DE102018220572A1 - PREDICTIVE ENERGY OPTIMIZATION OF AN ENTIRE RIDE - Google Patents

Abstract

A controller 200 for a vehicle 100, wherein the vehicle includes a traction battery 106, an electric traction motor 108, and an internal combustion engine 104, the controller comprising: prediction means 208 configured to predict the energy consumption of the vehicle during a trip; and scheduling means 210 configured in response to the prediction of when to cruise the vehicle in the first mode and when to steer the vehicle in the second mode while the vehicle is traveling, wherein the first mode is an increased discharge of traction battery power permitting, as permitted by the second mode, and wherein the planning means is configured so that the traction battery energy at the end of the journey is lower than the traction battery energy at the beginning of the journey.

Description

TECHNICAL AREA

The present disclosure relates to predictive energy optimization of an entire trip. In particular, but not exclusively, it refers to the predictive energy optimization of an entire ride in a vehicle.

Aspects of the invention relate to a controller, a control method and a computer program.

STATE OF THE ART

It is known that a hybrid vehicle includes an internal combustion engine and an electric traction motor or motors that are powered by a traction battery. The internal combustion engine and the electric traction motor serve to provide traction torque. The internal combustion engine and the electric traction motor may be arranged in a parallel hybrid configuration, so that the vehicle can be driven only with the electric traction motor and only with the internal combustion engine or with both the electric traction motor and the internal combustion engine.

It is common for a hybrid vehicle to use its electric traction motor at the beginning of a journey only until the electrical charge stored in the traction battery is depleted. Then the internal combustion engine is switched on for the remainder of the journey.

BRIEF SUMMARY OF THE INVENTION

The present invention aims to overcome the disadvantages of the prior art.

Aspects and embodiments of the invention provide apparatus, a vehicle, a method, and a computer program as claimed in the appended claims.

According to one aspect of the invention, there is provided a controller for a vehicle, the vehicle comprising a traction battery, an electric traction motor, and an internal combustion engine, the controller comprising: prediction means (predictor) configured to predict the power consumption of the vehicle for a ride ; and a scheduling means (scheduler) configured in dependence on the prediction of when to control the vehicle in a first mode while driving and when to control the vehicle in a second mode while driving, the first mode being increased Discharging the traction battery power (charge) allowed as permitted by the second mode, and wherein the scheduling means is configured so that the traction battery energy at the end of the journey is lower than the traction battery energy at the beginning of the journey.

The first mode may be a "discharge" mode in which the engine is used less than in the second mode, thereby increasing the dependency on the use of traction battery power. The second mode may be a "charge hold" mode in which the engine is used more than in the first mode, thereby reducing dependence on the use of traction battery power.

This offers the advantage of reduced energy consumption. This is because the controller maximizes the use of the unload mode, but only uses the unload mode when it is most efficient, such as during city driving at the beginning and end of a ride in low speed environments and / or in cities.

In some examples, the level of allowable discharge in the first mode is sufficient for a torque request to be fully satisfied by the discharge of the traction battery energy (reduction in state of charge) without the assistance of the internal combustion engine unless the torque request is completely satisfied by the discharge of the traction battery energy while a restriction is met. The constraint may be a capability limit of the electric traction motor, a capability limit (eg, power) of the traction battery, or the like. In some examples, the second mode implements a desired amount of traction battery power (state of charge) so that when an instantaneous amount of traction battery energy is below the set point, the traction battery energy needs to be increased toward the set point.

This offers the advantage of reduced energy consumption and reduction of urban emissions by avoiding the use of the low-energy municipal combustion engine. However, in some instances, the ability to implement "torque assist" for higher torque requests, and the ability to implement only electric drive motor drive for lower torque requests, are not necessarily prevented. In some examples, the torque assist mode is used when the torque request exceeds the capability of the currently active one of the electric traction motor or the internal combustion engine so that the torque from the active or traction motor is increased by the combined torque from the engine and the traction motor Torque request to comply.

In some examples, the scheduling means is configured to determine a prediction-based threshold to require the vehicle to be controlled in the first mode while not exceeding the threshold and to require the vehicle to be controlled in the second mode while the threshold is exceeded.

This offers the advantage of reduced energy consumption as the intended use of the modes is tailored to each specific ride. Further, a threshold is a quantitative condition so that it can be automatically determined based on quantitative information about the journey without requiring user input. In addition, the use of the single (total ride) threshold is computationally efficient, allowing for regular updates of the schedule during driving to account for real world deviations from the original forecasts.

In some examples, the threshold distinguishes a low speed and / or city driving environment from a high speed and / or extraurban driving environment.

This offers the advantage of reduced power consumption because the engine is preferred for high power driving and the electric traction motor is preferred for low power driving.

