Method of hybrid system control

Abstract

Methods of hybrid system control. Methods of using pre-determined or real-time transmitted route data information to optimize hybrid system efficiency are provided herein. For example, methods for energy recovery within one or more battery packs of a vehicle are provided. In other embodiments, methods of automatically implementing a neutral mode of a vehicle are provided. In further embodiments, methods of pre-loading the state of charge of a battery pack when necessary are provided.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to optimization of hybrid internal combustion engine power units with electrified power units. In particular, the present disclosure relates to the use of predictive road mapping and energy recovery in hybrid power units. BACKGROUND

[0002] Electrification of vehicles is a popular way to save on fuel costs and provide "cleaner" driving and other benefits. Many electrified vehicles are not purely electric, but include hybrid systems that utilize both electrical systems or battery packs and internal combustion engine power. Electrification of commercial vehicles has proven to be more difficult due to the heavy loads of commercial vehicles and the long distances most commercial vehicles travel. For example, battery capacity, the weight of the vehicle, and the electric motors that power such vehicles present greater challenges than the electrification of passenger cars. As a result, hybrid systems continue to play a vital role in the electrification of commercial vehicles.

[0003] Optimization of hybrid systems for large commercial vehicles is desirable. For example, recovery of energy within one or more battery packs in an efficient manner can save on fuel consumption, save on battery costs, and reduce brake hardware costs. Additionally, preloading of the state of charge of the battery pack when necessary can improve transportation efficiency and reduce trip time. Such applications can also be provided for passenger cars and other vehicles. SUMMARY

[0004] Methods for optimizing hybrid system efficiency using predetermined or real-time delivered route data information are provided herein. For example, methods for energy recovery within one or more battery packs of a vehicle are provided. In other embodiments, methods for automatically implementing a neutral mode of a vehicle are provided. In further embodiments, methods for preloading the state of charge of a battery pack when necessary are provided.

[0005] According to an exemplary embodiment of the present disclosure, a method of estimating a braking event is disclosed. The method includes providing route related information to a route data processor of a vehicle, identifying an upcoming uphill event or an upcoming downhill event from the route related information, estimating a speed of the vehicle at any given time during the upcoming uphill event or the upcoming downhill event based on at least one of a current speed of the vehicle, a grade of the upcoming uphill event or a grade of the upcoming downhill event, a length of the upcoming uphill event or a length of the upcoming downhill event, and a machine mass estimate of the vehicle, comparing the estimated vehicle speed to a predetermined speed threshold, and estimating an occurrence of a braking event.

[0006] The method can be performed using an intelligent coast management processor. The predetermined speed threshold can be a mandatory route speed limit. The predetermined speed threshold can be a speed set on a cruise controller of the vehicle. The braking event can not occur when the estimated vehicle speed is below the predetermined speed threshold. The method can further include issuing a command to a transmission control unit of the vehicle to shift the vehicle into neutral mode. The braking event can be estimated to be performed by an engine brake of the vehicle when the estimated vehicle speed is above the predetermined speed threshold. The braking event can be estimated to be performed by a wheel brake of the vehicle when the estimated vehicle speed is above the predetermined speed threshold. The method can further include performing energy recovery of at least one battery pack of the vehicle to increase a state of charge of the at least one battery pack prior to the actual occurrence of the braking event. The method can further include engaging an engine brake of the vehicle prior to the estimated occurrence of the braking event. The step of charging the at least one battery pack can begin prior to the occurrence of the terrain event. The information related to the route can be provided to the route data processor by a global positioning system.

[0007] According to another exemplary embodiment of the present disclosure, a method of managing vehicle speed is disclosed. The method includes providing information related to a route to a route data processor of a vehicle; identifying a predetermined speed threshold; identifying an upcoming terrain event from the information related to the route; estimating a speed of the vehicle at any given time during the upcoming terrain event based on at least one of a current speed of the vehicle, a grade of the upcoming terrain event, a length of the upcoming terrain event, and a machine mass estimate of the vehicle; comparing the estimated vehicle speed to the predetermined speed threshold; and reducing the speed of the vehicle by charging at least one battery pack of the vehicle with recovered energy.

[0008] The step of charging the at least one battery pack can occur prior to the topographical event. The information related to the route can be provided to the route data processor by a global positioning system. The information related to the route can be provided to the route data processor by a controller. The topographical event can be an uphill event. The predetermined speed threshold can be determined by using a vehicle cruise controller to set a desired speed value. The method can further include identifying a drivability condition of the uphill event. The method can further include comparing a state of charge of the at least one battery pack to a predetermined threshold of state of charge values. The estimated speed can be less than the predetermined speed threshold. The method can further include determining whether the vehicle is in an economy mode, a power mode, or a balance mode. When the vehicle is in the balance mode, the estimated speed can be less than a difference between the predetermined speed threshold and a calibrated value of the at least one battery pack. The topographical event can be a downhill event. The method can further include using a predictive cruise control processor to reduce a speed of the vehicle by a predetermined amount to a second speed when the estimated speed of the vehicle is greater than the predetermined speed threshold. The method can further include comparing the second speed to the predetermined speed threshold, wherein charging the at least one battery pack of the vehicle reduces the second speed of the vehicle to a third speed of the vehicle. The method can further include engaging an engine brake of the vehicle to reduce the speed of the vehicle. The step of charging the at least one battery pack can occur during the topographical event.

[0009] According to yet another embodiment of the present disclosure, a method of managing vehicle speed is disclosed. The method includes providing information related to a route to a route data processor of a vehicle, identifying a predetermined speed threshold, identifying an upcoming downhill event from the information related to the route, estimating a speed of the vehicle at any given time during the upcoming downhill event from at least one of a current speed of the vehicle, a grade of the upcoming downhill event, a length of the upcoming downhill event, and a machine mass estimate of the vehicle, comparing the estimated speed of the vehicle to the predetermined speed threshold, and selectively issuing a neutral command to a transmission control unit of the vehicle to cause the vehicle to enter a neutral mode, wherein the estimated speed of the vehicle is less than the predetermined speed threshold.

[0010] The information related to the route can be provided to the route data processor by a global positioning system. The information related to the route can be provided to the route data processor by a controller. The neutral command can be issued during the downhill event.

