Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles

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.

Charge Equalization Controller Algorithm for Series-Connected Lithium-Ion Battery Storage Systems: Modeling and Applications

“…A Li-ion battery of nominal 15.5 Ah and 3.7 V is chosen in EV applications because of its high output rating [6][7][8]12]. The generic model is applied for the Li-ion battery because this model is flexible to configuration at any values for Li-ion, lead-acid, Ni-Cd, and Ni-MH batteries [28]. The generic model is similar to the Shepherd model, except it is free of algebraic loop problems.…”

“…Equation (3) presents the approximate relationship between the OCV (V OC ) and SOC of the Li-ion battery found by curve fitting the experimental values with a norm of residuals of 0.016062 from the Figure 3. The SOC-OCV relationship is used to estimate SOC of the Li-ion battery [7,9,[28][29][30][31][32][33][34][35][36][37][38][39][40] for implementing the equalization control algorithm of the equalization controller model. During the process of charge equalization the controller records the terminal voltage value of each cell and converts to SOC value of the respective cell by using the relationship Equation 3, considering negligible temperature effect on the internal resistance of battery model.…”

Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

“…Operational cost can be employed as one of the main objective functions in MES scheduling. The purpose of MES scheduling optimization is to determine the amount of energy generated by the controlled units in the presence of uncontrolled units in a way that the operational cost is minimized while complying with the various operational constraints [30,31]. The optimization objective is to minimize the expected value of the total operating cost during the predictive horizon:…”

Approach to Hybrid Energy Storage Systems Dimensioning for Urban Electric Buses Regarding Efficiency and Battery Aging

“…For instance, a comparison of four rule-based energy management strategies is presented in [42]. On the other hand, advanced control strategies include predictive control algorithms capable of determining a power-mix in real time, based on the expected load demand and loss models for all system components [9] or aiming at the optimization of the system's performance [43].…”