ.sub.i(k+1)=φ.sub.i(k)+Δt|r.sub.i(k)|.sup.2,

[0113] wherein Δt is the time interval between subsequent samplings and r.sub.i is the current state residual value generated by estimator number i of the first set of estimators, |r.sub.i(k)|.sup.2 hence representing a current magnitude of the state residual at the time t.sub.k In the shown embodiment, three state residual sizes φ.sub.1, φ.sub.2, φ.sub.3 are generated by the respective first estimators 301, 302, 303.

[0114] In a fourth step 104, a first capacity estimate Q.sub.1* of the battery unit is determined based on the estimated set of state residual sizes φ.sub.i. By way of example, the step of using the set of first estimators 301, 302, 303 to determine the first capacity estimate Q.sub.1* of the battery unit based on the estimated set of state residual sizes φ.sub.i comprises the step of performing a curve fitting to find a residual function describing the state residual size as a function of capacity. One example of curve fitting is regression analysis. Typically, the curve fitting corresponds to a polynomial fit. One example of suitable polynomial fit is a second order polynomial. Another example of suitable polynomial fit is a fourth order polynomial. In FIG. 4, the fourth step 104 is carried out in block 304.

[0115] The step of determining the first capacity estimate Q.sub.1* may also comprise performing a convexity check of the residual function. Moreover, the step of determining the first capacity estimate Q.sub.1* herein comprises the step of identifying the value of the capacity that minimizes the residual function, e.g. the polynomial. In particular, the first capacity estimate Q.sub.1* of the battery unit is determined in block 304, by fitting a curve to the state residual sizes and finding the value of the capacity that corresponds to a minima on the fitted curve.

[0116] In a fifth step 105, the capacity Q*(t) of the battery unit 202 is estimated as a function of time based on at least the first capacity estimate Q.sub.1*. The capacity Q* of the battery unit 202 may e.g. be determined to be equal to the first capacity estimate Q.sub.1* at any given point in time. However, the capacity may also be determined based on additional estimates and using additional method steps, as will be further described below.

[0117] The method may e.g. comprise an optional sixth step 106 of filtering the first capacity estimate Q.sub.1* based on a priori information relating to a maximum expected rate of change of the capacity of the battery unit to obtain a filtered, or smoothed, first capacity estimate Q.sub.1,f*(t), based on which the capacity Q*(t) is determined. In this case, a non-linear observer 305, such as an extended Kalman filter, is used for the filtering.

[0118] Another embodiment of the method is illustrated in FIG. 5, in which optional steps are marked by dashed lines and in which the method steps 101-106 have already been described above with reference to FIG. 3. Reference is also in this case made to FIG. 4, and furthermore to FIG. 6, which is another block model illustrating computational blocks used by the method. In FIG. 6, the first estimators 301, 302, 303 and the subsequent block 304 are illustrated by a combined block 310.

[0119] In this embodiment, the method comprises a step 107 of determining an adjusted set of initial capacity values Q.sub.adj,i based on the first capacity estimate Q.sub.1*, given that a predefined condition is fulfilled. The predefined condition may e.g. be considered to be fulfilled after a predetermined time period or after an end of a predefined estimation episode. In the shown embodiment, the adjusted set of initial capacity values Q.sub.adj,i are determined based on the filtered (smoothed) first capacity estimate Q.sub.1,f* determined using the non-linear observer 305. The adjusted set of initial capacity values Q.sub.adj,i may typically be spread around the filtered first capacity estimate Q.sub.1,f*. In a step 108, the adjusted set of initial capacity values Q.sub.adj,i are fed back to the set of first estimators 301, 302, 303, where they are used for estimation of a new set of state residuals sizes φ.sub.i during an upcoming estimation episode.

[0120] The method illustrated in FIG. 5 and FIG. 6 further comprises a step 110 of determining a battery state, such as SOC, of the battery unit 202, based on the measurement data relating to battery current I.sub.b and battery terminal voltage V.sub.b. The battery state may either be measured under certain circumstances, or be estimated based on measurement data using a SOC estimator 306 as illustrated in FIG. 6. The battery state, in this case SOC, is in a subsequent step 111 used for determining a second capacity estimate Q.sub.2* based on the determined SOC and on the measured current I. In step 111, a second estimator 307 is used for the estimation of determining the second capacity estimate Q.sub.2*, wherein the second estimator 307 uses another algorithm than the set of first estimators 301, 302, 303. For example, the second estimator 307 may use enhanced Coulomb counting, in which the capacity estimate Q.sub.2* is calculated from measured battery current and from the SOC of the battery unit 202 as determined by the SOC estimator 306, compensating for random errors and bias in measured current and estimated SOC. The first and second capacity estimates Q.sub.1*, Q.sub.2* are fed to a fusion block 308, in which an optimum weighted average of the capacity Q*(t) is estimated based on those estimates.

[0121] When on one hand the set of first estimators 301, 302, 303 are used to determine a first capacity estimate Q.sub.1* and on the other hand the second estimator 307 is used to determine the second capacity estimate Q.sub.2*, the capacity Q*(t) of the battery unit as a function of time may in the step 105, in the fusion block 308, be estimated based on the first and the second capacity estimates Q.sub.1*, Q.sub.2*. How to select which of the capacity estimates Q.sub.1*, Q.sub.2* to base the capacity Q* of the battery unit on at any given point in time may be determined as described in the following.

