Classification and Review of Control Strategies for (2011)

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 1, JANUARY 2011 111

Classification and Review of Control Strategies forPlug-In Hybrid Electric Vehicles

Sanjaka G. Wirasingha, Student Member, IEEE, and Ali Emadi, Senior Member, IEEE

AbstractTo reduce fuel consumption and emissions in plug-inhybrid electric vehicles (PHEVs), it is equally important to selectan appropriate drive train topology as it is to develop a suitablepower flow control strategy. While there are many control strate-gies that have been developed and presented, most are expansionsof hybrid electric vehicle (HEV) control strategies and do notmaximize the true potential the PHEV offers as a result of itsability to operate in electric-only mode over a significant distance.In this paper, state-of-the-art control strategies are reviewed andclassified in detail. PHEV controllers mostly operate on either arule-based or an optimization-based algorithm, each having itsown advantages and disadvantages. An overview of the controllers

is given, and an analysis on which strategy is more suitable tomaximize PHEV performance in different drive cycle conditions isprovided. Finally, a new classification for PHEV control strategiesbased on the operation of the vehicle is presented and verifiedthrough simulation results.

Index TermsAll-electric range (AER), classification, controlstrategy, drive trains, efficiency, electric motor drives, electricvehicles (EVs), emissions, energy consumption, energy conversion,energy-storage systems (ESSs), fuel economy, hybrid EVs (HEVs),plug-in HEVs (PHEVs), power electronics, propulsion systems.

I. INTRODUCTION

GROWING consumer expectations, legislation pushing for

lower emissions, increasing fuel prices, and the realiza-tion that petroleum is a finite resource are factors leading to

groundbreaking changes in the automotive industry, particu-

larly in the realm of electrification of the drive train. Depending

on the degree of electrification, the combination of the internal

combustion engine (ICE) with an electric motor offers a wide

range of benefits from reduced fuel consumption and emission

reduction to enhance performance and supply of power-hungry

hotel loads.

Extensive research and development has been conducted

on alternative-fuel vehicles, including hybrid electric vehicles

(HEVs) and plug-in HEVs (PHEVs) [1]. PHEVs are essentially

a combination of an electric vehicle (EV) and an HEV, having

Manuscript received February 13, 2010; revised May 27, 2010 andAugust 16, 2010; accepted September26, 2010.Date of publicationOctober28,2010; date of current version January 20, 2011. This paper was presented inpart at the 2009 IEEE Vehicle Power and Propulsion Conference, Dearborn,MI, September 2009. The review of this paper was coordinated by Prof. J. Hur.

S. G. Wirasingha was with the Electrical and Computer Engineering De-partment, Illinois Institute of Technology, Chicago, IL 60616 USA. He isnow with the Chrysler Group LLC, Auburn Hills, MI 48326 USA (e-mail:[email protected]).

A. Emadi is with the Electrical and Computer Engineering Department, Illi-nois Institute of Technology, Chicago, IL 60616 USA (e-mail: [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TVT.2010.2090178

the all-electric capability of an EV in urban areas and a

smaller onboard ICE for extended range capability as an HEV.

This added layer of operation has made control strategies for

PHEVs significantly more complex than those of an HEV

as the all-electric range (AER) and the control strategy are

directly responsible for the fuel economy of a PHEV. Existing

PHEV control strategies are fine-tuned to achieve the best fuel

economy for specific driving conditions. These are therefore

not ideal in the real-world application of PHEVs due to

difficulties in predicting driving behavior. Control designers

are therefore shifting their focus to real-time control strategiesand many practical issues that need to be addressed prior to

commercial application. A classification for PHEVs based on

the control logic utilized will be presented.

Section II discusses the importance of PHEV control strate-

gies and presents an overview of these strategies. PHEV control

strategies operate on three modes, which are explained along

with other key characteristics that are unique to PHEVs, such

as AER and engine on/off criteria. This is followed by a

general control strategy classification based on the mathematic

approach that is commonly used today. Rule-based control

strategies are described in Section III along with the two main

approaches to implementing such controllers: 1) deterministic-

and 2) fuzzy rule-based controllers. The second control classi-fication, i.e., optimization-based control strategies, is discussed

in Section IV. The section will first discuss the advantages of

using optimization in terms mileage, efficiency, and emissions

of a PHEV. Two approaches, i.e., global optimization and

real-time optimization, are presented along with examples for

each. A classification of optimization-based controllers based

on controller behavior is subsequently presented. This is fol-

lowed by the three approaches to implementing optimization-

based controller strategies for PHEVs. They are the following:

1) dynamic programming (DP); 2) neural networks; and

3) stochastic DP. Examples of PHEV controllers that have

been proposed, developed, and implemented are discussed inSection V. These controllers vary in classification and ap-

proaches discussed in previous sections. Simulation results are

presented, which demonstrate a trend showing the advantages

of optimization-based versus rule-based controllers. However,

rule-based controllers are less complex and easier to implement.

PHEV control strategies are reviewed in detail in earlier sec-

tions, both from a theoretical and implementation perspective.

Current approaches to classifying PHEV control strategies are

also discussed. It is clear that these classifications are primarily

based in the mathematical model and approach of the controller.

A new classification, i.e., one that groups PHEVs based on how

the vehicle performs, is presented in Section VI. It is based on

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112 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 60, NO. 1, JANUARY 2011

the different modes of operation of a PHEV as a result of the

implemented controller and will only be true for PHEVs. They

are the following: 1) all-electric + conventional/hybrid; 2) rule-based blended; and 3) optimization-based blended control

strategies. This section will also include simulation results

demonstrating the viability of the new PHEV control strategy

classification. This will be followed by our conclusions inSection VII.

