Control Strategy Of Underground Mining Truck Based O N Driving Condition And Working Condition Recognition

With the development of the transportation industry, the population of vehicle is increasing rapidly. The world is facing urgent situation of energy tension and global warming, and countries have devoted to "energy conservation and emission reduction". The current research has started from reduce the passenger car and light vehicle fuel consumption to improve the off-road heavy vehicle fuel economy. Electric drive mining truck plays an important role in mine haulage. Its loading capacity is large and fuel consumption is huge. The fuel in the cost of mining occupies a large proportion, for which the research in the mining truck fuel economy has great significance.Electric drive system is the core of the electric drive mining truck, in the traction condition it consumes energy from diesel engine, generator to the traction motor, in the braking condition braking resistor consume the regenerative braking energy of traction motor in the form of heat. The performance of electric drive system have a great influence in the dynamic performance and ride quality of the mine truck, it also affects the fuel economy. In this paper, This paper did some research in control strategy of mine car in order to improve the vehicle’s fuel economy.First, in the view of driver-vehicle-road closed loop, this paper analyzed the traditional constant power control algorithm, for the purpose of exploring the influencing factor of vehicle fuel economy. Furthermore a new control strategy is proposed. The strategy identify the driving intention and vehicle running condition, then make the decision of the working point of electric drive system;on the premise vehicle dynamic.The article uses the method of fuzzy recognition to identify the driving intention of acceleration, braking and smooth driving.and applied the actual statistical data to guide the membership functions of identification parameter, which improve the accuracy of the procession of fuzzy recognition.For there is no research in road condition of mining truck, this paper carry out the research of the classification running condition for mining truck. Relying a large amount of real running data, then analyze the working condition based on k means clustering method, which obtained the classification of working condition with vehicle operating power information. Then apply the LVQ neural network technology to identify the four types of established running condition.On the basis of the working condition of driver intention recognition and identification the strategy of electric drive mining truck is put forward. And developethe forward simulation platform for electric drive mining truck with Maplesim/Simulink, simulation experiments are carried out under different conditions. It turned out that compared with constant power control strategy,t he control strategy of this paper is beneficial to improve the adaptability of working conditions and vehicle fuel economy.