Patent Application
20240001800
The present disclosure relates to a method for determining an electrical energy storage pack replacement configuration of a propulsion electrical energy storage of a vehicle comprising several electrical energy storage packs. The present disclosure also relates to a system, a control unit, and computer program. Although the method and system will be described in relation to a vehicle in the form of a truck, the method and system can also be efficiently incorporated in other vehicle types such as busses, light-weight trucks, passenger cars, construction equipment, marine vessels, and industrial equipment, as long as they are at least partly electrically propelled.
Electrical energy storage packs are becoming a more common source of energy for providing propulsion power to vehicles. Such electrical energy storage packs are rechargeable electrical energy storage packs and consist of several electrical energy storage cells that may be connected in series and/or in parallel forming a complete electrical energy storage pack for the vehicle.
However, electrical energy storage packs age and will with time need replacement. Different generation/types of electrical energy storage packs may exhibit different electro-thermal and ageing characteristics and may therefore show significantly different dynamic behaviour. The maximum power ability of battery pack is heavily influenced by internal resistance imbalance and internal voltage imbalance as well as the resistance and OCV as such. A perfectly homogenous multi-battery system is provided when zero parametric and power ability imbalances between battery packs is achieved and results in robust and safe operation due to equal sharing of power between battery packs.
However, such homogeneous multi-battery system is not likely due to intrinsic parametric variations between new cells, non-uniform operating conditions, differences in connection cable lengths, and ageing dispersion between packs. In particular, the imbalances are inevitable after replacement of a broken battery with a new one, which might be based on even new generation of cells. This results in a so-called heterogeneous multi-battery system.
The replacement decision is often based on evaluating a state of health (SOH) imbalance between battery packs. However, SOH is an estimated quantity, and its accuracy is difficult to estimate. This may lead to unnecessary or even incorrect battery packs replacements during service leading to a highly unbalanced multi-battery system.
Further, it may also be important to understand the impact of a replacement on the vehicle performance in terms of for example range and power.
Accordingly, it is desirable to improve the methods and systems for performing accurate battery pack replacements.
An object of the invention is to provide an improved method and system for determining an electrical energy storage pack replacement configurations of a propulsion electrical energy storage of a vehicle.
According to the first aspect of the invention, there is provided a method for determining an electrical energy storage pack replacement configuration of a propulsion electrical energy storage of a vehicle comprising several electrical energy storage packs, the method comprising: classifying a vehicle usage type using vehicle driving pattern data of the vehicle as input data; determining a minimum state of health required for usage according to the classified vehicle usage type; determining a state of health of each of the electrical energy storage packs of the vehicle; concluding a replacement of the electrical energy storage packs having state of health lower than the minimum state of health; determining a load distribution between electrical energy storage packs after reconfiguration including replacement electrical energy storage packs and maintained electrical energy storage packs; and once electrical energy storage pack replacement is performed, determining a load sharing factor indicative of a load distribution between the electrical energy storage packs, and depending on the outcome of comparing the load sharing factor to an predetermined condition, performing a reconfiguration of the electrical energy storage to more equally distribute the load across the electrical energy storage packs.
An output signal or message indicating the combination and locations of the reconfigured electrical energy storage is provided.
By the provision of the classifying the vehicle usage based on vehicle driving pattern data for the specific vehicle a more accurate use of state of health as indicator for replacement is obtained. More specifically, the minimum state of health that is considered acceptable is based on the usage type or profile or the vehicle itself. Thus, by evaluating logged vehicle data to first decide the driver style, driver aggressiveness, vehicle application, or the most common drive pattern of the vehicle, replacement decisions may be taken, and electrical energy storage pack configurations may be determined, based on more accurate information.
