APPARATUS AND METHOD FOR COOLING COMPONENTS OF A HEAVY-DUTY ELECTRIC VEHICLE

Abstract

A cooling arrangement for cooling components of a heavy-duty electric or hybrid electric vehicle, the cooling arrangement comprising a fan, a mount, an axial flux electric motor, and a control unit arranged to control the cooling arrangement. The control unit is configured to obtain a predicted cooling requirement of the vehicle for a future time by obtaining information about a speed of the vehicle at a future time and using a heat estimation model to estimate an amount of heat generated at the future time based on the speed of the vehicle, and/or obtaining information about a characteristic of a path of the vehicle at the future time and using the heat estimation model to estimate the amount of heat generated at the future time based on the characteristic of the path of the vehicle.

Claims

1. A heavy-duty electric or hybrid electric vehicle comprising: a cooling arrangement comprising: a fan arrangement comprising: a centrifugal fan assembly, an impeller, a stationary inlet shroud, and a stator comprising a plurality of stator blades located radially or semi-radially outside the impeller, a mount, and an axial flux electric motor, a control unit arranged to control the cooling arrangement, the control unit configured to: obtain a predicted cooling requirement of the vehicle for a future time by: obtaining information about a speed of the vehicle at a future time and using a heat estimation model to estimate an amount of heat generated at the future time based on the speed of the vehicle, and/or obtaining information about a characteristic of a path of the vehicle at the future time and using the heat estimation model to estimate the amount of heat generated at the future time based on the characteristic of the path of the vehicle.

2. The vehicle of claim 1, wherein the axial flux electric motor is arranged to be driven by a high voltage power supply.

3. The vehicle of claim 2, wherein the high voltage power supply is a main traction energy source of the vehicle.

4. The vehicle of claim 1, wherein the mount comprises a flat surface arranged to reduce air leakage.

5. The vehicle of claim 1, wherein the cooling arrangement further comprises an interface arranged to accept control input from the control unit and alter an operational parameter of the cooling arrangement in dependence of the control input.

6. The vehicle of claim 5, wherein the operational parameter is at least one of a fan speed, electric motor speed, or electric motor torque.

7. A method for controlling operation of a cooling arrangement in a heavy-duty electric or hybrid electric vehicle, comprising: obtaining, by a control unit, a predicted cooling requirement of a vehicle for a future time by: obtaining information about a speed of the vehicle at the future time and using a heat estimation model to estimate an amount of heat generated at the future time based on the speed of the vehicle; and/or obtaining information about a characteristic of a path of the vehicle at the future time and using the heat estimation model to estimate the amount of heat generated at the future time based on the characteristic of the path of the vehicle; calculating, by the control unit, an operational parameter of a cooling arrangement such that the generated cooling effect when using the operational parameter meets the predicted cooling requirement, the cooling arrangement comprising a fan arrangement, a mount, and an axial flux electric motor, the fan arrangement comprising a centrifugal fan assembly, an impeller, a stationary inlet shroud, and a stator comprising a plurality of stator blades located radially or semi-radially outside the impeller; and applying, by the control unit, the calculated operational parameter to control the cooling arrangement.

8. The method of claim 7, wherein the heat estimation model is adjusted in dependence of information about the amount of heat generated during a prior operation of the vehicle.

9. The method of claim 7, wherein calculating an operational parameter of the cooling arrangement comprises using a cooling model, the cooling model being determined as a model of a relation between the operational parameter of the cooling arrangement and a generated cooling effect.

10. The method of claim 9, wherein the cooling model is adjusted in dependence of information about the cooling effect generated during a prior operation of the vehicle.

