New Control Methodology for the Next Generation of Engine Management Systems
Lead Research Organisation:
University of Birmingham
Department Name: Mechanical Engineering
Abstract
Despite of the fact that electrical cars are under development and have the potential to provide alternatives for short distance light duty transport, the internal combustion engine will continue to be the main power unit in vehicles for several decades to come. Compared with extensive research on combustion and after-treatment systems, little work has been completed with respect to engine system control optimisation, leaving considerable room to improve fuel economy and lower emissions. Current engine calibration process relies on deriving static tabular relationships and the corresponding values between each calibrated engine operating point, with closed-loop feedback control to adjust the settings accordingly for air-fuel ratio control in real engine operation so as to meet the performance targets and emissions legislation. Such a widely adopted method, however, is not efficient in achieving the best fuel economy of the vehicle due to the constraints in the time duration and cost of engine-bed based calibration. Environmental conditions changes, the time required for the closed-loop control to respond, cycle-by-cycle variations, and cylinder-to-cylinder variations make the current engine control impossible to handle the the optimisation of the engine functionalities.
The development trend for future engines is towards an on-board intelligence for control and calibration and some research activities for the development of model based control systems are reported in literature. However, feasible strategies to control the engine operation cycle-by-cycle and cylinder-by-cylinder are not yet available.
Expanding the work of the applicants in the related areas for many years, the overall Goal of this project is to use a combination of joint efforts from 3 research groups with expertise of engine technology, control technology and computing algorithm in order to develop and test a new engine control and calibration methodology with on-line intelligence built in. This overall goal will be achieved through realising the following objectives:
(1) To develop a full real-time multi-cylinder engine model for cylinder-resolved-control purpose
(2) To develop a novel engine control strategy involving optimization of control points and control point locations, and multi-objective evaluation of test cycle performance
(3) To develop dynamic multi-objective evolutionary algorithms for online engine control optimization
(4) To demonstrate the implementation of the engine control models initially on Hardware-in-the-Loop (HIL) dSPACE system and then further rapid prototyping on a test engine.
(5) To compare the engine performance using the new techniques with traditional calibration and control approaches, and demonstrate improvements in terms of engine output, fuel consumption, and emissions.
The new engine control methodology will be evaluated on a new Jaguar gasoline direct injection (GDI) engine model.
The development trend for future engines is towards an on-board intelligence for control and calibration and some research activities for the development of model based control systems are reported in literature. However, feasible strategies to control the engine operation cycle-by-cycle and cylinder-by-cylinder are not yet available.
Expanding the work of the applicants in the related areas for many years, the overall Goal of this project is to use a combination of joint efforts from 3 research groups with expertise of engine technology, control technology and computing algorithm in order to develop and test a new engine control and calibration methodology with on-line intelligence built in. This overall goal will be achieved through realising the following objectives:
(1) To develop a full real-time multi-cylinder engine model for cylinder-resolved-control purpose
(2) To develop a novel engine control strategy involving optimization of control points and control point locations, and multi-objective evaluation of test cycle performance
(3) To develop dynamic multi-objective evolutionary algorithms for online engine control optimization
(4) To demonstrate the implementation of the engine control models initially on Hardware-in-the-Loop (HIL) dSPACE system and then further rapid prototyping on a test engine.
(5) To compare the engine performance using the new techniques with traditional calibration and control approaches, and demonstrate improvements in terms of engine output, fuel consumption, and emissions.
The new engine control methodology will be evaluated on a new Jaguar gasoline direct injection (GDI) engine model.
Planned Impact
While the control of the engine operation cycle-by-cycle with optimal calibration will be necessary but it is impossible without a new generation of engine management system. A successful conclusion to this project will lead to a new concept engine technology. This proposed project will combine the expertise of mechanical engineering, electrical/electronic engineering and computer science with the goal of developing the next generation of engine calibration and control strategy. It can be expected to have a significant impact on the current practice in the motor industry. It will take the R&D and application of new engine control technology in the UK to an international leading level and has great potential to make contributions to the reduction of global energy consumption and CO2 emissions and thus contribute to the environmental protection. A number of industry companies have shown a great interest in the proposed research and the impact of the project can be envisaged in the support letters supplied by the industry partners.
This project will generate a considerable amount of knowledge to advance the methodologies in engine calibration and control. The new knowledge will be exploited and used by the industry in order to explore this technical path. The result of the project will be passed to industry in the most effective way and the OEMs involved are currently producing over 1 millions engines. Opportunities to reduce the time required for engine calibration and optimised control of engine operation will have a significant cost implication which would be cascaded to the OEM partners and all partners will benefit by passing the knowledge on to their practice in production. The implication of the research results, however, will not be limited to the products of the industrial partners and shall be available for exploitation by all members of the consortium within the agreed distribution of IP.
