IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 1, NO. 3, OCTOBER 2015 223 A Supervisory Energy Management Control Strategy in a Battery/Ultracapacitor Hybrid Energy Storage System Junyi Shen and Alireza Khaligh, Senior Member, IEEE Abstract—One of the major challenges in a battery/ ultracapacitor hybrid energy storage system (HESS) is to design a supervisory controller for real-time implementation that can yield good power split performance. This paper presents the design of a supervisory energy management strategy that optimally addresses this issue. In this work, a multiobjective optimization problem is formulated to optimize the power split in order to prolong the battery lifetime and to reduce the HESS power losses. In this HESS energy management problem, a detailed dc–dc converter model is considered to include both the conduction losses and the switching losses. The optimization problem is numerically solved for various drive cycle data sets using dynamic programming (DP). Trained using the DP results, an effective and intelligent online implementation of the optimal power split is realized based on neural networks (NNs). The proposed online intelligent energy management controller is applied to a midsize electric vehicles (EV). A rule-based control strategy is also implemented in this work for comparison with the proposed energy management strategy. The proposed online energy management controller effectively splits the load demand and achieves excellent result of the energy efficiency. It is also estimated that the proposed online energy management controller can extend the battery life by over 60%, which greatly outperforms the rule-based control strategy. Index Terms—Electric vehicle (EV), hybrid energy storage system (HESS), multiobjective optimization, neural networks (NNs), ultracapacitor (UC). I. I NTRODUCTION B ATTERIES are used in electric vehicles (EV) as the main energy storage components, sized to provide both the required power and energy demand of the car. This leads to an oversizing of the batteries in order to deliver high power and to avoid unwanted degradation due to acceleration and braking. In order to improve the battery lifetime, ultracapacitor (UC)-based hybrid energy storage system (HESS) is proposed and investigated [1]–[3]. HESS, with complementary features of high energy and high power density, can have large energy storage and good power performances at the same time. In addition, battery lifetime can be significantly prolonged through peak shaving by UC. Manuscript received May 22, 2015; revised July 02, 2015; accepted August 01, 2015. Date of publication August 04, 2015; date of current version October 15, 2015. This work was partly supported by the National Science Foundation under Award 1307228 and by Genovation Cars Inc. The authors are with the Power Electronics, Energy Harvesting, and Renewable Energies Laboratory (PEHREL), Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742 USA (e-mail: jshen128@umd.edu; khaligh@ece.umd.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TTE.2015.2464690 With the two independent energy storage components, a degree of freedom arises for the load demand distribution. Effectively splitting the load demand between the UC pack and the battery pack is a major challenge. Different energy management strategies have been proposed in literature for decoupling power in HESSs [3]–[19]. Majority of the prior work is proposed for fuel cell systems hybridized with UC and/or batteries. Similar energy management techniques can be adopted for battery/UC hybridization. Strategies that are based on heuristics or empiric experience can be easily implemented by rule-based control algorithms [4]–[6] or using fuzzy logic methods [7]–[10]. The simple filter-based or frequency-based power split strategies have been implemented in [11] and [12]. However, the filter-based strategies have several deficiencies. One critical disadvantage of a simple filter-based method is that it introduces large phase shift as reported in [12]. The effectiveness of using UC system in reducing the battery current and losses is degraded due to this phase shift. In addition, one needs to adjust the cutoff frequency or other parameters in the filter design for different load demands in order to obtain effective power split results in different situations. Despite being simple and numerically efficient, the frequency-based methods are heuristic controllers, not optimal power split solutions. These heuristic controllers do not guarantee an effective control in different driving situations. In order to find the global optimal solution, optimization-based methods such as linear programming (LP) [13] and dynamic programming (DP) [14], [15] have been applied with the assumption that the entire drive cycle and load demand profile are known. Therefore, these global optimization techniques do not offer real-time or online solutions. The problem solved by DP can be used as a benchmark for the performance of other strategies. To provide online energy management, [16] formulated an instantaneous cost function and solved the problem using convex optimization at each time instant. To ensure that UC can provide sufficient power at each instant, a UC reference voltage is set based on experience, which is not guaranteed to be optimal. Another online implementation strategy is model predictive control (MPC) strategy [17], in which the optimization problem is set over a prediction horizon. However, it requires heavy computation for problems with complex structures and might affect the response time of the energy management. In this work, an accurate model of the dc–dc converter is included and presented to give insight on the HESS power flow control to avoid low-efficiency operation. The HESS power 2332-7782 © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. Authorized licensed use limited to: the Leddy Library at the University of Windsor. Downloaded on February 16,2022 at 05:00:46 UTC from IEEE Xplore. Restrictions apply. 224 IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 1, NO. 3, OCTOBER 2015 Fig. 1. Bidirectional dc–dc converter in the HESS. flow is optimized with two goals: 1) to minimize the HESS power losses and 2) to prolong the battery lifetime by minimizing battery current magnitude and variations. Although these two goals are important in HESS energy management problem, the prior work did not consider both of them simultaneously nor provide the optimal power split for these two goals [3]–[19]. In the HESS energy management problem, it is very challenging to design causal controllers for real-time implementation that can achieve optimal performances. In our work, we tackle this problem by combining the methodologies of DP and neural network (NN) to develop an efficient real-time energy management strategy. DP is used to obtain global optimal current flow in HESS. An online intelligent energy management controller is then implemented based on training an NN using the DP optimization results. This combination of DP and NN can approach the near-optimal control strategy in real-time [18], [19]. In this work, we combine and apply these methodologies to a power split problem in EV applications with battery/UC HESS, for the first time. This paper is organized as follows. Section II provides system-level model of HESS, including the efficiency model of the bidirectional dc–dc converter that interfaces the UC pack with the battery pack. A multiobjective optimization problem is formulated in Section III and solved using DP algorithm. Section IV presents the developed NN for the online energy management. The battery lifetime improvement of the offline and online optimization results is evaluated in Section V. Finally, the conclusion of this work is provided in Section VI. II. HESS M ODEL This section describes the models used in the HESS including the battery/UC and the bidirectional dc–dc converter. The propulsion machine and inverter group is modeled as a current source connected to its dc bus, which draws the current determined by the drive cycle profiles. In the HESS, a bidirectional dc–dc converter is used to interface the UC to the dc bus. Different HESS topologies have been discussed extensively in [1] and [2]. In this work, we adopt the topology of Fig. 1. This bidirectional dc–dc converter is composed of an inductor L, two power MOSFETs S1 and S2 , and two body diodes D1 and D2 . The low-side voltage of the dc–dc converter is equal to the UC terminal voltage Vuc ; and the high-side voltage is the battery terminal voltage Vb . Decoupled from the dc bus, the UC voltage can swing in a wider range; thus, it can improve the UC utilization factor. The circuit models of the HESS components are shown in Fig. 2. The following sections describe the HESS modeling based on these circuit models. Fig. 2. Model of: (a) battery/UC; (b) inductor; (c) MOSFET; and (d) body diode. TABLE I BATTERY AND UC C ELL C HARACTERISTICS A. Battery/UC Model The battery/UC circuit model is shown in Fig. 2(a). The voltage source Vj,oc represents the open-circuit voltage for battery/UC and Rj is the internal resistance, with j = {b, uc}. In this work, the battery open-circuit voltage Vb,oc is assumed constant during one drive cycle. This is because the adopted LiFePO4 battery has a flat discharge curve in the usable discharge range from 90% SoC to 30% SoC. This work uses a pack of K2 energy high