II. OVERVIEW OF PLU G-I N HYBRID ELECTRIC

VEHICLE CONTROL STRATEGIES

Attempts have been made to develop new controllers to better

utilize the fuel-saving potential of PHEVs. The main function

of all PHEV and HEV control strategies is the utilization of

input signals to calculate output signals that will enable the

vehicle to operate in a manner that will improve fuel economy,

performance, and emissions of the vehicle. Conventional strate-

gies are fundamentally consistent in the manner that they alter

input signals to produce output signals. This quality of consis-

tency has both advantages and disadvantages. An advantage is

that consistency results in great reliability. The disadvantage of

having this consistency is that it cannot adapt well to parameter

changes in the vehicles drive train. These changes are usu-

ally brought upon by changes in drive cycle, driving patterns,

and driver behavior. The wear and tear of the vehicle and

the simple fact that vehicles are not always utilized as they

were designed add to these changes. Fixed control strategies

cannot accommodate the many changes in driving conditions

and patterns resulting in a nonoptimized use of power, which

leads to bad fuel economy.

There has been much study done in developing, implement-ing, and optimizing control strategies for HEVs. There are

many commercially available HEVs today, which have allowed

for real-time testing and detailed analysis of these controllers.

Therefore, it is inevitable that expansions of these controllers

are proposed as PHEV controllers, resulting in some overlap

with HEV controllers. The many characteristics of HEV con-

trollers, including advantages and disadvantages, will also be

true for PHEVs. PHEVs demonstrate specific criteria, such as

electric-only drive capability, additional discharge range due to

the larger battery pack, and the option of turning the engine

off, which differentiates PHEVs from HEVs. They must be

addressed by the control strategy to take full advantage of allthe benefits of a PHEV.

A. Modes of Operation

A PHEV, as defined earlier, could have the potential for

providing energy for propulsion using only the engine, only the

electric machine, or the two sources in combination with each

other. The state of charge (SOC) of the energy-storage system

(ESS) will vary with time, depending on the energy source

providing the propulsion power. The behavior of the SOC is

used to describe in which mode the ESS operates, i.e., charger

sustaining (CS), EV, and charge-depleting (CD) modes [2], [3].

EV mode is when the vehicle operates in electric only modeusing energy from only the electric machine until it completes

Fig. 1. Modes of operation of a PHEV.

a predefined cycle or reaches a predefined SOC. This value is

generally the SOCL of the ESS and will be reached faster than

in CD mode. The engine will turn on if the electric machine

cannot meet the load demands of the vehicle forcing a mode

switch.

CD mode is when the vehicle operates using energy primarilyfrom the electric propulsion machine with a net decrease in

battery SOC, as seen in Fig. 1. The engine will be a secondary

source of energy, turning on when the electric machine is not

able to provide the required power or when the SOC drops too

low. Vehicles in this mode may opt to operate in CS mode to

supplement the SOC of the ESS when required.

Charge sustaining mode is when the vehicle is operating

in a manner that the propulsion is powered by the electric

machine, the engine, or both, with the constraint of maintaining

a constant battery SOC. The SOC curve in Fig. 1 shows how

the SOC of the ESS operates within a narrow range, which is

similar to the SOC of an HEV control strategy.

B. AER

While the AER is instrumental in improving mileage, ef-

ficiency, and emissions compared with an HEV, it also adds

significant complexity to the control design, creating chal-

lenges for design engineers. The electric energy is significantly

cheaper, and electric drive trains are more efficient. It is there-

fore advantageous to operate the vehicle in electric mode when-

ever possible, particularly during transient power demands

since ICEs are particularly inefficient during transients.

The performance of a PHEV is trip dependent. For example,

in the situation where the distance of the trip is close to theAER of the vehicle, a significant portion of the energy required

for propulsion will be provided by the electric drive system.

This will lead to high mileage and low emission value as little

fuel will be utilized by the vehicle. In the situation where the

size of the battery pack is increased or the distance of the trip is

reduced, the mileage of the vehicle will exponentially increase,

reaching a value of infinity when the distance is less than the

AER of the vehicle.

C. Engine On/Off

PHEVs have the ability to start in and to operate in electric

only mode over a significant distance. Several HEVs, includingthe Ford Fusion hybrid, boast the ability to turn the engine


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WIRASINGHA AND EMADI: CLASSIFICATION AND REVIEW OF CONTROL STRATEGIES FOR PHEVs 113

on and off during operation; however, it is executed more

effectively and efficiently by a PHEV. In an HEV, the engine

is required to turn on at a defined speed or when torque is

required regardless of the SOC of the ESS. PHEV control

strategies can be designed to turn the engine off rather than

idle when it is not required for propulsion. An idling engine

consumes significantly more energy than estimated by con-sumers. Another region of inefficiency of the engine is during

startup. The PHEV can limit this in traffic scenarios where the

electric motor provides the initial energy for propulsion and

the engine turns-on only when the required power is above a

predefined value. The vehicle topology will impact the turn-on

and turn-off procedure of the engine. For example, a parallel-

assist PHEV must start its engine upon every vehicle startup

and when full power is required. The latter is also true for

all parallel PHEVs. In topologies where there is EV start and

propulsion, one does not need to start the engine unless the

traction battery is depleted. Since the engine does not contribute

to acceleration, its load may be applied in a slow and controlled

manner, independent of the drivers requirements.

It is clear that engine on logic is generally based on three cri-

teria: 1) the requested power and threshold; 2) the battery SOC;

and 3) the ability of the electric motor to provide the required

power. Engine on and off times are assigned minimum time

intervals to ensure smooth operation of the vehicle. Delaying

the engine turning on will increase the regenerative potential of

the PHEV, further improving mileage. It is important to analyze

the effect of multiple engine starts on mileage and emissions

in the respective control strategies prior to defining the engine

on/off criteria.

D. General Control Strategy Classification

Controllers are commonly grouped depending on the math-

ematical models they are based on in its description. PHEV

controllers are thus divided into two groups: 1) rule-based

controllers and 2) optimization-based controllers. These groups

are further divided into two subsections each based on how they

are implemented.

Rule-based control strategies are tuned to achieve the best

fuel economy, efficiency, performance, and emissions for a

specific drive cycle. They operate based on a set of criteria

defined by the system. For example, PHEVs such as taxi cabs

and buses have control strategies that are tuned to perform best(highest fuel economy) during frequent stop-and-go driving.

Given the inherent rigidity of a rule-based approach, designers

have turned their attention to optimization-based controllers.

