IEEE Vehicle Power and Propulsion Conference (VPPC), September 3-5, 2008, Harbin, China
Development and Performance Validation of an ISG Diesel Hybrid Power-Train for City Buses – Part II: Control Strategy and Road Test Hu Zhong, Jing Feng, Xiaojian Mao, Zilin Ma, Feng Wang, Guoqiang Ao, and Bin Zhuo Institute of Automotive Electronic Technology, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China. Email:
[email protected]
Abstract—This is the second part of the two-part paper to illustrate the control system design and multi-energy strategy, it is the key to fulfill the fuel economy target for ISG hybrid bus. Based on a hierarchical structure ,-the control system is developed with the HCU (hybrid control unit) as the central controller to coordinate the motor, engine and CAN communication protocol. The multi-energy control strategy is consisted of the auxiliary strategy and the main strategy. The first one includes the finite state machine which decides the turning-point from one state to another state and the 2-quad tree of expert system which is adopted to realize the automatic start/stop function; the last one is called as the torque distribution strategy that satisfies the drive demand as well as implements the optimal torque distribution by fuzzy method. The parallel regenerative brake strategy and self-adaptive SOC balance strategy are all cooperated into the fuzzy based torque distribution strategy. The road tests are done to test the performance of ISG power-train and to validate the functions of the control system. The result shows that the fuel economy is 25% better than the traditional bus. Keywords—HEV , ISG, Multi-energy Strategy, Fuzzy Logic, Finite State Machine
I.
INTRODUCTION
From the pervious part of this paper, the ISG configuration, function and architecture are introduced, which provided the basis for the control system development. The control system and the strategy are the key to realize the fuel saving target for HEV. Firstly the control system for the ISG HEV is distributed system because it has several controllers to control and monitor the motor, batteries,diesel engine and vehcle. This system can be treated as a hierarchical structure with the HCU (Hybrid Control Unit) as the main controller[1]. Usually CAN (Controller Area Network) is adopted as the communication method. Thus functions of components controller and HCU should be clearly divided to avoid ambiguity and conflicts. The details of the control system are introduced in chapter II. The control strategy is a set of algorithm to fulfill functions like optimal torque distribution, regeneration brake, battery re-charge and automatic start/stop. The ISG HEV can be viewed as a multi-mode system which contains several typical operating states, and a fuzzy-
C 2008 IEEE 978-1-4244-1849-7/08/$25.00○
based algorithm with several rule bases is utilized to realize different targets in different states[2,3]. The strategy for ISG HEV is illustrated in chapter III. After the development and integration of the control system and the strategy, the acceleration and fuel economy tests are carried out to validate the performance of the ISG bus. II.
CONTROL SYSTEM CONFIGURATION
The hierarchical control topology is utilized by adopting the HCU as the main controller which receives the components’ feedbacks and sensors’ inputs to decide the torque distribution between the diesel engine and the electrical motor, referred figure 1. The CAN communication is adopted to send the torque demand and share the component conditions. 1) HCU sends the torque demand to ISG motor by CAN and sends the simulating APP (Acceleration Pedal) signal to ECU (Engine Control Unit) to realize the torque distribution. It also decides whetherthe diesel engine start or not by sending the ignition signal to ECU. It sends the connecting or disconnecting command to ADM (Automatic Disconnect Module) to control the high voltage connector. HCU is based on a highperformance, versatile Mono-chip processor, MC68376 with Touch CAN module, showed figure 2. 2) BPCM (Battery Package Control Module) monitors the batteries’ conditions,that is the module voltage and total voltage, current and temperature and so on. It calculates the remaining energy (state of charge, SOC) and diagnostic code and feed them back to the HCU. 3) DMCM (Drive Motor Control Module) responds the torque command and dedicate the IGBTs’ action to convert the direct current into alternative current according to the rotor position. 4) ADM controls the connecting, disconnecting and pre-charging process of the high voltage connector. It also monitors the isolation state of the high voltage battery and sends the diagnostic code to HCU.