Determining the load distribution between the electrical energy storage packs after reconfiguration may include to estimate energy and power de-rating after reconfiguration using a multi-battery system dynamic model. Using the multi-battery system dynamic model allows for analysing the impact on energy and power capability of a new electrical energy storage system configuration after a certain replacement decision where a combination of replacement electrical energy storage packs and maintained electrical energy storage packs are included. A multi-battery system dynamic model is configured to model power sharing/split dynamics between different electrical energy storage packs in the electrical energy storage system. Since energy and power of multi-battery electrical energy storage systems depends on the power sharing/split dynamics, it is advantageous to accurately model the power sharing/split dynamics for estimating total energy and power of the multi-battery electrical energy storage system.
Multi-battery system dynamic models may be realized in different ways, but can take as input, the type of battery packs in terms of cell chemistry, and their present state of health, and provides an output of how the power is split or shared between battery packs, and the total power and energy that can be obtained therefrom. A battery pack may be generally modelled as an equivalent circuit of electrical components such as a resistor in series with a parallel connected capacitor and second resistor. Several such parallel circuits may be connected in series. Several such battery packs may be connected in parallel to simulate a multi-battery system. A dynamic model of a multi-battery system can be developed using various approaches including equivalent circuit, electro-chemical, empirical, semi-empirical etc. The model can be mathematically represented in the form of differential equations, transfer functions, state-space form etc. The model is used for electro-thermal simulation of any multi-battery system, providing: prediction of current/power split between battery packs in electrical energy storage systems i.e., prediction of current flow through each battery pack for a given total energy storage system current, prediction of temperature evolution for each pack under given coolant flow and predicted current flow, prediction of SoC evolution for each battery pack under predicted current flow, in-rush current prediction through each battery pack under connection on the fly. Such a multi-battery system dynamic model may be configurable for any number of batteries along with individual setting of ageing level, SoQ and SoR, for each battery pack. In addition, cable connection resistance can be specified for each battery pack. The model may also be particularly useful for estimating overall SoP and SoE for heterogeneous ESS. As a reference, the disclosures EP2019/086833, EP2019/086835, EP2020/066874, and EP2020/066919, incorporated by reference, describes variations of such a multi-battery system dynamic model.
Determining the load distribution between the electrical energy storage packs after reconfiguration may further be based on other types of models or even look-up tables where given combinations of electrical energy storage packs are related to minimum state of health levels and load distribution parameters. Further types of models or estimations are also envisaged.
The minimum state of health required for usage according to the classified vehicle usage type may be concluded from mapping of the vehicle usage type with the electrical energy storage power and energy requirements. A given vehicle usage type require a given electrical energy storage total power and energy. With known required electrical energy storage total power and energy the minimum state of health levels can be estimated or models using a e.g., predetermined look-up tables or models such as a multi-battery model.
After the step of concluding a replacement, a model-based estimation of the energy and power de-rating provides for determining, in advance, how power sharing will occur between electrical energy storage packs after replacement of one of more electrical energy storage packs and how power sharing will affect the energy and power capability of the electrical energy storage system.
The vehicle drive pattern data is historical logged vehicle data regarding actual usage or driving pattern and/or data provided by a user or client regarding the vehicle's intended use.
The vehicle drive pattern data is used as input to a classification algorithm to perform classification of the vehicle usage pattern. For example, vehicle usage pattern can be classified into “benign”, “moderate, or “aggressive”. Other types of classifications are also conceivable such as classification according to performance requirements to complete a certain mission or set of missions for the vehicle. Example performance requirements include peak acceleration, average acceleration, top speed, range, uphill capability, downhill retardation braking capability, charging time capability, over-taking capability, total cargo weight carrying capability etc. In some implementations the classification can also be made according to vehicle type, e.g., construction truck, city distribution truck, reuse collection truck, flat road truck, hilly truck etc. As an example, using the logged vehicle data, the usage pattern is related to a “high/medium/low” peak acceleration, or any one of the other performance requirements.
Preferably, the method described herein is automated based on logged vehicle data and a multi-battery model. Thus, the decision to replace battery packs is autonomously made by an application running on-board the vehicle or as a cloud-based application.