capacity 26650 battery cells [20] with the energy storage of 34 kW·h. One hundred BCAP 2000 UC cells from Maxwell Technology [21] are connected in serial as the UC pack of 203-W·h energy storage. This 34-kW·h battery pack weighs 324 kg and the UC pack weighs 36 kg. The battery pack volume is 145 L while the UC pack is 29.2 L. The dc–dc converter weight is estimated as 30 kg. Given a packing factor of 1.4, the total mass and volume of the battery-UC HESS are 546 kg and 244 L, respectively. The battery and UC cell characteristics are listed in Table I. B. Bidirectional DC–DC Converter Model The bidirectional dc–dc converter works in two different operation modes, as Boost mode and Buck mode. The Boost and Buck operation modes are shown in Fig. 3 with the current direction indicated. In Boost mode, the bidirectional dc–dc converter transfers energy from UC to the dc bus by triggering the power MOSFET S2 ; in Buck mode, the UC pack captures the regenerative braking energy from dc bus through bidirectional dc–dc converter by triggering the power MOSFET S1 . The equivalent circuit model of each component is shown in Fig. 2. The inductance of the inductor is represented as L and the inductor winding resistance is represented by RL . The inductor current is IL , which is equal to the UC current Iuc . The MOSFET is modeled by the on-resistance RSW in its conducting state as shown in Fig. 2(c). The body diode is modeled Authorized licensed use limited to: the Leddy Library at the University of Windsor. Downloaded on February 16,2022 at 05:00:46 UTC from IEEE Xplore. Restrictions apply. SHEN AND KHALIGH: SUPERVISORY ENERGY MANAGEMENT CONTROL STRATEGY IN BATTERY/UC HESS 225 TABLE II PARAMETERS FOR DC–DC C ONVERTER Fig. 3. DC–DC converter operation modes. (a) and (b) are in Boost mode. (c) and (d) are in Buck mode. (a) S2 is ON and D1 is OFF. (b) S2 is OFF and D1 is ON . (c) S1 is ON and D2 is OFF. (d) S2 is ON and D2 is ON. by a resistance RD and a voltage source VD representing the voltage drop across the forward-biased diode in its conducting state as shown in Fig. 2(d). The output capacitance of the dc–dc converter is C and Vc denotes the voltage across. With these circuit parasitic included, a state-space average model is analyzed [22]. As a result, the duty cycle Dboost and Dbuck of the dc–dc converter, in the range of (0, 1), are derived for both boost and buck modes as √ Vb,oc +VD −Idmd Rb +IL (RD −Rsw +2Rb ) − Δ1 Dboost = 2IL Rb (1) 2 Δ1 = (Vb,oc +VD −Idmd Rb +IL (RD −Rsw +2Rb )) −4IL Rb (IL (RD +RL +Ruc +Rb )−Vuc,oc +Vb,oc −Idmd Rb +VD ) (2) √ −Vb,oc − VD + Idmd Rb − IL (Rsw − RD ) + Δ2 Dbuck = 2IL Rb (3) Δ2 = (Vb,oc + VD − Idmd Rb + IL (Rsw − RD )) 2 − 4IL Rb (IL (RD + RL + Ruc ) − Vuc,oc − VD ) . (4) The average output capacitor voltage Vc is also derived as Vb,oc + (1 − Dboost ) Rb IL − Rb Idmd , IL > 0 (5) Vc = Vb,oc + Dbuck Rb IL − Rb Idmd , IL < 0. The dc–dc converter switching losses is Psw,loss = fs 1 1 Vc |IL | (tr +tf ) + Vc2 Coss +Qt Vg +Vc Qrr . 2 2 (7) The switching frequency fs is 50 kHz. tr and tf denote the rise-time and fall-time transitions of MOSFETs during switching periods. Coss is the output capacitance of MOSFET. Qt is the gate charge due to charging the gate capacitance by gate voltage. Qrr denotes the reverse recovery charge. The part parameters for dc–dc converter are shown in Table II. Considering both the conduction losses and switching losses, the dc–dc converter efficiency is evaluated for both operation modes as ⎧ Vc (1 − Dboost ) Iuc ⎪ ⎪ , Iuc > 0 ⎨ V (1 − D c boost ) Iuc + Pdc,loss + Psw,loss η= ⎪ (Vuc,oc − Iuc Ruc ) |Iuc | ⎪ ⎩ , Iuc < 0. (Vuc,oc − Iuc Ruc ) |Iuc | + Pdc,loss + Psw,loss (8) Notice that the UC current Iuc is equal to the inductor current IL as the topology shown in Fig. 1 is used. Using the part parameters for dc–dc converter as given in Table II, the efficiency maps for both Boost mode and Buck mode are demonstrated in Fig. 4. In this efficiency map, the UC current value range is from 5 to 200 A and the UC open-circuit voltage varies from 135 to 270 V. It can be observed that the dc–dc converter has lower efficiency when it operates at light-load condition. The total power losses in HESS are the sum of power losses in the bidirectional dc–dc converter and in the battery/UC as 2 Ruc . Ploss = Pdc,loss + Psw,loss + Ib2 Rb + Iuc (9) C. Power Losses in HESS In this study, the conduction and switching losses of the dc– dc converter are considered for both Boost and Buck modes [22]. The dc–dc converter conduction losses is ⎧ ⎪ Dboost IL2 (RSW + RL ) + (1 − Dboost ) ⎪ ⎪ 2 ⎪ ⎪ ⎪ ⎨ IL (RD + RL ) + IL VD , IL > 0 Pdc,loss = Dbuck IL2 (RSW + RL ) + (1 − Dbuck ) (6) ⎪ 2 ⎪ ⎪ IL (RD + RL ) − IL VD , IL < 0 ⎪ ⎪ ⎪ ⎩0, I = 