[0011] Other features and advantages of the present disclosure will become apparent to those skilled in the art upon consideration of the following detailed description of illustrative embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0012] The foregoing and other features and advantages of the present disclosure, as well as the manner in which the same are attained, will become more fully apparent from the following description when reference is made to the accompanying drawings, in which:

[0013] Figure 1 is a flowchart illustrating a first example method of the present disclosure for controlling vehicle speed and energy recovery within a hybrid powertrain during a downhill event using an intelligent coast management processor of the present disclosure, and a second example method of the present disclosure for controlling vehicle speed during a downhill event using a predictive cruise control processor of the present disclosure;

[0014] Figure 2 is a graphical representation of the first example method of Figure 1 wherein vehicle speed is controlled during a downhill event using a neutral mode;

[0015] Figure 3 is a graphical representation of the first example method of Figure 1 or the second example method of Figure 1 wherein the vehicle is decelerated by engaging the vehicle’s engine brake, by recovery of energy, or by utilizing a predictive cruise control processor of the present disclosure;

[0016] Figure 4 is a graphical representation of the first example method of Figure 1 or the second example method of Figure 1 wherein the vehicle is initially decelerated by engaging the vehicle’s engine brake, by recovery of energy, or by utilizing a predictive cruise control processor of the present disclosure prior to the start of a downhill event;

[0017] Figure 5 is a flowchart illustrating an example method of the present disclosure for preloading one or more battery packs of a vehicle having a hybrid powertrain prior to the start of an uphill event;

[0018] Figure 6 is a graphical representation of the example method of Figure 5 wherein one or more battery packs of a vehicle are preloaded prior to the start of an uphill event;

[0019] Figure 7 is a comparative graphical representation of several aspects of vehicle operation between a vehicle utilizing predictive energy recovery and a vehicle utilizing brake energy recovery;

[0020] Figure 8A is a graphical representation of a vehicle utilizing brake energy recovery, and specifically a graphical representation of the relative amount of time spent by the vehicle charging, braking, and motoring during a downhill event;

[0021] Figure 8B is a plot of a vehicle utilizing predictive energy recovery, and specifically the relative amount of time spent by the vehicle charging, braking, and engine driving during a downhill event;

[0022] Figure 9 is a comparative plot of state of charge for a vehicle using predictive energy recovery compared to a vehicle using brake energy recovery; and

[0023] Figure 10 is a comparative plot of fuel consumption improvement between a vehicle using predictive energy recovery and a vehicle using brake energy recovery.

[0024] In the drawings, like reference numerals refer to like parts throughout the various views. Although the drawings illustrate embodiments of various features and components according to the present disclosure, the drawings are not necessarily drawn to scale and certain features can be exaggerated to illustrate and explain particular aspects of the present disclosure. The examples set forth herein illustrate various implementations of the present disclosure and are not intended to be exhaustive or limited to the precise forms set forth in the following detailed description. DETAILED DESCRIPTION

[0025] Reference is now made to the following descriptions in conjunction with the accompanying drawings, in which like reference numbers refer to like elements throughout the several views. The exemplary embodiments disclosed herein are not intended to be exhaustive or limited to the precise form disclosed in the following detailed description. Rather, the exemplary embodiments are chosen and described so that others skilled in the art can utilize their teachings.

[0026] Global Positioning Systems ("GPS") are commonly used during operation of vehicles for both short and long trips. In many vehicles, GPS processors have been integrated into the vehicle operating system. GPS can assist in predicting upcoming road conditions based on a route input by a user or driver. For example, a user or driver can input a start location and an end location and allow the GPS to automatically populate a route that best suits the user's needs and / or provide the user or driver with multiple selectable routes. For example, a user can choose between a fastest route, a shortest route, a route comprised of major highways, a route without major highways, a route including toll roads, a route not including toll roads, etc. In some embodiments, the GPS can detect the location of the vehicle without requiring manual input by the user or driver. The user or driver can utilize the GPS, with or without a predetermined route, to assist in predicting upcoming road conditions. Road conditions can include, but are not limited to, uphill events, downhill events, curve events, turn events, traffic events (including accidents, stalled vehicles, emergency vehicles, etc.), and the presence of congested or uncongested traffic. Such operations can include predictive road mapping. Vehicle systems can utilize predictive road mapping to optimize the operational efficiency of the vehicle. Such methods are further disclosed herein. In other embodiments, a route parameter manager block can be utilized that integrates map data with GPS sensors.

[0027] Further, many commercial vehicles frequently and repeatedly cycle through a drive having the same route or loop, sometimes several times a day. Such commercial vehicles can include transit vehicles and delivery vehicles. For example, a transit bus can have a fixed drive cycle that includes an exact route or exact loops that repeat to form a route and a schedule of stop times set by the responsible transit authority. Thus, the route characteristics or statistics of the myriad of drive cycles can be defined by associating these route characteristics with a known route identification reference, such as a particular route number. The route identification reference can be used to reference information such as the distance of individual loops of the route, the number of loops of the route per day or other unit of time, the number of opportunities for battery boost charging in each loop, the number of scheduled stops in each loop, the distance to and between stops, the nominal total energy required by the vehicle to complete an individual loop, elevation ranges, route surface ratings, route surface types, maximum speed limits, minimum speed limits, maximum route travel times, traffic conditions, and other statistics.

[0028] For hybrid vehicle systems, many of these route features can be provided to and stored in the hybrid controller. With relatively minimal computer memory overhead, such route feature statistics can be pre-programmed into the hybrid controller so that all operations required to adjust the decisions further identified and discussed herein are performed, for example, by inputting the current route identification reference via an operator interface or via the controller. Further information regarding the storage of route features and route identifications can be found in U.S. Patent Application Publication No. 2018 / 0134275A1, filed November 15, 2017, entitled “HYBRID VEHICLE DRIVE CYCLE OPTIMIZATION BASED ON ROUTE IDENTIFICATION” by Books et al., the disclosure of which is incorporated herein by reference in its entirety.

[0029] like Figure 1 As shown, a method 100 for determining vehicle operation in response to an upcoming downhill event is illustrated using intelligent coasting management. For an upcoming downhill event, the GPS processor 102 and / or controller 104 transmits information related to the vehicle's route to the vehicle's route data processor 106, which identifies the upcoming downhill event 114 based on the information received from the GPS processor 102 and / or controller 104. The route data processor 106 further identifies a speed limit 126 for the route via the information received from the GPS processor 102 and / or controller 104 to ensure vehicle compliance as further described herein.