The classification is preferably made on a relative scale in a fleet of vehicles. Thresholds or intervals in each performance related parameters or requirement and map them towards a certain type of vehicle usage or more generally a label. For example, if acceleration is between 0 and x1 then vehicle usage type is benign, if between x1 and x2 then vehicle usage type is moderate, and if above x2 then vehicle usage type is aggressive. However, other ways of performing classification and mapping are also envisaged.
Vehicle drive pattern data may be historical driving data as well as data provided by a customer or user for any new vehicle mission requirements.
A minimum state of health can be determined with respect to minimum level of performance required in terms of power and range. In other words, the minimum state of health is a threshold below which power and/or range requirements for the vehicle can no longer be met. The power and/or range requirements may be related to mission requirements determined as per usage pattern classification. Further, a minimum state of health may also be determined with respect to a required service lifetime of the electrical energy storage system. For example, the minimum state of health may be set so that a replacement with a battery pack of sufficient state of health such that the new electrical energy storage system after replacement can deliver a required energy and power at least for a remaining predetermined time period. A remaining predetermined time period can in some example implementations be a remaining warranty or contractual duration.
State of health may be defined as the loss in capacity relative to a capacity at the beginning of life of the battery, or the internal resistance increase of the battery relative the internal resistance at the beginning of life of the battery. Further, state of health may equally be defined of loss in range, loss in peak acceleration capability, efficiency loss etc.
State of health parameters that may be measured for determining a state of health may include at least a state of capacity and a state of resistance of the battery. These state of health parameters are well established and advantageously relatively straight-forward to measure and are typically available from automotive battery management systems.
Reconfiguration is herein understood to mean a suggested or performed replacement of one or more electrical energy storage packs in the electrical energy storage system to achieve more balanced load sharing factors among all electrical energy storage packs in the electrical energy storage system.
State of charge, SOC, which is mentioned herein is the present level of charge in the electrical energy storage compared to its full capacity and may be given as a percentage value.
The propulsion electrical energy storage should be interpreted as providing propulsion power to the vehicle. Thus, the vehicle may be an electrical, hybrid, or plug-in hybrid vehicle comprising an electrical engine, wherein the electrical energy storage provides power to the electrical engine for providing propulsion for the electrical, hybrid, or plug-in hybrid vehicle. The electrical energy storage may be Li-ion electrical energy storage comprising multiple cells arranged in series and in parallel as is known in the art.
An electrical energy storage may herein be considered a battery.
The methods described herein are preferably computer-implemented.
As an alternative to using the load sharing factor it is conceivable to compare total power ability, i.e., state of power, and total energy storage capability, i.e., state of energy of the electrical energy storage system with power and energy requirements for a given vehicle application. These requirements may be derived based on the first step of vehicle usage classification as described above. If the state of power and the state of energy are greater than the required power and energy levels then the new configuration of the electrical energy storage packs can be considered correct.
According to an example embodiment, wherein that the load sharing factor fulfils a predetermined conditions includes that the load sharing factor is within a predetermined interval, wherein when the load sharing factor is concluded to be within the predetermined interval, concluding that the electrical energy storage pack replacement is completed.
According to an example embodiment, when the load sharing factor is concluded to fall outside the predetermined threshold, repeat the step of determining a load distribution with a further combination of replacement electrical energy storage packs and maintained electrical energy storage packs, and the step of determining a load sharing factor and performing a reconfiguration.
The load sharing factor provides an accurate measure for concluding whether a new configuration of electrical energy storage packs provides a well-balanced electrical energy storage system. Further, it provides for a confirmation that the combination of replacement electrical energy storage packs and maintained electrical energy storage packs was indeed acceptable in terms of electrical energy storage balance. As discussed above, as an alternative to using the load sharing factor it is conceivable to compare total power ability, i.e., state of power, and total energy storage capability, i.e., state of energy of the electrical energy storage system with power and energy requirements for a given vehicle application.