0. L III. HESS O PTIMIZATION P ROBLEM The HESS energy management problem is to effectively split the load demand between battery and UC. The driving schedule {vs (k) , k = 1, . . . , N }, and the load demand current {Idmd (k) , k = 1, . . . , N } are known a priori for offline optimization problem. This optimization problem is a multistep decision problem: at each time instant, one decides the demand current split for battery and UC for the next time step, to achieve the minimum objective value while satisfying the constraints. To find the optimal control sequence, DP is applied. Authorized licensed use limited to: the Leddy Library at the University of Windsor. Downloaded on February 16,2022 at 05:00:46 UTC from IEEE Xplore. Restrictions apply. 226 IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 1, NO. 3, OCTOBER 2015 The UC current, with the same value as IL , can be derived based on the state-space average model of the bidirectional dc– dc converter as ⎧ ⎪ ⎨ Iconv (k) / (1 − Dboost (k)) , Iconv (k) > 0 Iuc (k) = Iconv (k) /Dbuck (k) , Iconv (k) < 0, k = 1, . . . , N ⎪ ⎩ 0, Iconv (k) = 0. (14) The constraints in this problem include the limits on the battery/UC current and voltage operation ranges Ib,min ≤ Ib (k) ≤ Ib,max Iuc,min ≤ Iuc (k) ≤ Iuc,max Vuc,min ≤ Vuc (k) ≤ Vuc,max , k = 1, . . . , N (15) where the subscripts of min and max denote the minimum and maximum value of each variable. Thus, the optimization problem is formulated as Minimize (12) s.t. (13)−(15). Iconv ∈RN Fig. 4. (a) Efficiency map of the dc–dc converter in Boost mode. (b) Efficiency map of the dc–dc converter in Buck mode. (16) A. Objective Functions C. Dynamic Programming Two cost functions are considered in the HESS energy management problem as The control variable Iconv is denoted as μ. The objective f2 (k) at time step k involves both Ib (k) and Ib (k − 1). While Ib (k) is determined by the control value μ(k) at time step k; Ib (k − 1) depends on the previous control value. To fit within the standard DP model, we make Ib (k − 1) a state variable x1 (k). In view of (13), we have f1 (k) = Ploss (k)/Ploss,max , k = 1, . . . , N 2 2 (Ib (k) − Ib (k − 1)) /(ΔIb,max ) , f2 (k) = 2 2 (Ib (k)) /(ΔIb,max ) , k = 1. (10) k = 2, . . . , N (11) Here, both cost functions are normalized because the scales of these two cost functions are different. The value of Ploss,max is set to 8000 W and the ΔIb,max is set to 40 A. Minimizing the objective function f1 results in reduced power losses. Minimizing the objective functions f2 will result in the battery current magnitude/variations reduction, which in turn will prolong the battery lifetime. The optimization problem, as given in (12), is to minimize a weighted sum of these two functions over the entire drive cycles, while satisfying several constraints for each time step N (w1 f1 (k) + w2 f2 (k)). (12) x1 (k + 1) = Ib (k) = Idmd (k) − μ (k) , which we write more compactly as x1 (k + 1) = g1k (μ (k)) , k = 1, . . . , N Here, w1 and w2 ≥ 0 are the weight factors of the two cost functions. (18) where the subscript k reflects the dependence on Idmd (k). Next, to allow for the constraint (15) on the UC voltage, we make the UC state-of-charge SoCuc , a second state variable x2 . The associated state equation is written as x2 (k + 1) = g2k (x2 (k) , μ (k)) , k = 1, . . . , N. (19) We then group (18) and (19) into a two-dimensional (2-D) state equation x (k + 1) = g (x (k) , μ (k)) , k=1 k = 1, . . . , N (17) k = 1, . . . , N (20) where x(k) = [x1 (k), x2 (k)]. We now rewrite the objective function (12) compactly as N In this optimization problem, the dc–dc converter output current Iconv (k) is chosen as the control variable, with k = 1, . . . , N . The battery current can be obtained based on the load demand current conservation equation as Ib (k) = Idmd (k) − Iconv (k) , Jk (x (k) , μ (k)) J (μ) = B. Problem Formulation k = 1, . . . , N. (13) (21) k=1 where μ denotes the N-vector μ(k), k = 1, . . . , N . The subscript k in Jk reflects dependence of f1 (k) on Idmd (k). The constraints in (15) are generalized on x and μ as ϕk (x (k) , μ (k)) ≤ 0, k = 1, . . . , N. (22) Authorized licensed use limited to: the Leddy Library at the University of Windsor. Downloaded on February 16,2022 at 05:00:46 UTC from IEEE Xplore. Restrictions apply. SHEN AND KHALIGH: SUPERVISORY ENERGY MANAGEMENT CONTROL STRATEGY IN BATTERY/UC HESS 227 Fig. 5. Flowchart of DP implementation. A cost-to-go function Vτ (ξ) is constructed for each time step τ and each state level ξ = [ξ1 , ξ2 ]. This cost-to-go function keeps track of the minimum objective function value that can be achieved when the initial time is τ and the initial state is ξ. With this cost-to-go function, the