IEEE Vehicle Power and Propulsion Conference (VPPC), September 3-5, 2008, Harbin, China
Figure 1.
5) 6)
Control system architecture of ISG HEV
ECU manages the fuel system of the diesel engine. IP (Instrument Panel) is the window for drivers to know how much electric energy is consumed and the instant current, voltage, and HEV status.
Figure 2.
III.
realize the optimal torque distribution, regenerative brake and the SOC balancing. One of these functions will be used at any time when the HCU is working.
Hybrid Control Unit
MULTI-ENERGY CONTROL STRATEGY
The HEV vehicle can be viewed as a multi-mode system which often switches from one state to another state. For example, when the vehicle is braking, the motor will generates to produce the brake torque;when the vehicle is accelerating, the motor will provide the assistant power to drive the vehicle. By classifying several operating states, a variable-structure strategy is built to fulfill the torque distribution and start-stop functions. It consists of the auxiliary strategy and the main strategy, illustrated in figure 3. The auxiliary strategy includes operating model switch, automatic start/stop strategy, engine-start strategy and so on. It also calculates the total drive demand torque and the limits the maximal motoring/generating torque of the ISG motor to protect the motor and the batteries. The main strategy concludes the key functions to
Figure 3.
Composition of Multi-Energy Strategy
A. State machine From the operating models analysis of Part I, the motor’s function is decided from the driver’s command and components status. But the input signals often present randomly and the meaning cannot be clear unless several signals presents in a specified logic. More signals, more complicated the control logic is. The Bool logic is hardly to handle multi-logic signals; therefore the Stateflow which is based on the finite state theory is adopted. the state machine actually interpreter the random and multilogic signals, decide the tuning point of the ISG system
IEEE Vehicle Power and Propulsion Conference (VPPC), September 3-5, 2008, Harbin, China
TVeh _ DmD == 0
SOC ≥ SOClow _ lmt
Figure 4.
State Machine for ISG bus
from one model to another model. This strategy is important to a sound and reliable control strategy to ISG hybrid system. In hybrid system, inputs are assigned with priorities to classify them. There are 3 levels of priority, the highest priority is the fault status, for example, the Isolation fault; the second priority is the driver’s command like the acceleration and brake pedal signals; and the last one is the components status. The signals with first and second priority are critical to the vehicle safety, and the third level signal is important to the fuel-saving target. The operating models are defined according to the signals with first and second priority and the signals of third level are considered in the main strategy. The state machine for ISG bus is illustrated in figure 4. B. Automatic start/stop strategy ISG system enables the engine stopped when the bus is waiting for the green light or during the deceleration process. When the driver presses the acceleration pedal, the engine will be dragged to the idle speed and start automatically in short time. This strategy is important to the fuel-saving target of the ISG bus. Different to the state machine, the start/stop strategy has only 2 status, engine start or engine stop. The decision-making process is the key. There are many considerations like the road conditions and the position of the transmission should be considered before stopping the engine. For example when the vehicle is stopped but the driver is pressing the clutch pedal and non-neutral is selected, the engine should not be stopped. The automatic stop function is summered in figure 5, seven factors are considered and the 2-quad tree structure is adopted. 1) 2) 3) 4) 5)
Veh_Status means that the vehicle is now braking, cruising or parked? Veh_Neutral_Flag means that the neutral gear should be selected to enable automatic stop. The vehicle total demand
TVeh _ DmD should be
zero . The battery SOC should be greater than its low limit. DM_Temp_Warn indicates that the motor’s temperature is normal or not? If temperature is too high, the automatic stop is not allowed.
Figure 5.
6)
7)
Automatic stop function
Timeb2stop means that the interval of two stop should be greater than a specified value to prevent the automatic stop’ frequency. Stop_Drive_Flag is a flag which combines the driver’s action which is used to indicates the road conditions are allowable to enable the automatic stop.