11. A control unit comprising: processing circuitry configured to: obtain a predicted cooling requirement of a vehicle for a future time by: obtaining information about a speed of the vehicle at the future time and using a heat estimation model to estimate an amount of heat generated at the future time based on the speed of the vehicle; and/or obtaining information about a characteristic of a path of the vehicle at the future time and using the heat estimation model to estimate the amount of heat generated at the future time based on the characteristic of the path of the vehicle; calculate an operational parameter of a cooling arrangement such that the generated cooling effect when using the operational parameter meets the predicted cooling requirement, the cooling arrangement comprising a fan arrangement, a mount, and an axial flux electric motor, the fan arrangement comprising a centrifugal fan assembly, an impeller, a stationary inlet shroud, and a stator comprising a plurality of stator blades located radially or semi-radially outside the impeller; and apply the calculated operational parameter to control the cooling arrangement.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] With reference to the appended drawings, below follows a more detailed description of embodiments of the invention cited as examples. In the drawings:

[0020] FIG. 1 shows an example of a heavy-duty vehicle;

[0021] FIG. 2 schematically illustrates a cooling arrangement;

[0022] FIG. 3 schematically illustrates an axial flux motor;

[0023] FIG. 4 A,B show a mounting means for a fan arrangement;

[0024] FIG. 5 shows a fan arrangement;

[0025] FIG. 6 illustrates an example driving scenario;

[0026] FIG. 7 is a flow chart illustrating methods;

[0027] FIG. 8 schematically illustrates a control unit; and

[0028] FIG. 9 shows an example computer program product.

[0029] DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE INVENTION

[0030] The invention will now be described more fully hereinafter with reference to the accompanying drawings, in which certain aspects of the invention are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments and aspects set forth herein; rather, these embodiments are provided by way of example so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like numbers refer to like elements throughout the description.

[0031] It is to be understood that the present invention is not limited to the embodiments described herein 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.

[0032] FIG. 1 shows an example heavy-duty electric or hybrid electric vehicle 100. The vehicle 100 may, for instance, be powered by a battery pack and/or by a fuel cell arrangement. The vehicle may also comprise a combustion engine, i.e., be a hybrid electric vehicle. An electric or hybrid-electric vehicle comprises at least one electric machine arranged to drive one or more of the wheels of the vehicle. An electric or hybrid-electric vehicle also comprises at least one energy storage system (ESS) arranged to power the electric machine or machines, i.e., a battery, a fuel cell, a super-capacitor, or a combination thereof.

[0033] The vehicle 100 may comprise a central main traction machine configured to drive two or more wheels, e.g., via a differential. Separate electric machines may also be arranged to drive respective wheels. Such electrical machines are referred to herein as wheel-end traction machines.

[0034] Herein, a heavy-duty vehicle 100 is taken to be a vehicle designed for the handling and transport of heavier objects or large quantities of cargo or passengers. As an example, a heavy-duty vehicle could be a semi-trailer vehicle or a truck as described above. As another example, a heavy-duty vehicle could be a vehicle designed for use in construction or farming. A heavy-duty vehicle could also be a bus.

[0035] The vehicle 100 is also shown to comprise a control unit 110, which will be described in more detail below. Furthermore, the vehicle comprises a cooling arrangement 200 for cooling one or more parts of the vehicle, such as the one or more electric machines, the ESS, or other components. Both the control unit 110 and the cooling system 200 are only schematically illustrated in FIG. 1.

[0036] FIG. 2 shows an example of a cooling arrangement 200 for cooling components of a heavy-duty electric or hybrid electric vehicle 100. The cooling arrangement 200 comprises a fan 230, a mounting means 210, and an electric motor 220. Notably, the electric motor 220 is an axial flux electric motor.

[0037] An axial flux electric motor (also known as an axial gap motor, or pancake motor) is a geometry of motor construction where the gap between the rotor and stator, and therefore the direction of magnetic flux between the two, is aligned parallel with the axis of rotation, rather than radially as with the concentric cylindrical geometry of the more common radial gap motor. Thus, in an axial flux electric motor, the magnetic flux is directed along the rotational axis of the rotor.

[0038] Although this geometry has been used since the first electromagnetic motors were developed, its usage was rare until the widespread availability of strong permanent magnets and the development of brushless DC motors, which could better exploit some of the advantages of the axial geometry. The axial geometry can be applied to almost any operating principle (e.g. brushed DC, induction, stepper, reluctance) that can be used in a radial motor, and can allow some topologies that would not be practical in a radial geometry, but even for the same operating principle there are considerations in the application and design that would cause one geometry to be more suitable than the other.

