GB2505416A - Computer implemented method of engine design optimisation - Google Patents

Computer implemented method of engine design optimisation Download PDF

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GB2505416A
GB2505416A GB1215224.5A GB201215224A GB2505416A GB 2505416 A GB2505416 A GB 2505416A GB 201215224 A GB201215224 A GB 201215224A GB 2505416 A GB2505416 A GB 2505416A
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engine
local
constraints
smoothness
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Mark Goodman
Xuefei Yin
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Jaguar Land Rover Ltd
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/02—Circuit arrangements for generating control signals
    • F02D41/14—Introducing closed-loop corrections
    • F02D41/1401—Introducing closed-loop corrections characterised by the control or regulation method
    • F02D41/1406—Introducing closed-loop corrections characterised by the control or regulation method with use of a optimisation method, e.g. iteration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/24—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means
    • F02D41/2406—Electrical control of supply of combustible mixture or its constituents characterised by the use of digital means using essentially read only memories
    • F02D41/2425—Particular ways of programming the data
    • F02D41/2429—Methods of calibrating or learning
    • F02D41/2438—Active learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
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    • G06Q10/10—Office automation; Time management
    • G06Q10/101—Collaborative creation, e.g. joint development of products or services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D2200/00—Input parameters for engine control
    • F02D2200/02—Input parameters for engine control the parameters being related to the engine
    • F02D2200/025—Engine noise, e.g. determined by using an acoustic sensor
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D35/00—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for
    • F02D35/02—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for on interior conditions
    • F02D35/023—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for on interior conditions by determining the cylinder pressure
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/02—Circuit arrangements for generating control signals
    • F02D41/14—Introducing closed-loop corrections
    • F02D41/1438—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor
    • F02D41/1444—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases
    • F02D41/1446—Introducing closed-loop corrections using means for determining characteristics of the combustion gases; Sensors therefor characterised by the characteristics of the combustion gases the characteristics being exhaust temperatures
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

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Abstract

A computer implemented method of optimising the design of an engine in a collaborative optimisation environment having a system level constraint to be met and a plurality of subsystems with one or more local constraints to be met. One of the local constraints to be met is the actuator change between a plurality of subsystem points. The method comprises identifying a system level constraint, such as fuel efficiency, to be met by the engine optimisation; passing the system level constraint to be met to the plurality of sub­systems and optimising a plurality of the sub-systems according to the system level and local constraints. The local constraint of actuator change between a plurality of sub-system points is defined as a minimisation in actuator change (local smoothness) between a plurality of sub-system points.