In some examples, the threshold is a performance threshold. In some examples, the power threshold depends on at least one of the following (predicted) variables: vehicle speed; Speed Limit; Road gradient; Vehicle mass; Road curvature; Type of road; additional electric charge. In some examples, the performance threshold depends on a combination of the variables. In some examples, the power threshold is implemented as a vehicle speed threshold.

This provides the advantage of more accurate planning, as the intended use of the modes is tailored to the quantitative characteristics of each specific ride.

In some examples, the planning means is configured to control a value of the threshold such that the energy of the traction battery is lower by a defined amount at the end of the journey than at the beginning of the journey.

This offers the advantage of reduced power consumption since the threshold favors maximum utilization of the electric traction motor in parts of the journey in which the charge hold mode is inefficient.

In some examples, the controller is configured such that during travel while the vehicle is being controlled in the first mode, exceeding the threshold is not sufficient alone to switch the engine from an off state to an on state to meet the torque request.

This has the advantage that the schedule can not simply be overridden manually. In some examples, the only way to use the internal combustion engine during the first mode is to increase the torque request until the torque assist mode is activated. In the torque assist mode, the torque from the engine is increased by the electric traction motor to increase the vehicle acceleration.

In some examples, the controller is configured such that during travel, while the vehicle is being controlled in the second mode, not exceeding the threshold alone is sufficient to cause the engine to transition from an on state to an off state Meet torque requirement.

This has the advantage that the schedule can not simply be overridden manually. In some examples, the only way to use the electric traction motor to decay the traction battery during the second mode is to activate the torque assist mode.

In some examples, the ride includes a plurality of segments, wherein the prediction means is configured to predict a value indicative of the energy consumption of the vehicle for each segment. In some examples, the total number of segments does not exceed 1500.

This provides the advantage of improved computational efficiency because where the / each parameter indicative of power consumption is a constant value for each segment, the level of detail used by the scheduling means is reduced, allowing for faster processing and therefore regular scheduling updates while driving.

In some examples, the controller includes aggregation means (an aggregator) configured to collect from the segments information indicative of the predicted energy usage, wherein the scheduling means is configured to determine the threshold in dependence on the aggregation. In some examples, aggregation serves for data binning in a variety of dimensions. In some examples, determining the threshold includes calculating the energy consumption over each segment.

This offers the advantage of improved computational efficiency and reduced memory usage because instead of calculating and storing the energy for each segment, energy is computed for each bin in which information has been aggregated. When determining the power threshold, the computational iterations decrease from n1 = number of segments to n2 = number of bins. The memory is reduced because segments n1 and their corresponding distances along the route do not need to be stored in memory. This allows regular updates of the schedule during driving to account for real deviations from the original forecasts.

In some examples, aggregation is done using no more than 2000 bins.

This offers the advantage of improved computational efficiency, allowing for regular scheduling updates while driving, since the number of computation cycles required to perform operations on the clustered data is minimal.

In some examples, the controller includes an updating means (updater) configured to update a power consumption prediction during the journey over the remainder of the journey and to update a schedule depending on the updated prediction when the rest of the trip Vehicle is to be controlled in the first mode and when during the remainder of the journey, the vehicle is to be controlled in the second mode.

This has the advantage that less accurate information is needed for the initial prediction, as real deviations from the original predictions are regularly taken into account.

In some examples, the prediction means is configured to predict the power consumption based on information indicative of the predicted power consumption detected by a vehicle navigation system and / or predicted by machine learning.

This offers the advantage of less frequent driver disturbances since the control functions are performed automatically and can be hidden from the driver.

In some examples, the information indicative of the predicted energy usage includes one or more of: a distance-dependent parameter; a speed-dependent parameter; a gradient-dependent parameter.

This offers the advantage that information indicative of the predicted power usage can be obtained from existing vehicle subsystems, such as a navigation system.

In some examples, the scheduling means is configured to switch between the first mode and the second mode multiple times during driving.

This has the advantage that the energy consumption for trips that vary several times between high performance and low power consumption can be minimized.

According to one aspect of the invention, there is provided a controller for a vehicle, the vehicle including a traction battery, an electric traction motor, and an internal combustion engine, the controller comprising: at least one electronic processor; and at least one electronic storage device electrically coupled to and having stored therein instructions (a computer program) having at least one electronic storage device and the instructions configured with the at least one electronic processor: predicting the energy consumption of the vehicle on a trip; in response to the prediction of when to control the vehicle in a first mode during driving and when to control the vehicle in a second mode while driving, the first mode allows increased traction battery energy discharge through the second mode and wherein the schedule is configured to require traction battery energy at the end of the journey to be lower than the traction battery energy at the beginning of the journey. According to another aspect of the invention, there is provided a control method for a vehicle, the vehicle including a traction battery, an electric traction motor, and an internal combustion engine, the control method comprising: predicting the power consumption of the vehicle on one trip; in response to the prediction of when to control the vehicle in a first mode during driving and when to control the vehicle in a second mode while driving, the first mode allows increased traction battery energy discharge through the second mode and wherein the scheduling requires the traction battery energy at the end of the journey to be lower than the traction battery energy at the beginning of the journey.