They are used to develop an optimal control strategy for

PHEVs by minimizing a cost function. This cost function is

derived based on the vehicle and component parameters and

the performance expectations of the vehicle. Some controllers

are optimized for specific drive cycles using past and future

(expected) information regarding the trip and components and

are therefore termed acausal systems. More advanced control

techniques are based on real-time optimization. Also referred

to as causal systems, they rely on real-time feedback to opti-

mize a cost function that is developed using past information(see Fig. 2).

Fig. 2. Optimization classification. PHEV control strategy tree.

III. RUL E-BASED CONTROL STRATEGIES

The main goal of any rule-based control strategy is to operate

the PHEV at its highest efficiency point. This is achieved by

running the engine and the electric machine at their most effi-

cient points utilizing the AER and maximizing the regenerative

potential of the PHEV. Predefined rules are initially set based on

desirable outputs and expectations without any prior knowledge

of the trip. Flowcharts and state diagrams are commonly used torepresent the power flow of a given driving schedule. The tran-

sition from one mode to another depends on the predefined cri-

teria, such as the power requirements of the engine and motor,

acceleration and deceleration, vehicle speed, and the batteries

SOC. Rule-based control strategies are a combination of CS,

CD, EV, and conventional modes used in different combinations

and periods of time and are implemented using deterministic

rule-based methods and fuzzy logic rule-based methods.

A. Deterministic Rule-Based Methods

The deterministic rule-based controllers operate on a set of

rules that have been defined and implemented prior to actualoperation, and state machines are proposed as a viable method

of implementing them [4], [5]. It uses state machine models

reflected in flowcharts and control parameter tablesin addi-

tion to operating conditions, i.e., instantaneous inputs for the

decision-making process. State-machine-based control logic is

a model composed of a number of states, transitions between

those states, and actions. The states in this case are the various

vehicle modes of operation. The most basic determined rule-

based controller would be the thermostat controller. It operates

on the simple principle that the engine will be turned on and off

based on the SOC value of the energy storage and the torque

demands of the vehicle. This controller has limited success in aseries hybrid drive train, where the electric motor provides all

the propulsion, and the engine is required to sustain a defined

SOC in the ESS. The most popular is the power follower, which

is used by Toyota Prius and Honda Insight HEVs. It operates

on the criteria of sustaining a charge in the ESS and focuses

on parallel topologies, where the electric machine serves as a

torque-assist device. The power follower, while being a practi-

cal and successful solution, is not ideal as it does not focus on

optimizing the complete drive train for PHEV applications.

B. Fuzzy-Rule-Based Controller

Fuzzy logic is ideal for nonlinear time-varying systems,such as a PHEV drive train, as they are robust, adaptable to


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Fig. 3. SOC profile.

variations, and easily adjustable. Fuzzy logic controllers reduce

computational burden and provide a higher level of abstraction

to the controllers. Developed by Lee and Sul [6], these con-

trollers improved fuel economy over simple rule-based con-

trollers. However, they are still based on predetermined rules

and can only be optimized for specific drive cycles. An adaptivecontroller implemented using fuzzy logic will understand the

average behavior of the respective driver and will optimize itself

for these situations. For example, a controller that will select

the operating points with the least impact on fuel economy, as

implemented by Johnson [7], demonstrated better fuel economy

when compared with a simple rule-based controller. However,

the computational burden does not allow the controller to be

used easily in a real-time implementation. Another key limita-

tion is that it is difficult to optimize a system that has two or

more variables, such as mileage and emissions, as this leads to

more than one set of rules. Lee and Sul proposed a fuzzy logic-

based control strategy to minimize emissions while maintaining

SOC. They further developed a system with two fuzzy logiccontrollers, a drivers intention predictor, and a power balance

controller.

IV. OPTIMIZATION-BASED CONTROL STRATEGIES

Rule-based control strategies optimize the performance of

each component individually. Local optimization has a major

disadvantage in that they are not able to find the global mini-

mum, thus does not optimize the PHEV as a whole. There are

two criteria to global control strategies: 1) optimization based

on historical data and 2) optimization based on real-time data.

System optimization takes place as a result of system learn-ing and adapting to the condition within a framework of rules

or constraints. A predefined set of rules will however limit

the validity of the optimization to a specific drive cycle. Opti-

mization also provides the ability to incorporate two variables,

i.e., mileage and emission goals, in a cost function that can be

optimized. Studies on global optimization have shown that the

optimal pathway is based on maximizing the CD condition, i.e.,

the SOC reaching SOCL value at the end of the drive cycle

[8]. In other words, a system optimized to operate in CD mode

instead of in CDCS mode (or EVCS mode) will provide the

best fuel efficiency improvement (see Fig. 3).

The fuel economy of a PHEV is dependent on the ability

of the controller to optimize each segment of the trip. Con-trollers have, however, fallen short as it is nearly impossible

to accurately predict future trip scenarios. Having accurate trip

information and component conditions is vital in developing an

optimum controller. Technological advances such as the Global

Positioning System (GPS), Internet maps, and real-time traffic

data have made trip planning a simpler task [9], [10]. This,

coupled with the advances in information transfer to and from

vehicles, has been instrumental in real-time control strategiesgathering momentum (see Fig. 4).

A. Global Optimization (Acausal)

Optimization-based control strategies are routinely divided

into two categories as they are applied to HEVs and PHEVs

[11]. They are global optimization, which are acausal systems,

and real-time optimization, which are casual systems with the

key difference being the ability to adapt in real time. The cost

function is defined using historical data and is minimized based

on future expectations and results. The controllers are often

optimized offline and then implemented on PHEVs. They are

noncausal systems by definition because the system dependson future values with the possibility of depending on past

and present variables. However, it is physically improbable for

such a system to depend on present values. Therefore, these

controllers are defined as systems that will minimize the cost

function of the system by using past and future variables/inputs

of the PHEV drive train.