[0122] For example, a step 112 of determining a level of uncertainty of the first and/or the second capacity estimate Q.sub.1*, Q.sub.2* may be carried out. A level of uncertainty of the first capacity estimate Q.sub.1* may be determined from the convexity, or slope, of the residual function. The smaller the convexity, i.e. the flatter the curve, the larger is the level of uncertainty in the estimate. A level of uncertainty of the second estimate Q.sub.2* may be determined based on a variance of the SOC value used for calculating the second estimate Q.sub.2*. The capacity Q*(t) of the battery unit is determined from one of the capacity estimates Q.sub.1*, Q.sub.2*, wherein the value to be used is selected based on a first predefined criterion relating to said level of uncertainty. For example, a threshold may be set relating to an acceptable level of uncertainty. Also a second criterion may be predefined, relating to an operating condition of the battery unit. For example, the second predefined criterion may be related to an operational SOC window ΔSOC of the battery unit 202, as determined for a predetermined time period or estimation episode. The second predefined criterion may also be related to a degree of relaxation of the battery unit 202, wherein the open circuit voltage V.sub.oc is used as input to the fusion block 308. Both the first and the second criteria may be taken into account for the estimation of the capacity Q*(t).

[0123] Generally, when the second estimator 307 uses enhanced Coulomb counting, the second estimator 307 is particularly suitable for estimating capacity when the battery unit 202 is in a relaxed condition at the start and end of a charge or discharge process. It is also particularly useful when the operational SOC window ΔSOC of the battery unit is relatively narrow, in particular when the SOC window is in a lower range of the total SOC, e.g. when the SOC window is from 20-60% of the total SOC. At wide SOC windows and at higher ranges of the total SOC, the set of first estimators 301, 302, 303 may be used to provide a more accurate capacity estimate. If the SOC window is wide, regardless of whether the battery unit is in a relaxed condition or not, using output from the set of first estimators 301, 302, 303 is generally preferable over using output from the second estimator 307, mainly due to a higher likelihood of large error accumulation during open-loop Coulomb counting over a wide SOC window for biased current sensing. However, if the current sensor used to measure battery current is known to have insignificant current bias, output from the second estimator 307 may also be used. In short, the choice depends on operating conditions and on the level of uncertainty.

[0124] The method as illustrated in FIG. 5 further comprises a step 113 of iteratively estimating an impedance R of the battery unit 202 based on the measurement data using an impedance estimator 309. The estimated impedance is fed to the set of first estimators 301, 302, 303 and used in the estimation of the set of state residual sizes φ.sub.i of the battery unit to compensate for estimation errors arising due to impedance growth. The impedance estimator 309 may also use the estimated capacity Q*(t) from the fusion block 308 as an input.

[0125] The method as illustrated in FIG. 5 further comprises a step 114 of mapping the estimated capacity Q*(t) of the battery unit under present operating conditions to the capacity Q.sub.std*(t) under standard operating conditions in order to isolate operating-condition-dependent short-term capacity variation from long-term variation due to ageing. This may e.g. be performed in a mapping block 311 using look-up tables and average or root-mean-squared values of measured current I.sub.b and temperature T.sub.b over the estimation episode. The estimated capacity mapped to standard conditions Q.sub.std*(t) may also be fed to the impedance estimator 309 mentioned above to be used as input in the impedance estimation.

[0126] The method may also comprise a step (not shown) of predicting expected operating conditions of the battery unit 202, and performing the estimation of the capacity Q*(t) only given that the expected operating conditions fulfill predetermined requirements relating to usage of the battery unit 202. For example, it may be determined that the method can estimate the capacity of the battery unit only when the operating condition is within a given operating condition range.

[0127] Although the figures may show a sequence, the order of the steps may differ from what is depicted. Also two or more steps may be performed concurrently or with partial concurrence. Such variation will depend on the software and hardware systems chosen and on designer choice. All such variations are within the scope of the disclosure. Likewise, software implementations could be accomplished with standard programming techniques with rule based logic and other logic to accomplish the various connection steps, processing steps, comparison steps and decision steps. Additionally, even though the invention has been described with reference to specific exemplifying embodiments thereof, many different alterations, modifications and the like will become apparent for those skilled in the art.

[0128] The estimated battery operating parameter may e.g. be communicated to an electronic control unit of the vehicle, such as an engine control unit (ECU). The estimated operating parameter may be communicated at time intervals depending on e.g. operating conditions of the ESS, or in real time.

[0129] The control functionality of the example embodiments may be implemented using existing computer processors, or by a special purpose computer processor for an appropriate system, incorporated for this or another purpose, or by a hardwire system. Embodiments within the scope of the present disclosure include program products comprising machine-readable medium for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available media that can be accessed by a general purpose or special purpose computer or other machine with a processor. By way of example, such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code in the form of machine-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer or other machine with a processor. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a machine, the machine properly views the connection as a machine-readable medium. Thus, any such connection is properly termed a machine-readable medium. Combinations of the above are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing machines to perform a certain function or group of functions.

[0130] It is to be understood that the present invention is not limited to the embodiments described above and illustrated in the drawings; rather, the skilled person will recognize that many changes and modifications may be made within the scope of the appended claims. For example, although the present invention has mainly been described in relation to an electrical bus, the invention should be understood to be equally applicable for any type of electric vehicle, in particular an electric truck or the like.