Dissemination of outcome of the research will be through various arrangements: (1) The results will be reported in various publications, and presentations will be made through participating in national and international seminars and conferences in the UK/ Europe and the world including SAE World Congress in Detroit, and SAE Powertrain, Fuels and Lubricants Meetings, and IEEE conferences, as well as in international journals with professional organisations such as the IMechE, ASME and IEEE. (2) A userful forum for timely dissemation across the country's engine commnuminty will be UnICEG (University IC Engines Group), through which 3 seminars are organised to share research findings with scientists, reseachers, engineers, and PhD students. (3) Archives will be filed by academic theses, papers and, where appropriate, patent applications. (4) A publically accessible webpage will be created at the beginning of the project and updated regularly to report findings and progress. (5) Regular project meetings are to be mutually hosted by University and industrial partners to review the project progress and to discuss the challenges encountered and technical direction. (6) Progress at all stages will be written into reports which will be distributed to all partners. (7) The principal investigators will organize all the engagement activities in a timely manner. (8) The anticipated intellectual property rights (IPR) will be covered and protected by standard University/Industry practice.
A showcase will be created to demonstrate the merits of the project outcomes and to encourage industrial engagement from other motor companies and engine manufacturers. Also, the research fellows and the research students will work closely with the industrial partners and engage in their potential future products development. Development in this present research area can be expected to have a significant impact on the current movement to new engine calibration and control strategies in the motor industry.
This project will generate a considerable amount of knowledge to advance the methodologies in engine calibration and control. The new knowledge will be exploited and used by the industry in order to explore this technical path. The result of the project will be passed to industry in the most effective way and the OEMs involved are currently producing over 1 millions engines. Opportunities to reduce the time required for engine calibration and optimised control of engine operation will have a significant cost implication which would be cascaded to the OEM partners and all partners will benefit by passing the knowledge on to their practice in production. The implication of the research results, however, will not be limited to the products of the industrial partners and shall be available for exploitation by all members of the consortium within the agreed distribution of IP.
Dissemination of outcome of the research will be through various arrangements: (1) The results will be reported in various publications, and presentations will be made through participating in national and international seminars and conferences in the UK/ Europe and the world including SAE World Congress in Detroit, and SAE Powertrain, Fuels and Lubricants Meetings, and IEEE conferences, as well as in international journals with professional organisations such as the IMechE, ASME and IEEE. (2) A userful forum for timely dissemation across the country's engine commnuminty will be UnICEG (University IC Engines Group), through which 3 seminars are organised to share research findings with scientists, reseachers, engineers, and PhD students. (3) Archives will be filed by academic theses, papers and, where appropriate, patent applications. (4) A publically accessible webpage will be created at the beginning of the project and updated regularly to report findings and progress. (5) Regular project meetings are to be mutually hosted by University and industrial partners to review the project progress and to discuss the challenges encountered and technical direction. (6) Progress at all stages will be written into reports which will be distributed to all partners. (7) The principal investigators will organize all the engagement activities in a timely manner. (8) The anticipated intellectual property rights (IPR) will be covered and protected by standard University/Industry practice.
A showcase will be created to demonstrate the merits of the project outcomes and to encourage industrial engagement from other motor companies and engine manufacturers. Also, the research fellows and the research students will work closely with the industrial partners and engage in their potential future products development. Development in this present research area can be expected to have a significant impact on the current movement to new engine calibration and control strategies in the motor industry.