[0039] FIG. 3 schematically illustrates an example axial flux electric motor 220 comprising at least one rotor 310a, 310b and at least one stator 320. The stator 320 comprises at least one stator winding 321. The stator windings 321 comprise a winding or coil of electrically conductive material arranged to operate as an electromagnet. Herein, the term electrically conductive material is used to refer to a material such as copper or silver, or with an electric conductivity substantially above 10000 (Ωm).sup.−. The stator windings may also comprise a ferromagnetic core.

[0040] The rotor 310a, 310b normally comprises at least one permanent magnet 311. The permanent magnet may comprise ferromagnetic materials such as iron, nickel, cobalt, or a neodymium alloy. Although FIG. 3 shows two rotors, an axial flux electric motor may have a larger or smaller number of rotors.

[0041] The rotor and the stator in an axial flux electric motor may be thought of as discs, i.e. substantially circular objects with a radius, where the thickness of the disc is smaller than the radius, i.e., a pancake shape. The rotor and stator are placed next to each other with the axis of rotation 305 of the rotor perpendicular to the surface of both discs. During operation, the magnetic flux between rotor and stator will then be parallel to the axis of rotation 305.

[0042] Compared to radial flux electric motors, in which the magnetic flux is perpendicular to the axis of rotation of the rotor, axial flux electric motors can be constructed to have a higher power density, i.e., an axial flux motor may be physically smaller than a radial flux motor capable of generating a similar amount of power. This is an advantage in applications such as the present one where the amount of space available in the fan axial direction is limited.

[0043] The cooling arrangement 200 may be arranged to cool any component that generates or is exposed to excessive heat during operation of the vehicle. As an example, it may be arranged to cool a main traction machine or a wheel-end traction machine, the ESS, or any other heat-sensitive components on the vehicle.

[0044] According to aspects, the axial flux electric motor 220 may be arranged to be driven by a high voltage power supply, preferably a power supply with an output voltage of 400 V or more. The high voltage power supply may be the main traction energy source of the vehicle. This way the often significant energy storage capacity of the vehicle can be re-used to also power the cooling system, which is an advantage. It is also an advantage to make use of the available high voltage in an electric vehicle, since this high voltage allows for a more powerful fan operation and therefore a higher cooling capacity.

[0045] FIGS. 4A and 4B illustrate an example mounting means 210 comprising a flat surface 410 arranged to reduce air leakage. FIG. 4A is a front view and FIG. 4B is a side view. A fan assembly, such as the fan assembly shown in FIG. 5, can be attached to the motor 220 by the mounting means 210.

[0046] As an example, the fan 230 may be an axial flow fan. As another example, the fan 230 may be a centrifugal flow fan or a cross-flow fan.

[0047] FIG. 5 shows a perspective view of an example fan arrangement 500 comprising a fan assembly 510, a stationary inlet shroud 520 and a stator 530. The stator 530 may comprise a plurality of stator blades located radially or semi-radially outside the impeller 540. The stationary inlet shroud 520 serves to guide air towards the fan assembly 510, reducing leakage of air. Such a fan arrangement is, e.g., described in WO2015005832A1. The present cooling arrangements are particularly suitable for use with the types of fans described in WO2015005832A1, since the combination yields a particularly effective cooling arrangement with very small foot-print, at least in the axial direction of the fan, since the package can be made flat to conserve space in the axial direction of the fan and motor.

[0048] According to aspects, the cooling arrangement may also comprise an interface 240, schematically illustrated in FIG. 2, arranged to accept control input from a control unit 110 and alter an operational parameter of the cooling arrangement in dependence of said control input. The interface 240 may comprise one or more transmitters and receivers, comprising analogue and digital components and a suitable number of ports for wireline or wireless communication.