Description

Optimisation process for engine calibration
Technical field
The inyention relates to a method of optimisation for calibration of engines, in particular diesel engines.
Background to the invention
Engines, particularly automotive engines, are becoming more sophisticated and complex. It is often a goal when designing such engines to reduce fuel consumption. It is also important to ensure that the engines meet regulatory specified limitations with regards to emissions. Such emission targets are applicable over a specified drive cycle.
Furthermore, when designing such engines it is important to ensure that any changes made to the engine do not affect the "driveability" of the engine. For example, a change in an actuator setting which rcsuhs in a decrease in fuel consumption may not necessarily be desirable if it results in a decrease in driveability by, for example, a step change in actuator position.
Thus there is a challenge in calibrating engines to find the optimal combination of settings which minimise fuel consumption and ensure that the engine's emissions remain within the specified legal constraints as well as ensuring that the end-user's experience of driving is not affected.
It is also known that calibration optimisation of engines can be expensive computationally. Engine calibration optimisation typically analyses a number of discrete operating points in the engine design space, known as minimap points. For a given engine design there may be several tens of minimap points, each of which is modelled. Therefore, when optimising such engines it is important to ensure that the computational cost of such optimisation does not become prohibitive.
In the prior art, Traditional Collaborative Optimisation (TCO) has been applied to engine optimisation in order to reduce computational expense. Engine optimisation may be considered as a hierarchical problem, and as such collaborative optimisation may be used for engine optimisation. In such examples the optimisation problem is considered at both a system level and at a local level. The engine drive cycle outcomes and actuator gradient changes are the system level targets, and at each minimap point in the model, local calibration targets are defined.
In the Diesel engine calibration optimisation problem, it is found that the computational effort, and therefore expense, lies partially within the constraints analysis. TCO may be considered to impose constraints in both the solution space, mainly reflecting calibrator's preferences for strategy (i.e. domain constraints for actuators) and smootlmess of the actuator map, and in the objective domain (associated with emissions performance and other attributes such as noise, combustion stability, etc).
Whilst the TCO optimisation method provides acceptable solutions, these are seen as computationally expensive. It is known from Forrester et al (2008) Engineering design via surrogate modelling: a practical guide, that computational costs can be reduced by removing constraints. Braun and Kroo Implementation and performance issues in collaborative optimization. Analysis, 10, I., (1996) suggest that it is better to change global constraints (such as gradient change (GC) constraints in the engine optimisation example) to a local level, as opposed to the system level. Though such solutions are still computationally expensive.
It is an aim in the engine calibration process to produce an accurate optimum solution for engine operation without unnecessary computational expense.
According to an aspect of the invention there is provided a methodology to refonriulate the engine calibration problem, in order to achieve the engine calibration targets and constraints, by utilising an efficient optimisation framework as a multi-level optimisation structure. There is provided a computer implemented method of engine optimisation in a collaborative optimisation environment having one or more system level constraints to be met and a plurality of sub-systems with one or more local constraints to be met, wherein one of the local constraints to be met is actuator change between a plurality of sub-system points, the method comprising: identiting a system level constraint to be met by the engine model; passing the system level constraint to be met to the plurality of sub-systems and optimising a plurality of the sub-systems according to the system level and local constraints; wherein the local constraint of actuator change between a plurality of sub-system points is defined as a minimisation in actuator change,or maximisation of local smoothness, between a plurality of sub-system points.
Preferably, wherein the system level constraint is the minimisation of fuel consumption. Preferably, wherein further local constraints to be met are selected from the group containing: engine noise, exhaust temperature, mean effective pressure, combustion stability. Preferably, wherein the minimisation in actuator change, local smoothness (Ini,th), between minimap points is defined as the sum of the absolute values of the actuator changes associated with all possible transitions from sub-system ito sub-system] Ismth(X) LX. -X.
Preferably, wherein the formulation of smoothness is normalised by a factor representative of a predetermined allowable actuator change. Optionally wherein the ((x.-x.) 2 formulation of smoothness is defined as smooth = 1 GCiirnij wherein CC11111 is the allowable actuator change between sub-system points / and].
Preferably, wherein the formulation of smoothness is based on the principle of minimisation of maximum change. Optionally, wherein the formulation of smoothness (xxi2 is given as (X) = max GC K 1imy wherein GChm is the allowable actuator change between sub-sytem points / and i.
Preferably, wherein the system level constraint is defined as (X-Xh)2 0; (Xj-Xmj 0 where Xj is the target value passed to sub-system /, I by the system level objective and is the optimised value returned by the system.
There is also provided an engine, for use in a motor vehicle, wherein the engine is optimised using the optimised values determined by the disclosed method.
There is also provided a vehicle having an engine which is optimised using the optimised values determined by the disclosed method.
Other aspects of the invention will be apparent from the appended claim set.
Brief description of the figures
Embodiments of the invention are now described, by way of example only, with reference to the accompanying drawing in which: Figure 1 is a data flow diagram according to an aspect of the invention.
Detailed description of an embodiment