Organisations
- University of Birmingham (Lead Research Organisation)
- Fraunhofer Society (Collaboration)
- Automotive Research Association of India (Collaboration)
- Mahle Engine Systems UK Ltd (Collaboration)
- BYD (Collaboration)
- AVL (Collaboration)
- Jiangsu Industry Technology Research Institute (Collaboration)
- Tsinghua University China (Collaboration)
- Geely (Collaboration)
- Southeast University China (Collaboration)
- Oxsensis1 (Project Partner)
- SAIC Motor (United Kingdom) (Project Partner)
- Tata Motors (United Kingdom) (Project Partner)
- Shell (United Kingdom) (Project Partner)
Publications
Zhou Q
(2019)
Multi-step reinforcement learning for model-free predictive energy management of an electrified off-highway vehicle
in Applied Energy
Wang C
(2014)
Impact of fuel and injection system on particle emissions from a GDI engine
in Applied Energy
He Y
(2020)
Multiobjective component sizing of a hybrid ethanol-electric vehicle propulsion system
in Applied Energy
Lattimore T
(2016)
Investigation of EGR Effect on Combustion and PM Emissions in a DISI Engine
in Applied Energy
Li J
(2022)
Fuzzy-tree-constructed data-efficient modelling methodology for volumetric efficiency of dedicated hybrid engines
in Applied Energy
Cash S
(2019)
Development of a Series Hybrid Electric Aircraft Pushback Vehicle: A Case Study
in Engineering
Wang Z
(2015)
Fuel injection and combustion study by the combination of mass flow rate and heat release rate with single and multiple injection strategies
in Fuel Processing Technology
Tayarani-N. M
(2015)
Meta-Heuristic Algorithms in Car Engine Design: A Literature Survey
in IEEE Transactions on Evolutionary Computation
Li J
(2020)
Driver-Identified Supervisory Control System of Hybrid Electric Vehicles Based on Spectrum-Guided Fuzzy Feature Extraction
in IEEE Transactions on Fuzzy Systems
Li J
(2020)
Back-to-Back Competitive Learning Mechanism for Fuzzy Logic Based Supervisory Control System of Hybrid Electric Vehicles
in IEEE Transactions on Industrial Electronics
Zhou Q
(2018)
Cyber-Physical Energy-Saving Control for Hybrid Aircraft-Towing Tractor Based on Online Swarm Intelligent Programming
in IEEE Transactions on Industrial Informatics
Li J
(2023)
Statistics-Guided Accelerated Swarm Feature Selection in Data-Driven Soft Sensors for Hybrid Engine Performance Prediction
in IEEE Transactions on Industrial Informatics
Li J
(2022)
Cyber-Physical Data Fusion in Surrogate- Assisted Strength Pareto Evolutionary Algorithm for PHEV Energy Management Optimization
in IEEE Transactions on Industrial Informatics
Zhou Q
(2021)
Knowledge Implementation and Transfer With an Adaptive Learning Network for Real-Time Power Management of the Plug-in Hybrid Vehicle.
in IEEE transactions on neural networks and learning systems
Wang J
(2022)
A Moment-of-Inertia-Driven Engine Start-Up Strategy for Four-Wheel-Drive Hybrid Electric Vehicles
in IEEE Transactions on Transportation Electrification
Sun Z
(2022)
A Novel Hybrid Battery Thermal Management System for Prevention of Thermal Runaway Propagation
in IEEE Transactions on Transportation Electrification
Li J
(2022)
Distributed Cooperative Energy Management System of Connected Hybrid Electric Vehicles With Personalized Non-Stationary Inference
in IEEE Transactions on Transportation Electrification
Zhou Q
(2021)
Modified Particle Swarm Optimization With Chaotic Attraction Strategy for Modular Design of Hybrid Powertrains
in IEEE Transactions on Transportation Electrification
Li J
(2022)
Pedestrian-Aware Supervisory Control System Interactive Optimization of Connected Hybrid Electric Vehicles via Fuzzy Adaptive Cost Map and Bees Algorithm
in IEEE Transactions on Transportation Electrification
Liu Z
(2023)
Safe Deep Reinforcement Learning-based Constrained Optimal Control Scheme for HEV Energy Management
in IEEE Transactions on Transportation Electrification
Zhang C
(2023)
Dedicated Adaptive Particle Swarm Optimization Algorithm for Digital Twin Based Control Optimization of the Plug-In Hybrid Vehicle
in IEEE Transactions on Transportation Electrification
He Y
(2020)
Multiobjective Co-Optimization of Cooperative Adaptive Cruise Control and Energy Management Strategy for PHEVs
in IEEE Transactions on Transportation Electrification
Ma H
(2015)
Model-Based Multiobjective Evolutionary Algorithm Optimization for HCCI Engines
in IEEE Transactions on Vehicular Technology
Zhou Q
(2021)
Global Optimization of the Hydraulic-Electromagnetic Energy-Harvesting Shock Absorber for Road Vehicles With Human-Knowledge-Integrated Particle Swarm Optimization Scheme
in IEEE/ASME Transactions on Mechatronics
Li J
(2022)
Electrothermal Dynamics-Conscious Many-Objective Modular Design for Power-Split Plug-in Hybrid Electric Vehicles
in IEEE/ASME Transactions on Mechatronics
Li J
(2023)
Driver-Centric Velocity Prediction With Multidimensional Fuzzy Granulation
in IEEE/CAA Journal of Automatica Sinica
Li J
(2018)
Improved scheme of membership function optimisation for fuzzy air-fuel ratio control of GDI engines
in IET Intelligent Transport Systems
Cash S
(2018)
New traction motor sizing strategy for an HEV/EV based on an overcurrent-tolerant prediction model
in IET Intelligent Transport Systems
He Y
(2019)
Adaptive Cruise Control Strategies Implemented on Experimental Vehicles: A Review
in IFAC-PapersOnLine
Zhang C
(2021)
The Digital Twin Modelling of the Electrified Vehicle Based on A Hybrid Terminating Control of Particle Swarm Optimization
in IFAC-PapersOnLine
Cash S
(2021)
Space vector modulation of multilevel inverters with online vector identification and reduced switching losses for HEV/EV applications