[0049] According to one example, an operational parameter may be the fan speed of rotation or drive torque. According to another example, the operational parameter may be an output power of the electric motor 220, or a drawn current by the electric motor 220. These parameters affect the amount of cooling provided by the cooling arrangement, since they affect the flow of cooling air generated by the cooling arrangement.

[0050] There is also herein disclosed herein a method for predictive master cooling control (PMCC) exemplified by the flow chart in FIG. 7. The method, performed in the control unit 110, controls the operation of the cooling arrangement 200 described above. The method comprises obtaining S1 a predicted cooling requirement for a future time t and calculating S2 an operational parameter of the cooling arrangement such that the generated cooling effect meets the predicted cooling requirement. The method also comprises applying S3 the calculated operational parameter to the cooling arrangement 200.

[0051] The predicted cooling requirement represents a future need for cooling by the vehicle 100 as it traverses some planned driving path. A steep climb is normally associated with a larger cooling requirement compared to a more level road. A long downhill drive may also be associated with an increased cooling need, in particular by the electric machines of the vehicles if these are used for braking the vehicles.

[0052] The cooling effect is the cooling generated by the cooling arrangement. Increased cooling effect can, for instance, be generated by driving the fan at a higher speed or torque. A reduced cooling effect can be obtained by reducing fan speed or torque. The fan may also be turned off for periods of time when no significant cooling effect is required.

[0053] The predicted cooling requirement may be obtained as a predicted amount of heat generated by a component that the cooling arrangement 200 is arranged to cool, measured, e.g., in Joules or some similar quantity. The predicted amount of heat generated by the component may be obtained from a heat estimation model arranged to estimate the amount of heat generated by the component under different operating conditions. In this case, conditions could for example refer to different vehicle speeds or road inclines and varying vehicle load, but also weather conditions such as ambient temperature. The heat estimation model is a mathematical model, such as a look-up table (LUT) or function which takes a number of input parameters such as vehicle speed and vehicle load, road incline, and so on, and determines a prediction of generated heat. This type of model can, e.g., be initialized by computer simulation and testing, and then updated in field to better suit a given vehicle or vehicle type. Thus, as an example, the heat estimation model may comprise a lookup table comprising measured or pre-calculated values of the amount of heat generated by some vehicle component. As another example, the heat estimation model may comprise a mathematical function representing the relationship between a parameter such as vehicle speed or road incline and the amount of heat generated. As a third example, the heat estimation model may comprise a machine learning model such as a neural network. This machine learning model may be trained to represent a given vehicle or a given vehicle type by operating the vehicle during varying driving conditions, road altitude profiles, and loads. The actual generated heat can be measured and used to adjust the neural network to better predict generated heat based on the available input data.

[0054] It is advantageous if the heat estimation model can obtain predicted values of for example the vehicle speed or road incline for a future time t. These values could be obtained from a traffic situation management (TSM) unit or path planning module such as a navigation unit with access to maps and other information about the upcoming stretch of road. Generally, a TSM unit plans vehicle operation with a time horizon of, e.g., 10-60 seconds. This time frame for instance corresponds to the time it takes for the vehicle to negotiate a curve, i.e., to transition from driving straight to entering the curve and then exiting the curve again, or driving up a hill. A path planning module can also provide information related to a planned path by the vehicle, as well as associated altitude profiles of the path, road conditions, speed limits, and so on.

[0055] The heat estimation model may be adjusted in dependence of a parameter describing the vehicle. A parameter describing the vehicle may for example be the vehicle weight or current load. The heat estimation model may be adjusted during vehicle operation by measuring generated heat in dependence of vehicle parameters such as load and road condition.

[0056] According to aspects, obtaining S1 a predicted cooling requirement for a future time t may also comprise obtaining information about a speed of the vehicle at the future time t and using a heat estimation model to estimate S11 the amount of heat generated at the future time t based on the speed of the vehicle. As an example, if the component that the cooling arrangement 200 is arranged to cool is a main traction machine or a wheel-end traction machine, an increase in vehicle speed normally means a higher motor output power, which may lead to a higher cooling requirement. Conversely, a reduction in the vehicle speed may lead to a lower cooling requirement. This may, however, not always be the case. For instance, an electric machine used for braking during down-hill driving can be expected to generate a significant amount of heat.