In accordance with another aspect of the invention, there is provided a computer program that, when executed on at least one electronic processor, at least: predicts the energy consumption of a vehicle on a journey, the vehicle including a traction battery, an electric traction motor, and an internal combustion engine; in response to the prediction of when to control the vehicle in a first mode during driving and when to control the vehicle in a second mode while driving, the first mode allows increased traction battery energy discharge through the second mode and wherein the scheduling requires the traction battery energy at the end of the journey to be lower than the traction battery energy at the beginning of the journey.

In accordance with another aspect of the present invention, a non-transitory computer-readable storage medium is provided.

It is expressly intended, within the scope of this application, that the various aspects, embodiments, examples and alternatives presented in the preceding paragraphs, in the claims and / or in the following description and drawings, and in particular, their individual features, be independent of each other or in any combination. This means that all embodiments and / or features of any embodiment can be combined in any manner and / or in any combination, provided that these features are not incompatible. The Applicant reserves the right to change any claim originally filed or to submit any new claim, including the right to alter any originally filed claim, in order to depend on and / or disregard any feature of any other claim although it was previously unclaimed in this way.

list of figures

• 1 ein Beispiel für ein Fahrzeug zeigt,
• 2A ein Beispiel einer Steuerung und 2B ein Beispiel eines computerlesbaren Speichermediums zeigt;
• 3 ein beispielhaftes Verfahren zeigt;
• 4A ein Beispiel für Informationen zeigt, die indikativ für die vorhergesagte Energieverwendung, und ein Beispiel für einen Schwellenwert sind, und 4B ein Beispiel für einen Zeitplan des ersten und zweiten Modus zeigt.

One or more embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

• 1 an example of a vehicle shows
• 2A an example of a control and 2 B shows an example of a computer-readable storage medium;
• 3 shows an exemplary method;
• 4A shows an example of information indicative of the predicted energy use, and an example of a threshold, and 4B shows an example of a schedule of the first and second modes.

DETAILED DESCRIPTION

1 illustrates an example of a vehicle 100 in which embodiments of the invention can be implemented. In some, but not necessarily all, examples is the vehicle 100 a passenger vehicle, which is also referred to as a passenger car or as an automobile. Passenger cars generally have a curb weight of less than 5000 kg. In other examples, embodiments of the invention may be useful for other applications, such as e.g. As commercial vehicles, air or water vehicles are implemented.

The vehicle 100 in 1 includes an internal combustion engine 104 , The internal combustion engine 104 can be a reciprocating internal combustion engine. The vehicle 100 is a hybrid vehicle because the vehicle 100 also a traction battery 106 and at least one electric traction motor 108 includes. The traction battery 106 is an electrical energy source for the electric traction motor 108 , In some examples, the electric traction motor 108 a motor generator, while in other examples a separate generator is provided. In some examples, the electric traction motor 108 a starter generator operable to perform the function of a starter motor. The purpose of the generator is to use the traction battery 106 charging, for example, by implementing a regenerative braking function.

The vehicle 100 is a parallel hybrid vehicle, so the electric traction motor 108 and the internal combustion engine 104 can be operated in a variety of modes.

In a "discharge" mode (first mode) is the electric traction motor 108 in an on state to produce a traction torque. This consumes electrical charge in the traction battery 106 is stored. When the torque request in the discharge mode is high, the internal combustion engine may 104 be operated simultaneously in an on state to provide a "torque assist" function.

In a "charge-up" mode, the traction battery becomes 106 recharged by the generator when the engine generates a torque that exceeds the driver torque request. In one example, in the charge-up mode, a regenerative brake function is operable to drive the engine 104 In an off state, it could request energy recovery while the vehicle is braking 100 to maximize.

In a "charge hold" mode (second mode) is the internal combustion engine 104 in an on state to generate a traction torque, and the electric traction motor 108 can be used to provide torque assistance or short periods of an all-electric traction motor; however, this must be compensated by providing a charge to maintain a targeted state of charge, for example, using regenerative braking or shifting the engine load to a higher point. For example, the target state of the charge may be a constant value for the entire time that the charge hold mode is in operation. The level of allowable discharge in the charge hold mode is insufficient for medium to high range torque requirements (eg, within the range X-100%, where 100% full throttle corresponds) fully covered by the discharge of the traction battery power. without contribution of the internal combustion engine 104 , Therefore, the internal combustion engine 104 during the charge hold mode to be mainly in its on state. The torque request 'X' corresponds to either the request for a torque at zero or a request for a torque threshold that is greater than zero, under which driving only one electric traction motor is allowed.