in International Journal of Power Electronics
He X
(2022)
Fuzzy Logic based Power-Split Hybrid Propulsion Control System using Digital Twin Assisted Parallel Learning
in International Journal of Powertrains
Shuai B
(2022)
Supervisory control of the hybrid off-highway vehicle for fuel economy improvement using predictive double Q-learning with backup models
in Journal of Central South University
Ma H
(2018)
Computational Intelligence Nonmodel-Based Calibration Approach for Internal Combustion Engines
in Journal of Dynamic Systems, Measurement, and Control
Zhou Q
(2021)
Human-Knowledge-Augmented Gaussian Process Regression for State-of-Health Prediction of Lithium-Ion Batteries With Charging Curves
in Journal of Electrochemical Energy Conversion and Storage
Xu B
(2022)
Hierarchical Q-learning network for online simultaneous optimization of energy efficiency and battery life of the battery/ultracapacitor electric vehicle
in Journal of Energy Storage
Ma H
(2018)
Model-based computational intelligence multi-objective optimization for gasoline direct injection engine calibration
in Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Li Z
(2018)
Intelligent air/fuel ratio control strategy with a PI-like fuzzy knowledge-based controller for gasoline direct injection engines
in Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Zhang Y
(2018)
Intelligent transient calibration of a dual-loop EGR diesel engine using chaos-enhanced accelerated particle swarm optimization algorithm
in Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
| Description | It should be aware that nearly 80% of the time in the design stage of engine development is dedicated to the calibration and optimization of the set of control system parameters. The model-based calibration using Design of Experiments (DoE) is a common solution to engine calibration in the recent years. The idea behind it is to carry out calibration on a statistical engine model rather than a real internal combustion engine. This approach needs a great deal of experimental data to develop the statistical engine model, as well as experienced calibration engineers to design the corresponding experiments by DoE. It also appears that there is no great potential of this method for future development due to limitation of further measurement time reduction. Moreover, the increasingly high requirements of engine model accuracy and large number of variables also constrain the future of model-based engine calibration method. The purpose of this project is to reduce the time and efforts put into the calibration process for the optimization of an internal combustion engine by Computational Intelligence Non-model-based Calibration Approach (CINCA). Compared to the model-based calibration approach, the introduced CINCA bypasses the DoE-based test planning and statistical engine model development processes. Additionally, in the CINCA, the engine test bench measurements and optimization processes are combined into one process. In other words, compared to executing the calibration processes gradually, the CINCA operates these steps in parallel. In the proposed CINCA method, the fuel consumption and PM emissions are continuously measured throughout the engine test bench experiments. Meanwhile, the engine calibration variables, Intake Valves Opening (IVO) phase, Exhaust Valves Closing (EVC) phase, Spark timing (Spk_t), Injection timing (Inj_t) and Fuel Rail Pressure (Rail_P), are tuned correspondingly, based on the Strength Pareto Evolutionary Algorithm 2 (SPEA2). To implement the CINCA on a V6 GDI engine test bench, a Simulink based CINCA model was developed and applied on the real engine by Rapid Control Prototyping (RCP) and external ECU bypass technology. The key findings are: 1. From the research so far, it is found that the evolutionary algorithm (EA) has a very strong potential to provide a complete solution package to improve the current engine calibration process, either offline or online. The researchers of the University of Birmingham have developed Strength Pareto Evolutionary Algorithm (SPEA2) based engine calibration optimizer. The researchers at the University of Birmingham for validating the engine optimization method have also developed a GDI engine model. From the co-simulation study and experimental validation results, it is found that the developed engine optimization approach is able to find the optimal engine parameter set, for the best fuel economy and minimum PM emissions, with a good accuracy and much higher speed. The researchers at the University of Birmingham have also commissioned a new V6 GDI engine test bed, for implementing and validating the novel developed engine optimization methods on a real engine. 2. The developed Computational Intelligence Non-model-based Calibration Approach (CINCA) has the potential to help engine calibration engineers improve online calibration process remarkably. The CINCA was implemented on a V6 GDI engine test bench, the experiment results show that: a. The CINCA is capable of automatically finding the optimal engine variables set, which is very similar to the default ECU set, from blank after 50 generations searching (2.5 hours). It shows a more efficient and economical way to optimize the engine performance; b. The CINCA can even find the variables setting with better fuel consumptions (3.1% at the maximum) and PM emissions emissions (6.9% at the maximum) compared to the default ECU set; c. Compared to the conventional model-based engine calibration, the CINCA is able to predict the engine variables set for better engine performance generation-by-generation intelligently without using DoE and engine model. The conclusions above show that the developed CINCA has great potential to improve the automatic level for engine calibration. 