Generally, a load sharing factor that is too low or too high compared to its power ability factor, i.e. not in the predetermined interval is indicative of unacceptable large imbalance between electrical energy storage packs and thus potentially much lower power and energy performance of the electrical energy storage system due to constraints posed by “weakest-link-in-chain”, stated differently, due constraints posed by the electrical energy storage pack with the lowest state of health.
In embodiments, each vehicle usage type may be based on modelled power and energy requirements for the associated vehicle usage type. Thus, by logging vehicle drive pattern data, and input this data to a model of data related to power and energy requirements the vehicle usage type may be determined or classified. Various types of models are envisaged, and a few examples are mentioned herein. For example, the model may be based on histograms to capture distribution of average or peak power and total energy consumption per trip. In addition, power histogram can also be represented in correlation with SoC i.e., how much power is consumed at different SoC levels and how frequently that power level.
Further, it is conceivable to use models based on machine learning methods. For example, a model such as a neural network, logistic regression, support vector machine, etc may be trained by providing electrical energy storage system usage inputs including power usage, energy usage, etc, and providing output labels regarding “classes of loads” including herein discussed benign, moderate, and aggressive, etc. The trained models will then be configured to classify any given usage in terms of load types or vehicle type.
Since each vehicle usage type may be based on modelled power and energy requirements for the associated vehicle usage type, the step of reconfiguring may further comprise performing reconfiguration in such a way that vehicle performance requirements are met.
According to an example embodiment, the method may comprise acquiring logged vehicle driving pattern data including charging session data from an on-board memory storage device or from a remote server through cloud connectivity. Although the vehicle drive pattern data is typically obtained or logged by the on-board control unit and memory, the vehicle drive pattern data is transmitted to the cloud on a regular basis to reduce the vehicle on-board memory requirements. The vehicle usage type is classified also based on the charging session data indicating amount of energy charged and frequency of charging sessions.
According to an example embodiment, the drive pattern data may include at least one of: average current flow through each of the electrical energy storage packs, peak power points for the electrical energy storage packs, duration of the power peaks, and a statistical distribution of power for a specific vehicle usage type.
According to an example embodiment, the method may comprise continuously monitoring the state of health of the electrical energy storage packs to evaluate whether a replacement is needed, and if a replacement of an electrical energy storage is conclusive, provide a signal indicative thereof to a user. This may further be achieved by comparing available energy and power ability with the required energy and power respectively
According to an example embodiment, reconfiguring the electrical energy storage packs may comprise: determining which electrical energy storage packs to replace; determining a combination of replacement electrical energy storage packs and maintained electrical energy storage packs depending on the classified vehicle usage type and the associated minimum state of health; performing modelling the power distribution across the combination of replacement electrical energy storage packs and maintained electrical energy storage packs to estimate energy and power de-rating after reconfiguration to determine load distribution between electrical energy storage packs after reconfiguration; reconfigure the electrical energy storage packs based on the outcome of the modelling.
Preferably, the method is performed as an on-board application of the vehicle. However, it is envisaged that the method may be performed as a cloud-based service.
According to a second aspect of the invention, the object is achieved by a system according to claim 11.
According to the second aspect of the invention, there is provided a system for determining an electrical energy storage pack replacement configuration of an electrical energy storage pack of a vehicle comprising several electrical energy storage packs, the system comprising: sensors for collecting driving pattern data indicative of the use pattern of the vehicle and for collecting electrical energy storage sensor data, a memory storage for storing vehicle driving data acquired when the vehicle is operational, and for storing a power requirement model associating classified vehicle usage types with minimum state of health requirements, and a control unit configured to: acquire the vehicle driving pattern data, classify a vehicle usage type using a classification model and the vehicle driving pattern data; determine, using the power requirement model, a minimum state of health required for vehicle usage according to the classified vehicle usage type; determine a state of health of each of the electrical energy storage packs of the vehicle using the electrical energy storage sensor data; conclude to replace the electrical energy storage packs having state of health lower than the minimum state of health; determine a load distribution between electrical energy storage packs after reconfiguration including replacement electrical energy storage packs and maintained electrical energy storage packs; and once electrical energy storage pack replacement is performed, determine a load sharing factor indicative of a load distribution between the electrical energy storage packs, and depending on the outcome of comparing the load sharing factor to an predetermined condition, perform a reconfiguration of the electrical energy storage pack to more equally distribute the load across the electrical energy storage packs.