optimal control decision at time τ and the state ξ can be found, according to Bellman’s principle of optimality 0, τ =N +1 Vτ (ξ) = min {Jτ (ξ, u) + Vτ +1 (gτ (ξ, u))} , τ = 1, . . . , N. u∈Uτ ξ (23) This cost-to-go function is recursively evaluated using backward induction from τ = N to τ = 1 and for all the states x ∈ R2 . The optimal decision at time τ with state ξ is obtained as μτ (ξ) = arg min {Jτ (ξ, u) + Vτ +1 (gτ (ξ, u))} u∈Uτ ξ (24) where Uτ ξ , the set of admissible controls at time τ and state ξ, is given by Uτ ξ = {u : ϕτ (ξ, u) ≤ 0} . (25) D. DP Implementations and Optimization Results The flowchart of the DP algorithm is shown in Fig. 5. As shown in Fig. 5, there are three iteration loops in this DP algorithm. The algorithm starts backward iteration from time k = N to k = 1. At each time step, the cost-to-go function is updated based on the control variable iteration results at each state by minimizing the objective function value. After the state iterations, DP generates a look-up table of the optimal decision variable at each state and each time step. With an initial state, the optimal decision at this state can be determined and performed according to the generated lookup table. Based on the state transition process, the state and the corresponding optimal decision at the next time step are found. With the optimal decision performed at each time step, the optimal decision sequence is determined. Fig. 6. Optimization results of DP algorithm under UDDS cycle. E. Numerical Results The DP algorithm is applied to the Urban Dynamometer Driving Schedule (UDDS) with the weight factors set as w1 = w2 = 1 to give the equal importance for the two objective functions. With the standard drive cycle data, the load demand is obtained based on the EV model parameters. In this work, the total vehicle mass of the EV model is 1600 kg. The frontal area of the EV is 2.123 m2 , the aerodynamic drag coefficient is 0.24, the rolling coefficient is 0.008, the wheel radius is 0.328 m, and the dc-bus voltage is 360 V. With these EV model parameters, the load demand for the HESS is derived [23]. The feasible area of the UC voltage is set by the bounds of Vuc,max = 270 and Vuc,min = 135. The limits on the battery/UC current are set as Ib,max = 360 A, Ib,min = −90 A and Iuc,max = 120 A, Iuc,min = −120 A, according to the battery/UC characteristics. Fig. 6 shows the optimization results, including the load current, the battery/UC current, and the dc–dc converter output current. The dc–dc converter efficiency is evaluated according to (8). The optimization result shows that UC effectively reduces the battery current peak. In this optimized HESS, the peak battery discharging and charging currents have been reduced by 46% and 82% in comparison to the battery-only ESS. In addition, the dc–dc converter works with high efficiency during the entire drive cycle, which minimizes the HESS power losses. This offline optimization framework using DP algorithm has been applied to various drive cycles, obtaining the training data to construct the NN. IV. O NLINE E NERGY M ANAGEMENT The goal is to construct an effective online implementation of the control strategy described in offline optimization. Authorized licensed use limited to: the Leddy Library at the University of Windsor. Downloaded on February 16,2022 at 05:00:46 UTC from IEEE Xplore. Restrictions apply. 228 IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 1, NO. 3, OCTOBER 2015 Fig. 7. NN architecture for online energy management controller. As mentioned in Section III, DP algorithm explicitly depends on the future load demand or the future vehicle trip information, thus it is infeasible for real-time implementations. It is also difficult to derive a deterministic equation of the optimal energy management control strategy due to the system complexity. NN, with the capacity to represent or emulate human knowledge, can be effectively used for system approximation of complex systems. In this way, NN is applied to learn the optimal results by DP. Fig. 8. Performances of NN. (a) Train data. (b) Test data. A. NN Training Data As a first step in constructing the NN, the offline optimization program is solved for different representative drive cycle data sets, obtaining the required input and target data to train the NN. In this work, the standard drive cycle data sets are used, obtained from Autonomie software by Argonne National Laboratory, including UDDS, New European Driving Cycle (NEDC), Extra-Urban Driving Cycle (EUDC), Unified LA92, etc. [24]. For each drive cycle, the DP optimization problem described in Section III is solved to obtain the optimal decision sequences of the control variable. With the optimal data