C. Fuzzy-based torque distribution strategy The dominant strategy is about the torque distribution which will carry out the functions like motor assistance, SOC balance and regeneration. The fuzzy logic with Mamdani rule mechanism is adopted to handle the torque distribution. It adopts the vehicle torque demand TVeh _ DmD , SOC and engine speed and the maximal engine pedal factor
nICE as inputs
α ICE and the motor
torque TDM as outputs. Four sub rule-bases and more than 100 rules are built, seeing the figure 6. The fuzzy method[2,3] is reviewed as the extension of the rule-based strategy, but it will further improve the system efficiency by adopting the optimal thought. The optimal method normally uses the cost function to select the best combination of the motor torque and engine torque to minimize the instant fuel economy. But because of the heavy calculation burden it is replaced by the static maps which have been optimized off-line. Therefore a 3dimension map that has the motor torque demand as the output is formed according to TVeh _ DmD , SOC and nICE , so a point-to-point optimization can be implemented. The fuzzy logic adopts proper methods to implement the interpolation mechanism to guarantee the coverage of the rule-base to the scope of all inputs[4]. When the vehicle is driving forward,
TVeh _ DmD is
IEEE Vehicle Power and Propulsion Conference (VPPC), September 3-5, 2008, Harbin, China
Figure 6.
Figure 7.
The fuzzy-based energy control strategy
Vehicle load zone
Figure 8.
λ-curve
positive. From figure 7, TVeh _ DmD can be classified as high
proportional to
power demand zone, engine optimal zone and low torque zone. In high power zone, the motor should help the vehicle because the engine power is not enough to propel the vehicle. In low torque zone, the motor will generator to improve the engine’s load and charge the batteries. Several torque line is inserted into to refine these zones,
and should be limited when it locates in heavy brake zone where the vehicle is undergoing the heavy deceleration for safety consideration. The SOC and nICE also should
TVeh _ DmD can be more
When SOC is in very low zone (VL), the charge-sustain strategy will be activated to command the engine produce more power to charge the battery even the vehicle is
thus the output for a specified
accurate. For any point of ( TVeh _ DmD , array of under
nICE ), the torque
Ti is gotten by interpolating each torque line
nICE . For each torque zone an index of λi
is
assigned. Thus a λ-curve is formed and the torque zone factor
λ
of
TVeh _ DmD is get from the
λ represents
which zone the
λ-curve, referring figure 8.
TVeh _ DmD locates and the
optimal output is deduced from the rule base. When the vehicle is braking,
TVeh _ DmD is negative. In
these cases, the motor generates to recover parts of motive energy. The motor generating torque should be
TVeh _ DmD if it locates in light brake zone,
be considered. The regenerative brake is forbidden if SOC is too high or
nICE is too small.
accelerating. And when
TVeh _ DmD is in optimal zone, the
motor is also used to charge the battery, and the engine’s efficiency is improved also. In these cases, the off-line optimization is conducted first and the best combination of ( TICE ， TDM )is found. Then
TDM is converted into
fuzzy rules and the optimal and charge-sustain rule-base are built. IV.
STRATEGY OPTIMIZATION
The control strategy should be validate and optimized before the road test is carried out. In fuzzy based strategy,
IEEE Vehicle Power and Propulsion Conference (VPPC), September 3-5, 2008, Harbin, China
the inputs membership function and the rule-base are all should be optimized firstly. Optimization is conducted by Hardware-in-the-loop test bench[5], referring figure 9.
soaring up. At the end of the test the vehicle brakes down, the ISG motor generates to produces the brake effect to recover partial motive energy.
'
In figure 9, two lines of the motor torque ( TDM , TDM ) are illustrated. The first one TDM is the result of the untested algorithm. The motor torque should be continuously increasing when the vehicle is accelerating, but there are two gaps of
TDM in this process which are
indicated by two circles in figure 9. Thus the bugs of the control algorithm are detected. After the correction, the ' DM
motor torque ( T
) calculated by the new algorithm is
showed in figure 9 also. Through Hardware-in-the-loop test, the bugs of software can be eliminated at earlier stage of development to ensure a higher reliability, therefore the performance of the ISG bus is guaranteed.