[0030] Additionally, during vehicle operation, the machine mass estimate 108 or vehicle mass is calculated to determine the gross vehicle weight 110 of the vehicle in operation. The gross vehicle weight may include the vehicle chassis, body, engine, engine coolant, fuel, accessories, driver, other parts of the vehicle, and the weight of any cargo carried by the vehicle (including but not limited to articles, equipment, passengers, or anything attached to or supported by the vehicle). The vehicle also includes internal brakes or engine brakes 112.

[0031] The route data processor 106 communicates information related to upcoming downhill events 114 and speed limits 126 of the route to the intelligent coast management processor 116. In one embodiment, the speed limits 126 can be speed limits enforced by local, state, regional, or federal law. In another embodiment, the speed limits 126 can also be predetermined limits set for travel safety or any other goal. Other information related to upcoming downhill events 114 communicated by the route data processor 106 can include route grade, length of the hill event, or any other information related to the downhill event 114. The intelligent coast management processor 116 also receives the vehicle gross weight 110 of the vehicle calculated from the machine mass estimate 108, data related to the efficiency of the engine brake 112, and / or any potential vehicle speed change rate due to use of the engine brake 112. In performing intelligent coast management, the processor 116 is configured to perform processing to determine the need for the vehicle to enter the neutral coast mode, the engine brake mode, or the wheel brake mode to achieve a desired speed during the downhill event 114 from the information received from each of the route data processor 106, the machine mass estimate 108, and the engine brake 112.

[0032] Still referring to the method 100 of Figure 1 The intelligent coast management processor 116 can implement a vehicle speed model to estimate the vehicle speed throughout the downhill event 114. For example, the intelligent coast management processor 116 can use a calculation consisting of the current speed of the vehicle, the route grade, gravity, the vehicle mass, and engine drive (i.e., friction generated by the engine when the downhill motion is controlled by gravity but the engine is still operating) to calculate an estimated speed of the vehicle at any given time during the upcoming downhill event 114. This estimated speed is communicated to block 138 for use with the intelligent coast management predictive controller, as discussed further herein.

[0033] The user or driver of the vehicle can select whether to implement intelligent coast management. For example, as discussed above, the user or driver can select a mode of operation of the vehicle that implements intelligent coast management. In another embodiment, the user or driver can select a mode of operation of the vehicle that does not implement intelligent coast management. In another embodiment, the user or driver can select a mode of operation of the vehicle that implements intelligent coast management for some downhill events and not for others. In another embodiment, the user or driver can select a mode of operation of the vehicle that implements intelligent coast management for some downhill events and not for others based on the length of the downhill event, the grade of the downhill event, the speed limit of the route, or any other factor. Figure 1As shown, the intelligent coast management processor 116 communicates with the transmission control unit 128 such that the transmission control unit 128 can transition the vehicle to neutral mode upon receiving a neutral command 130 from the intelligent coast management processor 116. Prior to implementing the neutral command 130, the intelligent coast management processor 116 determines whether the neutral mode is required. If the neutral mode is zero, as shown in block 132, then the user or driver of the vehicle has disabled the neutral mode, and the method 100 ends at block 122. If the neutral mode is not disabled at block 132, i.e., if the neutral mode is not equal to zero, then the intelligent coast management processor 116 determines the mode of the intelligent coast management predictive controller at block 136. For example, if an overspeed event is not estimated, then the method 100 ends at block 122.

[0034] If an overspeed event is estimated, then the vehicle speed model is used at block 138 to compare the estimated speed of the vehicle during the downhill event 114 with the speed limit 126 communicated by the route data processor 106 at block 134 to determine whether the estimated speed of the vehicle during the downhill event 114 will exceed the speed limit 126. If the estimated speed is less than the speed limit, then the intelligent coast management processor 116 issues a neutral command 130 to the transmission control unit 128. The transmission control unit 128 transitions the vehicle to neutral mode, and the method 100 ends at block 124. If the estimated speed is greater than or equal to the speed limit 126, then energy recovery is enabled at block 144 to charge one or more battery packs within the vehicle, as shown in block 146, which also begins to slow the vehicle down. After power recovery begins, the intelligent coast management processor can perform additional vehicle speed estimation and comparison according to the speed of the vehicle. If a new estimation is performed and the new estimated speed is greater than or equal to the speed limit 126, then the intelligent coast management processor 116 also engages the engine brake 112 to reduce the vehicle speed by a predetermined amount (e.g., about 1 km / h, about 3 km / h, about 5 km / h, or other amount) at block 140. If a new estimation is not performed, then the intelligent coast management processor 116 also engages the engine brake 112 to reduce the vehicle speed by a predetermined amount at block 140, which correspondingly enables energy recovery at block 144.

[0035] The intelligent coast management processor 116 can then calculate another estimated speed of the vehicle according to the reduced vehicle speed. The new estimated vehicle speed is then compared to the speed limit 126 at block 142. If the new estimated vehicle speed is still above the speed limit 126, then energy recovery continues at block 144, and a wheel brake event is expected to be initiated by the user or driver to slow the vehicle during the downhill event 114. If the new estimated vehicle speed is less than the speed limit 126, then the method 100 ends at block 150. Energy recovery can or can not continue.

[0036] Still referring to Figure 1 A method 200 of using a predictive cruise controller to determine vehicle operation for an upcoming downhill event is also disclosed. Similar to method 100, the GPS processor 102 and / or controller 104 communicates information related to the vehicle route to the route data processor 106 of the vehicle, which identifies terrain events of the route based on the information received from the GPS processor 102 and / or controller 104. The route data processor 106 also identifies speed limits 126 of the route via the information received from the GPS processor 102 and / or controller 104 to ensure smoothness of the vehicle. Additionally, as described above, the machine mass estimate 108 is calculated during operation of the vehicle to determine the vehicle gross weight 110 of the vehicle in operation.

[0037] The route data processor 106 communicates information related to upcoming terrain events of the route and speed limits 126 of the route to the predictive cruise control processor 118, similar to the communication to the intelligent coast management processor 116 in method 100 described above. The information communicated by the route data processor 106 regarding upcoming terrain events can include an identification of a substantially flat terrain, a pre- hill event, a hill event, a pre-downhill event, or a downhill event. Further communicated information can include route grade, length of hill event, or any other information. The predictive cruise control processor 118 also receives the vehicle gross weight 110 of the vehicle calculated from the machine mass estimate 108. The predictive cruise control processor 118 also receives the current set speed of the vehicle from the cruise controller 148 of the vehicle.

