Study On Fuel Cell Hybrid Vehicle Integrated Thermal Management System Using Cooperative Distributed Optimal Control

With the implementation of the Emission Peak and Carbon Neutrality policy,Fuel Cell Hybrid Vehicle(FCHV)has been focused widely due to its characteristics of high energy efficiency,low noise,zero pollution and so on,among which thermal management is the key to improve the energy efficiency of FCHV.The existing thermal management system structure of most vehicles is usually designed independently for different high-power heat generating parts,resulting in complex system structure,high cost and low heat utilization rate.Therefore,the integrated design of thermal management system and its optimization management are explored to further reduce system energy consumption while ensuring that all power components work at the optimal temperature,thus improving the heat efficiency of the vehicle heat,which has high practical engineering significance and theoretical research value.In this paper,a high-power FCHV of 75 k W is considered.First,the integrated structure design is carried out for the thermal management system composed of fuel cell system,lithium battery,motor,power control unit(PCU)and heat pump air-conditioning heat exchanger system.The thermal energy consumption between various components is fully coupled and exchanged through the coolant and heat exchanger,providing hardware basic support for system-level thermal management.Based on the design of integrated thermal management system,the coupling characteristics of electric and heat field of each component are modeled according to the theory of heat transfer and electrochemistry.At the same time,the heat generation model of each component of the system,the heat transfer model of solid wall and fluid,and the heat transfer model of fluid and fluid are considered in order to accurately describe the temperature stability/dynamic characteristics of the integrated system.On this basis,the system variables are decomposed optimally based on Weighted Directed Network Community Detection Algorithm.Firstly,the nonlinear system model is linearized at common operating points,system variables are regarded as nodes,and information transfer paths between variables are regarded as edges,so the directed network model of the system is obtained.On this basis,Graph Entropy is used to describe the importance of each edge,and Dijkstra algorithm is used to find the shortest path of information transfer between variables to obtain the Weighted Adjacency Matrix,which can accurately describe the weight of each edge.Finally,based on the matrix,the Modularity evaluation index and the Fast Unfolding Algorithm were used to search the subsystems presenting strong coupling relationship in the system,and the system was decomposed into two subsystems,which providing a theoretical basis for the design of the controller in the next step.Based on the decomposed subsystems,Cooperative Distributed Model Predictive Control(CO-DMPC)algorithm was developed to track the optimal temperature of each component.Under the condition of satisfying the coolant and cooling air mass flow constraints,the coordinated and optimized temperature control of fuel cell system,lithium battery,motor and PCU is realized.As a comparison,Centralized Model Predictive Control(CMPC)algorithm is also designed.Finally,we build the system simulation environment and control algorithm based on Matlab and Simulink,and evaluate the performance of the control system from three aspects: optimal temperature tracking accuracy,solving time and robustness.The results show that the distributed control can achieve similar control effect to the centralized control in terms of tracking accuracy,and the working temperature of each component is controlled within the optimal temperature range.The absolute value of temperature fluctuation of each component is accurately controlled within 4℃ in the whole operating condition.Compared with centralized control,the average single-step solution time of distributed control is reduced by 17.95%.In terms of robustness,it can converge to steady state when the sensor and actuator are interrupted by intermittent faults.