3. The application of a computational intelligence multi-objective calibration approach on a GDI engine model is introduced, and validated by co-simulation study. The Co-simulation result shows that: a. The optimization approach can lead the reference indicators to converge to the area of the best engine performance; b. For the GDI engine cases, there are up to 3.2%, 16.5% and 10.3% improvements over the given experimental data which can be achieved for ISFC, ISPMN and ISPMM respectively by the developed optimizer. It means that the optimizer is able to find better performance points based on the limited experimental data; c. More loops leads to better optimization results, but longer time the optimizer takes a longer time. And bigger population size can help to make finding the best results easier. |
| Exploitation Route | 1. Due to the increasingly stringent emission regulations and the demand for automotive manufacturers on improvement of fuel economy, the complexity of modern engines is increasing. Moreover, due to the combinatory explosion of the parameter space, the traditional manual engine calibration approach is thus becoming technically inadequate, economically expensive and time consuming. Vehicle manufacturers have to spend more money and time on the engine optimization process. The developed EA based engine optimization method is very promising to help the automotive industry saving money and time from tedious engine calibration work and control optimization. 2. The Computational Intelligence Non-model-based Calibration Approach (CINCA), which has the potential to help engine calibration engineers improve calibration process remarkably, can be put to use in vehicle industry for engine calibration and optimization. 3. Two papers, "Model-based Computational Intelligence Multi-objective Optimization for GDI Engine Calibration" and "Computational Intelligence Non-model-based Calibration Approach for Internal Combustion Engines", based on the findings have been submitted to the journal publications. Both of them has been published. They will improve the intelligent control behaviors of the GDI engine. And the advanced control technology will be implemented into the engine based on the key findings. 4. Two papers, which used the evolutionary algorithm (i.e., chaos-enhanced accelerated particle swarm optimization) for component sizing and modular design, have been published. The advanced AI method has been implemented into the hybrid powertrain design and control. 5. Two papers, which developed reinforcement learning algorithms (multi-step Q learning and double Q learning) for online optimization of energy management control, have been published. The advanced AI method has been implemented into the hybrid powertrain control and been validated via hardware-in-the-loop testing. 6. Two new project sponsored by industry have started in April 2020 and January 2021 respectively to use the AI based control technology for hybrid vehicle powertrain development. 7. Professor Xu is invited to give a Keynote in the World Congress of ICE in April 2021 to present the work on the AI control of vehicle powertrain. |
| Sectors | Energy,Environment |
| URL | https://www.birmingham.ac.uk/research/activity/mechanical-engineering/vehicle-technology/case-v/index.aspx |
| Description | Our AI based toolbox train namely SMARTX has received interest from industry and the techniques we have developed is used by the industry as tools for product design and optimization. |
| First Year Of Impact | 2016 |
| Sector | Energy,Environment |
| Impact Types | Economic |
| Description | software licence was sold to FORD |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Influenced training of practitioners or researchers |
| Description | AI strategy for hybrid engine development |
| Amount | £116,098 (GBP) |
| Organisation | BYD |
| Sector | Private |
| Country | China |
| Start | 01/2021 |
| End | 12/2021 |
| Description | Fuels Quality and Effects on Fuel Injection Equipment and Engine Operation |
| Amount | £542,246 (GBP) |
| Organisation | Jaguar Land Rover Automotive PLC |
| Department | Jaguar Land Rover |
| Sector | Private |
| Country | United Kingdom |
| Start | 01/2013 |
| End | 12/2015 |
| Description | Hybrid Electric Push-Back Tractor |
| Amount | £622,978 (GBP) |
| Funding ID | 102253 |
| Organisation | Innovate UK |
| Sector | Public |
| Country | United Kingdom |
| Start | 07/2015 |
| End | 09/2017 |
| Description | Innovation to commercialisation of University Research (ICURe) programme |
| Amount | £35,000 (GBP) |
| Organisation | Innovate UK |
| Sector | Public |
| Country | United Kingdom |
| Start | 10/2018 |
| End | 03/2019 |
| Description | Jaguar new high performance engine |
| Amount | £214,000 (GBP) |
| Organisation | Jaguar Land Rover Automotive PLC |
| Department | Jaguar Land Rover |
| Sector | Private |
| Country | United Kingdom |
| Start | 10/2016 |
| End | 11/2017 |
| Description | Research on Real-time Optimisation System for Plug-in Hybrid Electric Vehicle based on Artificial Intelligence Digital Twin Technology |
| Amount | £101,449 (GBP) |
| Organisation | Jiangsu Industry Technology Research Institute |
| Sector | Public |
| Country | China |
| Start | 04/2020 |
| End | 03/2022 |
| Title | Computational Intelligence Non-model-based Calibration Approach |