According to an aspect of the invention there is provided a hybrid collaborative optimisation framework which is used to deliver engines with reduced lItel consumption whilst ensuring that driveability constraints, in particular those related to actuator smoothness, are met. In particular, an aspect of the invention is to provide a methodology for engine optimisation which reduces the computational requirement whilst producing accurate results.
In an example of the invention there is provided a refined collaborative optimisation framework which reduces the computational expense required to provide an acceptable solution, both in terms of the global and local requirements. in an example of the invention there is provided a methodology which provides solutions which ensure smooth actuator changes within the engine, thus ensuring that the user experience, in terms of drivability, is not affected.
There is provided a method of reducing the computational expense associated with engine optimisation by replacing computationally expensive gradient change constraints in the engine optimisation with a cost function in which the gradient change for actuators is minimised between minimap points.
The following is described with specific reference to a diesel engine though the principles discussed herein may be applied to other forms of engine.
In Diesel engine calibration optimisation it is found that the computational effort, and therefore expense, lies mostly with the constraints analysis. Typically there are several different kinds of constraints imposed, reflecting both legislative requirements for emissions and the calibrator's preferences for engineering attributes (such as noise and driveability). From a calibration optimisation point of view, constraints are imposed both in the solution space (reflecting the smoothness of the actuator map) and in the objective domain (associated with emissions performance and other attributes such as noise, combustion stability, etc).
There are several types of constraints which must be met, such as: constraints which define each engine actuator's valid range (i.e. the physically acceptable values in an engine); engine performance local constraints (such as noise, combustion stability and exhaust temperature); cycle emission constraints which define the legal limits of emissions over the whole drive cycle calculated as the weighted sum of the emissions at each minimap point (i.e. the legal constraints to which the engine must conform); and gradient change (GC) constraints define the actuator maximum allowable change between different minimap points (i.e. drivability constraints). These constraints need to be met in order to acceptably operate the engine.
It has been found that the cycle emission constraints and the GC constraints are typically the most difficult constraints to be satisfied. In a typical diesel engine model with 10 minimap points there are over 250 actuator gradient changes between minimap points resulting in a substantial computational burden. The OC constraint has been imposed in the prior art in commercial engine control optimisation software, and informally through historical calibration engineers' experience from a similar engine.
The concept is also described by (Roudenko et al Application of a Pareto-based evolutionary algorithm to fuel injection optimization. In: Intemational Conferene On Statistics And Analytical Methods in Automotive Engineering Professional Engineering Publishing, Vol. 4, pp. 81-92., 2002). The computational cost of such optimisation can be reduced, without any adverse effect on the quality of optimum solutions, by reformulating the OC constraint. An aspect of the invention is based on removing the actuator gradient constraints altogether, to reduce computational cost, and replacing the UC constraints with a minimisation requirement i.e. the change between minimap points for an actuator is minimised. This approach is justifiable from an engineering analysis of the calibration of the engine, where the calibration preference is for a "smooth actuator map" which ensures drivcability. A smooth actuator map in the optimisation process means that for each minimap point in design space, there is a minimum actuator change, which translates as smooth actuator changes in the engine thereby avoiding step changes in the actuator which are associated with poor driveability.
The implication for the Collaborative Optimisation implementation is that in order to address the requirement for a smooth actuator map, the subsystem optimisation problem should be redefined from the "discipline feasible solution analysis" used in the prior art, to be one of "minimisation of actuator gradient / change" or local smoothness.
Thus sublevel optimisation of the engine produces an engine control map that is smooth as possible in every local area (load/speed space) presented in each discipline, as well as ensuring consistency with the system level optimisation requirement.
Figure 1 is a data flow diagram which expresses the hybrid optimisation concept.
There is shown the system level constraint which passes down the target value to each subsystem or discipline. The subsystem (or minimap point) optimiscs the minimap point with the target objective as passed down from the system level constraint.
As shown in Figure 1 at the system level (which is the global objective) the total fuel consumption is minimised subject to cycle emissions constraints and the consistency constraints of subsystem responses. The system target value is passed down to each discipline, and at each discipline the "local smoothness" (i.e. actuator change) is minimised subject to the local constraints. In order to ensure compatibility with the system level optimisation a constraint is imposed on the system overall objective, i.e. the subsystem task is to deliver the minimal actuator changes with the not worse' fuel economy as the system target as shown in Figure 1.
This cnsures that the returned solution at the subsystem level does not result in a global system with worse fuel consumption. In an embodiment this constraint is expressed as 0; (X-X)2 0 where Xii is the target value passed to subsystem i,j by the system levc objcctive and XbI.J is the optimiscd v&uc rcturned by the system. Tn further embodiments, other formulations of the constraint may be used.
In order to replace the GC constraints from the optimisation methodology with the ocal smoothness constraint, a mathematical formulation of "smoothness" is required.