A commonly used optimization method is linear program-

ming, where a linear model for the PHEV is first built and a

controller that would find the global optimum for the model

is subsequently constructed. This method is drive train topol-

ogy dependent. Furthermore, building a linear model for an

advanced topology is significantly more complex. PHEVs are

powered by both an engine and an electric machine, and the

torques and speeds of the two components are directly related

in parallel topologies. The control theory approach takes ad-

vantage of this relationship to define a cost function using only

two decision variables [12]. While this controller is easier to

implement, it will not adapt to drive train changes as well

as numerical- or iterative-based controllers would. The Bell-

man principle [13] isolates control patterns, which are both

dependent and independent of the drive cycle, and develops a

real-time control strategy in Matlab Simulink. SOC is the key

parameter, the engine torque is the command of the system, and

the cumulative energy loss throughout the drive cycle is mini-

mized. The genetic algorithm [14] is a search technique usedto find exact or approximate solutions for complex nonlinear

optimization. Categorized as global search heuristics, they are

generally implemented in simulations where the cost function

will evolve toward its minimum solution. Some of the other

optimization methods that have been proposed for developing

a controller for PHEVs are particle swarm optimization [15],

simulated annealing, DIRECT global optimization algorithm

[16], two-scale DP [17], gas-kinetic traffic flow model [18], and

game theory [19].

B. Real-Time Optimization (Causal)

There is much interest in real-time optimization controllersas their ability to optimize itself in real time will enable a


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Fig. 4. Control classificaion.

PHEV to achieve its potential. Since these controllers attempt

to optimize a cost function that was developed using past

information, they can be classified as causal systems.

Various techniques have been introduced to the power man-

agement problem to obtain a globally optimal control system. A

good example is DP [20]. DP is a feedback (closed-loop) form

of control and is used to find the optimum solution to the control

problem of the plug-in hybrid drive train based on historic

data, such as drive cycles and past traffic reports. Stochastic DP

optimization is not a real-time solution by nature; however, the

control values can be used in real-time implementation since thevalues are an average result of many different drive cycles [21].

Adaptive controllers are being studied as they show the most

promise for optimizing the components in a PHEV for maxi-

mum performance in real time. It is clear that the daily driving

range is the biggest factor in optimizing a PHEV controller.

An adaptive controller will understand the average behav-

ior of the respective driver and will optimize itself for these

situations.

C. Classification Based on Behavior

Optimization-based controllers are also grouped based on themathematical models they are based on and the characteristics

the models will display.

Local Versus Global Optimization: Local optimization is

somewhat similar to rule-based controllers in that only a certain

component and operation is optimized. Since a PHEV system is

multimodal, it is required that the system is globally optimized

to find the global minimum and is not just one of the many local

minimum points of the system. Global optimization is also ideal

in systems that are noisy and discontinuous as these factors may

lead to skewed results over a small region of operation.

Deterministic Optimization Versus Stochastic Optimization:

Deterministic systems will consistently provide a similar outputto a set of input variables as development of future states will

follow a set procedure. Stochastic optimization will, however,

determine the output of the system by following both a set

procedure and a random element. Stochastic optimization has

its advantages when optimizing a system where a variable is

not known as a result of uncertainty or unavailability.

Gradient Versus Derivative Free: Nonsmooth unconstrained

optimization problems appear in many applications and in

particular in PHEV control. There are many different methods

that have been developed to solve this problem, including

algorithms based on soothing techniques [22] and the random

gradient sampling [23]. The computation of at least one subgra-

dient or approximating gradient is required for each iterationin a gradient approach; however, there are many practical

problems where the computation of even one subgradient is

a difficult task. Therefore, in such situations, derivative-free

methods seem to be a better choice since they do not use

explicit computation of subgradients. Among derivative-free

methods, the generalized pattern search methods are well suited

for nonsmooth optimizations [24].

D. Control Strategy Implementation

There are three primary approaches to implementing an

optimization-based control strategy, a brute-force approach us-

ing DP, neural networks, and stochastic optimization. The brute

force approach can be utilized using DP. DP is a powerful tool

to solve general dynamic optimization problems, particularly

in the situation where the cost function is more complex and

cannot be simplified to be optimized by global optimization ap-

proaches such as linear programming, as previously explained.

This is what Lin et al. did in their two implementations of DP

[25], [26]. In their first approach, they used pure DP, a cost

function, which they tried to minimize for the duration of a

drive cycle. The second approach was based on stochastic DP

and used a Markov chain to find a policy that would maximize

the fuel efficiency of their vehicle. The strategy is optimized fora range of drive cycles, and the average values are considered.

An advantage of the two described DP approaches is that it

allows for a global optimum to be found.

A key disadvantage of DP is that it is a recursive approach

that is based on a specific drive cycle and that it requires full

knowledge of this drive cycle to be effective. Therefore, the

controller is not able to adapt to any changes and cannot be

utilized in a real-time controller since drive cycle information

will not be available or does not exist in this situation.

Artificial neural networks (ANNs) have been used to design

optimization-based controllers that will minimize the fuel con-

sumption of the vehicle, regardless of the driving pattern. Anoptimized control strategy is created by the ANN learning from

the experience rendered by existing control strategies over

different drive cycles. The many advantages of implementing a

controller using neural networks include the following: 1) The

controller is not specific to a drive cycle or user; 2) the more

comprehensive the training table, the more robust the controller

will be; and 3) the training set can be updated if required. The

Monte Carlo method, a statistical approach, has garnered some

interest for gathering information for the neural network train-

ing set when it is unfeasible to compute an exact result with a

deterministic algorithm. The probabilities for each action of the

vehicle are computed based on different driving scenarios, and

this information is used to train the neural network. Therefore,a broader spectrum of possible scenarios can be addressed.


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It is important to be able to update a neural network in the

situation where the driving behavior or the drive cycle changes

significantly. A second processor can be utilized to gather

operational information over a significant period to develop a

new training set. Criteria for retraining must be set so as to

not overtrain the neural network as this will inversely impact

the final results of the controller. Other disadvantages of neuralnetwork approaches include the need for a large processor, slow

processing times, and the need for propagation to retrain the

controller. This also requires an additional processor, which

adds to the complexity and cost of the system.

