Medical, Pharma, Engineering, Science, Technology and Business

Research scholar from Nha Trang University, Institute of Neuro Immune Pharmacology, Vietnam

- Corresponding Author:
- Long VT

Research scholar from Nha Trang University

Institute of Neuro Immune Pharmacology, Vietnam

**Tel:**+ 8458-383-1149

**E-mail:**[email protected]

**Received date:** August 25, 2014; **Accepted date:** December 31, 2014; **Published date: **January 06, 2015

**Citation:** Long VT (2015) Application of a Pheromone-Based Bees Algorithm for Simultaneous Optimisation of Key Component Sizes and Control Strategy for Hybrid Electric Vehicles. Int J Swarm Intel Evol Comput 4:114. doi:

**Copyright:** © 2015 Long VT. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

**Visit for more related articles at** International Journal of Swarm Intelligence and Evolutionary Computation

Hybrid electric vehicles; Basic bees algorithm; Pheromone-based bees algorithm; Intelligent optimization; Parallel HEV control strategy

Increasing concern about environmental issues, such as global warming and greenhouse gas emissions, as well as the predicted scarcity of oil supplies have made energy efficiency and reduced emissions a primary designing point for automobiles. HEVs have demonstrated improved fuel economy with lower emissions than conventional vehicles. Superior HEV performance in terms of higher fuel economy and lower emissions, with satisfaction of driving performance, necessitates a careful balance of key component sizes as well as control strategy parameter monitoring and tuning.

Optimal parameter value of component sizes and control strategy for HEVs have been studied previously [1-3]. The parametric optimization based on rule-based control was widely used in early studies whereas control concepts based on optimal theories such as Dynamic Programming (DP) or Pontryagin’s Minimum Principle (PMP) [4,5] is more current. The optimal control parameters are obtained if the driving-cycle and vehicle performance such as fuel consumption, exhaust emission, and acceleration performance are known. In this case, the DP approach can find the global optimal solution [11-13]. However, DP has to use one more step, a post-processing step, such as neural networks to approximate the results of the optimal control pattern. Even then DP cannot cover all driving conditions. Hence, the real-time controller based on DP is effective only for the driving cycle that is used for rule extraction.

Another approach based on optimal control theory is PMP requiring less computing time than DP. While, control based on PMP can reduce the computational time for getting an optimal trajectory, it is a local optimal solution, not a global solution in general problems [7-9]. In addition, some other approaches have used for the optimization of HEV. Asians (1996) tried to find optimal input variables including the sizes of ICE, EM and battery pack. The optimization objective was to improve the FC when the driving performances were kept within the standard limits. However, they did not account for the exhaust emissions [13]. Montazeri (2006) used Genetic Algorithm (GA) to find optimal component sizes and control strategy. Their objective was to minimize a weighted sum of FC and emissions while the PNGV (the Partnership for a New Generation of Vehicles) performance requirements were considered as constraints [6]. Wu used Particle Swarm Optimization to achieve optimal parameters for both the powertrain and control strategy, and vehicle performances were also defined as constraints. This research aimed to reduce FC, emissions, and manufacturing costs of HEVs. To solve this problem, they used a single objective problem with a goal-attainment method to replace the original multi-objective optimization problem.

In 2012, Long, used a basic Bees Algorithm to optimize parallel HEV component sizes and control strategy. The parameters include three parameters of component size and six parameters of control strategy. In this paper, the number of parameters of control strategy is expanded to seven [14]. In order to enhance convergence speed, the component size and control strategy parameters of parallel HEVs are optimized simultaneously by using the new version of Bees Algorithm, the Pheromone-Based Bees Algorithm, to obtain the minimization of weighted sum of FC and emissions when the PNGV driving performances such as acceleration and grad-ability of parallel HEVs are maintained.