[0038] The predictive cruise control processor 118 is configured to operate according to five different modes of predictive cruise control. When the vehicle is operating along a substantially flat terrain, the predictive cruise control processor 118 is in mode A configuration. When the vehicle is in a pre-hill position, for example, when the route data processor 106 identifies an upcoming hill event on the vehicle route, the predictive cruise control processor 118 is in mode B configuration. When the vehicle is in a hill event position, the predictive cruise control processor 118 is in mode C configuration. When the vehicle is in a pre-downhill position, for example, when the route data processor 106 identifies an upcoming downhill event on the vehicle route, the predictive cruise control processor 118 is in mode D configuration. When the vehicle is in a downhill event position, the predictive cruise control processor 118 is in mode E configuration.

[0039] Referring to Figure 1As described above, when the route data processor 106 identifies an upcoming downhill event, the predictive cruise control processor 118 enters the Mode D configuration, as shown in block 127. In the Mode D configuration, the predictive cruise control processor 118 can implement a vehicle speed model to estimate the vehicle speed throughout the downhill event 114, as shown in block 129. For example, the predictive cruise control processor 118 can use a calculation consisting of the speed set by the cruise controller 148, the route grade, gravity, vehicle mass, and engine drag (i.e., the friction generated by the engine when the downhill motion is controlled by gravity but the engine is still running) to calculate the estimated speed of the vehicle at any given time during the upcoming downhill event 114.

[0040] The predictive cruise control processor 118 then compares the estimated speed of the vehicle during the downhill event 114 to the speed limit 126 communicated by the route data processor 106 at block 131 to determine whether the estimated speed of the vehicle during the downhill event 114 will exceed the speed limit 126. If the estimated speed of the vehicle during the downhill event 114 will exceed the speed limit 126, then the predictive cruise control processor 118 changes the set speed of the cruise controller 148 by a predetermined amount (e.g., about 1 km / h, about 3 km / h, about 5 km / h, or other amount) at block 140. The predictive cruise control processor 118 can then calculate a new estimated speed of the vehicle according to the reduced vehicle speed. The new estimated vehicle speed is then compared to the speed limit 126 again at block 142. If the new estimated vehicle speed is still above the speed limit 126, then energy recovery is enabled to charge one or more battery packs within the vehicle at block 144, as shown in block 146, and also causes the vehicle to decelerate in anticipation of an engine braking event or a wheel braking event initiated by the user or driver, thereby decelerating the vehicle during the downhill event 114. If the estimated vehicle speed is less than the speed limit 126, then the method 200 ends at block 150.

[0041] Referring now to Figures 2 to 4 , the predictive control of energy recovery described in the method 100 and the method 200 is shown. With particular reference to Figure 2 , an exemplary downhill event is shown by the terrain profile 225. A satellite system or GPS 227 communicates with a controller 229 of a vehicle 231 describing an upcoming downhill event. In one embodiment, the controller 229 can communicate with the GPS processor 102 (FIG. 1) via a wired or wireless connection 233 to receive the route data 104 (FIG. 1) and the speed limit 126 (FIG. 1) from the GPS processor 102 (FIG. 1). The controller 229 can also communicate with the predictive cruise control processor 118 (FIG. 1) via a wired or wireless connection 235 to receive the estimated vehicle speed 128 (FIG. 1) from the predictive cruise control processor 118 (FIG. 1). Figure 1A similar GPS processor is used to receive GPS information. In another embodiment, controller 229 may include a GPS processor. In yet another embodiment, controller 229 can identify an upcoming downhill event from memory without using GPS 227. Line 233 shows the predetermined speed for engaging the engine brake of vehicle 231 and / or the speed limit 126 of vehicle 231. Figure 1 Line 235 shows the speed of vehicle 231 when it is coasting along terrain profile 225 in gear. Line 237 shows the speed of vehicle 231 when it is coasting along terrain profile 225 in neutral mode.

[0042] For example, still refer to Figure 2 According to method 100 ( Figure 1 ), Route Data Processor 106 ( Figure 1 ) Identify the upcoming downhill event indicated by terrain profile 225 and send it to the intelligent gliding management processor 116 ( Figure 1 The downhill event is transmitted. If the intelligent coasting management processor 116 ( Figure 1 If the intelligent coasting management processor 116 is working and neutral mode is enabled, then the intelligent coasting management processor 116 ( Figure 1 It can be determined that vehicle 231 will not exceed the speed limit 126 shown by 233. Figure 1 It travels at a speed of ) and by sending a signal to the transmission control unit 128 ( Figure 1 Send neutral command 130 ( Figure 1 This puts vehicle 231 into neutral mode. By comparing line 233 with line 235, it can be seen that when in neutral coasting mode, vehicle 231 completes terrain contour 225 without reaching or exceeding speed limit 233.

[0043] Now for reference Figure 3 This illustrates another exemplary terrain profile 250 associated with a downhill event. (See above regarding...) Figure 2 As described, the satellite system or GPS 227 communicates with the controller 229 of the vehicle 231 to describe an upcoming downhill event. In one embodiment, the controller 229 may communicate with the GPS processor 102 ( Figure 1 A similar GPS processor is used to receive GPS information. In another embodiment, controller 229 may include a GPS processor. In yet another embodiment, controller 229 can identify an upcoming downhill event from memory without using GPS 227. Line 252 shows a predetermined speed at which the engine brakes of vehicle 231 are engaged and / or a speed limit 126 for vehicle 231. Figure 1). Line 254 shows the speed of vehicle 231 when coasting along terrain profile 250 with no predictive energy recovery. Line 256 shows the speed of vehicle 231 when coasting along terrain profile 250 with predictive energy recovery.

[0044] For example, still referring to Figure 3 , according to method 100( Figure 1 ), route data processor 106( Figure 1 ) identifies the upcoming downhill event shown by terrain profile 250 and communicates the downhill event to intelligent coast management processor 116( Figure 1 ). If intelligent coast management processor 116( Figure 1 ) is active and engages intelligent coast management predictive controller 134( Figure 1 ), intelligent coast management processor 116( Figure 1 ) can determine that vehicle 231 will reach a speed higher than the engine brake limit speed shown by line 252. Intelligent coast management processor 116( Figure 1 ) can also determine that the rate of engine brake in combination with the grade of the terrain only requires the engine brake to be activated when the vehicle reaches such a speed. In this case, intelligent coast management processor 116( Figure 1 ) enables energy recovery at engine brake limit line 252 to slow vehicle 231 down, and correspondingly engages engine brake 112( Figure 1 ) to further slow the vehicle down. It can be seen by comparing line 256 to line 252 that vehicle 231 completes terrain profile 250 without reaching or exceeding the speed limit 252 when in energy recovery mode.