| Description | The present work developed a new computational intelligence approach to calibrate internal combustion engine without the engine model. The Strength Pareto Evolutionary Algorithm 2 (SPEA2) is applied to this automatic engine calibration process. In order to implement the approach on a V6 GDI engine test bench, we developed a Simulink based real-time embedded system, before implemented it to engine ECU through Rapid Control Prototyping (RCP) and external ECU bypass technology. Experimental validations proved that this novel developed engine calibration approach is capable of automatically finding the optimal engine variable set, which can provide the optimal fuel consumption and PM emissions, with good accuracy and high efficiency. The proposed engine calibration approach does not rely on either the engine model, or massive test bench experimental data. It has great potential to improve the engine calibration process for industries. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2018 |
| Provided To Others? | Yes |
| Impact | The Computational Intelligence Non-model-based Calibration Approach (CINCA) is capable of automatically finding the optimal engine variables set, which is very similar to the default ECU set, from blank after 50 generations searching (2.5 hours). It shows a more efficient and economical way to optimize the engine performance. The CINCA can even find the variables setting with better fuel consumptions (3.1% at the maximum) and PM emissions (6.9% at the maximum) compared to the default ECU set. Compared to the conventional model-based engine calibration, the CINCA is able to predict the engine variables set for better engine performance generation-by-generation intelligently without using DoE and engine model. In summary, the developed CINCA has great potential to improve the automatic level for engine calibration. It is a promising approach to help the calibration engineer to get rid of numerous engine maps tests. |
| URL | http://dynamicsystems.asmedigitalcollection.asme.org/article.aspx?articleID=2653469 |
| Title | Model-Based Multi-objective Evolutionary Algorithm Optimization for HCCI Engines |
| Description | In this study, interdisciplinary research on a multi-objective evolutionary algorithm (MOEA) based global optimization approach is developed for a homogeneous charge compression ignition (HCCI) engine. The performance of the HCCI engine optimizer is demonstrated by the co-simulation between an HCCI engine Simulink model and a Strength Pareto Evolutionary Algorithm 2 (SPEA2) based multi-objective optimizer Java code. The HCCI engine model is developed by Simulink and validated with different engine speeds (1500-2250 r/min) and indicated mean effective pressures (IMEPs) (3-4.5 bar). The model can simulate the HCCI engine's indicated specific fuel consumption (ISFC) and indicated specific hydrocarbon (ISHC) emissions with good accuracy. The introduced MOEA optimization is an approach to efficiently optimize the engine ISFC and ISHC simultaneously by adjusting the settings of the engine's actuators automatically through the SPEA2. The settings of the HCCI engine's actuators are intake valve opening (IVO) timing, exhaust valve closing (EVC) timing, and relative air-to-fuel ratio ?. The co-simulation study and experimental validation results show that the MOEA engine optimizer can find the optimal HCCI engine actuators' settings with satisfactory accuracy and a much lower time consumption than usual. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2015 |
| Provided To Others? | Yes |
| Impact | The proposed optimization approach is able to find the optimal engine parameters set (IVO, EVC, and ?) for the best ISFC and ISHC with good accuracy and speed. It also can be observed that the optimized results for both ISFC and ISHC are significantly better than the performance of the pre-obtained experimental data. The HCCI engine optimizer only needs around 20 min to obtain the best engine performance points for one optimization case. For most of the cases, after 40 loops, the Pareto optimal points are located in or very near the optimal zones. It means the optimizer is able to find the best performance points based on the limited experimental data, which suggests that the approach has great potential to help industries to reduce the HCCI engine calibration efforts, even for traditional SI gasoline and compression ignition (CI) diesel engines. |
| URL | http://ieeexplore.ieee.org/document/6942279/ |
| Title | Model-based Computational Intelligence Multi-objective Optimization for GDI Engine Calibration |
| Description | The present work developed a computational intelligence based multi-objective optimization approach for the Gasoline Direct Injection (GDI) engine, which can optimize the engine's Indicated Specific Fuel Consumption (ISFC), Indicated Specific Particulate Matter by Mass (ISPMM) and Indicated Specific Particulate Matter simultaneously by intelligently adjusting the engine actuators' settings through Strength Pareto Evolutionary Algorithm 2 (SPEA2). A mean value model of GDI engine was developed to predict the performance of ISFC, ISPMM and ISPMN with given values of throttle position, spark timing, injection timing, Intake Valves Opening (IVO) and Exhaust Valves Closing (EVC). Then a Co-simulation platform was established for the computational intelligence multi-objective calibration simulation in the given engine driving condition. The co-simulation study and experimental validation results suggest that the developed intelligence calibration approach can find the optimal GDI engine actuators' settings with acceptable accuracy in much less time, compared to the traditional approach. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2016 |