Figure 9.
Figure 11. 0-50km/h acceleration test result
B. Fuel Economy Test The fuel economy test adopts the 4-speed cycle which is the national standards, referring figure 12. The motor will assist the vehicle when it is starting or accelerating, and generates when the vehicle is cruising to charge the batteries. Thus the batteries are charged during the cycle and SOC remains unchanged. After repeated tests, the fuel economy is 25% better than the original bus and the SOC difference at the end of test is zero.
Hard Acceleration Test Using RT-HILS
V.
ROAD TEST
The acceleration and fuel economy test are conducted to validate the performance of the ISG prototype bus. The 4-cycle is designed to test the fuel economy and the fuel economy of the original bus is tested previously. The prototype bus is illustrated in figure 10.
Figure 12. 4-speed cycle test
CONCLUSION 1)
2)
3) Figure 10.
ISG prototype bus
A. Acceleration test Acceleration test includes two experiments: the 050km/h and 0-80km/h, the time of 0-50km/h is 9.5% shorter than the original bus, and the 0-80km/h is almost the same. The test result is shown in figure 11. The ISG motor produces its maximal torque to assist the vehicle when the acceleration pedal is kicked down, and gradually quit the assistance when the engine speed is
4)
5)
The hierarchical control system is set up. The HCU based on 32bits Microcontroller can handle various signals. The CAN protocol with 250kbit/s speed rate is defined and compatible with the SAE J1939 protocol. The multi-energy strategy using the variable structure and fuzzy logic algorithm is developed to fulfill the optimal torque distribution and auto start-stop function. The Stateflow base on finite state machine theory is adopted to realize a sound and reliable state machine to decide the tuning point from one model to another. The 2-quad tree is employed to handle the automatic start/stop function and seven considerations including the driver’s judgment are included. The main strategy is based on the fuzzy logic algorithm which consists of the Mamdani rule reference. It adopts ( TVeh _ DmD , SOC , nICE )as inputs and ( TDM , α ICE )as outputs. Four rule-
IEEE Vehicle Power and Propulsion Conference (VPPC), September 3-5, 2008, Harbin, China
6)
bases are built to fulfill different functions. The acceleration and fuel economy tests are carried out. The time of 0-50km/h test is 9.5% shorter than the original bus. And the fuel economy is improved 20%. ACKNOWLEDGMENT
The authors would like to acknowledge Dr. Wenyong Xiao, Dr. Feng Liang, and Jie Ye from Yuchai Machine Cooperation for their contributions offered in this project. REFERENCES
[1]
[2]
[3]
[4] [5]
Anthony M. Phillips, Miroslava Jankovic, Kathleen E. Bailey, Vehicle System Controller Design for a Hybrid Electric Vehicle, International Conference on Control Applications, Proceedings of the 2000 IEEE, page 297-302 PU;, J.-H., C.-L. YIN;, and J.-W. ZHANG. FUZZY TORQUE CONTROL STRATEGY FOR PARALLEL HYBRID ELECTRIC VEHICLES. International Journal of Automotive Technology, 2005. 6: p. 592-536. B. M. Baumann, G. Washington, B. C. Glenn, et al. Mechatronic design and control of hybrid electric vehicles. IEEE/ASME Transactions on Mechatronics, 2000, vol. 5:58-72. Zhi-hong, X. and Guang, R. Interpolation models of typical fuzzy controllers. Fuzzy Systems and Mathematics, 2004, 18, 67-75. Hu Zhong Ao, G.uoqiang, Yang, L., and Bin, Z., "Development of a real-time hardware-in-loop simulation test bench for hybrid electric vehicle based on multit-hread technologies," , IEEE International Conference on Vehicular Electronics and Safety, 2006. pp. 470-476, Shanghai China, 2006