[0057] According to other aspects, obtaining a predicted cooling requirement for a future time t may comprise obtaining information about a characteristic of the path of the vehicle at the future time t and using a heat estimation model to estimate S11 the amount of heat generated at the future time t based on the characteristic of the path of the vehicle.

[0058] As mentioned above, a characteristic of the path of the vehicle may for example be a road incline or slope, a change in direction of the vehicle path, or a property of the road surface such as if it is an asphalt road or a dirt road.

[0059] FIG. 6 shows an example driving scenario and a requested fan speed, or fan speed demand, during the driving scenario. In this scenario the component that the cooling arrangement 200 is arranged to cool is a component that generates additional heat when the vehicle 100 encounters an incline, such as a main traction machine or a wheel-end traction machine.

[0060] At the beginning of the driving scenario, in section A of FIG. 6, the vehicle 100 travels across substantially flat ground. The requested fan speed remains low both for an example temperature-driven regular fan control (RFC) method indicated by a dash-dotted line, and with a predictive PMCC method indicated by a solid line, i.e., the method illustrated in FIG. 7. Here, regular fan control is taken to mean conventional methods of controlling a cooling arrangement, comprising e.g., obtaining input from temperature sensors and adjusting the fan speed in dependence of the measured temperature.

[0061] In section B the vehicle drives up a slope. According to the method above the resulting increase in the cooling requirement is anticipated, resulting in a higher PMCC fan speed demand before the vehicle 100 enters section B. In section C the vehicle 100 drives up a steeper slope. At a time t, which in FIG. 6 corresponds to the time when the vehicle 100 is driving up the steepest part of the slope, the fan speed demand from PMCC is at a maximum. In section D the vehicle is again driving across substantially flat ground and the PMCC fan speed demand is rapidly lowered.

[0062] This way, by proactively generating cooling before the temperature actually rises, over-heating is more efficiently prevented by the cooling system. This, in turn, means that vehicle performance does not become temperature-limited as easily as if the cooling arrangement is controlled using the RFC type of methods.

[0063] In addition to the heat estimation model, it may also be possible to measure the amount of heat generated by a component of the vehicle 100 under different driving conditions. For example, the vehicle may be equipped with temperature sensors arranged in proximity to the component. The heat estimation model can then be altered or adjusted in such a way that the difference between the measured and estimated amount of heat generated by the component is reduced. The heat estimation model may thus be adjusted S13 in dependence of information about the amount of heat generated during prior operation of the vehicle. For example, if the predicted amount of generated heat is always below the actual generated heat, then the heat estimation model can be adjusted to reduce the discrepancy. Similarly, if the predicted amount of generated heat is constantly above the actual generated heat, the heat estimation model can be adjusted to predict a lower generated amount of heat.

[0064] For instance, the heat estimation model may be implemented as any of a Kalman filter, a particle filter, or a neural network.

[0065] Alternatively, data on heat generation may be collected from a plurality of vehicles, for example vehicles of a similar type or equipped with similar components and stored in a data storage unit such as a server. Referring again to FIG. 1, the vehicle may be connected to the server 140 via a wireless connection 130 to a base station 135. The collected data may then be used to improve a heat estimation model that is subsequently redistributed to the vehicle. For example, the server 140 may maintain a plurality of machine learning models which are regularly updated, i.e., trained, based on the data received from vehicle. The updated machine learning models can then be fed back to the vehicles, thereby improving the predictive cooling operation.

[0066] According to other aspects, calculating an operational parameter of the cooling arrangement comprises using a cooling model S21, the cooling model being a model of the relation between the operational parameter of the cooling arrangement and a generated cooling effect.

[0067] As an example, the cooling model may comprise a lookup table comprising measured or pre-calculated values of the cooling effect for different values of the operational parameter of the cooling arrangement. As another example, the cooling model may comprise a mathematical function describing the relationship between the operational parameter of the cooling arrangement and the cooling effect. As a third example, the cooling model may comprise a machine learning model such as a neural network.