In the charge hold mode, if only one drive of the electric traction motor is allowed in response to a low torque request, in one example X may be any value in the range of 0-40%. In some examples, a condition (such as X) for switching to only depends on Driving the electric traction motor also from another parameter. The other parameter may include one or more of: the state of charge of the traction battery; Road gradient; the temperature of an exhaust aftertreatment device, such as a catalyst temperature; the temperature of an engine system, such as a coolant temperature; or vehicle speed.

In some, but not necessarily all, examples, the torque assist mode is available within the charge hold mode while the current state of charge of the traction battery 104 above the setpoint. In some examples, the torque assist mode may also be available within the charge hold mode, even if the current state of charge of the traction battery 104 is below the setpoint.

In some, but not necessarily all, examples, the torque assist mode is implemented in the charge hold mode when the torque request is not complete by use of the internal combustion engine 104 can be met while a constraint is met. In the charge hold mode, the restriction may take the form of a capability limit of the internal combustion engine 104 accept. The ability of the internal combustion engine 104 may be expressed as a threshold torque or in any other suitable manner.

The discharge mode allows for increased discharge of the traction battery energy, as permitted by the charge hold mode, since the discharge mode does not require a constant desired state of charge. In fact, the level of allowable discharge in the discharge mode is sufficient to satisfy torque requirements in the low to mid range (eg, within the 0-Y% range) completely by the discharge of traction battery energy without the intervention of the internal combustion engine 104 to fulfill. For example, a maximum discharge rate could be allowed. The torque assist mode is still available within the unload mode when the torque request exceeds a limit, such as the torque request. B. the ability of the electric traction motor 108 , The ability of the electric traction motor 108 may be expressed as a threshold torque or in any other suitable manner. The threshold value may be the same as or different from that of the internal combustion engine 104 his. The ability of the electric traction motor 108 may correspond to a torque requirement of Y%, where Y may be a 100% torque request, or any other value from the 70% -100% range in other examples.

Additionally or alternatively, the or any other restriction may take the form of a capability limit of the traction battery 106 , such as a power limit. The power limit can act as a bottleneck, making the electric traction motor 108 can not be used to its full capacity. For example, the power limit may depend on the temperature, as the temperature affects battery performance. If this limitation is not met, the internal combustion engine can 104 be used to at least partially meet the torque request, so that the condition remains satisfied.

Accordingly, the different behavior between the charge hold mode and the discharge mode is particularly evident while an average torque request is applied, e.g. B. a torque requirement value in the range of 40% -70%. For this torque request would be the internal combustion engine 104 in accordance with some, but not necessarily all, examples in the charge hold mode but not in the discharge mode.

In some, but not necessarily all, examples includes the vehicle 100 a vehicle navigation system 102 , The vehicle navigation system 102 may be a satellite navigation system; For example, the vehicle navigation system 102 be configured as a Global Positioning System (GPS) navigation system. The vehicle 100 includes a controller 200 in which embodiments of the invention can be implemented. Any existing vehicle controller could be used to implement the functionality disclosed herein. In other to that in 1 Examples shown could be the controller 200 the vehicle navigation system 102 his.

2A illustrates an example of a controller 200 in again 1 represented control 200 , The control 200 includes means for causing one or more of the methods described herein to be performed.

The electronic control device 200 the 2A includes at least one electronic processor 202 and at least one electronic storage device 204 that with the electronic processor 202 is coupled and on the commands 206 are stored, wherein the at least one electronic storage device 204 and the commands 206 configured with the at least one electronic processor 202 to cause one or more of the methods described herein to be carried out. In 2A but not necessarily in all examples, the instructions include 206 prediction means 208 (a predictor), planning means 210 (a dispatcher), aggregation means 212 (an aggregator) and updating means 214 (an updater) whose functions will be described below.

2A also shows an example of a computer program that, if it is on at least one electronic processor 202 causes one or more of the methods described herein to be performed.

2 B illustrates an example of a computer-readable storage medium 216 that the computer program 206 includes.

3 illustrates an example of a control method 300 ("Method") according to aspects of the present invention. The procedure 300 can by the above system 200 be executed.

The procedure 300 includes at block 302 the prediction of the energy consumption of the vehicle 100 on a ride. This is a function of the prediction means 208 ,

In some, but not necessarily all, examples, the ride is a route between a starting location and a destination, optionally via one or more waypoints.

In one example, the route is through the vehicle navigation system 102 released. In this example, the starting location, the destination, and the waypoints may be deterministic, as they may be through user input to the vehicle navigation system 102 are specified. Therefore, the route is deterministic.

In another example, the route of machine learning is predicted. Machine learning could indicate where the vehicle is 100 before and at what times it was driven. This allows a probabilistic determination of the route. For example, the driver normally drives to work at 8 o'clock on a day of the week, which trains a predictive algorithm to determine that if the driver is on a weekday at 8 o'clock in the morning his vehicle 100 enters, he will follow a certain route.