Effects and features of the second aspect of the invention are largely analogous to those described above in connection with the first aspect.
According to a third aspect of the invention, there is provided a vehicle comprising the system according to the second aspect.
According to a fourth aspect of the invention, there is provided a computer program comprising program code means for performing the steps the first aspect when the program is run on a computer.
According to a fifth aspect of the invention, there is provided a computer readable medium carrying a computer program comprising program code means for performing the steps of the first aspect when the program product is run on a computer.
According to a sixth aspect of the invention, there is provided a control unit for determining an electrical energy storage pack replacement configuration of an electrical energy storage system of a vehicle comprising several electrical energy storage packs, the control unit being configured to perform the steps of the method according to the first aspect.
Effects and features of the third, fourth, fifth, and sixth aspects are largely analogous to those described above in relation to the first aspect.
Further features of, and advantages will become apparent when studying the appended claims and the following description. The skilled person will realize that different features may be combined to create embodiments other than those described in the following, without departing from the scope of the present disclosure.
With reference to the appended drawings, below follows a more detailed description of embodiments of the invention cited as examples.
In the drawings:
The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments of the invention are shown. The invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided for thoroughness and completeness. The skilled person will recognize that many changes and modifications may be made within the scope of the appended claims.
Like reference character refer to like elements throughout the description.
Although the vehicle in
It can be reasonably assumed that a decrease in power ability and capacity is substantially proportional to an increase in internal resistance, which occurs as a battery pack ages, whereby a power split between battery packs will be according to their power abilities i.e., the old battery pack, BP3, will provide lower power and the less aged battery pack BP2 will provide higher power according to their maximum power abilities. However, the assumption may not always be accurate, which may lead to power flow through an old battery pack, BP3 that is higher than its maximum power ability. This is particularly true for mixed generation multi-battery systems where old and new battery packs are based on slightly different cells chemistries. In addition, other imbalances including operating conditions, cable lengths etc. also have an impact on power distribution across the battery packs. Under these heterogeneities, there is instead unlikely that power distribution will be according to the individual power abilities of the battery packs. For example, a more aged battery pack BP3 may reach its power limit first, leading to under-utilization of a newer battery pack BP2. In short, the total power of the electrical energy storage system 200 may get severely limited by the weakest link. The embodiments presented herein addresses how to configure a replacement of battery packs in such a way to avoid or alleviate the above issues. Now, turning to specific embodiments.
State of health is typically estimated based on capacity loss and/or impedance growth. The end-of-life (EoL) of a battery can be defined according to a certain minimum threshold on remaining capacity or a maximum threshold on impedance increase. For example, when a battery loses 30% of its capacity (i.e., 70% remaining capacity) or gains 50% resistance (i.e., 150% internal impedance) then this can be set as end of life for that battery. These thresholds are dependent on battery chemistry and vehicle application as per performance requirements. Based on this, the value of State of health can be represented in percentage in a relative scale or in an absolute scale.
In a relative scale, state of health is represented on a scale of 0 to 100% relative to the end-of-life threshold where 100% means battery is new and 0% means battery has reached its end-of-life (EoL).
In an absolute scale state of health is represented in percentage according to the actual percentage loss of capacity or percentage increase in impedance. For example, SoH=80% when the capacity loss is 20%.
Note that typically maximum 20 to 30% loss in capacity or 50 to 70% increase in impedance is considered in electric vehicle applications.
In the drawings, the percentages in the figures should be interpreted appropriately based on one of the relative scale and absolute scale. If a relative scale is used then 0% may be end of life, and if the absolute scale is used then, for example, a percentage in the range of 70%-80% may be considered as end of life.