[0068] As with the generated heat, the cooling effect of the cooling arrangement may be measured during operation of the vehicle, for example using temperature sensors arranged in proximity to the cooling arrangement, air flow sensors, and the like. The measured cooling effect may then be used to improve the cooling model by adjusting the cooling model to reduce the difference between the estimated and measured cooling effect in a manner similar to the heat estimation model discussed above. Thus, the method may also comprise adjusting S22 the cooling model in dependence of information about the cooling effect generated during prior operation of the vehicle.

[0069] Information about the cooling effect of a cooling arrangement 200 may also be collected for a plurality of vehicles 100 comprising cooling arrangements 200 and stored in a data storage unit such as a server. Referring again to FIG. 1, the vehicle may be connected to the server 140 via a wireless connection 130 to a base station 135. The information may be used to improve a cooling model that can subsequently be used in any vehicle of the plurality of vehicles. This way cooling data can be collected from a plurality of different sources and used to build a model of cooling needs and cooling efficiency in various driving scenarios.

[0070] A vehicle 100 may be arranged to upload data associated with component temperatures and the like in different driving scenarios to the server 140, and optionally also receive updated models from the server 140. The uploaded data may, e.g., comprise vehicle speed, road incline angle, vehicle load and so on, along with temperatures of the different vehicle components being cooled. The server 140 may use this data to adjust models for predicting cooling requirements in different scenarios. The server 140 can maintain, e.g., trained neural networks for a plurality of different vehicle types, where each neural network is configured to take driving scenario as input and generate a predicted cooling requirement as output. These trained neural networks can be downloaded to vehicles in order to improve control of cooling operations.

[0071] There is also herein disclosed a control unit 110 comprising processing circuitry 910 configured to perform a method as described above. There is also disclosed a heavy-duty vehicle 100 comprising such a control unit and a cooling arrangement 200 as previously described

[0072] FIG. 8 schematically illustrates, in terms of a number of functional units, the components of a control unit 110 according to embodiments of the discussions and methods disclosed herein. This control unit 110 may be comprised in the vehicle 100, e.g., in the form of a vehicle motion management (VMM) unit. Processing circuitry 810 is provided using any combination of one or more of a suitable central processing unit CPU, multiprocessor, microcontroller, digital signal processor DSP, etc., capable of executing software instructions stored in a computer program product, e.g. in the form of a storage medium 830. The processing circuitry 810 may further be provided as at least one application specific integrated circuit ASIC, or field programmable gate array FPGA.

[0073] Particularly, the processing circuitry 810 is configured to cause the control unit 110 to perform a set of operations, or steps, such as the methods discussed in connection to FIG. 7. For example, the storage medium 830 may store the set of operations, and the processing circuitry 810 may be configured to retrieve the set of operations from the storage medium 830 to cause the control unit 110 to perform the set of operations. The set of operations may be provided as a set of executable instructions. Thus, the processing circuitry 810 is thereby arranged to execute methods as herein disclosed.

[0074] The storage medium 830 may also comprise persistent storage, which, for example, can be any single one or combination of magnetic memory, optical memory, solid state memory or even remotely mounted memory.

[0075] The control unit 110 may further comprise an interface 820 for communications with at least one external device, such as an electric machine, a cooling arrangement or a gearbox. As such the interface 820 may comprise one or more transmitters and receivers, comprising analogue and digital components and a suitable number of ports for wireline or wireless communication.

[0076] The processing circuitry 810 controls the general operation of the control unit 110, e.g., by sending data and control signals to the interface 820 and the storage medium 830, by receiving data and reports from the interface 820, and by retrieving data and instructions from the storage medium 830. Other components, as well as the related functionality, of the control node are omitted in order not to obscure the concepts presented herein.

[0077] FIG. 9 illustrates a computer readable medium 910 carrying a computer program comprising program code means 920 for performing, e.g., the methods illustrated in FIG. 7, when said program product is run on a computer. The computer readable medium and the code means may together form a computer program product 900.