[0045] According to method 200( Figure 1 ), route data processor 106( Figure 1 ) can communicate the upcoming downhill event shown by terrain profile 250 to predictive cruise control processor 118( Figure 1 ). If cruise controller 148( Figure 1 ) remains at the current setting, predictive cruise control processor 118( Figure 4 ) can determine that vehicle 231 will reach a speed higher than the engine brake limit speed shown by line 252. In this case, predictive cruise control processor 118( Figures 2 to 3 ) can change the cruise controller setting of vehicle 231 to slow the vehicle down during the downhill event to prevent overspeeding, while also enabling energy recovery mode.

[0046] Referring now to Figure 1This illustrates another exemplary terrain profile 275 associated with a downhill event. (See above regarding...) Figure 1 The satellite system or GPS 227 communicates with the controller 229 of the vehicle 231 to describe an upcoming downhill event. In one embodiment, the controller 229 may communicate with the GPS processor 102 (… Figure 4 A similar GPS processor is used to receive GPS information. In another embodiment, controller 229 may include a GPS processor. In yet another embodiment, controller 229 can identify upcoming downhill events from memory without using GPS 227. Line 277 shows a predetermined speed at which the engine brakes of vehicle 231 are engaged and / or a speed limit 126 for vehicle 231. Figure 1 Line 279 shows the wheel brake limit of vehicle 231, or the speed at which the driver engages the wheel brakes to successfully complete a downhill event. Line 281 shows the speed of vehicle 231 when it coasts along terrain profile 275 in gear without predictive energy recovery. Line 283 shows the speed of vehicle 231 when it coasts along terrain profile 275 in gear and with predictive energy recovery.

[0047] For example, still refer to Figure 1 According to method 100 ( Figure 1 ), Route Data Processor 106 ( Figure 1 ) Identify the upcoming downhill event indicated by the terrain profile 275 and send it to the intelligent gliding management processor 116 ( Figure 1 The downhill event is transmitted. If the intelligent coasting management processor 116 ( Figure 1 ) is working and is integrated with the intelligent gliding management predictive controller 134 ( Figure 1 If the intelligent coasting management processor determines that vehicle 231 will reach a speed higher than the engine brake limit speed shown in line 277, then the intelligent coasting management processor 116 ( Figure 1 It can also determine the rate requirement of the engine brake in conjunction with the slope of the terrain for an early engine braking event, and activate energy recovery at engine brake limit line 252 to decelerate vehicle 231 before the start of a downhill event, and correspondingly engage engine brake 112. Figure 1 This further decelerates the vehicle. By comparing line 281 with line 283, it can be seen that when the vehicle 231 is in energy recovery mode, it completes the terrain profile 275 without reaching or exceeding the speed limit 277; without predictive control or early recovery mode, the driver or user would have to engage the wheel brakes to successfully complete the downhill event (as shown in line 281) while still ignoring the speed limit 277.

[0048] According to method 200 ( Figure 1 ), Route Data Processor 106 ( Figure 1 It can also send data to the predictive cruise control processor 118 ( Figure 1 The cruise controller 148 transmits information about an upcoming downhill event as shown by terrain profile 275. Figure 5 If the current settings are maintained, then the predictive cruise control processor 118 ( Figure 1 It can be determined that vehicle 231 will reach a speed higher than the engine brake limit speed shown by line 277. In this case, the predictive cruise control processor 118 ( Figure 1 It can change the cruise control settings of vehicle 231 to slow down the vehicle before a downhill event occurs to prevent speeding, while also enabling the energy recovery mode.

[0049] Now for reference Figure 1 A method 500 for determining vehicle operation in response to an upcoming uphill event using a predictive cruise controller is disclosed. Similar to the discussion above. Figure 1 In method 200, the GPS processor 502 and / or controller 504 transmit information related to the vehicle's route to the vehicle's route data processor 50. The route data processor 506 identifies terrain events 558 on the route and transmits the route information to the predictive cruise control processor 518. Additionally, during vehicle operation, a machine mass estimate 508 is calculated to determine the total vehicle weight 510 of the vehicle in operation, and the machine mass estimate 508 is transmitted to the predictive cruise control processor 518. The predictive cruise control processor 518 also receives the vehicle's current set speed from the vehicle's cruise controller 548.

[0050] You can find information about it above. Figure 1 More information relating to the vehicle's GPS processor 502, controller 504, predictive cruise controller 518, machine mass estimate 508, vehicle gross weight 510, and speed limit 526 can be found in the discussion of methods 100 and 200. That is, GPS processor 102 ( Figure 1 ), controller 104 ( Figure 1 Predictive cruise controller 118 Figure 6 Machine quality estimate 108 ( Figure 5 ), Total vehicle weight 110 ( Figure 6 ) and speed limit 126 ( Figure 5 It includes the same functions and features as the GPS processor 502, controller 504, predictive cruise controller 518, machine mass estimate 508, vehicle gross weight 510, and speed limit 526 of the vehicle discussed herein.

[0051] When the route data processor 506 identifies an upcoming hill event 558, at block 552, the predictive cruise control processor 518 identifies its current mode configuration. If the predictive cruise control processor 518 is in mode A, i.e., if the vehicle is traveling along generally flat terrain, the predictive cruise control processor moves to block 554. If the predictive cruise control processor 518 is not in mode A, the method 500 ends at block 556. At block 554, the predictive cruise control processor 518 uses the information identified by the route data processor 506 to determine whether the predictive cruise control processor 518 will enter mode B, i.e., whether the vehicle is entering a pre- hill range. If the predictive cruise control processor 518 will not enter mode B, then the method 500 ends at block 556. If the predictive cruise control processor 518 will enter mode B during the pre-hill event, the drivability of the upcoming hill event 558 will be analyzed by the predictive cruise control processor 518.

[0052] The predictive cruise control processor 518 can use a calculation consisting of the speed set by the cruise controller 548, the route grade, gravity, vehicle mass, and other factors to calculate an estimated speed of the vehicle at any given time during the upcoming hill event 558. The predictive cruise control processor 518 then determines whether the vehicle can maintain the speed set by the cruise controller 548 throughout the hill event 558 without additional power. If the estimated speed of the vehicle during the hill event 558 is low, i.e., if the estimated speed of the vehicle during the hill event 558 is less than the speed set by the cruise controller 548, at block 560, the predictive cruise control processor 118 identifies that the vehicle drivability condition is low for the upcoming hill event 558.