In step S102 of the method a vehicle usage type is classified using vehicle driving pattern data of the vehicle as input data. For this, the system 200 comprises sensors 202a-b for collecting driving pattern data, i.e., sensor 202a, indicative of the use pattern of the vehicle and for collecting electrical energy storage sensor data, by sensor 202b. Here only two sensors are schematically shown but is it generally understood that more than two sensors may be utilized, this means that the sensors 202a-b may be considered sensor assemblies comprises multiple sensors.
The sensors 202a are configured to acquired drive pattern data that may indicate a certain driver style or vehicle usage type. For example, the sensors 202a may collect data indicating yaw rates, roll rates, pitch rates, lateral and longitudinal acceleration, speed, and may for example include gyroscopes and accelerometers part of an inertial measurement unit of the vehicle 1.
The sensors 202b are configured to collect data indicative of characteristics and parameters of the electrical energy storage system 2. Such sensors 202b may be voltage and/or current sensors for detecting a voltage of each battery pack BP1-BP4, or each cell of the battery packs BP1-BP4, or an electrical discharge of charge current of each battery pack BP1-BP4, or each cell of the battery packs BP1-BP4. The sensors 202b may also include temperature sensors for measuring a temperature of each battery pack BP1-BP4. Data from the sensors 202b may be used for estimating e.g., SOC, SOH, SOP, SOE, SOQ, etc.
Preferably, the drive pattern data includes at least one of: average current flow through each of the electrical energy storage packs BP1-BP4, peak power points for the electrical energy storage packs BP1-BP4, and duration of the power peaks, and a statistical distribution of power between the battery packs BP1-BP4 for a specific vehicle usage type.
The data from the sensors 202a-b are logged on-board and are preferably transmitted to a remote server 102, i.e., to the cloud for storage. However, it would be possible to store the data from the sensors 202a-b in an on-board data memory storage 204. Further, the memory storage 204 may also store a power and energy requirement model associating classified vehicle usage types with minimum state of health requirements, as will be discussed in more detail herein.
The control unit 101 is configured to acquire the vehicle driving pattern data from the cloud-based server 102, or optionally from the memory storage 204 of the vehicle 1, and to classify a vehicle usage type using a classification model 206 and the vehicle driving pattern data.
Subsequently, a minimum state of health required for usage according to the classified vehicle usage type is determined in step S104. Each vehicle usage type is based on modelled power and energy requirements for the associated vehicle usage type. Thus, the control unit 101, which has access to the model 208 via the memory storage 204 or server 102, is configured to determine, using the power and energy requirement model 208, a minimum state of health required for the vehicle usage according to the classified vehicle usage type. The output from the classification model 206 is used as input to the power and energy requirement model 208.
The power and energy requirement model 208 generally relates a minimum level of performance required in terms of power, energy, and range for the vehicle provided its intended use, assignments, or mission, to a minimum state of health of the battery packs BP1-BP4. The minimum state of health is a threshold below which power and/or range requirements can no longer be met. This requirement model 208 may be based on a multi-battery model which takes vehicle performance requirements such as power, energy, and range requirements as input and then output whether these requirements can be met without violating limits of each battery pack in the energy storage system. If a limit of any battery pack is violated, it is concluded that at least one of the battery pack does not have a sufficient state of health level. It is also envisaged that the model 208 may for example be a look-up table relating vehicle usage type to energy and power requirements and minimum state of health.
In step S106 a state of health of each of the electrical energy storage packs BP1-BP4 of the vehicle 1 is estimated. The control unit 101 is configured to determine the state of health's using the sensor data collected from the sensors 202a-b.