Stochastic optimization is another way to model uncertainty

in the data by assuming that part of the input is specified in

terms of a probability distribution rather than by deterministic

data given in advance. As in deterministic DP, the recursive

cost equation is solved backwards to obtain the solution. The

future cost becomes an expected value and can be calculated

based on probability distributions of inflows. Stochastic dy-

namic processing using Markhov decision process is used to

develop an algorithm that allows for the controller to be used

in a real-time implementation. It treats the combination of the

electric machine and the ICE in a parallel hybrid as a single

power plant. The vehicle is treated as an intelligent agent that

interacts with the environment and learns to optimize itself

for a specific driving cycle. At each decision moment, the

controller is presented with a torque demand by the driver,

and the controller will decide on the percentage of torque

that will be provided by each energy source. The controller

is awarded a certain reward in the form of fuel consumption,

and the system transitions to the next state. The goal of the

controller is to maximize its rewards in the worst-case scenario

while providing the required torque demanded by the driver andkeeping the SOC of the battery to a steady level. This approach

requires prior knowledge of the drive cycle. It is, however, faster

and requires less processing that a neural network.

V. EXAMPLES OF PLU G-I N HYBRID ELECTRIC

VEHICLE CONTROLLERS

The switching logic/circuit controller presented by the PSG

College of Technology, India, is one example of a rule-

based controller [27]. The PHEV will operate in three modes:

1) electric mode; 2) power mode; and 3) speed-restriction

mode. In electric mode, only the battery provides power fortraction, whereas in power mode, the auxiliary unit comes on

and operates at its maximum efficiency point. The additional

energy developed in this mode is utilized to charge the now-

depleted ESS. Speed-restriction mode is activated when the

SOC drops below the threshold value that is too low to provide

energy for electric propulsion. The PHEV is thus limited to

a speed that requires a quantity of energy that can directly

be supplied by the alternator. Once the SOC reaches another

predefined value as a result of onboard charging, the PHEV will

switch out of this mode back to either electric or power mode

based on a set of predefined rules. This is a rule-based controller

for a series hybrid using a microprocessor-based control unit.

The vehicle stays in EV mode until the SOC reaches 50%, andthen, the engine turns on to provide power directly to the motor

and recharge battery, thus operating in CS mode. Note that the

system will shut the engine off at idle and restart on electric

until the speed reaches a set value.

Another example of a rule-based control strategy is the

energy management strategy proposed by Pisu and Rizzoni

[28] for HEVs. This is a rule-based control technique that

uses heuristic knowledge to develop a set of event-triggeredrules and can easily be expanded to be utilized for PHEVs.

This strategy is presented for a parallel PHEV, where the

electric machine is primarily a torque-assist machine, whereas

the engine is responsible for providing the base torque required

for propulsion. Eight states of operation are defined, and each

state corresponds to a different control action and is influenced

by inputs such as acceleration/brake pedal positions, operating

points of components, and SOC.

Some examples for the other rule-based control strategies are

presented in brief in [3]. They are listed as follows: 1) differen-

tial engine power, which sets the engine turn-on value at a lower

power rating than in an EVCS strategy, which will extend the

range of the vehicle in CD mode; 2) full engine power, where

once the engine is turned on, it will provide all the required

power at the wheel. As a result, the power provided by the

electric motor will go to zero, mimicking a vehicle in the first

classification provided in this paper. This will also force the

engine to operate at a higher efficiency value; 3) optimal engine

power, where, once the engine is turned on, it will operate at its

highest efficiency point. If at these operating points the engine

is providing all the required power, the motor power will go to

zero, and if not, the motor will continue to operate, providing

the remaining power requirement at the wheels.

DIRECT optimization was used by the team at Argonne

National Laboratory, Argonne, IL, to a CDCD control topol-ogy [29]. It was a pretransmission parallel hybrid, and the

engine turn-on/off was determined by SOC limits and torque

requirements with a function to limit engine coming on during

just power spikes of the drive demand. The fuel energy ratio

to range to battery energy was studied, and it was clear that

DIRECT optimization was successful in improving the vehicle

performance. However, since DIRECT is a local optimization

method, it was observed that once the vehicle cycle range

varied, the performance improvement was not as significant.

The adaptive control strategy can be implemented via a

neural network that is continually updated by the network

training subcomponent. The training feature takes input andoutput signal data collected over time and calculates the vehicle

efficiency associated with that data. The training feature then

modifies the neural network. All neural network modifications

over the life of the vehicle are made to achieve high-efficiency

operation with higher frequency. The ability to alter the neural

network throughout the life of the vehicle is very beneficial as

the vehicle ages. In addition, the training set can detect driving

habits (i.e., vehicles used by multiple drivers) and adjust the

neural network accordingly. For example, aggressive driving

requires hard acceleration, whereas conservative driving does

not. A conventional constant control strategy cannot produce

optimal power use in that situation. The data preprocessing sub-

component can discriminate between the input/output signalsgenerated from aggressive driving and conservative driving.


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Therefore, the adaptive control strategy can adapt rather quickly

from one driving style to another, resulting in optimal fuel

economy.

Gong proposes an optimal charge-depletion strategy for

PHEV power management [30]. The problem is to minimize

J

k [x(k)] = minu(k)L [x(k), u(k)] + J

k+1 [x(k + 1)]

(1)

where Jk

[x(k)] is the optimal cost-to-go function, x(k) is thestate vector of the system starting from time stage k, L is

the instantaneous cost, and u(k) represents the vector controlvariables (output torque from engine, output torque from motor,

etc.). The preceding equation is solved backwards to find the

control strategy using quantization and interpolation. Three

power management strategies were simulated in ADVISOR:

1) DP-based charge depletion control; 2) rule-based control;

and 3) a depletion-sustenance control. For all cases, the initial

and final battery SOCs were set at 0.8 and 0.3, respectively.

The simulation results concluded that using a DP for globaloptimization demonstrated the best fuel efficiency, which is

an improvement of over 50% compared with other controllers

implemented.

A stochastic DP approach is used to optimize PHEV power

management over various drive cycles by Moura et al. [21]. The

paper uses a power-split PHEV similar in design to the 2002

Toyota Prius with a potentially larger battery pack. The defined

control inputs are the engine, the motor, and the generator

torques. The state variables are the crankshaft speed, the vehicle

velocity, and the battery SOC, i.e.,

J = limN

EPdemN1k=0

kg (x(k), u(k)). (2)

In addition to the variables that have been defined in the

foregoing study, is a constant, and Pdem is the instantaneous

power demand. The problem is solved using stochastic DP.

