Abstract
This paper deals with the energy management strategy (EMS) for an on-board semi-active hybrid energy storage system (HESS) composed of a Li-ion battery (LiB) and ultracapacitor (UC). Considering both the nonlinearity of the semi-active structure and driving condition uncertainty, while ensuring HESS operation within constraints, an adaptive model predictive control (AMPC) method is adopted to design the EMS. Within AMPC, LiB Ah-throughput is minimized online to extend its life. The proposed AMPC determines the optimal control action by solving a quadratic programming (QP) problem at each control interval, in which the QP solver receives control-oriented model matrices and current states for calculation. The control-oriented model is constructed by linearizing HESS online to approximate the original nonlinear model. Besides, a time-varying Kalman filter (TVKF) is introduced as the estimator to improve the state estimation accuracy. At the same time, sampling time, prediction horizon and scaling factors of AMPC are determined through simulation. Compared with standard MPC, TVKF reduces the estimation error by 1~3 orders of magnitude, and AMPC reduces LiB Ah-throughput by 4.3% under Urban Dynamometer Driving Schedule (UDDS) driving cycle condition, indicating superior model adaptivity. Furthermore, LiB Ah-throughput of AMPC under various classical driving cycles differs from that of dynamic programming by an average of 6.5% and reduces by an average of 10.6% compared to rule-based strategy of LiB Ah-throughput, showing excellent adaptation to driving condition uncertainty.
Highlights
As concerns about energy crisis and environmental issues mount, electric vehicles (EVs) have been considered as the most promising substitutes for internal combustion engine vehicles
The results reveal that receding optimization process of adaptive model predictive control (AMPC) effectively resists disturbance uncertainty caused by varying receding optimization process of AMPC effectively resists disturbance uncertainty caused by varying driving condition, while different cycles have different characteristics and constant rule-based controllers (RBCs) parameters driving condition, while different cycles have different characteristics and constant RBC parameters can’t adapt to cycle change
In contrast to standard model predictive control (MPC), the AMPC solves the control action by calculating a quadratic programming (QP) problem in which prediction model matrices are updated online and model states are estimated by time-varying Kalman filter (TVKF)
Summary
As concerns about energy crisis and environmental issues mount, electric vehicles (EVs) have been considered as the most promising substitutes for internal combustion engine vehicles. CVX is an effective tool in dealing with optimization problems with multi-states, whereas, it is only applicable to convex cost functions with convex constraints, and its application in nonto extend battery life, as well as reduce total energy consumption. From the literature described above, optimization-based EMSs are preferred over RBC-based paper, an AMPC-based EMS for semi-active HESS is proposed to handle the model nonlinearity. Simulation results under various driving cycles differ from those of DP by indicating that AMPC is able to minimize the LiB Ah-throughput online effectively when the HESS an average of 6.5% and reduce an average of 10.6% compared to RBC, indicating that AMPC is able is nonlinear and the driving conditions are uncertain.
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