[0053] At block 562, the state of charge of the vehicle's battery pack is compared to a threshold or predetermined value. If the state of charge of the battery pack is equal to or greater than the threshold, the method 500 ends at block 556. If the state of charge of the battery pack is less than the threshold, at block 564, the predictive cruise control processor 518 determines the vehicle state selected by the driver. For example, the user or driver of the vehicle can selectively choose between an economy mode state 566, a balance mode state 568, and a power mode state 570. If the user or driver of the vehicle has placed the vehicle in the economy mode state 566, the method 500 ends at block 572. If the user or driver of the vehicle has placed the vehicle in the balance mode state 568 or the power mode state 570, at block 573, the predictive cruise control processor 518 enables the estimated vehicle speed to determine whether a pre-load recovery event should occur.

[0054] For example, if the user or driver of the vehicle has placed the vehicle in a power mode state 570, at block 582 the predictive cruise control processor 518 compares the estimated speed of the vehicle throughout the uphill event 558 to the speed set by the cruise controller 548. If the estimated speed is less than the speed set by the cruise controller 548, energy recovery is enabled at block 576 to pre-load or pre-charge the battery pack of the vehicle at block 578. If the estimated speed is equal to or greater than the speed set by the cruise controller 548, the method 500 ends at block 577. If the user or driver of the vehicle has placed the vehicle in a balance mode state 568, at block 580 the predictive cruise control processor 518 compares the estimated speed of the vehicle throughout the uphill event 558 to the speed set by the cruise controller 548, while also considering the calibration value of the battery pack of the vehicle. If the estimated speed is less than the difference between the speed set by the cruise controller 548 and the calibration value of the battery pack, energy recovery is enabled at block 576 to pre-load or pre-charge the battery pack of the vehicle at block 578. If the estimated speed is equal to or greater than the speed set by the cruise controller 548, the method 500 ends at block 575.

[0055] Referring now to Figure 5 , predictive control of the method 500 for an uphill event is shown. An exemplary uphill event is shown by the terrain profile 600. A satellite system or GPS 602 communicates with a controller 604 of a vehicle 606 describing an upcoming downhill event. In one embodiment, the controller 604 can receive GPS information via a GPS processor similar to the GPS processor 502 Figure 5 ) of the vehicle 606. In another embodiment, the controller 604 can include a GPS processor. In yet another embodiment, the controller 604 can identify an upcoming uphill event from memory without using the GPS 602. Line 608a shows the speed of the vehicle 606 during the uphill event 600 without using pre-charge control. Line 608b shows the speed of the vehicle 606 during the uphill event 600 with pre-charge control. Line 610a shows the state of charge of the battery pack of the vehicle 606 during the uphill event 606 without using pre-charge control. Line 610b shows the state of charge of the battery pack of the vehicle 606 with pre-charge control. Line 612a shows the engine power of the vehicle 606 without using pre-charge control. Line 612b shows the engine power of the vehicle 606 with pre-charge control.

[0056] For example, still referring to Figure 5 , according to the method 500 Figure 5 ), the route data processor 506 Figure 6identifies an upcoming uphill event shown by the terrain profile 600 and communicates the uphill event to the predictive cruise control processor 518 Figure 5 determines that for the upcoming uphill event 600 the vehicle drivability state is low and the state of charge of the battery pack is below a threshold amount, the predictive cruise control processor 518 Figure 6 determines which state of the vehicle 606 the driver has selected to enable. Figure 5

[0057] In the embodiment shown in Figure 5 the economy mode state 566 Figure 5 has been selected, therefore no pre-charge control is utilized. In other embodiments, the vehicle 606 can not utilize the function because the driver or user has selected not to enable predictive cruise control, or because predictive cruise control has failed or is otherwise unavailable. In such embodiments, the vehicle 606 operates according to lines 608a, 610a and 612a throughout the uphill event 600.

[0058] In the embodiment shown in Example 1 the balance mode state 568 Figure 7 or the power mode state 570 Figure 7 has been selected. In such embodiments, the predictive cruise control processor 518 Figure 1 enables energy recovery prior to the occurrence of the uphill event 600. As shown by line 610b, the state of charge of the battery pack is increased prior to the occurrence of the uphill event 600, thereby providing the vehicle 606 with potential power to complete the uphill event 600 smoothly without multiple gear shifting events. Further, as shown by the comparison of line 608a to line 608b, the use of pre-charge control results in a more consistent speed throughout the uphill event 600.

[0059] Figure 1

[0060] Referring to Figure 1 ​FIG. 3 shows a comparison of predicted performance energy recovery and brake energy recovery. Chart 300 shows the elevation of the vehicle during a downhill event, where the position between position 4.4 and position 5.2 on the x-axis represents the position of the vehicle, and the y-axis represents the elevation in meters. Line 302 represents the elevation of the vehicle as a function of vehicle position. Chart 310 shows the speed of the vehicle represented by the elevation in chart 300 during the downhill event, where the position between position 4.4 and position 5.2 on the x-axis represents the position of the vehicle, and the y-axis represents the speed of the vehicle in kilometers per hour (km / h). Line 312 represents the actual speed of the vehicle as a function of vehicle position. Line 314 represents the set or preferred speed of the vehicle as a function of vehicle position. Line 316 represents the speed of the vehicle using predicted performance energy recovery, which is described in further detail herein. Chart 320 shows the intelligent coasting management mode of the vehicle represented by the elevation in chart 300 during the downhill event, where the position between position 4.4 and position 5.2 on the x-axis represents the position of the vehicle, and the y-axis represents the four modes of the intelligent coasting management system. The intelligent coasting management system is configured to selectively be in one of the four modes: i.e., OFF, REQ, ACK, and ACT. Line 322 represents the mode of the intelligent coasting management system as a function of vehicle position.

[0061] Still referring to Figure 7 Chart 330 shows the motor generator power of the vehicle represented by the elevation in chart 300 during the downhill event, where the position between position 4.4 and position 5.2 on the x-axis represents the position of the vehicle, and the y-axis represents the motor generator power in kilowatts (“kw”). Line 332 represents the motor generator power as a function of vehicle position. Chart 340 shows the state of charge of the battery pack of the vehicle represented by the elevation in chart 300 during the downhill event, where the position between position 4.4 and position 5.2 on the x-axis represents the position of the vehicle, and the y-axis represents the percentage of battery charge. Line 342 represents the state of charge of the battery pack as a function of vehicle position. Chart 350 shows the braking events of the vehicle represented by the elevation in chart 300 during the downhill event, where the position between position 4.4 and position 5.2 on the x-axis represents the position of the vehicle, and the y-axis represents the brake engagement events. Line 352 represents the engagement of the wheel brakes as a function of vehicle position. Line 354 represents the engagement of the engine brakes as a function of vehicle position.