vectors/sequences of the battery current (Ib ), UC current (Iuc ) determined, the UC SoC (SoCuc ) sequence is also obtained. These obtained data vectors are used as the net input including the load current (Idmd ), the UC SoC and the battery current at the previous time step (Ib_prev ). These inputs are used because the energy management control strategy is influenced by these factors according to the DP algorithm. In addition, the vehicle speed (vs ) and the vehicle acceleration (a) are used as the net inputs. Note that unlike the DP algorithm, the NN does not depend on future load demand or future drive cycle information. However, it does not preclude the opportunity to use the current or past drive cycle information to improve the energy management performances. The vehicle speed and acceleration data provide knowledge about the driving trend, which also influence the energy management control strategy. Given these net input data, the optimal decision of the dc–dc converter current is used as the target data. With these training data, the NN is trained with the net architecture shown in Fig. 7. Fig. 9. Online energy management results versus DP optimization results. relationship between the outputs of the NN and the actual target value is plotted as shown in Fig. 8. Fig. 8(a) illustrates the results in the training phase, while Fig. 8(b) shows the results in the test phase, respectively. Two ±25% error lines are added for comparisons. As shown in Fig. 8, the NN model performs well in terms of prediction accuracy. C. Intelligent Online Energy Management Controller The trained and validated NN is implemented as an intelligent online energy management controller. By taking the real-time data for the NN inputs, NN outputs the command value of the dc–dc converter current. B. NN Training D. Test Results This NN is designed and trained using the NN Toolbox software in MATLAB [25]. To train the NN, Levenberg–Marquardt algorithm is used as it is characterized by its fast convergence and robustness. The NN training performance is measured by the mean quadratic errors (MSEs) [25]. To validate the NN, the The proposed online energy management controller is applied to test drive cycles. The online power split results are illustrated in Fig. 9 over a test drive cycle. The power split performance generated by NN is analyzed and compared with those generated by DP in offline, which is Authorized licensed use limited to: the Leddy Library at the University of Windsor. Downloaded on February 16,2022 at 05:00:46 UTC from IEEE Xplore. Restrictions apply. SHEN AND KHALIGH: SUPERVISORY ENERGY MANAGEMENT CONTROL STRATEGY IN BATTERY/UC HESS 229 TABLE III B IDIRECTIONAL DC–DC C ONVERTER O PERATION E FFICIENCY IN B OTH P ROPULSION M ODE AND R EGENERATIVE B RAKING M ODE (%) TABLE IV BATTERY SOH E STIMATION A FTER D IFFERENT D RIVE C YCLES Fig. 10. System integration of the controller and the HESS plant. used as the benchmark of the optimal performances. The online results are shown in dotted lines and the offline results are presented in solid lines. It can be observed that the power split performance generated by the NN closely follows the offline optimization results. The study also involves the analysis of the battery peak shaving effect by UC and the battery lifetime. TABLE V BATTERY L IFETIME E STIMATION FOR T EST D RIVE C YCLES (Y EAR ) V. S YSTEM I NTEGRATION AND R ESULTS A. System Integration Fig. 10 presents the system integration of the controller system and HESS. The controller system consists of two loops. The outer loop is the proposed supervisory energy management control algorithm, which optimizes the power split and computes the reference inductor current. The inner loop can be designed as a PI controller, which commands the inductor current to follow the reference signal by generating the PWM signal to control the dc–dc converter. In this work, the dc–dc converter control works at 50 kHz; nevertheless, the supervisory controller system is characterized for its slow dynamics. The reference signal generated by the supervisory controller can be updated at 10 Hz. B. Results and Comparisons In this work, the dc–dc converter operation efficiency in both propulsion mode and regeneration mode is evaluated. These results of the proposed online intelligent energy management controller are analyzed through comparison with the offline optimization results and the results obtained by a rule-based control strategy as described below. In this rule-based algorithm, the UC voltage reference is set based on heuristics as described. Typically, when the vehicle starts, it requires a large amount of power. Thus, the UC voltage references are set to high levels when the vehicle is stopped or at very low speed in order to prepare sufficient power to the motor whenever needed for accelerations. In urban driving situation, UC captures regenerative braking energy when the vehicle speed decreases from high speed. Therefore, the control strategy sets UC voltage references to allow room for receiving the regenerative braking energy. The UC voltage reference profile is given as [6] Vuc,ref (k) = V uc,oc 1− 3vs (k) . 