**Parallel HEV component sizing and control strategy**

**The parallel HEV component sizing:** The parallel HEV
configuration is shown in (**Figure 1**). In this configuration, both ICE
and EM are mechanically connected to the driving wheels. The EM
plays the role of assisting the ICE in supplying the required power. The
ICE can also drive the EM as a generator to charge the battery [15-17].
In this research, the ICE, EM and battery are treated as key components
in the design process of parallel HEVs.

**HEV control strategy: **There are some control strategies that
are proposed for parallel HEVs. The Electric Assist Control Strategy
(EACS) has been used in this research. Using EACS, the main energy
provider is ICE and the EM is used as ICE assistance. The EACS is
described in (**Figures 2-4**) [8] and [2].

The EACS can use the EM in a variety of ways [10]:

1. If the required speed is less than the electric launch speed
(which is dependent on the SOC), the ICE could be turned off. In
(**Figure 4**), above solid line the ICE is on and below solid line the vehicle
attempts to run all electrically.

2. If the SOC is higher than its low limit, the ICE could be turned off. If the requested speed is less than the launch speed and the SOC is higher than the low limit, the ICE will be turned off.

3. If the required torque is less than a cutoff torque, *cs_off_trq_
frac *fraction of the maximum torque T_{ max}, the ICE could be turned off.
If the requested torque is lower than this cutoff and the SOC is higher
than the low limit, the ICE will be turned off.

4. When the battery SOC is below *cs_lo_soc,* additional torque
is required from the ICE to charge the battery. This additional charging
torque is proportional to the difference between SOC and the average
of *cs_lo_soc *and *cs_hi_soc*. This ICE torque is prevented from being
below a certain fraction,* cs_min_trq_frac*, of the maximum ICE torque
T _{max} at the current operating speed. This is intended to prevent the ICE
from operating at an inefficiently low torque.

**Optimization targets: **The HEV research objective is to minimize
the weighted sum of FC and exhaust emissions (HC, CO and NOx)
while still satisfying charge sustaining requirement and driving
performances. The PNGV passenger car constraints described in (**Table 2**) [19] are used as dynamic performance requirements to show that
vehicle performance is not sacrificed during optimization.

The objective function is defined as follows:

G(x) = f_{1}FC + f_{2}HC + f_{3}CO + f_{4}NO_{x} (1)

Where, f_{1} to f_{4} are also defined as weighting factors used to
investigate the effect of different objectives on the optimization results.

**Bees algorithm for simultaneous optimization of component
sizes and control strategy**

Bees Algorithm mimics the food foraging behavior of a swarm of honey bees. This algorithm performs a type of neighborhood search combined with random search.

**Basic bees algorithm:** The basic bee’s algorithm is an intelligent
optimization tool imitating the food foraging behavior of honey bees
found in nature. In the natural environment bees are able to discover
food sources using two kinds of search methods, namely, a global
random search and a local search. The former consists of sending the
bees at random around the hive. Once these bees, which are called the
scout bees, discover potential food sources they return to their hive and
start recruiting more bees to exploit those food sources which were
discovered during the random search attempt. The bees waiting in the
hive receive their instructions from the returning scout bees in the form
of a waggle dance which gives them the following useful information:
the location of the nearest food source, the quality of that food source,
and the amount of energy needed to harvest the food. Logically, the
better the food source and the closer to the hive the more numerous
the recruited bees will be. The search performed by the recruited bees is
similar to a local search. While some bees are recruited to conduct local
search, a percentage of the bee population continues the global random
search to look for other promising food sources. This ensures that the
search continues cycle after cycle in an iterative manner until all the
good food sources including the best food source in the vicinity of the
hive are found. This is similar to an intelligent optimization process
and can be formulated into an algorithmic form as in the basic Bees
Algorithm [15].

**Pheromone-based bees algorithm: **In nature, the bees are known
to secrete pheromones in a liquid form which is transmitted by coming
into direct contact with it or as it is a vapor. The pheromones release
chemical signals proportional to the amount which has been deposited by scout bees for marking potential food sources, marking their hive,
scenting potential hive sites, and assembling or recruiting other bees.
The scent arising from the secreted pheromones can intensify or
diminish over time depending on the level of bee activity at that site.
A strong scent will help to recruit bees in larger numbers to the food
source while a mild scent will indicate the depletion of nectar in a
previously marked food source.