[0062] Graphs 300, 310, 320, 330, 340, and 350 show, respectively, elevation, vehicle speed, intelligent coasting management system mode, motor generator power, state of charge of the battery pack, and braking events measured within the vehicle during the downhill event represented by the elevation in graph 300 without using predictive energy recovery. In such an example, it can be seen by comparing the state of charge indicator line 342 with the engine brake line 354 at vehicle locations between 4.6 and 4.7 on graph 340 and graph 350 that energy recovery did not begin until a braking event occurred. Then, the same measurements were made using predictive energy recovery, as discussed further herein.

[0063] When the vehicle utilizes predictive energy recovery, braking events are estimated by the methods 100 and 200 discussed above with respect to Example 2 For example, the intelligent coasting management processor 116 Figures 8A to 10 ) and / or the predictive cruise control processor 118 Figures 8A to 10 ) estimate any needed braking events during the downhill event and control vehicle operation accordingly, for example, to begin energy recovery before the braking event actually occurs. Graph 335 is a reproduction of graph 330 with the addition of line 337, which represents motor generator power of the vehicle using predictive energy recovery during the downhill event represented by the elevation in graph 300. Graph 345 is a reproduction of graph 340 with the addition of line 347, which represents state of charge of the battery pack of the vehicle using predictive energy recovery during the downhill event represented by the elevation in graph 300. Graph 355 is a reproduction of graph 350 with the addition of line 357, which represents engine braking events of the vehicle using predictive energy recovery during the downhill event represented by the elevation in graph 300, and line 359, which represents wheel braking events or no braking events in the vehicle using predictive energy recovery during the downhill event represented by the elevation in graph 300.

[0064] It can be seen by comparing line 332 and line 337 in graph 335 that the motor generator of the vehicle using predictive energy recovery used less power throughout almost the entire downhill event compared to the vehicle not using predictive energy recovery. Similarly, comparing line 342 and line 347 in graph 345, the battery pack in the vehicle using predictive energy recovery began to recover energy sooner compared to the vehicle not using predictive energy recovery. As shown by line 347 in graph 345, at the end of the downhill event, the state of charge of the battery pack in the vehicle using predictive energy recovery was much higher, allowing that vehicle to have more potential energy available during the use of that vehicle.

[0065] Still referring to Figure 8A, particularly chart 355, a vehicle using predictive energy recovery does not need to utilize wheel brakes to successfully complete a downhill event, as shown by comparing line 359 to line 352. For example, line 352 represents a wheel brake event that occurs when a vehicle not using predictive energy recovery is between position 4.6 and position 5; however, line 359 remains at the y-axis 0 throughout the entire downhill event for a vehicle using predictive energy recovery. Further, as shown by comparing chart 345 to chart 355, a vehicle using predictive energy recovery does not need to wait for a brake event to occur before energy recovery begins. Instead, predictive energy recovery allows the vehicle to begin recovering energy before the vehicle reaches position 4.6 (line 347), even though an engine brake event does not occur until approximately 4.65 (line 357). In comparison, in a vehicle not utilizing predictive energy recovery, energy recovery does not begin until a brake event occurs, as seen by comparing line 342 to line 354. By comparing line 357 to line 354, the vehicle using predictive energy recovery further utilizes the engine brake to maintain the vehicle at the set speed 314 (shown by line 316 of chart 310), rather than requiring the driver or user to engage the wheel brakes. By beginning energy recovery before any brake event occurs, the vehicle further decelerates during the downhill event, such that relying solely on the engine brake is sufficient to successfully complete the downhill event, thus not requiring wheel brakes.

[0066] Figure 8B

[0067] Referring now to Figure 8A , further results of the comparison use of vehicles are shown. For each of Figure 8B , a vehicle not using predictive energy recovery to complete a downhill event is compared to a vehicle using predictive energy recovery to complete a downhill event. For example, Figure 9 chart 400 of graph 400 shows the relative amount of time a vehicle spends in energy recovery mode 402, brake mode 404, and engine drive mode 406. The vehicle of chart 400 does not utilize a predictive energy recovery system, and spends about 79.13% of the time during the downhill event in energy recovery mode 402, about 11.11% of the time during the downhill event in brake mode 404, and about 9.76% of the time during the downhill event in engine drive mode 406.

[0068] In comparison, Figure 9Chart 410 of FIG. 4 shows the relative amount of time spent by the vehicle in energy recovery mode 412, braking mode 414, and engine drive mode 416. The vehicle of chart 410 utilizes a predictive energy recovery system, and spends about 80.45% of the time during the downhill event in energy recovery mode 412, about 10.07% of the time during the downhill event in braking mode, and about 9.48% of the time during the downhill event in engine drive mode 416. By comparing Figure 10 chart 400 of FIG. 4 to ​ chart 410 of FIG. 4, it can be concluded that a vehicle using predictive energy recovery spends less time braking during a downhill event, and spends more time charging the vehicle’s battery pack.

[0069] The impact of this difference in the relative time spent in each mode can be seen in ​ FIG. 5. ​ Chart 420 of FIG. 4 compares the state of charge of the battery pack of the various vehicles completing the downhill event. The y-axis represents the level of state of charge of the battery pack, while the x-axis represents the position of the vehicle during the downhill event. Line 422 represents the state of charge of the battery pack of a vehicle not utilizing predictive energy recovery at any given position of the vehicle. Line 424 represents the state of charge of the battery pack of a vehicle utilizing predictive energy recovery at any given position of the vehicle. Comparing line 422 to line 424, it can be seen that a vehicle utilizing predictive energy recovery begins to recover energy within the vehicle’s battery pack earlier than a vehicle not utilizing predictive energy recovery. As a result, a vehicle utilizing predictive energy recovery remains in energy recovery mode for a longer period of time, resulting in a higher state of charge of the battery pack during the downhill event. This allows the vehicle to rely on the battery pack to provide power for a longer period of time, resulting in reduced fuel consumption.