In one example, the journey over which energy usage is predicted extends to the destination. In another example, the prediction extends only to one waypoint.

Once the route is known, the energy consumption of the vehicle 100 for the trip following this route, predicted from any available information indicating the energy consumption. Particularly useful variables include a distance-dependent parameter such as distance, a speed-dependent parameter such as speed or permissible speed limit, and a gradient-dependent parameter such as road gradient or altitude points from which the gradient can be determined. Other useful parameters include vehicle mass, aerodynamic drag coefficients, road curvature (eg, curve radius dependent or a number of curves), road type (eg, road classification, number of lanes). The control 200 may be configured to perform a force analysis of the available parameters to predict the power to use for vehicle travel according to Newton's Second Law of Motion. In some examples, a model of the powertrain and / or powertrain of the vehicle 100 and auxiliary electrical loads (eg, lighting, heating, cooling, engine accessories) may be used to account for losses.

In some, but not necessarily all, examples, a driving style identification algorithm is implemented to predict the aggressiveness of the driver. For example, a stored database is matched by drivers with certain key identifiers. If a particular key fob or other device with the vehicle 100 interacts (eg unlocked), the driver's file is queried to determine who the vehicle 100 and to determine a predictive power modifier that varies depending on the relative driving style of the driver compared to other drivers. The modifier may depend on drive parameters that include one or more of torque request, use of brakes, use of power consuming vehicle accessory subsystems. The modifier could increase or decrease the predicted power to account for the driving style as indicated by the driving parameters. The driving parameters can be set dynamically via a Surveillance and stored in the manner of machine learning against this driver file.

In one example, information indicative of energy usage for the journey may be provided by the vehicle navigation system 102 to be published. For example, speed limits and / or average traffic speeds and altitude points may be provided.

Additionally or alternatively, information indicative of power consumption for the ride may be determined from machine learning. For example, machine learning may involve changing the state of charge of the traction battery 106 in terms of time, determine a past route that is identical to the route to follow in the current trip.

4A Figure 2 shows two graphs representing two types of information indicating a predicted power usage. The x-axis represents time or distance. The y -Axis of the upper graph represents the vehicle speed. The y -Axis of the lower graph represents a road gradient that can be positive (uphill) or negative (downhill) and can be expressed as a percentage or any other suitable shape.

In some, but not necessarily all, examples, the ride includes a plurality of segments. In 4A but not necessarily in all examples, includes the ride 14 Segments. In other examples, the ride may include up to 1500 segments or more. The speed, the gradient and other information are controlled by the controller 200 quantized to a constant value over each segment. The number of segments into which the journey is divided corresponds to a spatial and / or temporal resolution. The width of each segment may be a certain time and / or a given distance (width on the x Axis) and may differ from the width of at least one other segment. In one example, each segment represents a line between two nodes in a graph representing a road network. The graph may be that derived from a route finding algorithm, such as the Dijkstra algorithm, used in the vehicle navigation system 102 is implemented, used to find the route. The node-to-node distance is variable because each node can correspond to a real intersection or an auxiliary node to improve spatial resolution (eg, taking into account road curves). Therefore, the segment widths are variable. The segmentation may be in the vehicle navigation system 102 for the purpose of route calculation occur before receiving the information or in other examples, the controller 200 perform the segmentation.

Once the prediction at block 302 has been made, the process proceeds 300 to block 304 which plans, when while driving the vehicle 100 in the first mode (unloading mode) should be controlled and when the vehicle while driving 100 in the second mode (charge hold mode).

A first goal of the planning tools 210 is to schedule the use of the discharge mode and the charge hold mode so that the energy of the traction battery (state of charge) at the end of the journey is less than the energy of the traction battery at the beginning of the journey (eg 100% charged) , In particular, but not exclusively, the aim is that the state of charge at the end of the journey has a certain value, for example, a value between 0% to 30%.

A second goal of the planning tools 210 is to plan the use of the discharge mode and the charge hold mode so that the vehicle 100 is low in the unload mode during any sections (eg, segments) of the ride in which the predicted energy is used and controlled, and in the charge hold mode during any other sections (eg, segments) of the ride, in which the predicted energy consumption is high, to be controlled.

The schedule provides an output condition for switching between the modes while the vehicle 100 in operation and takes the ride. While using the vehicle 100 the controller is configured to implement the output condition so that the internal combustion engine 104 and the electric traction motor 108 be controlled so that they switch between modes, as required by the output condition. In some examples, periodic updates are made by the vehicle navigation system 102 be sure that the implementation of the schedule is synchronized with the actual trip, which may be subject to delays, for example.

In the following paragraphs, an advantageous planning approach for block 304 discloses including the calculation of a threshold as an output condition. While using the vehicle 100 For example, a mode switching operation for switching from the first mode to the second mode is always executed when a continuously measured variable on which the threshold is based (eg, power, vehicle speed) exceeds the threshold. The mode switching operation switches from the second mode to the first mode when the continuously-measured variable falls below the threshold. In this example, mode switching is not performed at segment boundaries, but this could be the case in other examples. In some, but not necessarily all, examples, the threshold for the total predicted trip assumes a constant value.

