Hybrid electric vehicles (HEVs) combined with more than one power source offer additional flexibility to improve the fuel economy and to reduce pollutant emissions. The dynamic-programming-based supervisory controller (DPSC) presented here investigates the fuel economy improvement and emissions reduction potential and demonstrates the trade-off between fuel economy and the emission of nitrogen oxides (NO x) for a state-of-charge-sustaining parallel HEV. A weighted cost function consisting of fuel economy and emissions is proposed in this paper. Any possible engine-motor power pairs meeting with the power requirement is considered to minimize the weighted cost function over the given driving cycles through this dynamic program algorithm. The fuel-economy-only case, the NO x-only case, and the fuel-NO x case have been achieved by adjusting specific weighting factors, which demonstrates the flexibility and advantages of the DPSC. Compared with operating the engine in the NO x-only case, there is 17.4 per cent potential improvement in the fuel-economy-only case. The fuel-NO x case yields a 15.2 per cent reduction in NO x emission only at the cost of 5.5 per cent increase in fuel consumption compared with the fuel-economy-only case.

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The hardware-in-the-loop (HIL) platform for I. INTRODUCTION hybrid electric vehicle in this paper features the real-time Hybrid electric vehicle are the hot choices for automakers characteristic and flexibility with a simple architecture as the next generation of alternative power train to satisfyT which consist of a PC to display the simulation result and ... calculate the model, a simulation board (HIL-ECU) to mor an mor srnetmiiorguaosadthwrl generlate the analogue signalsaton measre tHeL Hybri ever-increasing petrol price. But the development of control generate the analogue signals and measure the Hybrid system for hybrid vehicle requires lots of test and validation Control Unit (HCU) output, and a USB-CAN card to stefohyrdviceequie oso etadvldto which are very costly and time-consuming. Besides there implement CAN communication. The RT-HIL adopts exist many types of different hybrid configuration which three methods to guarantee the real-time ability: 1) means the difficulty to build the prototypes for each one. So utilizing the multi-thread technology based on windows the HIL test is employed to simulate the hybrid vehicles and operating system with high-resolution timing function to will be used to study the control strategy of HCU. proceed the model calculation; 2) adopting high speed MCU as key component of the simulation ECU; 3) using HL[]pafr isntaovldeloruomie the high speedcAonet a the ommuniation mEthod. The ucontroller development, and there exist many applications of thei-hread ChN is ahe tom realizethedelTie them, like dSPACE Autobox [2], Ford Laboratory's HIL simulation by create three independent threads to handle system[3], Mathwork xPC target system[4]. There are two the model calculation, the monitor-control interface and

This paper presents a systemic design method of a multi-energy management control strategy by using fuzzy logic control to realize the optimal torque distribution between the internal combustion engine and electric motor. The controller which is the brain of the hybrid electric vehicle receives vehicle information such as the acceleration and brake pedal, the engine speed, and the absolute state of charge of the battery package as inputs and sends a direct torque command to control the electric motor and the engine throttle angle to command the diesel engine. Fuzzy control logic consists of three parts to realize the interpolation mechanism: the trapezoid membership, the Mamdani rule reference machine, and the centre of gravity as the defuzzification method. A novel technique to fuzzify the vehicle torque demand that can enable a point-to-point optimization is introduced and more than 130 rules are classified into four subrule bases. Hardware-in-the-loop simulation results reveal that the efficiency of the integrated starter—generator hybrid system has been improved greatly and the fuel economy is better than the default rule-based control strategy.

A torque distribution strategy was designed by using fuzzy logic to realize the optimal control. The vehicle load zones were dynamically divided into several zones by several torque lines to indicate the drivers demand and the high or low efficient operating areas of the diesel engine. The fuzzy logic controller with trapezoid membership function and Mamdani rule reference mechanism was utilized. There are over 100 rules used in this fuzzy-based torque distribution strategy which are sorted into four rule-bases. The fuel economy and acceleration tests were designed to test and validate the integrated starter/generator (ISG) bus performance using fuzzy-based torque distribution strategy. The fuel economy is improved 7.7% compared with the rule-based strategy. Finally the road test results reveal that there is about 15% improvement of fuel economy. And the 0-50 km/h acceleration time is 9.5% shorter than the original bus.