[0070] ​ A comparison of fuel consumption between a vehicle utilizing predictive energy recovery and a vehicle not utilizing predictive energy recovery is shown. For example, in a mild hybrid 48V system, when predictive energy recovery is not used, the vehicle fuel consumption improvement is about 4.59%, while when predictive energy recovery is used, the vehicle fuel consumption improvement is about 4.91%. This results in a 0.32% improvement in fuel consumption for a mild hybrid 48V system using predictive energy recovery compared to a mild hybrid 48V system not using predictive energy recovery. Stronger hybrid vehicles can have even higher performance improvements.

[0071] While the application has been described with reference to the example designs, it can be further modified within the spirit and scope of the disclosure. This application is therefore intended to cover any variations, uses, or adaptations of the application using its general principles. Further, this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which the application pertains.

Claims

1. A method of estimating a braking event, the method comprising the steps of: providing a route data processor of a vehicle with information relating to a route; identifying an upcoming uphill event or an upcoming downhill event from the information relating to the route; estimating a speed of the vehicle at any given time during the upcoming uphill event or the upcoming downhill event in dependence on at least one of: a current speed of the vehicle, a gradient of the upcoming uphill event or a gradient of the upcoming downhill event, a length of the upcoming uphill event or a length of the upcoming downhill event, and a machine mass estimate of the vehicle; comparing the estimated vehicle speed to a predetermined speed threshold; estimating an occurrence of a braking event; when there is an upcoming uphill event, if the estimated vehicle speed at any given time during the upcoming uphill event is less than the predetermined speed threshold, recovering energy from at least one battery pack of the vehicle to increase a state of charge of the at least one battery pack prior to the actual occurrence of the uphill event; and when there is an upcoming downhill event, charging the at least one battery pack of the vehicle during the downhill event when the estimated vehicle speed at any given time during the upcoming downhill event is greater than the predetermined speed threshold. the method is performed using an intelligent coasting management processor.

2. The method of claim 1, wherein, the predetermined speed threshold is a mandatory route speed limit.

3. The method of claim 1, wherein, the predetermined speed threshold is a speed set on a cruise controller of the vehicle.

4. The method of claim 1, wherein, issuing a command to a transmission control unit of the vehicle to shift the vehicle into neutral mode.

5. The method of claim 1, further comprising the step of: the braking event is estimated to be performed by an engine brake of the vehicle when the estimated vehicle speed is above the predetermined speed threshold.

6. The method of claim 1, wherein, the braking event is estimated to be performed by a wheel brake of the vehicle when the estimated vehicle speed is above the predetermined speed threshold.

7. The method of claim 1, wherein, engaging an engine brake of the vehicle prior to the estimation of the braking event occurring.

8. The method of claim 1, further comprising: the step of charging the at least one battery pack begins prior to the occurrence of the braking event.

9. The method of claim 1, wherein, the information relating to the route is provided to the route data processor by a global positioning system.

10. The method of claim 1, wherein, 11. A method of managing a speed of a vehicle, the method comprising: providing a route data processor of a vehicle with information relating to a route; identifying a predetermined speed threshold; identifying an upcoming terrain event from the information relating to the route, wherein the terrain event is at least one of an uphill event and a downhill event; estimating a speed of the vehicle at any given time during the upcoming terrain event in dependence on at least one of: a current speed of the vehicle, a gradient of the upcoming terrain event, a length of the upcoming terrain event and a machine mass estimate of the vehicle; comparing the estimated vehicle speed to the predetermined speed threshold; charging at least one battery pack of the vehicle by recovering energy to reduce the speed of the vehicle; ​ when there is an upcoming uphill event, energy recovery of at least one battery pack of the vehicle is performed to increase a state of charge of the at least one battery pack prior to the actual occurrence of the uphill event if the estimated vehicle speed is less than the predetermined speed threshold at any given time during the upcoming uphill event; and when there is an upcoming downhill event, the at least one battery pack of the vehicle is charged during the downhill event when the estimated vehicle speed is greater than the predetermined speed threshold at any given time during the upcoming downhill event.

12. The method of claim 11, wherein, The step of charging the at least one battery pack begins prior to the occurrence of the terrain event.

13. The method of claim 11, wherein, The information relating to the route is provided to the route data processor by a global positioning system.

14. The method of claim 11, wherein, The information relating to the route is provided to the route data processor by a controller.

15. The method of claim 11, wherein, The terrain event is an uphill event.

16. The method of claim 15, wherein, The predetermined speed threshold is determined by using a vehicle cruise control to set a desired speed value.

17. The method of claim 15, further comprising: A driving nature of the uphill event is identified.

18. The method of claim 15, further comprising: The state of charge of the at least one battery pack is compared to a predetermined threshold of state of charge.

19. The method of claim 15, further comprising: It is determined whether the vehicle is in an economy mode, a power mode, or a balance mode.

20. The method of claim 19, wherein, The vehicle is in the balance mode and the estimated speed is less than a difference between the predetermined speed threshold and a calibrated value of the at least one battery pack.

21. The method of claim 11, wherein, The terrain event is a downhill event.

22. The method of claim 21, further comprising: The speed of the vehicle is reduced by a predetermined amount to a second speed using a predictive cruise control processor when the estimated vehicle speed is greater than the predetermined speed threshold.

23. The method of claim 22, further comprising: The second speed is compared to the predetermined speed threshold, wherein charging the at least one battery pack of the vehicle reduces the second speed of the vehicle to a third speed of the vehicle.

24. The method of claim 21, further comprising: An engine brake of the vehicle is engaged to reduce the speed of the vehicle.

25. A method of managing vehicle speed, the method comprising: providing information relating to a route to a route data processor of a vehicle; identifying a predetermined speed threshold; identifying an upcoming downhill event from the information relating to the route; estimating a speed of the vehicle at any given time during the upcoming downhill event from at least one of: a current speed of the vehicle, a grade of the upcoming downhill event, a length of the upcoming downhill event, and a machine mass estimate of the vehicle; comparing the estimated vehicle speed to the predetermined speed threshold; selectively issuing a neutral command to a transmission control unit of the vehicle to place the vehicle in a neutral mode, wherein the estimated vehicle speed is less than the predetermined speed threshold; and charging at least one battery pack of the vehicle during the downhill event when the estimated vehicle speed is greater than the predetermined speed threshold at any given time during the upcoming downhill event.

26. The method of claim 25, wherein, The information relating to the route is provided to the route data processor by a global positioning system.

27. The method of claim 25, wherein, The information relating to the route is provided to the route data processor by a controller.

28. The method of claim 25, wherein, The neutral command is issued during the downhill event.

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