In the Pheromone-Based Bees Algorithm the number of scout
bees allocated for global random search is defined by parameter “n”
and the number of bees assigned to search around the selected site
“e” is defined by parameter “**m**”. In order to facilitate the search
within a sphere centered on the selected sites, the parameter “**n _{gh}**” is
used to define the neighborhood size. In the Pheromone-Based Bees
Algorithm, pheromones are used to recruit bees to search around
each selected site. In every iteration, the bees deposit pheromones on
the sites they are drawn to and the exact amount on a particular site
depend on the quantity of pheromones already present on that site
which is influenced by a decay rate, the fitness of that site, and the
number of bees found on that site. The amount of pheromones found
on a site will gradually evaporate to nothing, over time, if there is no
bee activity there. Due to pheromone evaporation, the older the site,
the less attractive it is (because it has been exploited and the nectar
in it might have exhausted). As a consequence, the number of bees
recruited to each site will be proportional to the quantity of pheromone
already present on that site, and the fitness of that site. Thus the use of pheromones allows an automatic and dynamic recruitment of bees
across the search space. The pheromones are used to recruit bees to
a particular site, uses not only the quality of that site, i.e. fitness, but
also the amount of pheromone found on the site. The precise amount
of pheromone accumulated on each site will be calculated in each
iteration using a pheromone update equation which will show either
an increase or decrease in its level [15].

The Pheromone-Based Bees Algorithm is shown as in **Figure 5**, and
its parameters are described in **Table 3**.

The algorithm starts with the initial population of n scout bees to search randomly in the solution space. Then, the fitness of the scout bees associated with their respective sites is evaluated in step 2. However, only bees with the highest fitness are chosen as “selected bees” and sites visited by them are selected for neighborhood search in step 3. After that, in steps 4, 5 and 6, the algorithm will search in the neighborhood of the selected sites, the number of bees “m” recruited for each selected site depends on the pheromone deposited on that site. At the end of each neighborhood search, the bee having the highest fitness value associated with its visited patch is selected to form the next bee population

In order to avoid local optima, in step 7, the remaining bees (n-e) in the population have to search randomly around the solution space to find new potential sites. The iteration of these above steps will not be finished until a stopping criterion is met and the best bee of the last population is treated as the optimal solution [21,22].

Pheromone-based bees algorithm in parallel HEV optimization: In order to apply PBA to the simultaneous optimization of parallel HEVs, the fitness in step 2 is the inverse of objective function G (x) in Equation (1). However, the optimization task is required to maintain the on road performances such as acceleration and grad-ability of parallel HEVs. Unfortunately, the PBA cannot work directly with constrained optimization problem. To solve this problem, it is necessary to add penalty functions into objective function G(x) [23].

Min G (x) x = (x_{1}, x_{2}---x_{9}) (2)

Subject to h_{i} (x) ≤ 0 i = 1, 2,…, 7

(3)

(4)

Where, x_{1}, x_{2}… x_{9} are parameters of component sizes and control
strategy listed in (**Table 1**)

C_{i} (S_{j})(x), αi and F_{i} (x) are penalty function, desired value and
evaluated value related to ith constrain h_{i} (x) in (**Table 2**)

The penalty function is used to penalize infeasible solutions
by reducing their fitness values. C_{i} (S_{j})(x) = 0, if the constrain h_{i} (x) is
satisfied.

ki is penalty factor chosen by trial and error as given in (**Table 2**)

fitness (S_{j})(x) is the fitness value of site S_{j}

The optimization process using PBA for parallel HEVs can be stated as follows:

Step 1: Initialize the population of scout bees, each scout is a set of
specific values of all variables of component sizes and control strategy
in (**Table 1**)

Step 2: Evaluate the FC, HC, CO, NO_{x} and penalty functions C_{i}(x)
for each scout bee by combining between PBA and ADVISOR software