160 (26) Here, the unit of vs (k) is mi/h. According to this defined UC voltage reference, the UC discharge and charge current can be controlled based on the rules [6]. In this rule-based algorithm, UC is used to receive the regenerative braking current as much as possible while battery receives the rest within the battery charging current limit. In discharging mode, a battery discharging current limit is set. When there is a large demand current, the battery delivers the maximum discharging current while UC supplies any remaining current if UC voltage is above the reference voltage. In this way, this rule-based algorithm aims at reducing the battery peak current through peak shaving by UC. In this work, we implemented this rule-based control strategy for result comparisons. Tables III–V summarize the results obtained by DP, the proposed online controller, and the rule-based control strategy, under different drive cycles. Table IV presents dc–dc converter operation efficiency in both propulsion and regeneration modes. The dc–dc converter efficiency is evaluated according to (8). As shown in Table III, the proposed supervisory energy management controller has improved the bidirectional dc–dc converter operation efficiency by about 4% in comparison to the rule-based control strategy. Fig. 11 presents the simulation results of the battery peak current under different drive cycles. The proposed energy management controller effectively reduces the battery peak current by 60% in comparison to the battery-only ESS, while the rule-based strategy can only reduce 20% of the battery peak current, in average. The proposed energy management controller can achieve good battery peak current shaving results for all the test drive cycles; while the rule-based controller cannot guarantee effective performance under different driving situations. Authorized licensed use limited to: the Leddy Library at the University of Windsor. Downloaded on February 16,2022 at 05:00:46 UTC from IEEE Xplore. Restrictions apply. 230 IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 1, NO. 3, OCTOBER 2015 battery lifetime, under the daily commute assumption based on the New York drive cycles. ACKNOWLEDGMENT Fig. 11. Battery peak current value |Ib | under different drive cycles (A). In addition, the battery remaining state-of-health (SOH) after 10 years are estimated based on previous work in [26] and presented in Table V. Here, a daily commute is constructed as a repeated sequence of the corresponding drive cycles to cover 40 miles. The battery remaining SOH after 10 years is estimated based on the assumption that the EV repeats the same daily commute every day. In Tables IV and V, the results in a batteryonly ESS are also evaluated for comparison. The improvement achieved by the proposed online controller over the battery-only ESS is shown in the last column. The proposed online intelligent energy management controller obtains effective results in comparison to the benchmark results obtained by DP algorithm. In addition, this proposed online controller improves the battery SOH by 15% in average, in comparison to the battery-only ESS, while the rule-based strategy achieves very small improvement of the battery SOH. The battery lifetime (in years) before the battery reaches its end-of-life (EOL) is also evaluated based on the daily commute assumption. The proposed online energy management controller extends the battery lifetime by 64.8% in comparison to the battery-only ESS, while the rule-based control strategy only improves the battery lifetime by 4.2%, using the New York city drive cycles as daily commute. VI. C ONCLUSION This paper presented a supervisory energy management strategy for a battery/UC HESS. A detailed dc–dc converter model is considered in order to include both the conduction losses and the switching losses in the energy management problem. With switching losses considered, it improves the accuracy of the loss model especially for high-frequency application. This model provides an accurate reference for the hardware implementation of the supervisory energy management controller. The supervisory energy management controller is implemented by solving a multiobjective optimization problem in order to prolong the battery lifetime and achieve high-efficiency operation of the HESS. The problem is solved by DP and the results are used to develop the NN. The proposed online energy management controller effectively splits the load demand and achieves excellent result of both the energy efficiency and the battery lifetime extension. It is estimated that the proposed online energy management controller extends the battery lifetime by 64.8% in comparison to the battery-only energy storage system, while the rule-based strategy only achieves 4.2% extension of the The authors would like to express their sincere appreciation to Dr. A. Tits for his advice and suggestions throughout this research. 