In step S108, the control unit 101 is configured to conclude to replace the electrical energy storage packs having state of health lower than the minimum state of health. As an example, the battery packs BP3 and BP4 both have SOH below a minimum state of health of 80%, for example as required by a vehicle usage classified as harsh. In other words, the battery packs BP3 and BP4 need to be replaced in order to fulfil the minimum level of performance for the intended usage or specific application for the vehicle 1. Stated otherwise, firstly, it is determined which electrical energy storage packs to replace. In
In step S110, the power split and sharing of a combination of replacement electrical energy storage pack and maintained electrical energy storage pack may be modelled using e.g., a multi-battery model to determine a load distribution between the electrical energy storage packs after reconfiguration. The control unit 101 is configured to use models, generally indicated as 210, of the added, new or recycled battery packs, i.e., the using the multi-battery model or other types of models or look-up tables, to estimate the distribution of the load between the prior battery packs BP1, BP2, BP3′, BP4′. Here, battery packs BP1 and BP2 are maintained battery packs that have not been replaced, and battery packs BP3′, BP4′ are added new, repaired, refurbished, remanufactured, and/or recycled battery packs that have replaced battery packs BP3 and BP4. Here, the mix or combination of replacement battery packs and maintained battery packs is determined based on what mix and combination that is allowed depending on the energy and power requirements. The vehicle usage type is used as input for determining the energy and power requirements. As discussed above, model-based estimation of expected power and energy de-rating after replacement may be performed using a multi-battery electrical energy storage system model which allows determining in advance how power sharing occur between batteries after replacement and how that affects the total state of power and state of energy of the electrical energy storage system.
Preferably, the combination of added replacement electrical energy storage packs, BP3′ and BP4′, and prior electrical energy storage packs BP1 and BP2 is determined depending on the classified vehicle usage type and the associated minimum state of health. The combination is determined so that the electrical energy storage system 2 can achieve the minimum performance requirement for the vehicle. The distribution of power between packs of the combination of replacement electrical energy storage packs and maintained electrical energy storage packs may be modelled using a multi-battery model or another type of model, to estimate energy and power de-rating after reconfiguration to determine load distribution between electrical energy storage packs after reconfiguration. The electrical energy storage packs are reconfigured based on the outcome of the modelling step.
A replacement battery pack may be a new, refurbished, remanufactured, or repaired battery pack.
Once electrical energy storage pack replacement is performed, a load sharing factor indicative of a load distribution between the electrical energy storage packs is determined by the control unit 101 in step S112. The load sharing factor essentially indicates how well balanced the new electrical energy storage system is. If the load sharing factor fulfils a predetermined condition, a reconfiguration of the electrical energy storage pack is performed in step S114 to more equally distribute the load across the electrical energy storage packs. As discussed above, as an alternative to using the load sharing factor it is conceivable to compare total power ability, i.e., state of power, and total energy storage capability, i.e., state of energy of the electrical energy storage system with power and energy requirements for a given vehicle application.
The computation of the load sharing factor needs active usage of the electrical energy storage system, i.e., some power flow out of electrical energy storage system. A load sharing factor may be computed after some trial run of a vehicle i.e., after a model-based reconfiguration and replacement decision, e.g., a workshop technician may perform a short vehicle test under varying driving style where a varying power should flow into and out of the electrical energy storage system. At the end of this test, the load sharing factor may be automatically computed by a control unit and green or red signal will be given to a technician to indicate validation of new configuration. This signal can be given either in the instrument cluster or can be read via OBD port after the test.
The load sharing factor will be continuously computed by a control unit even during normal operation of a vehicle and thus it can also be used for diagnostics i.e., it can be used to indicate when it is time to make battery pack replacement.
Preferably, that the load sharing factor fulfils a predetermined conditions includes that the load sharing factor is within a predetermined interval. Thus, when the load sharing factor is determined to be within the predetermined interval, the control unit 101 can conclude that the electrical energy storage pack replacement is completed. Thus, the vehicle can initiate operation using the energy storage system now including energy storage packs BP1, BP2, BP3′, and BP4′.
However, when the control unit 101 concludes that the load sharing factor is outside the predetermined threshold, the step S110 of modelling with a further combination of new electrical energy storage packs and prior electrical energy storage packs, and the step S112 of determining a load sharing factor and performing S114 a reconfiguration, are repeated with a new configuration of battery packs.