Since all the coefficients are set to nonzeroes, both electricity

and fuel consumption are taken into consideration, which is

referred to as the blended control strategy. It will operate as

a CDCS controller when only fuel consumption is considered

and all the other coefficients are set to zeroes. Both approaches

were simulated on ADVISOR in this paper. The results proved

that a blended approach reduced fuel consumption by 8.2%and had a 6.4% lower energy cost compared with the CDCS

strategy. It is concluded that blending the battery and the engine

power improves engine efficiency. A stochastic DP approach

not requiring future information allows it be implemented in

more than one drive cycle.

A feedforward ANN has been used for modeling and de-

veloping a PHEV controller for a freeway trip model around

on/off ramps by Gong [31]. The neural network is trained by

conducting a real test with drive pattern data sets obtained

through highway detectors. A data set of ramp flow information

(Q1) obtained from WisTransPortal is chosen for validation.The neural network is trained and used to simulate the PHEV

over a new speed profile developed by combining the twodata sets. The simulation results showed 16.7% deterioration in

fuel economy if using WisTransPortal data set alone compared

with the real test data. A 17.5% improvement in fuel economy

was witnessed when combining WisTransPortal with neural

networks. Note that, in this paper, DP was used to optimize the

power management algorithm, and a neural network was used

to make the trip model more accurate.

Optimization algorithms based on ANN are not always guar-anteed to provide optimal optimization for the entire operating

domain. Some other real-time-based control strategies that have

been proposed include the equivalent fuel consumption control

method [12]. Pisu and Rizzoni have implemented several real-

time optimization-based control strategies. The adaptive equiv-

alent consumption minimization technique [28] formulates a

cost function to minimize the fuel consumption subject to

constraints of the system, including emissions and drivability.

Pisu also proposed a decoupling controller based on the vehicle

model to improve drivability and optimizing SOC.

Two scale DPs have also been proposed [17] as means

for trip-based power management of plug-in hybrid vehicles.

Drive cycle modeling and prediction is possible due to the

developments in intelligent transportation systems, geograph-

ical information systems (GIS), GPS, and real-time access to

historical and current traffic patterns. The trip is defined using

this information and is divided into smaller segments, with

the SOC optimized for each segment. Different optimization

methods can be used for optimizing each segment, and DP was

used by the authors. However, DP is not applicable for real time

as the trip model would require future information.

VI. PROPOSED PLU G-I N HYBRID ELECTRIC VEHICLE

CONTROL STRATEGY CLASSIFICATION

As PHEVs are gathering momentum, different classifications

for describing and comparing PHEVs have been proposed.

These classifications are from different perspectives as PHEVs

vary in technology, components, drive trains, and control algo-

rithms. Some of the more common classifications of PHEVs

are mileage, drive train topology [32], PHEVx based on AER

[33], PHEVx based on petroleum displacement [33], power

level based [13], and, more recently, plug-in hybrid electric

factor (Pihef) [34] with the Pihef providing a comprehensive

comparison/classification for PHEVs. Despite the controller

playing a vital role in the optimization of PHEV, as addressed

in detail in this paper, there is only one classification thatclassifies PHEVs based on how it performs. It is the power

level classification, which separates PHEVs into three groups

based on their performance: 1) blended; 2) range extender; and

3) in-between. PHEVs have also been classified based on the

mathematical models driving the controller used in the vehicle.

However, this approach does not specifically address the PHEV,

but the mathematical model and the classification remain true

for any system using a similar controller. Numerous control

strategies have been proposed for PHEVs. This paper proposes

that all PHEV control strategies could be grouped into three

key classifications based on the different modes of operation of

a PHEV as a result of the implemented controller. They are the

following: 1) all-electric + conventional/hybrid; 2) rule-basedblended; and 3) optimization-based blended control strategies.


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TABLE IVEHICLE PARAMETERS

Fig. 5. Schematic layout of a PHEV.

The Powertrain System Analysis Toolkit (PSAT), which has

been developed by Argonne National Labs, was used for simu-

lation purposes. PSAT is a forward-looking simulation program

that provides real-time information of the drive train [35]. The

impact of each classification on the performance of the PHEV

and components, including but not limited to the ESS, engine,

and electric machine, is studied. The chassis model used for

the simulation analysis of Pihef in this paper was that of a

midsize sports utility vehicle. It consisted of a pretransmission

parallel hybrid configuration, and key parameters of the vehicle

are presented in Table I. Two loops of the UDDS drive cycle

set by the U.S. Environmental Protection Authority (EPA) were

used. The results and analysis are presented along with thedefinitions of each classification (see Fig. 5).

A. All-Electric + Conventional/Hybrid

This classification consists of PHEVs that initially operate

in EV mode prior to switching to operating as either a con-

ventional nonhybrid or a hybrid vehicle. Due to its simplicity,

this method is of initial interest for after-market retrofit plug-

in conversions, as proposed by companies such as Hybrid

Electric Vehicle Technologies, Inc. In the first instance, the

available energy from the ESS is first used up, as observed in

the SOC plot [see Fig. 6(a)], and all the energy for propulsion

of the remainder of the trip will be supplied by the engine. Inthe second instance, the vehicle will operate in EV mode for

Fig. 6. SOC of (a) all-electric+ conventional and (b) all-electric+ hybrid.

a predefined SOC range, i.e., 30% in this case, after which

the vehicle will operate as a hybrid vehicle in CS mode, as

illustrated in Fig. 6(b).

The behavior of the electrical and mechanical drive trains

for each case in the PHEV control classification is further

analyzed. In the instance where the vehicle continues to operate

as a conventional vehicle, the election drive train will cease to

provide any assistance for vehicle propulsion. This is verified

by the energy and power output of the motor presented in

Fig. 7(a) and (b).