The planning approach calculates a threshold 402 , as in 4A shown to demand that the vehicle 100 is controlled in the discharge mode while the threshold 402 is not exceeded, and to demand that the vehicle 100 is controlled in the charge hold mode while the threshold value 402 is exceeded. The threshold 402 depends on the predicted power usage as described above, therefore the threshold is 402 different for each different ride. The value of the threshold 402 is defined so that the traction battery energy is lower for the required amount at the end of the journey than at the beginning of the journey; For example, the threshold represents 402 ensure that the charge state at the end of the trip at the above value is in the range of 0% to 30%. A method of calculating the threshold 402 will be discussed in more detail below.

4B illustrates the effect of applying the threshold 402 , 4B illustrates the state of charge of the traction battery 106 on the y-axis, and the x-axis is like for 4A Are defined. The predicted vehicle speed over the drive sections 1 . 2 . 3 . 10 . 12 . 13 and 14 is below the threshold 402 so that the unloading mode is used everywhere in these segments when the vehicle speed during the running according to the prediction in those areas of 4B which are marked with "CD" (Discharge Mode) aligned under these segments where the state of charge decreases. The vehicle speed during travel may be determined by an actual vehicle speed obtained from a speed sensor and / or an estimated or predicted vehicle speed determined by the navigation system 102 will be presented. The predicted vehicle speed over the drive sections 4 - 9 and 11 is above the threshold 402 so that the charge hold mode is used in these segments when the vehicle speed during the drive is in accordance with the prediction as in the ranges in 4B which are labeled "CS" (Charge Hold Mode) and are below those segments where the state of charge is a constant set point.

In the example of 4A-4B is the threshold 402 a vehicle speed threshold, therefore, a ride segment is above the threshold 402 if the representative constant vehicle speed (or speed limit) for that segment is above the vehicle speed threshold. In other examples, the threshold could be 402 however, be any power threshold that is not limited to a vehicle speed threshold. The threshold 402 Could represent at least one of, or a combination of, the following predicted variables: vehicle speed (or speed limit), road gradient, vehicle mass, road curvature, road type or electrical supplemental load, or any other variables of the information described above indicating the predicted energy consumption. For example, the power threshold could represent kilojoules / second, with one drive segment above that threshold 402 when the power derived from the force analysis for this segment is above the threshold 402 lies.

One technical effect of using a vehicle speed threshold is the ease of monitoring whether the threshold is exceeded while the vehicle is running 100 is driven. Further, the vehicle speed threshold could better distinguish between city traffic and out-of-town driving, for example, in urban areas that are hilly and therefore require high energy consumption. This means that electric-only driving in urban areas is preferred so that vehicle emissions are removed from urban areas. A technical effect of a power threshold taking into account a combination of the above performance variables is that power consumption is reduced because of the internal combustion engine 104 and the electric traction motor 108 only be used when they are most efficient.

During driving, it is important that the preferred state (on or off) of the internal combustion engine 104 and the electric traction motor 108 as by the block 304 planned mode determines not too easy to override by driver input because this could cause excessive discharge or underutilization of traction battery power. It is envisaged that while driving while the vehicle 100 controlled in discharge mode, exceeding the threshold 402 not alone sufficient to cause the engine to switch from an off state to an on state to meet a torque request. Exceeding another stricter threshold may be required. Similarly, while the vehicle 100 is controlled in the charge hold mode, the threshold is not exceeded 402 not alone sufficient to cause the motor to switch to the off state. For example, when the torque request approaches 100% (eg, in the range Y% -100%), this indicates a significantly higher power demand than the threshold 402 is assigned, and therefore exceeds a stricter threshold than the threshold 402 Only then would a torque assist function be used in which both the internal combustion engine 104 as well as the electric traction motor 108 used to increase the traction torque.

An exemplary algorithm for quickly calculating the threshold 402 will now be described, that of the aggregation agent 212 the controller 200 for example in block 304 is carried out. The calculation takes into account the vehicle speed (or speed limit), the road gradients, and the segment distance, however, in other examples, additional or fewer variables associated with the predicted energy usage may be used.