The control unit may provide a signal 207 indicating the new configuration of electrical energy storage packs including which electrical energy storage packs to include, where they should be arranged within the electrical energy storage system relative to the other electrical energy storage packs and relative to the loads taking the length and resistance of the connection cables into account. Other conditions that may be taken into account for the new configuration is ambient conditions, for example ambient temperature at the place where each battery pack is located.
The load sharing factor provides an indication of how the total load of the electrical energy storage system is shared between the battery packs BP1-BP4′. The load sharing factor is determined for each battery pack and for a well-functioning combination of battery packs, the load sharing factor should be similar for each of the battery packs of the new configuration.
The load sharing factor (α) for a battery pack, i, may be given by:
where
is the average electrical current (I) of the battery pack, i.
A power ability factor/ratio for a charge current for each battery pack, i, is defined by:
Where ĪChmax is mean/average of maximum charge abilities of all battery packs and Ii,Chmax is the maximum charge ability of pack i.
A power ability factor/ratio for a discharge current for each battery pack, i, is defined by:
Where ĪDchmax is the mean/average of maximum discharge abilities of all battery packs, and Ii,Dchmax is the maximum discharge ability of battery pack i.
Ideally, for an electrical energy storage pack:
αi=βi
Which means that each battery pack is sharing power according to its ability.
However, an acceptable load-sharing-factor is within an allowed interval given by:
|αi−βi|≤δ
Where δ is determined from or defined based on a desired power buffer. For example, if total installed electrical energy storage system power is higher than the required power then some imbalance may be allowed as the power and energy requirements may still be met. In short, this delta, δ, is defined based on how much buffer/margin in power that is desirable.
A not acceptable load sharing factor is much lower or much higher than power ability factor i.e.,
α<<βi or αi>>βi
Generally, any load sharing factor which leads to a battery pack power below the required level is considered too low. On the other hand, if one battery pack has too high load sharing factor then some other battery pack has too low load sharing factor and vice versa.
The method described herein may be performed as an on-board application of the vehicle 1. Further, the method may comprise continuously monitoring the state of health of the electrical energy storage packs BP1-BP4 to evaluate whether a replacement is needed, and if a replacement of an electrical energy storage is conclusive, provide a signal indicative thereof to a user. This provides for an autonomous decision proves for deciding when an electrical energy storage pack replacement is needed. The signal may be provided as an indication to an application running on a portable device or to a dashboard of the vehicle 1. The signal may be generated by the control unit 101. In other words, the method described herein is preferably automated based on logged vehicle data and a multi-battery model.
In addition to monitoring of state of health, it is also envisaged to monitor the load sharing factor online for diagnosing issue with power ability. These load sharing factors can be computed online, in the cloud, on a remote server, from beginning of life, when all battery packs are new, and it is possible to keep track of their variations and compare against a benchmark or signature value, i.e., their value when all packs were new, to determine if sharing is happening as per expectation or if battery pack ability have drifted too much from each other.
A control unit may include a microprocessor, microcontroller, programmable digital signal processor or another programmable device. Thus, the control unit comprises electronic circuits and connections (not shown) as well as processing circuitry (not shown) such that the control unit can communicate with different parts of the truck such as the brakes, suspension, driveline, in particular an electrical engine, an electric machine, a clutch, and a gearbox in order to at least partly operate the truck. The control unit may comprise modules in either hardware or software, or partially in hardware or software and communicate using known transmission buses such as CAN-bus and/or wireless communication capabilities. The processing circuitry may be a general purpose processor or a specific processor. The control unit comprises a non-transitory memory for storing computer program code and data upon. Thus, the skilled addressee realizes that the control unit may be embodied by many different constructions.
The control functionality of the present disclosure 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.
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.
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.
| Number | Date | Country | Kind |
|---|---|---|---|
| 22182283.6 | Jun 2022 | EP | regional |