Step 3: Calculate the fitness value of all scout bees according to Equation (3) and (4)

Step 4: Choose e bees with highest fitness

Step 5: Recruit bees for selected “e” sites according to the
pheromone levels at those sites (local search) to conduct searches in the
neighborhood of the selected e sites and choose a bee with the highest
fitness for each site. The number of bees given by nb (S_{j}, t) recruited for
a site S_{j} of e sites at time t is calculated from Equation (5)

Step 6: Assign the remaining (n-e) bees to search randomly around the search space for new potential solutions

Step 7: At the end of the local and global search, the best bees from all the sites are sorted according to their fitness

Step 8: Update new population

Step 9: Update pheromone level on each site by using Equation (7)

Step 10: Stop the program if the convergence criteria is satisfied, otherwise go to step 4.

(5)

(6)

(7)

Where, fs (S_{j}) is the fitness score of site S_{j},. S_{e+1} are the best
performing site among the non-selected sites. Note that the fitness
score fs (S_{j}) is normalized to smooth noise and suppress systematic
variations. The optimization process is programmed and linked with
ADVISOR by using *.m file in Matlab [7]. The linkage configuration
between ADVISOR and PBA is described in (**Figure 6**). The parameters

Where, up. Bound _{(i)} and lo. Bound _{(i)} are the upper bound and
lower bound of variable i^{th} listed in (**Table 6**)

ADVISOR software gives different component modules such as
fuel converter, energy storage, motor, etc. to build a vehicle system. In order to continuously adjust component sizes in the search space, the
fixed parameters are used for the simulation of the parallel HEV shown
in (**Table 5**). To vary component size, the baseline ICE of Geo Metro
1.0L SI engine is used. The engine torque scale factor, *fc_trq_scale*,
is also used to vary the ICE size. In addition, for the baseline electric
motor, a Westinghouse AC induction motor is employed. The same
as ICE, the motor torque scale factor, *mc_trq_scale*, is used to vary the
EM size. Similarly, the Hawker Genesis Valve-Regulated Lead-Acid
(VRLA) battery is used for battery sizing. To vary the battery size, the
number of battery modules, *ess_module_num*, is changed [8].

The range of component size and control strategy variables is given
in (**Table 6**).

**Simulation results and analysis: **In order to eliminate the influence
of energy from the battery on fuel consumption, the simulation has
been run several times starting with different initial SOC values until
the final SOC is close to the initial SOC. After running the optimization
program with PBA parameters in (**Table 4**) following three driving
cycles, FTP, ECE-EUDC and UDDS. The optimal parameters, FC,
emissions and dynamic performances, are shown in (**Tables 7-9**).

The results in the above tables prove the power of the PBA. With
the optimal parameters of component sizes and control strategy listed
in (**Table 7**), the FC, HC, CO and NOx are improved and dynamic
performances are satisfied the PNGV constrains. The FC, emissions and
vehicle performances obtained by using PBA with the driving cycles
FTP, ECE-EUDC and UDDS are nearly same as ones employed by BBA.
However, the rate of convergence of PBA is faster than that optimized by BBA [13,14]. The optimization process in this research will be
stopped after 30 iterations or when the value of objective function does
not reduce after 15 iterations. The set of component size and control
strategy variables of the last best bee at the last iteration is considered
as the best solution for optimization of the parallel HEVs. Compared to
the BBA, the new version, PBA, showed an improvement of about 25 %
in convergence speed. This indicates the good performance of the PBA
approach in saving time to achieve the optimal parameters.

The paper presents a simultaneous optimization of parallel HEV component sizes and control strategy to minimize the weighted sum of FC and emissions without sacrificing road performance by using a new approach, Pheromone-Based Bees Algorithm. Similar to the BBA, the PBA employs a type of neighborhood search (local search) combined with a random search (global search) in the solution space, so the results of component size and control strategy parameters of parallel HEVs are ensured to be global solutions. However, as the PBA employs pheromones to attract bees to explore promising regions of the search space, it can find the best solution approximately 25% faster than the basic Bees Algorithm. The results show that, the PBA approach is powerful in searching the best parameters of parallel HEVs in the solution space resulting in improvement of FC and reduction of HC, CO and NOx, while PNGV constrains are maintained.