First, a data binning operation is performed in which each segment is assigned to a particular bin in a multi-dimensional array. Each bin along a first dimension (eg, columns) represents an interval of a first variable, such as vehicle speed. Each bin along a second dimension (eg, rows) represents an interval of a second variable, for example, street gradients. The segment distance is assigned to the bin. If two segments belonging to a particular bin are identified, their distances in the bin are added up (aggregated). An example of a binning array is shown in Table 1, where km / h is percent per kilometer, km by kilometer, and% for road gradients: Table 1: Binning Array > 90 km / h (56 mph) 30-90 km / h (18.7-56 mph) 0-30 km / h (0-18.7 mph) > 5% 0.2 km (0.1 mph) 0 km 0.2 km (0.1 mph) -5% to 5% 3 km (1.9 mph) 5 km (3.1 mph) 4 km (2.5 mph) <-5% 0.1 km (0.07 mph) 0 km 0 km

In a practical implementation, the number of data bins may differ from the example of Table 1. Ten, hundreds or thousands of bins could be used at correspondingly smaller intervals. Not more than 2000 bins in total are computationally efficient, d. H. the array is smaller than 64x64.

In an exemplary implementation, the speed threshold is determined by calculating a value of the total energy consumption for each bin. The energy consumption calculation in this example is a function of the vehicle speed, the gradient and the distance. In other examples, the calculation may use other and / or additional variables associated with the predicted energy usage.

The result of the accumulation can be at a certain threshold 402 be imaged in the electronic storage device. For example, with reference to 4A a vehicle speed threshold 402 of 15 km / h (9.4 mph). In an exemplary implementation, each row (or column) of bins is summed to a single scalar value, resulting, for example, in a vector of total energy bins classified only by speed (not speed and gradient). The vehicle speed threshold is determined by accumulating the total energy consumption values in vehicle speed bins, starting from the lowest vehicle speed limit and moving to the highest vehicle speed limit, until the available traction battery energy matches the cumulative energy. This process ensures that the vehicle speed threshold is optimized so that the vehicle 100 the discharge mode so far as possible at times when the unloading mode is most efficient (low speed driving) and at the desired level of unloading at the end of the ride.

It would be appreciated that the vehicle speed threshold 402 can be calculated using alternative methods to the method shown above. Further, other forms of the threshold than a vehicle speed threshold may be applied, for example, by controlling how the vector of bins is classified in the summing operation. In addition, considering additional variables requiring the use of additional arrays, an optimization algorithm may be built in to the optimal threshold 402 to find. In another example, arrays can not be used and the threshold 402 could be guessed by simulation and iterated. However, the method described above is particularly computationally efficient, thereby allowing the prediction and scheduling blocks 302 and 304 be repeated during a journey to take into account the changing real driving conditions.

The updating means 214 is responsible for driving the block 302 to repeat a prediction of the energy consumption over the remainder of the journey and, depending on the updated prediction, the block 304 to repeat, to update a schedule, when during the rest of the ride, the vehicle 100 in the discharge mode, and when during the rest of the drive the vehicle 100 to be controlled in the charge hold mode. In one example, the schedule is the threshold 402 Therefore, updating the schedule means that the value of the threshold 402 is updated. The update may be performed periodically to repeat several times during travel (eg, repeat in the order of milliseconds or seconds) and / or in response to manual input and / or in response to feedback, e.g. As state of charge, a change of the planned route of the ride or other deviations of the driver from the predicted behavior. In an exemplary implementation, when the vehicle is 100 consumed more traction battery energy than originally planned, the threshold 402 by the updating means 214 lowered, so that a greater proportion of the remaining drive in charge-holding mode is completed.

For the purposes of this disclosure, it is pointed out that the controller (s) 200 as described herein may each comprise a control unit or a computing device having one or more electronic processors. A vehicle 100 and / or a system thereof may comprise a single control unit or an electronic control, or alternatively different functions of the control (s) may be implemented or accommodated in different control units or controls. A set of instructions may be provided which, when executed, causes the controller (s) or controller (s) to perform the control techniques described herein (including the described method [s]). The set of instructions may be incorporated in one or more electronic processors; alternatively, the set of instructions may be provided as software on the computing device executed by one or more electronic processors. For example, a first controller may be implemented in software running on one or more electronic processors, and one or more other controllers may also be implemented in one or more processors, optionally the same one or more electronic processors as the first controller. implemented software. It should be understood, however, that other arrangements are also useful, and thus, the present disclosure is not intended to limit a particular arrangement. In any event, the embodiments described above may be embedded in a computer-readable storage medium (eg, a non-volatile storage medium) that includes any mechanism for storing information in a form readable by a machine or electronic processor / computing device, including, but without limitation: a magnetic storage medium (eg, a floppy disk); an optical storage medium (eg, CD-ROM); a magnetic optical storage medium; a read-only memory (ROM); a random access memory (RAM); an erasable programmable memory (eg EPROM and EEPROM); a flash memory; or electrical or other types of media for storing such information / instructions.

In the 3 The illustrated blocks may include steps in a method and / or sections of code in the computer program 206 represent. The representation of a particular order for the blocks does not necessarily imply that there is a required or preferred order for the blocks, and the order and arrangement of the block may be varied. In addition, it may be possible that some steps are omitted.

Although embodiments of the present invention have been described in the preceding paragraphs with reference to various examples, it should be understood that modifications may be made to the given examples without departing from the scope of the claimed invention.