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Authorized licensed use limited to: the Leddy Library at the University of Windsor. Downloaded on February 16,2022 at 05:00:46 UTC from IEEE Xplore. Restrictions apply. SHEN AND KHALIGH: SUPERVISORY ENERGY MANAGEMENT CONTROL STRATEGY IN BATTERY/UC HESS [19] Y. Murphey et al., “Intelligent hybrid vehicle power control—Part II: Online intelligent energy management,” IEEE Trans. Veh. Technol., vol. 62, no. 1, pp. 69–79, Jan. 2013. [20] K2 Energy Inc., “Technical datasheet of high capacity LFP26650 power cell data,” Henderson, NV, USA, 2012. [21] Maxwell’ Technologies Inc., “Technical datasheet of K2 series ultracapacitors,” Document No. 1015370.5, 2013. [22] R. W. Erickson and D. Maksimovic, Fundamentals of Power Electronics. New York: Springer Science & Business Media, 2007. [23] M. Ehsani, Y. Gao, and A. Emadi, Modern Electric, Hybrid Electric, and Fuel Cell Vehicles: Fundamentals, Theory, and Design, 2nd ed. Boca Raton, FL, USA: CRC Press, 2010. [24] Argonne National Laboratory. (2009). Autonomie (Version 1.0), Computer Software. Argonne, IL, USA: Argonne National Laboratory [Online]. Available: http://www.autonomie.net [25] The Mathworks, Inc., Neural Networks Toolbox: User’s Guide (MATLAB R2012b). Natick, Massachusetts, USA, 2012. [26] J. Shen, S. Dusmez, and A. Khaligh, “Optimization of sizing and battery cycle life in battery/UC hybrid energy storage system for electric vehicle applications,” IEEE Trans. Ind. Informat., vol. 10, no. 4, pp. 2112–2121, Nov. 2014. Junyi Shen received the B.S. degree in electrical engineering from Zhejiang University, Hangzhou, China, in 2011. Currently, she is pursuing the Ph.D. degree in electrical engineering at the University of Maryland, College Park, MD, USA. She is a Graduate Research Assistant with the Power Electronics, Energy Harvesting, and Renewable Energies Laboratory (PEHREL), Electrical and Computer Engineering Department, University of Maryland. Her research interests include energy management strategies for hybrid energy storage systems for electric vehicle applications. 231 Alireza Khaligh (S’04–M’06–SM’09) is an Associate Professor with the Electrical and Computer Engineering (ECE) Department and the Institute for Systems Research (ISR), University of Maryland (UMD), College Park, MD, USA. He is an author/coauthor of over 130 journal and conference papers. His major research interests include modeling, analysis, design, and control of power electronic converters for transportation electrification, renewable energies, energy harvesting, and microrobotics. Dr. Khaligh is an Editor of the IEEE T RANSACTIONS ON V EHICULAR T ECHNOLOGY (TVT), and an Associate Editor for the IEEE T RANSACTIONS ON T RANSPORTATION E LECTRIFICATION . He was a Guest Associate Editor for the Special Issue of the IEEE T RANSACTIONS ON P OWER E LECTRONICS on Transportation Electrification and Vehicle Systems, and a Guest Editor for the Special Section of IEEE TVT on Sustainable Transportation Systems. He is a Distinguished Lecturer of the IEEE Vehicular Technology Society. He was the Program Chair of the 2015 IEEE Applied Power Electronics Conference and Expo (APEC), the Assistant Program Chair of the 2014 APEC, the General Chair of the 2013 IEEE Transportation Electrification Conference and Expo (ITEC), and the Program Chair of the 2011 IEEE Vehicle Power and Propulsion Conference (VPPC). He is the General Chair of the 2016 IEEE Applied Power Electronic Conference and Expo (APEC). He is the recipient of various awards and recognitions including the 2015 Inaugural ISR Junior Faculty Fellowship from the Institute for Systems Research, UMD, the 2013 George Corcoran Memorial Award from the ECE Department, UMD, the 2013 Best Vehicular Electronics Paper Award from the IEEE Vehicular Technology Society, and the 2010 Ralph R. Teetor Educational Award from the Society of Automotive Engineers. Authorized licensed use limited to: the Leddy Library at the University of Windsor. Downloaded on February 16,2022 at 05:00:46 UTC from IEEE Xplore. Restrictions apply.