In an example of the invention, the formulation of smoothness at a local discipline, or minimap point /, can bc exprcssed as the sum of thc abso]utc valucs of thc actuator changes associated with all possible transitions from minimap point i. This is expressed in equation 1.
I sniootj, (x) = -Equation 1.
Whcrc X is thc actuator settings at minimap point I and X is thc actuator setting at minimap point j with the possible transitions from minimap point. Whilst such a formulation of smoothness may produce acceptable results, it is found that a problem with this smootimess formulation is that it involves a number of actuator changes that are expressed in different engineering units (scales). For example, the scale of mass air flow and injection timing are expressed differently and are typical considered during engine optimisation.
In a frirther example of the invention, in order to increase the effectiveness of the formulation of smoothness is expressed as the sum of squared normalised actuator smoothness at the particular minimap point, as expressed in equation 2.
= xi)9 2 Equation 2.
The subsystem optimisation with the smoothness in Equation 2 not only standardiscs the actuator change between the actuator control variables, but also standardises gradient change across the map. Specifically, the defined allowed gradient is defined as smaller on load increases (as larger gradient changes during load increases are found to cause driveability issues) than, say, on speed increases (which are found to be less significant in terms of driveability). By dividing by the allowable actuator change i.e. OCtim (preferably defined during calibration) the invention is able to account for such chances.
In a further example the local smoothness function is based on a mm-max principle, i.e. minimise the maximum normalised actuator change around a minimap point. This is expressed in equation 3.
((x. -x [moc,th(X) = maxL Equation 3.
Using the above formulations of smoothness, a hybrid collaborative framework can be defined. In the following, the minimisation of ifiel consumption is defined as the system level objective (Is(X)).
Therefore the objective is to minimise: J(X) = F(X) Subject to the global constraints: G(X,) = * Einission <Emission1 and consistency constraints of control variables between system and subsystem levels: H(X1) = (x1 -xcuh1) where w is the residency time of each minimap point i contributing to the drive cycle, in an example the cycle is the NEDC cycle; F is the fuel consumption for minimap point i; and X is the vector of control variables at system level; Xsub is the vector of control variaNes at subsystem leve(G(X) denotes the total emission constraints).
Furthermore, the solutions must &so satisfy the consistency constraint.
At subsystem level the optimisation problem may therefore be described as: Minimise: Js,noothX4ytsub) subject to the following local constraints: NoJsE(x;zth) < Local 101SF Exhaust TEMP(X,8th)< Local,.)lC,,T,.,,.
IMEP(X;" ) < Local,,.,.
FCX?Lb) «= FCIXT) wherein Jsmooth is the subsystem objective representing the ocal smoothness; XIb is the vector of local variables at minimap point i and XT is the vector of target variables passed from system level, which are from the system variables X; NOJSE(X,SUh) and LocaINOISE are the noise level and the noise limit at minimap point i; Exhaust TEMP(X)and Local,/CbZ,JJ..M/, are the exhaust temperature and the exhaust temperature limit at minimap point i; IMEP(X7") and LocalJ4ffi are the Indicated Mean Fffective Pressure(IMEP) and the IMEP limit at minimap point i; PC(X) and Pc(xf) are the estimated fuel consumption from subsystem optimisation and system optimisation for minimap point i.
In further embodiments the local constraints may be adapted according to the aims of the simulation and engine constraints to be considered.
Importantly, in the present invention the computationally expensive CC constraints have been removed from the simulation as local smoothness has been used as the objective at subsystem level. Therefore the computational cost of performing the simulation is greatly reduced, as the computationally expensive CC constraints have been replaced by the less computationally expensive local smoothness objective.
Furthermore, as the local constraints are used to satisfy engine performance requirements and to ensure that fuel consumption target is not worse than the system target (i.e. as passed down in Figure 1) the use of local smoothness does not impact on the ultimate end aim of the simulation i.e. reduction in ifiel consumption.
In order to effectively optimise the local smoothness Jsmooth, it is necessary to define the local smoothness. Jsmcjoth is the subsystem objective, is the vector of local variables at minimap point / and XT the is the vector of target variables passed from system level (which are the same as the system variables X). The smoothness formulation can be written as: _xi2 srnooth (x) = I
K
((x;b -xf) 2 Wl?th (x) = max
K
Such optimisation using the above formulations of local smoothness may be implemented in MATLAB or any other suitable program. In further examples of the invention other formulations of local smoothness may be used.
Therefore the present invention provides an improved MDO/TCO framework, via a hybrid CO (MDO/HCO) framework. This hybrid CO framework significantly reduces the computational expense associated with such optimisation by eliminating costly (in terms of computational cost) actuator change constraints, by introducing a new strategy for discipline level optimisation -to optimise the local actuator "smoothness" rather than simply satisfying the actuator change constraints.
This innovative formulation of local actuator smoothness not only reduces the complexity of the calibration optimisation problem (by removing the actuator change constraints), but also significantly reduces the amount of prior calibration knowledge as there is no longer any need to specify the limits of actuator change between minimap points as in existing prior art systems. It has been found that such a formulation produces comparable results to existing methodologies in terms of fuel efficiency.
Such techniques can therefore be used to construct optimised engines with the optimised variables within a complex set of constraints. In further examples engines are physically created used the parameters determined by the disclosed optimisation method. Such engines may installed and used in motor vehicles.
Whilst the above techniques have been described for use in the optimisation of engines motor vehicles, they may be applied to other types of engine.