Features described in the foregoing description may be used in combinations that differ from the expressly described combinations.

Although functions have been described with reference to certain features, these functions are executable by other features, whether described or not.

Although features have been described with respect to particular embodiments, these features may be embodied in other embodiments as well, whether described or not.

While it has been attempted in the foregoing specification to draw attention to those features of the invention which have been believed to be particularly important, it should be understood that applicant claims protection with respect to each patentable feature or patentable combination of features referred to above and / or illustrated in the drawings, regardless of whether or not they have been given particular significance.

Claims (12)

A controller (200) for a vehicle (100) comprising the vehicle; a traction battery (106), an electric traction motor (108), and an internal combustion engine (104), the controller comprising: Prediction means (208) configured to predict the energy consumption of the vehicle (100) on a journey; and Planning means (210) configured to schedule, in response to the prediction, when during travel the vehicle (100) is to be controlled in a first mode and when in motion the vehicle (100) is controlled in a second mode should, wherein the first mode allows increased discharge of the traction battery energy as permitted by the second mode, and wherein the scheduling means (210) is configured to require that the traction battery energy at the end of the journey be lower than the traction battery energy at the beginning of the journey. The controller (200) after Claim 1 wherein the scheduling means (210) is configured to determine a prediction-based threshold (402) to require that the vehicle (100) be controlled in the first mode while not exceeding the threshold and to demand in that the vehicle (100) is controlled in the second mode while the threshold is exceeded. The controller (200) after Claim 2 wherein the threshold distinguishes a low speed and / or city driving environment from a high speed and / or extraurban driving environment, wherein the threshold is optionally a power threshold that depends on at least one of the following variables: vehicle speed; Speed Limit; Road gradient; Vehicle mass; Road curvature; Type of road; additional electrical charge. The controller (200) after Claim 3 wherein the scheduling means (210) is configured to control a value of the threshold such that the energy of the traction battery is lower by a defined amount at the end of the journey than at the beginning of the journey. The controller (200) after Claim 3 or Claim 4 configured to, during travel, while the vehicle (100) is being controlled in the first mode, not exceeding the threshold alone not sufficient to cause the motor (104) to switch from an off to a on state; to meet the torque requirement. The controller (200) according to one of Claims 3 to 5 configured so that during travel while the vehicle (100) is being controlled in the second mode, the threshold not exceeding is not sufficient alone to cause the motor (104) to switch from a powered to a power off state to meet the torque request. The controller (200) of any one of the preceding claims, wherein the ride comprises a plurality of segments, and wherein the prediction means (208) is configured to predict a value indicative of the energy consumption of the vehicle (100) for each segment, optionally comprehensive aggregation means (212) configured to aggregate from the segments, information indicating the predicted power consumption, and wherein the scheduling means (210) is configured to determine the threshold depending on the aggregation. The controller (200) of any one of the preceding claims, updating means (214) configured to update, during the ride, the power consumption prediction over the remainder of the journey, and in response to the updated forecast to update a schedule, when the vehicle (100) is to be controlled during the remainder of the journey in the first mode, and when during the remainder of the journey the vehicle (100) is to be controlled in the second mode. The controller (200) of any one of the preceding claims, wherein the prediction means (208) is configured to predict the power consumption in response to information indicating the predicted power consumption acquired by a vehicle navigation system (102) and / or optionally predicted from machine learning wherein the information indicates the predicted energy consumption comprising one or more of the following: a distance-dependent parameter; a speed-dependent parameter; a gradient-dependent parameter. The controller (200) of any one of the preceding claims, wherein the scheduling means (210) is configured to be capable of switching between the first mode and the second mode multiple times during travel. A control method (300) for a vehicle (100), the vehicle comprising a traction battery (106), an electric traction motor (108), and an internal combustion engine (104), the control method comprising: Predicting (302) the power consumption of the vehicle (100) on a trip; depending on the prediction, the scheduling (304), when during travel the vehicle (100) is to be controlled in the first mode and when during the journey the vehicle (100) is to be controlled in the second mode, wherein the first mode allows increased discharge of the traction battery energy as permitted by the second mode, and wherein the planning requires that the energy of the traction battery at the end of the journey is lower than the energy of the traction battery at the beginning of the journey. A computer program (206) that, when executed on at least one electronic processor, causes at least: Predicting (302) the energy consumption of a vehicle (100) on a journey, the vehicle comprising a traction battery (106), an electric traction motor (108), and an internal combustion engine (104); depending on the prediction, the scheduling (304), when during travel the vehicle (100) is to be controlled in the first mode and when during the journey the vehicle (100) is to be controlled in the second mode, wherein the first mode allows increased discharge of the traction battery energy as permitted by the second mode, and wherein the planning requires that the energy of the traction battery at the end of the journey is lower than the energy of the traction battery at the beginning of the journey.

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