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
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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
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SHEN AND KHALIGH: SUPERVISORY ENERGY MANAGEMENT CONTROL STRATEGY IN BATTERY/UC HESS
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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.
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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)
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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.
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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
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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.
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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.
Any opinions, findings and conclusions or recommendations
expressed in this material are those of the author(s) and do not
necessarily reflect the views of the National Science Foundation
(NSF).
R EFERENCES
[1] A. Khaligh and Z. Li, “Battery, ultracapacitor, fuel cell, and hybrid energy
storage systems for electric, hybrid electric, fuel cell, and plug-in hybrid
electric vehicles: State of the art,” IEEE Trans. Veh. Technol., vol. 59,
no. 6, pp. 2806–2814, Jul. 2010.
[2] S. Lukic, S. Wirasingha, F. Rodriguez, J. Cao, and A. Emadi, “Power
management of an ultracapacitor/battery hybrid energy storage system in
an HEV,” in Proc. IEEE Veh. Power Propul. Conf., Sep. 2006, pp. 1–6.
[3] Y. Gurkaynak, A. Khaligh, and A. Emadi, “State of the art power management algorithms for hybrid electric vehicles,” in Proc. IEEE Veh. Power
Propul. Conf., Sep. 2009, pp. 388–394.
[4] P. Garcia, L. Fernandez, C. Garcia, and F. Jurado, “Energy management
system of fuel-cell-battery hybrid tramway,” IEEE Trans. Ind. Electron.,
vol. 57, no. 12, pp. 4013–4023, Dec. 2010.
[5] K. Jin, X. Ruan, M. Yang, and M. Xu, “A hybrid fuel cell power system,”
IEEE Trans. Ind. Electron., vol. 56, no. 4, pp. 1212–1222, Apr. 2009.
[6] R. Carter, A. Cruden, and P. Hall, “Optimizing for efficiency or battery life in a battery/supercapacitor electric vehicle,” IEEE Trans. Veh.
Technol., vol. 61, no. 4, pp. 1526–1533, May 2012.
[7] H. D. Lee and S. K. Sul, “Fuzzy-logic-based torque control strategy for
parallel-type hybrid electric vehicle,” IEEE Trans. Ind. Electron., vol. 45,
no. 4, pp. 625–632, Aug. 1998.
[8] E.-S. Koo, H.-D. Lee, S.-K. Sul, and J.-S. Kim, “Torque control strategy
for a parallel hybrid vehicle using fuzzy logic,” in Proc. IEEE 33rd IAS
Annu. Meeting Ind. Appl. Conf., Oct. 1998, vol. 3, pp. 1715–1720.
[9] N. Schouten, M. Salman, and N. Kheir, “Fuzzy logic control for parallel hybrid vehicles,” IEEE Trans. Control Syst. Technol., vol. 10, no. 3,
pp. 460–468, May 2002.
[10] S. Dusmez and A. Khaligh, “A supervisory power-splitting approach for a
new ultracapacitor–Battery vehicle deploying two propulsion machines,”
IEEE Trans. Ind. Informat., vol. 10, no. 3, pp. 1960–1971, Aug. 2014.
[11] A. Santucci, A. Sorniotti, and C. Lekakou, “Power split strategies for
hybrid energy storage systems for vehicular applications,” J. Power
Sources, vol. 258, pp. 395–407, 2014.
[12] J. J. Awerbuch and C. R. Sullivan, “Filter-based power splitting in
ultracapacitor-battery hybrids for vehicular applications,” in Proc. IEEE
12th Workshop Control Model. Power Electron. (COMPEL), 2010,
pp. 1–8.
[13] E. D. Tate and S. P. Boyd, “Finding ultimate limits of performance
for hybrid electric vehicles,” Rep. 2000-01-3099, SAE Technical Paper,
2000.
[14] C. Lin, H. Peng, J. Grizzle, and J. M. Kang, “Power management strategy
for a parallel hybrid electric truck,” IEEE Trans. Control Syst. Technol.,
vol. 11, no. 6, pp. 839–849, Nov. 2003.
[15] Z. Chen, C. Mi, J. Xu, X. Gong, and C. You, “Energy management for
a power-split plug-in hybrid electric vehicle based on dynamic programming and neural networks,” IEEE Trans. Veh. Technol., vol. 63, no. 4,
pp. 1567–1580, May 2014.
[16] M. Choi, J. Lee, and S. Seo, “Real-time optimization for power management systems of a battery/supercapacitor hybrid energy storage system in
electric vehicles,” IEEE Trans. Veh. Technol., vol. 63, no. 8, pp. 3600–
3611, Oct. 2014.
[17] H. Borhan, A. Vahidi, A. Phillips, M. Kuang, I. Kolmanovsky, and
S. Di Cairano, “MPC-based energy management of a power-split hybrid
electric vehicle,” IEEE Trans. Control Syst. Technol., vol. 20, no. 3,
pp. 593–603, May 2012.
[18] Y. Murphey, J. Park, Z. Chen, M. Kuang, M. Masrur, and A. Phillips,
“Intelligent hybrid vehicle power control: Part I: Machine learning of
optimal vehicle power,” IEEE Trans. Veh. Technol., vol. 61, no. 8,
pp. 3519–3530, Oct. 2012.
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.
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