A Multiobjective Linear Time-varying Model Predictive Control Strategy for a Battery/Supercapacitor Hybrid Energy Storage System

Chao Jia Author

03/29/2021 Added

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This work proposes a multiobjective, linear, time-varying model predictive control strategy is proposed to optimize the current split between battery and supercapacitor of a hybrid energy storage system used in electric vehicles.

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[00:00:00.660]Hello. Welcome to my presentation.
[00:00:02.700]I'm Chao, a PhD student of electrical engineering.
[00:00:06.780]Here, I'm going to present my research that I conducted with Dr.
[00:00:10.350]Wei Qiao on multiobjective linear time-varying model
[00:00:14.370]predictive control strategy for a battery supercapacitor hybrid energy storage
[00:00:19.320]system.
[00:00:21.090]Battery and supercapacitor are two types of energy storage devices.
[00:00:25.080]Since they have complimentary power properties,
[00:00:28.980]they can be integrated as a battery supercapacitor hybrid energy storage
[00:00:33.480]system, which is referred to as the HESS hereafter.
[00:00:37.890]HESS has been widely used in electric vehicles,
[00:00:40.740]storage-enabled microgrid, and uninterruptible power supplies.
[00:00:45.630]With a suitable power management strategy, hybridization of battery
[00:00:49.740]and supercapacitor can take advantage of both energy storage devices
[00:00:54.810]to improve the energy efficiency of the HESS while prolonging battery lifetime.
[00:00:59.070]Based on the literature study,
[00:01:02.490]the HESS prediction models in prior literatures do not consider parameter
[00:01:07.050]variations with respect to the batteries state-of-charge and,
[00:01:11.880]therefore, cannot guarantee the satisfactory model accuracy
[00:01:16.770]over the entire SOC range. To bridge this research gap,
[00:01:22.290]this work proposes a new, linear time-varying (LTV for short) prediction model.
[00:01:27.150]for the HESS. Furthermore, according to the proposed model,
[00:01:31.830]a multiobjective LTV-MPC strategy is proposed to
[00:01:36.540]optimally split the current between the battery and the supercapacitor.
[00:01:41.250]At last, a scaled-down experimental
[00:01:44.250]setup is developed to validate the proposed strategy.
[00:01:50.100]Figure 1 shows you the block diagram of the electric vehicle powertrain
[00:01:53.790]configuration studied in this work.
[00:01:56.700]The major components include the battery, supercapacitor.
[00:01:59.790]DC/DC converter, inverter,
[00:02:01.920]electric motor, and the power management strategy. The battery equivalent circuit
[00:02:06.750]model is shown in Figure 2,
[00:02:09.710]which is composed of three pairs of resistor-capacitor branches,
[00:02:15.360]an open circuit voltage, and a series resistance.
[00:02:19.620]The values of these eight parameters depend on the batteries state-of-charge.
[00:02:24.900]Figure 3 shows the equivalent model of the supercapacitor
[00:02:29.870]The three resistor-capacitor branches represent different time constants.
[00:02:35.220]These two equivalent circuit models
[00:02:39.750]can be expressed as a group of differential equations,
[00:02:43.740]which include unknown parameters that
[00:02:46.710]are required to be identified by the testing data.
[00:02:51.480]Figure 4 and Figure 5 show the battery and supercapacitor voltage curves
[00:02:55.140]of the test and simulation results, respectively,
[00:03:00.490]The simulation results agree well with the experimental results,
[00:03:05.590]so the equivalent circuit models proposed in this work
[00:03:09.460]offer a good modeling accuracy.
[00:03:13.270]Next, I will introduce the proposed
[00:03:17.590]LTV-MPC strategy.
[00:03:19.780]The model developed in the last section can be stacked into a discrete-time
[00:03:24.220]state-space model of the HESS,
[00:03:28.180]given by these two equations.
[00:03:29.680]where x is the state vector, u is the control variable, D is the
[00:03:34.120]measured disturbance,
[00:03:36.430]y is the output vector, and A, B, C,
[00:03:40.210]and D are matrices of the state-space model.
[00:03:44.860]There cost functions J1, J2, and J3 are considered in the MPC
[00:03:49.450]strategy of the HESS. J1 is the power losses of HESS.
[00:03:54.010]J2 represents the battery
[00:03:56.320]current variations. J3 is a penalty
[00:03:59.320]term on the state-of-charge of the supercapacitor.
[00:04:02.890]Then, the following combined objective function J is used to evaluate
[00:04:07.870]the overall performance.
[00:04:10.660]where omega1 and omega2 are weighting factors. By minimizing the
[00:04:15.280]combined objective function J, the energy efficiency of the HESS
[00:04:19.480]can be improved to offer a longer driving range
[00:04:22.900]via J1 and the battery lefttime can be prolonged via J2
[00:04:29.680]According to the LTV prediction model, the control action at time k
[00:04:33.700]is obtained by minimizing the following multiobjective function
[00:04:38.440]over a prediction horizon Np steps. We need to solve this
[00:04:43.390]online constrainted optimization problem. where Nc is the
[00:04:47.860]control horizon. U(k) is the sequence or the supercapacitor currrent
[00:04:52.690]to be optimized. At the time step k,
[00:04:56.830]the MPC receives the new measurements or estimations of the current states and
[00:05:01.390]then solves the constrained
[00:05:04.740]optimization problem to obtain the optimal value of U(k).
[00:05:09.700]Then, the controller only applies the first optimal action to the plant.
[00:05:16.560]The proposed LTV-MPC-based
[00:05:17.790]current split strategy is compared
[00:05:22.710]with two state-of-the-art power management strategies,
[00:05:27.310]a rule-based strategy and a frequency-decouping
[00:05:29.560]strategy for the HESS in EV application.
[00:05:34.590]Figure 6 shows a scaled-down experimental setup for the HESS,
[00:05:38.760]including two battery module, two supercapacitor modules, a bidirectional DC
[00:05:43.230]power supply, a bidirectional DC/DC converter,
[00:05:46.500]and a real-time controller.
[00:05:51.420]Figure 7 compares accumulated energy losses and Figure 8 compares
[00:05:56.340]accumulated current variations of three strategies for a vehicle driving cycle.
[00:06:01.910]The proposal strategy has the least total energy losses.
[00:06:06.560]The accumulated
[00:06:07.730]current variation of the proposed strategy is less than 6.5%
[00:06:12.560]of those of the other two strategies.
[00:06:17.380]Finally, the conclusions are summarized here.
[00:06:21.400]A multiobjective
[00:06:22.570]LTV-MPC strategy was proposed properly distribute the
[00:06:28.030]load current between battery and the supercapacitor to reduce the
[00:06:32.770]energy losses and battery current variations.
[00:06:36.640]The simulation and experimental results validated superiority of the
[00:06:41.440]proposed LTV-MPC
[00:06:42.670]strategy over a rule-based strategy and a frequency
[00:06:47.590]-decoupling strategy.
[00:06:50.200]This work has been accepted on this conference regarding the power electronics
[00:06:55.630]for distributed generation systems.
[00:06:58.720]I would like to thank the Nebraska Center for Energy Sciences Research
[00:07:03.490]and the Nebraska Public Power District for supporting this work.

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A Multiobjective Linear Time-varying Model Predictive Control Strategy for a Battery/Supercapacitor Hybrid Energy Storage System

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