| Provided To Others? | No |
| Impact | The optimizer developed in this study can lead the reference indicators in the co-simulation system to converge to the best engine performance area. For the investigated GDI engine, there are up to 3.2%, 16.5% and 10.3% improvements over the original calibration data for ISFC, ISPMN and ISPMM respectively by the optimizer generated data. This means that the optimizer is able to find better performance points based on the limited experimental data. Using more loops will lead to better results, but the process takes a longer time. A bigger population size in the optimization can help finding the best results. In summary, this study proves that the computational intelligence multi-objective optimization approach can find optimal GDI engine actuators' settings with acceptable accuracy based on a mean-value engine model with limited experimental data. It has great potential to help calibration engineers improve the calibration process. |
| Description | AVL partnership in developing new EPSRC proposal |
| Organisation | AVL |
| Department | AVL UK |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | AVL will join the steering board of an EPSRC project |
| Collaborator Contribution | AVL will join the steering board and providing invitations to speak at AVL events. |
| Impact | A new EPSRC proposal will be submitted by March 2021 |
| Start Year | 2020 |
| Description | Collaborative research on intelligent optimization of vehicle electric systems |
| Organisation | Tsinghua University China |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Software support |
| Collaborator Contribution | Hareware support |
| Impact | Not yet |
| Start Year | 2022 |
| Description | Comercilisation of the intelligence calibration tool with BYD motor company |
| Organisation | BYD |
| Department | BYD Motors Inc |
| Country | United States |
| Sector | Private |
| PI Contribution | BYD motor company has shown great interest in commercialisation of the research outcome. A testing water project is being set up for a hybrid engine calibration application. |
| Collaborator Contribution | BYD will provide a prototype vehicle for the testing. They will also cover the essential cost including a travel budget, consumables and engineer's time for this project. |
| Impact | A contract will be signed in March 2020. We will publish the research result within the following year. |
| Start Year | 2019 |
| Description | Cooperative study of key technologies for virtual development of DHT hybrid powertrain system |
| Organisation | Southeast University China |
| Country | China |
| Sector | Academic/University |
| PI Contribution | Software support |
| Collaborator Contribution | Hardware support |
| Impact | Not yet |
| Start Year | 2022 |
| Description | Joint development of high-efficiency and high-performance electric drive systems for vehicles |
| Organisation | Jiangsu Industry Technology Research Institute |
| Country | China |
| Sector | Public |
| PI Contribution | Software support |
| Collaborator Contribution | Hardware support |
| Impact | Not yet |
| Start Year | 2022 |
| Description | Link with Fraunhofer Germany |
| Organisation | Fraunhofer Society |
| Department | Fraunhofer Institute for Environmental, Safety, and Energy Technology |
| Country | Germany |
| Sector | Public |
| PI Contribution | We have shared research outcome and discussions during two of their visits and meetings. The discussions focussed on the production technology of MF/MF-c based bio-oil and characterizing the fuel properties. |
| Collaborator Contribution | The Institute provides technical support |
| Impact | this collaboration is multi-disciplinary |
| Start Year | 2016 |
| Description | MAHLE partnership in developing new research proposal |
| Organisation | Mahle Engine Systems UK Ltd |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | MAHLE has joined a steering committee for the development of a new EPSRC proposal and will supply their flexible ECU as a platform for the implementation of embedded machine learning software for control strategy optimization. |
| Collaborator Contribution | MAHLE has joined a steering committee for the development of a new EPSRC proposal and will supply their flexible ECU as a platform for the implementation of embedded machine learning software for control strategy optimization. |
| Impact | A new EPSRC proposal will be submitted by March 2021 |
| Start Year | 2020 |
| Description | Partnership with Geely Royal Powertrain Ltd. |
| Organisation | Geely |
| Country | China |
| Sector | Private |
| PI Contribution | MOU signed with Geely Royal Powertrain Ltd. to promote collaboration in engineering research and education. |
| Collaborator Contribution | Submitted a research proposal to the Ningbo government. Industry project contract in a discussion. |
| Impact | Submitted a research proposal to the Ningbo government. Industry project contract in a discussion. |
| Start Year | 2023 |
| Description | University of Birmingham teams up with The Automotive Research Association of India on transport research |
| Organisation | Automotive Research Association of India |
| Country | India |
| Sector | Public |
