The team is dedicated to applications of artificial intelligence in future vehicles for more than 10 years. We have been fully exploring the optimization potential of the three major algorithms in engineering problems. These are fuzzy logic, evolutionary algorithms and machine learning. Their specific applications and achievements are as follows:
1. Fuzzy logic based development
(a) Dual-loop Online Intelligent Programming
- Dual-loop Online Intelligent Programming is proposed for PHEV energy management.
- Deep fuzzy predictor is created for vehicle velocity forecast via fuzzy granulation technology.
- The proposed DFP has the ability to differentiate driving behaviours at each driving state in real time. Its prediction result shows excellent accuracy with the lowest maximum error (2.22%), compared with the NNP (14.81%) and the FEP (4.81%).
- During real world driving, up to 9.37% total energy can be saved compared with the rule-based control strategy. The DP-based control strategy cannot work in this online environment.
- The computational time of the DOIP algorithm is investigated. It is feasible to operate the DOIP algorithm for 20s look-ahead horizon and computing resources still have a surplus.
Fig. 3-1. Fuzzy granulation evolution for MC models
Fig. 3-2. Online prediction results over real-world driving
(b) Back-to-back competitive learning mechanism
- During the study case of this research, the proposed mechanism has an ability to adapt to the change of driving behaviours and performs 957 updates of the MF parameters in the FL-based control system.
- Under different initial SoC conditions (SoC=0.3/0.4/0.5), the FL-based control system driven by the proposed mechanism can significantly improve the fuel consumption when compared to less advanced control strategies (CD/CS and conventional FL-based).
- Compared to the CD/CS-based system, the improved FL-based control system reduces fuel consumption by at least 9% over the testing real-world cycle.
- Compared to the conventional FL-based system, the improved FL-based control system reduces fuel consumption by at least 7% over the testing real-world cycle.
- The 2-second observation window is best for learning from backward horizons achieving the lowest fuel consumption of 5.88 litres/100km, compared to the fuel consumption obtained when using other lengths of observation windows (1/3/4/5/10/20/30/40/50s).Fig. 3-3. Concept of back-to-back competitive learning mechanism
Fig. 3-4. Real-time performance boosted by the BCLM
(c) AFR Controlling and Optimization of GDI engines
- A discrete fuzzy logic PI controller proposed in this research has been proved with simple structure, easy tuning, excellent tracking performance, and high robustness.
- It has the same linear structure as that of the conventional PI controller, but has non-constant coefficient and self-tuned control gains (they are the nonlinear functions of the input signal);
- The controller is designed based on the classical discrete PI controller, from which the fuzzy control law is derived. Membership functions are simple triangular ones with fuzzy logic if-then rules.
- The controller can reduce the damping of oscillations occurring in the fuel injection system and improve the air/fuel ratio dynamic stability.Fig. 3-5. Research target Jaguar V6 GDI engineFig. 3-6. Schematic diagram of AFR fuzzy control system
- The proposed MFO scheme uses CA approach and can visualise the relationship between engine step gain scenarios and designed MF patterns to precisely determine its scalar parameters for AFR regulation of GDI engines.
- According to the CA-based MFO, the customised MF has the more efficient adjustment in with its homogenization can keep trend characteristics of designed MFs while repairing the insensitive interval.
- A comparison is performed with a commonly PI controller at the absence of PI-like fuzzy control policy. The FKBC optimised by CA-based MFO outperforms the PI controller, in term of both ITAE and convergence time. At 8-10% step gain, the fuzzy logic strategy at most decreases ITAE and convergence time by 50.0% and 52.2% respectively.
- A comparative study with existing typical MF in the FLC indicates that, it is worth mentioning that the FKBC optimised by CA-based MFO can reduce ITAE up to 82% with 8-16% step gain compared to typical MFs.Fig. 3-7. Results of corresponding analysis
- An enhanced intelligent PI-like Fuzzy Knowledge Based Controller (FKBC) is developed;
- Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm is applied for the transient calibration of controller parameters;
- An alternative time-domain objective function is applied for the transient calibration program without the need for prior selection of the search-domain;
- The real-time transient performance of the enhanced PI-like FKBC is investigated on the Air/Fuel Ratio (AFR) control system of a Gasoline Direct Injection (GDI) engine;
- The enhanced PI-like FKBC is able to reduce the settling time and oscillations more effectively with better efficiency and accuracy.
