Research On Energy Management Strategy And Capacity Configuration Optimization Of Hybrid Energy Storage System For Electric Vehicle

As a low-carbon mode of transport,electric vehicles are of great significance in solving problems such as energy shortage and environmental pollution.The use of lithium batteries as the main power source for electric vehicles can effectively reduce greenhouse gas emissions,thus achieving environmental and low carbon goals.In the automotive context,single lithium battery systems are often subjected to large multiplier and high-frequency charging and discharging conditions.When operating under these conditions for long periods of time,lithium batteries can suffer a rapid decline in performance and capacity.Hybrid energy storage systems(HESSs)for vehicles consisting of supercapacitors and lithium batteries are an effective solution to replace single lithium battery energy storage systems.However,the difference in electrical behaviour between batteries and supercapacitors leads to a difference in their ability to withstand energy and power.Therefore,how to efficiently manage the two different energy sources,batteries and supercapacitors,coordinate the power distribution between them and give full play to their respective advantages has become a key issue in the research of HESSs.In addition,due to the deep coupling of the capacity configuration and energy management strategies of the automotive HESS,uncoordinated capacity configuration and management strategies not only fail to optimise the performance of the system but also accelerate system ageing and even create safety problems.Therefore,it is of great research significance and value to design a joint optimal capacity configuration and energy management strategy for automotive HESSs to improve system power performance,extend the overall system life,guarantee safe system operation and provide solutions for future power sources for intelligent vehicles.This dissertation models the external behaviour characteristics of the automotive HESS,and proposes a power state estimation method,an energy management strategy and a capacity allocation method for the automotive HESS based on a comprehensive electro-thermal coupling model.The main contribution and innovations of this work can be summarized as:1)Aiming at the electrical and thermal behaviour description of the automotive HESS under complex currents and temperatures,a comprehensive electro-thermal coupling model of the system is proposed.Firstly,the equivalent circuit model and thermal model of the battery and supercapacitor are constructed based on Kirchhoff’s voltagecurrent theorem and the principle of energy conservation.Secondly,a comprehensive spatial representation of the electrical and thermal states of the HESS is established in conjunction with a bidirectional DC/DC efficiency model.Thirdly,the parameters of the open-circuit voltage equation of the battery,the thermal model at standard temperature and the electrical behaviour of the system at different temperature points are identified based on the recursive least squares method.Finally,the integrated electrothermal coupling model of the HESS is validated at multiple temperature points and multiple sets of current conditions,providing a theoretical basis and data foundation for subsequent power state estimation and energy management strategies.2)Aiming at the quantitative power estimation of HESS,a state of power estimation method considering temperature constraints is proposed.Firstly,based on the thermal state space expression of the HESS and the generated thermal model,a direct relationship equation from the temperature constraint to the power constraint is constructed.Secondly,cooperating with the current constraint,voltage constraint and charge state constraint,a multi-constraint power state estimation method for the HESS is constructed.Finally,the effectiveness of the multi-constraint-based state of power estimation method is verified under different temperatures and initial charge states.The proposed method can provide a solution for the quantitative description of the safe working zone of the HESS and guarantee the safe and reliable operation of the system.3)Aiming at the problem of designing real-time and efficient energy management strategies for the automotive HESS,an adaptive model predictive control energy management strategy suitable for the automotive environment is proposed by combining power state estimation with rolling optimization concepts.Firstly,the predictive model is constructed based on a comprehensive electro-thermal coupling model of the system,and the corresponding rolling optimization function and feedback correction method are designed.Secondly,the application of multi-constraint-based power state estimation in energy management strategies is implemented by designing inequality constraints in rolling optimization.Finally,the optimal control domain and the prediction domain of the adaptive model predictive control are selected through experiments.The proposed energy management strategy is also verified under three operating conditions.The results show that the adaptive model predictive control can reduce the average current of the Li-ion battery,extend the life of the Li-ion battery and reduce the system energy loss,etc.4)Aiming at the global optimization of the capacity configuration and energy management strategy of HESSs,a decomposition-based multi-objective joint optimization method is proposed.The proposed method can simultaneously solve for the optimal capacity configuration and the optimal key parameters of the energy management strategy.Firstly,the initial capacity of the HESS is designed based on an improved continuous energy density function method.Secondly,a decomposition-based multi-objective optimization method is used to obtain uniform optimal sizing results and different optimal parameter sets for different types of working conditions.Thirdly,based on the grey wolf support vector machine algorithm,a classification and identification method for different types of working conditions is established.Then,the trained grey wolf support vector machine is used to identify the categories of real-time load conditions and to obtain the optimal parameter sets under the corresponding condition categories.Finally,the superiority of the proposed method in terms of computational speed and accuracy is verified by comparing with other multi-objective optimization algorithms and other support vector machine algorithms.5)Aiming at efficient control of the HESS,a complete energy management control system is designed and a prototype of the HESS is built.Through the design of the energy management controller hardware scheme,the construction of the underlying software and the development of the monitoring and control software,the hybrid energy storage system power cooperative control and energy refinement management are achieved,which is of great engineering significance to ensure the safe,reliable and efficient operation of the system.The reliability and effectiveness of the designed energy management control system is verified on a prototype hybrid energy storage system built by combining the supervisory software and programmable DC load.