The energy output of the engine and motor of a PHEV thatwill continue to operate in CS mode is plotted in Fig. 7(c) and

(d). The electric motor initially provides the energy for propul-

sion and then tapers off, even retrieving some of the depleted

energy toward the end of the drive cycle. This is possible since

the engine in hybrid mode will generate the additional energy

required to charge the ESS. The engine modeled in this paper

does provide some energy when the PHEV is in EV mode. This

is due to the engine turning on and continuing to idle until it

is required to provide propulsion energy. The impact of engine

turn-on times is discussed in the following section.

B. Rule-Based Blended

Blended mode by definition consists of both the engine and

the motor providing energy for propulsion of the vehicle in

conjunction with each other for maximum efficiency all through

the drive cycle. It is important to consider how this strategy

is implemented as it will significantly impact the overall effi-

ciency and performance of the vehicle. Control strategies that

utilize both the electrical and mechanical drive trains based on

predefined criteria are classified as rule-base blended control

strategies. There is a strong possibility that the first genera-

tion of commercially available PHEVs will operate on such

controllers.

While it is theoretically possible for a PHEV in this classi-fication to operate in CD mode for the duration of the trip, it


9/12


Fig. 7. Output for EV-conventional (a) energy of motor, (b) power of motor,(c) energy of engine, and (d) energy of motor for EVCS.

is practically improbable as it is implausible to forecast a drive

cycle. PHEVs in this classification will therefore first operate

in CD mode until the PHEV has achieved a predefined criterion(i.e., the SOC of the ESS reaches a predefined value, etc.) and

then continue to operate as an HEV by switching to CS mode,

as seen in Fig. 8(a). The SOC variation band once the vehicle

switches to charge sustaining mode is narrower since the ESS

is discharged close to its low value before switching over.

It was the goal of the implemented controller to maximize the

electric potential of the PHEV. To that end, the controller was

designed to activate the engine under two conditions, namely,

if the demanded power was higher than a predefined value

and if the SOC of the ESS reached a predefined value. This

enabled the vehicle to utilize the available electrical energy

without sacrificing performance in CD mode. Fig. 8(b) clearly

demonstrates that the electric motor initially has a bigger energyimpact, which is replaced by engine energy after the PHEV

Fig. 8. (a) SOC of ESS. (b) Output energy of engine and motor. (c) Outputpower of engine. (d) Output power of motor.

switches to CS mode. Fig. 8(c) and (d) illustrates the engine

only initially providing peak power demands when in CD

mode, whereas the motor is in operation all through the drivecycle.

C. Optimization-Based Blended

As previously defined, blended control strategies use both

the electric machine and the engine for propulsion all through

the trip. Control strategies that will attempt to employ each

subsystem at their best operating points for maximum effi-

ciency of the PHEV drive train by means of an optimization-

based algorithm are classified in this category. These control

strategies are based on algorithms that are adaptable to each

driving scenario, whereas rule-based blended controllers are

predefined and will not provide the best possible controllerfor all scenarios. It is the goal of optimization-based control


10/12


Fig. 9. (a) SOC of ESS. (b) Output power of engine and motor. (c) Outputenergy of engine. (d) Output energy of motor.

strategies to reach a SOCL value at the end, thereby utilizing

the full potential of the ESS. While this is regarded as anexcellent scenario, it is common to have these controllers end

up as CDCS mode control strategies. The many factors that

could lead to this result are addressed in the optimization-based

controller section of this paper.

The PHEV controller operates in blended mode all through

the drive cycle, as seen in Fig. 9(a). The electric motor and

the engine will supply the required power and energy blended

with each other, as seen in Fig. 9(b)(d). Fig. 9(b) further

demonstrates how the engine provides all the high power de-

mands while the motor provides for the transients, which is

an area where engines are particularly inefficient. The energy

output curves reiterate the blending of the two sources where

the engine output increase somewhat smoothly while the motoroutput fluctuates with the demands of the drive cycle.

Fig. 10. ESS SOC for CS mode.

Fig. 11. SOC depletion for each classification.

D. Hybrid Control

We can also consider a fourth category for vehicles that

operate in CS mode throughout the drive cycle. These vehicles

are practically HEVs with a larger battery pack. However, this

larger pack enables the vehicle to have a much larger range

of SOC swing, allowing the vehicle to discharge significantly

more electric energy, thus providing more electric power on an

average drive cycle. In addition, they have the added flexibil-

ity of operating in electric mode during stop-and-go driving,allowing the battery to discharge without having to operate the

engine soon after to recharge the batteries, as is required in a

conventional hybrid (see Fig. 10).

E. Comparison

It is clearly documented that all PHEV control strategies can

be grouped into three PHEV control classifications and hybrids.

(While hybrid control does not constitute a PHEV control

strategy, it is important to consider them as there are many

retrofit PHEV conversions that are simply a result of a bigger

battery pack, which is added on to HEVs with no modificationto the existing control strategy.) The SOC curves for each case

are plotted below. The different modes of operation in each case

are evident (see Fig. 11).

As discussed in Section II-C, the engine-on/off criteria is an

important component of PHEV control strategies. The engine

turn-on for three cases is subsequently presented. The first

case is clearly a CDCS strategy. The engine periodically turns

on at the beginning to counter high load demands and, once

the PHEV switches to CS mode, stays on continually. In an

EV mode first approach, the engine will turn on only once,

i.e., when the PHEV switches to either conventional or hybrid

mode. The second and third cases are for rule-based blended

and optimization-based blended controllers, respectively. Bothcontrollers have multiple engine starts, with the optimization


11/12


Fig. 12. Engine on behavior for PHEVs in each classification.

TABLE IICONTROL CLASSIFICATION COMPARISON

approach demonstrating engine turn-on that is spread somewhat

evenly throughout the drive cycle. The rule-based approach also

demonstrates some areas where the engine turns on and off

multiple times over a short period of time. Engine starts are

particularly inefficient, and the additional multiple starts of the

rule-based approach will have a definite negative impact on the

overall efficiency of the PHEV (see Fig. 12).

Each control strategy has its advantages and disadvantages.