Fig. 3-8. Performance comparison of AFR regulation
2. Nature-inspired Algorithm based Development
(a) Design optimisation and real-time control of HEVs
- A novel algorithm for hybrid electric powertrain intelligent sizing is introduced and applied.
- The proposed CAPSO algorithm is capable of finding the real optimal result with much higher reputation.
- Logistic mapping is the most effective strategy to build CAPSO.
- The CAPSO gave more reliable results and increased the efficiency by 1.71%.
Fig. 3-9. Electrified off-road vehicles
Fig. 3-10. Improved CAPSO algorithm interface
- The proposed has the capacity of finding global optima with much faster computing speed comparing with GA.
- The OSIP can optimize the vehicle performance in real-time with a maximum prediction horizon size of 35s.
- The vehicle with OSIP outperforms the system without it in energy saving at all initial battery SoC level, and it has more potential in fuel saving when initial battery SoC is high.
- The proposed energy management method is robust and reliable, and up to 17% fuel and 13% total energy loss can be saved via the proposed cyber-physical control.Fig. 3-11. workflow of swarm intelligent optimization programming
(b) Multi-objective Optimization of V2V-based Cooperative Driving
Multi-objective optimization of the V2V-based cooperative driving and hybrid energy management, involving both the external vehicular dynamics and the internal powertrain dynamics.
This study implemented and compared two optimization methods, Pareto and weighted sum, to simultaneously improve energy consumption, tracking capability and ride comfort.
The optimized control scheme can provide significant reduction regarding tracking error (by ~ 70 %) and energy consumption (by ~ 8 %) under various driving conditions, while at the same time satisfy ride comfort need.Fig. 3-12. Multi-objective optimization with Pareto and weighted sum methodsFig. 3-13. Pareto optimal results for 5*WLTC driving cycle
(c) Intelligent Calibration of a Dual-loop EGR Controller
- A novel calibration method for the controllers on diesel engine is introduced and applied;
- The intelligent tuning method based on CAPSO could reduce the overshoot by at least 59.9% and reduce the settling time by at least 35.4% when compared with the baseline setting. A 0.69% drop of the BSFC result is found when comparing the TMPC controller with the PID controller during transient scenarios
- The proposed calibration algorithm is capable of finding the real optimal result with much higher reputation.
- The experiments prove that these improvements through the proposed calibration method could be reappeared on a real engine.Fig. 3-14. Algorithm interfaceFig. 3-15. HiL results
(d) Engine Modelling and Optimal Feedback Control
- A new computational intelligence approach to calibrate internal combustion engine without the need to an engine model or massive test bench experimental data is developed;
- The Strength Pareto Evolutionary Algorithm 2 (SPEA2) is applied to this automatic engine calibration process;
- The developed calibration approach can automatically find the optimal engine variable set which can provide the best fuel consumption and PM emissions, with good accuracy and high efficiency;
- The developed calibration approach can improve the automatic level and reduce the time for engine calibration process dramatically.Fig. 3-16. Found optimal solutions
3. Machine Learning based Development
(a) Model-Free Predictive Energy Management
- Model-free predictive energy management method can improve the vehicle efficiency from increasing 1) the learning time from real-world interaction and 2) the prediction step length;
- The proposed learning strategies can optimise the control policy in real-time with a maximum prediction step size of 65;
- R2T learning strategy is the most effective n-step reinforcement learning strategy for model-free predictive energy management, which outperforms other proposed strategies in terms of same prediction step length and full performance in real-time;
- The proposed model-free predictive energy management method is robust and reliable for energy saving and it outperforms the conventional model-based method by saving up to 14% energy.Fig. 3-17. Aircraft towing scenario with V2X communicationFig. 3-18. Distributed control framework for model-free control
Fig. 3-19. Self-learning process
(b) Double-Q Learning based Energy Management
- Charge-sustaining control strategy is optimised online with double Q-learning;
- Two action execution policies are proposed for overestimation prevention;
- The random execution policy is shown the most effective and stable action execution policy;
- The method can save energy by more than 4% in selected real-world driving conditions.
Fig. 3-20. Layered control framework for model-free charge-sustaining control
Fig. 3-21. Learning performance of three control strategies