Claims (11)

  1. Claims 1. A computer implemented method of engine optimisation in a collaborative optimisation environment having one or more system level constraints to be met and a plurality of sub-systems with one or more local constraints to be met, wherein one of the local constraints to be met is actuator change between a plurality of sub-system points, the method comprising: identifying a system level constraint to be met by the engine model; passing the system level constraint to be met to the plurality of sub-systems and optimising a plurality of the sub-systems according to the system level and local constraints; wherein the local constraint of actuator change between a plurality of sub-system points is defined as a minimisation in actuator change,or maximisation of local smoothness, between a plurality of sub-system points.
  2. 2. The method claim 1 wherein the system level constraint is the minimisation of fuel consumption.
  3. 3. The method of any preceding claims wherein frirther local constraints to be met are selected from the group containing: engine noise, exhaust temperature, mean effective pressure, combustion stability.
  4. 4. The method of any preceding claims wherein the minimisation in actuator change, local smoothness (smooth), between minimap points is defined as the sum of the absolute values of the actuator changes associated with all possible transitions from sub-system ito sub-system/ Tsmooth(X) = X
  5. 5. The method of any preceding claims wherein the formulation of smoothness is normalised by a factor representative of a predetermined allowable actuator change.
  6. 6. The method of claim S wherein the formulation of smoothness is defined as I -v ______ -L..j ccT j 1 ç Iuti,j wherein GChm is the allowable actuator change between sub-system points / and].
  7. 7. The method of any of claims I to 4 wherein the formulation of smoothness is based on the principle of minimisation of maximum change.
  8. 8. The method of claim 7 wherein the formulation of smoothness is given as ((x.-x.)t /smooth(A') = max GC, wherein GCi is the allowable actuator change between sub-sytem points i and].
  9. 9. The method of any preceding claims wherein the system level constraint is defined as (Xi-Xsahi)2 0; (Xj-Xh)2 0 where X is the target value passed to sub-system 4/ by the system level objective and Xh Lj is the optimised value returned by the system.
  10. 10. An engine, for use in a motor vehicle, wherein the engine is optimised using the optimised values determined by any of the preceding method claims.
  11. 11. A vehicle having an engine according to claim 10.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103942362A (en) * 2014-03-20 2014-07-23 北京理工大学 Method for designing AMT hydraulic gear shifting mechanism

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