A tradeoff between efficiency, complexity, and cost is important

from a production point of view. A simple comparison of the

control strategies in each classification is conducted. It is notedthat there are many algorithms that can be implemented for each

classification, with each demonstrating different results. How-

ever, this comparison goes to demonstrate the average trends for

mileage, CO2 emissions, and efficiency. An optimization-based

blended control strategy is clearly the best across the board (see

Table II).

VII. CONCLUSION

Plug-in hybrid vehicles promise high efficiency, improved

performance, and lower emissions, which can only be achieved

through a suitable power management strategy. It is thereforevital that one follows a systematic process of optimization

when designing a controller for PHEVs. Many controllers,

which are mostly extensions of HEV controllers, have been

proposed, as discussed in earlier sections. They are com-

monly grouped based on their mathematical approach: rule-

and optimization-based controllers. Each is described in detail

along with advantages, disadvantages, and examples. While

rule-based control strategies are simpler to implement, it is clear

that they do not provide an optimum solution to maximizing

fuel savings and minimizing emissions. Global optimization

methods will improve the performance of the vehicle. They will

allow for integrating multiple variables to the cost function,

which is important since minimizing emissions are just asimportant as increasing mileage. Adaptive controllers imple-

mented using fuzzy logic, DP, neural networks, and stochastic

dynamic process have been proposed and have shown promise.

However, since one would require drive-cycle information be-

forehand, and driving patterns are very difficult to predict,

they too fall short in providing a globally optimum solution

for PHEVs.

It is clear that a global control strategy that will be optimized

in real time would be the ideal solution. The controller willstrive to minimize a fuel and emission cost function using real-

time data. Real-time controllers that access trip information

via GIS and GPS have been proposed. The success of these

controllers will depend on the ability to access this information

in real time. It will also be limited since this information is

not always available. An adaptive control strategy that will

optimize itself in real time based on easily available vehicle

parameters is seen as the best solution.

Current approaches to classifying PHEV control strategies

are also discussed. It is clear that these classifications are

primarily based on the mathematical model and approach of the

controller. A new classification, i.e., one that groups PHEVsbased on how the vehicle performs, is presented. It is based

on the different modes of operation of a PHEV as a result of

the implemented controller and will only be true for PHEVs.

They are the following: 1) all-electric + conventional/hybrid;2) rule-based blended; and 3) optimization-based blended con-

trol strategies. The viability of this classification is demon-

strated through a simulation study.

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Amer. Control Conf., St. Louis, MO, Jun. 2009, pp. 46014606.[32] V. Freyermuth, E. Fallas, and A. Rousseau, Comparison of powertrain

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Sanjaka G. Wirasingha (S03) received the Ph.D.degree in electrical engineering from Illinois In-stitute of Technology (IIT), Chicago, in 2010.His Ph.D. dissertation, under the supervision ofDr. A. Emadi, was entitled System level analysisof PHEVs: classification, electrification, energy ef-ficiency, and control strategies.

He was a Lab Manager with the Power Electronicsand Motor Drives Laboratory, IIT. He has industryand project experience from a technical, business,and management perspective. He is currently a Hy-

brid Engineer with the Electrified Power Train Group, Chrysler Group LLC,where he is responsible for system requirements, integration, and validation ofthe power electronic subsystem.

Dr. Wirasingha is the recipient of numerous awards, including the ClintonE. Stryker Distinguished Service Award for leadership and contributions in thearea of campus life and the Grand Prize for M.S. research on the hybridizationof a transit bus from India (his M.S. Thesis).

Ali Emadi (S98M00SM03) received the B.S.and M.S. degrees in electrical engineering (withhighest distinction) from Sharif University of Tech-nology, Tehran, Iran, in 1995 and 1997, respectively,and the Ph.D. degree in electrical engineering fromTexas A&M University, College Station, in 2000.

He is currentlythe HarrisPerlstein Endowed Chair

Professor of Engineering and the Director of theElectric Power and Power Electronics Center andGrainger Laboratories with Illinois Institute of Tech-nology (IIT), Chicago, where he has established

research and teaching facilities as well as courses in power electronics, motordrives, and vehicular power systems. In addition, he is the Founder andPresident of Hybrid Electric Vehicle Technologies, Inc. (HEVT)a universityspin-off company of IIT. He is the principal author/coauthor of over 250 journaland conference papers, as well as several books, including Vehicular Elec-tric Power Systems (Marcel Dekker, 2003), Energy Efficient Electric Motors(Marcel Dekker, 2004), Uninterruptible Power Supplies and Active Filters(CRC, 2004), Modern Electric, Hybrid Electric, and Fuel Cell Vehicles, Second

Edition (CRC, 2009), and Integrated Power Electronic Converters and DigitalControl (CRC, 2009). He is also the editor of the Handbook of AutomotivePower Electronics and Motor Drives (Marcel Dekker, 2005).

Dr. Emadi was the General Chair of the First IEEE Vehicle Power andPropulsion Conference in 2005, which was colocated under his chairman-

ship with the Society of Automotive Engineers (SAE) International FutureTransportation Technology Conference. He is currently the Chair of the IEEEVehicle Power and Propulsion Steering Committee, the Chair of the TechnicalCommittee on Vehicle and Transportation Systems of the IEEE Power Elec-tronics Society, and the Chair of the Power Electronics Technical Committeeof the IEEE Industrial Electronics Society. He has also served as the Chair ofthe 2007 IEEE International Future Energy Challenge. He is the recipient ofnumerous awards and recognitions. He was named a Chicago Matters GlobalVisionary in 2009. He was named the 2003 Eta Kappa Nu Outstanding YoungElectrical Engineer by virtue of his outstanding contributions to hybrid electricvehicles. He also received the 2005 Richard M. Bass Outstanding Young PowerElectronics Engineer Award from the IEEE Power Electronics Society. In 2005,he was selected as the Best Professor of the Year by the students at IIT. He isthe recipient of the 2002 University Excellence in Teaching Award from IIT aswell as the 2004 Sigma Xi/IIT Award for Excellence in University Research. Hedirected a team of students to design and build a novel motor drive, which won

the First Place Overall Award at the 2003 IEEE International Future EnergyChallenge for Motor Competition.

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