| PI Contribution | Prof. Xu and his team member, Dawei Wu and Haoye Liu applied a IGI/ IAS Global Challenges Funding Research Co-Design Sandpits "The Challenge of Particular Matter (PM) Emission Reduction in India", In this project, as the leader team, we united multiple organisations like Automotive Research Association of India (ARAI), Pune, India and Indian Oil Corporation Limited (IOCL), India and Academic Institutions like Indian institute of Technology (IIT) Delhi and National Institute of Technology (NIT) Calicut as a research consortium on India PM reduction roadmap. During the project, ARAI has shown great interesting in cooperation with us in the fields of air quality management, alternative fuels, power train and electric vehicle technology. And the MOU has been approved recently in March 2021, and the detailed information can be found in the following link: https://www.birmingham.ac.uk/news/latest/2021/03/arai-transport-research.aspx. |
| Collaborator Contribution | ARAI is India's premier automotive research and development institute set up by the automotive industry with the Government of India. ARAI is an autonomous body affiliated to the Ministry of Heavy Industries and Public Enterprises, Government of India and is recognized by the Department of Scientific and Industrial Research, Government of India. The partnership will see British and Indian air pollution experts working together to create a blueprint to tackle the challenge of particulate emissions in India - looking to develop and deliver solutions identified in the plan.The partners also plan to support the development of education programmes that will help produce future transport leaders and world-leading research. |
| Impact | The collaboration has just been established in March 2021. The partnership will see British and Indian air pollution experts working together to create a blueprint to tackle the challenge of particulate emissions in India - looking to develop and deliver solutions identified in the plan.The partners also plan to support the development of education programmes that will help produce future transport leaders and world-leading research. |
| Start Year | 2021 |
| Title | CINCA - Intelligent Engine Calibration Toolbox |
| Description | Software License sold to Ford |
| IP Reference | |
| Protection | Copyrighted (e.g. software) |
| Year Protection Granted | 2016 |
| Licensed | Yes |
| Impact | The software license is formally sold to Ford motor company |
| Title | VEHICLE POWER MANAGEMENT SYSTEM AND METHOD |
| Description | A vehicle power management system (100) for optimising power efficiency in a vehicle (400), by managing a power distribution between a first power source (410) and a second power source (420). A receiver (110) receives a plurality of samples from the vehicle (400), each sample comprising vehicle state data, a power distribution and reward data measured at a respective point in time. A data store (350) stores estimated merit function values for a plurality of power distributions. A control system (200) selects, from the data store (350), a power distribution having the highest merit function value for the vehicle state data at a current time, and transmits the selected power distribution to be implemented at the vehicle (400). A learning system (300) updates the estimated merit function values in the data store (350), based on the plurality of samples. |
| IP Reference | WO2020002880 |
| Protection | Patent application published |
| Year Protection Granted | 2020 |
| Licensed | Yes |
| Impact | A university spin-off company is set up for further development and commercialisation of this technology. |
| Title | SMART X- online embedded optimisation toolbox |
| Description | Smart X is a software platform for embedded systems. It complies with state-of-the-art testing facilities in the automotive industry. Intelligent calibration is one of its most successful application cases. The early version of SMART X was sold to Ford. We are developing the upgraded version for engine start control with BYD and for hybrid powertrain development with JITRI. |
| Type Of Technology | Software |
| Year Produced | 2019 |
| Impact | The software has been sold to Ford for engine calibration. Two additional funding applications are in processing. |
| Company Name | DEEPPOWER INNOVATION LIMITED |
| Description | A university spin-off company for commercialization of real-time optimization software for automotive. |
| Year Established | 2018 |
| Impact | Engagement with several venture capitalists. A testing water project has been set up with the BYD motor company. |
| Description | Orgnising invited session on IFAC E-COSM conference - topic AI, Connectivities, and Digital Twins for Future Powertrain Systems |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | We organized an invited session in a reputational academic conference sponsored by IFAC. This invited session collects the recent advances in the development of CAE tools for powertrain design (e.g. system modeling, diagnosis, modular design, component sizing, energy management, and controls at different levels) and demonstrates how the state-of-the-art AI and IT advancements help improve vehicle performance. |
| Year(s) Of Engagement Activity | 2021 |
| Description | Participation in meeting with AVL |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Meeting with professionals from AVL regarding the operation of 4-cylinder Puma Engine in acquiring controlling systems to operate the engine and testing emissions. |
| Year(s) Of Engagement Activity | 2016 |
| URL | https://www.avl.com/ |
| Description | University combustion and Engines Group (UNICeG) Conference |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Attended the UNICeG meeting held at the University of Birmingham, Birmingham. 2018 |
| Year(s) Of Engagement Activity | 2018 |