- Assanis D, Delagrammatikas G, Fellini R, Filipi Z, Liedtke J, et al. (1996) An optimization approach to hybrid electric propulsion system design.
- Chirag D (2010) Design and optimization of hybrid electric vehicle drivetrain and control strategy parameters using evolutionary algorithms. A Thesis in The Department of Electrical and Computer engineering, Concordia University, Canada.
- Han Z, Yuan Z, Guangyu T, Quanshi C, Yaobin C, et al. (2004) Optimal Energy Management Strategy for Hybrid Electric Vehicles. SAE Paper 1: 576.
- Karaboga D, Akay B (2009) A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214: 108-132.
- Kim N, Cha S, Peng H (2011) Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle. IEEE Trans Control System Technology 19: 1279-1287.
- Moore TC, Lovins AB (1995) Vehicle Design Strategy to Meet and Exceed PNGV Goals. SAE95-27.
- Markel T, Brooker A, Hendricks T, Johnson V, Kelly K, et al. (2002) Advisor: a systems analysis tool for advanced vehicle modelling. Journal of Power Sources
- Montazeri-Gh, M, Poursamad A (2006) Appliacation of Genetic Algorithm for Simultaneous Optimization of HEV Component Sizing and Control Strategy. Int. J. Alternative Propulsion1
**:**63-78. - Namwook K, Sukwon C, Huei P (2011) Optimal Control of Hybrid Electric Vehicles Based on Pontryaginâ€™s Minimum Principle. Control Systems Technology IEEE Transactions on 19: 1279-1287.
- National Renewable Energy Laboratory (2001) Documentation ADVISOR software 3.2.
- Lin CC, Peng H, Grizzle JW, Kang J (2003) Power Management Strategy for a Parallel hybrid Electric Truck. IEEE Trans Control Syst. technol 11: 839-849.
- Liu J, Peng H (2006) Control Optimization for a Power-Split Hybrid Vehicle. American Control ConfMinneapolis Minnesota 466-471.
- Long VT, Nhan NV (2012) Bees-algorithm-based optimization of component size and control strategy parameters for parallel hybrid electric vehicles. International Journal of Automotive Technology13: 1177 â€“ 1183.
- Long VT (2012) Application of Bees Algorithm for simultaneous optimisation of HEV key component sizes and control strategy. The 2nd international conference on automotive technology, engine and alternative fuels 6: 37â€“43.
- Packianather MS, Landy M, Pham DT (2009) Enhancing the speed of the Bees Algorithm using Pheromone-based Recruitment. 7th IEEE International Conference on Industrial Informatics789-794.
- Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, et al. (2005) The Bees Algorithm Technical Note Manufacturing Engineering Centre. Cardiff University UK.
- Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, et al. (2006) The Bees Algorithm - A Novel Tool for Complex Optimisation Problems. Proceedings of IPROMS Conference454-461.
- Pu JH, Yin CL, Zhang JW (2005) Fuzzy torque control strategy for parallel hybrid electronic vehicles. Int. J. Automotive Technology6: 529-536.
- Srdjan ML, Ali E (2004) Effects of Drivetrain Hybridization on Fuel Economy and Dynamic Performance of Parallel Hybrid Electric Vehicles. IEEE Transaction on Vehicular Technology53: 385-389.
- Suh B, Frank A, Chung YJ, Lee EY, Chang YH, et al. (2011) Powertrain System Optimization for A Heavy-duty Hybrid Electric Bus. Int. J. Automotive Technology12: 131-139.
- Vadim F (1996) Global Methods in optimal control theory. Marcel DekkerInc140.
- Wu J, Zhang CH, Cui NX (2008) Pso Algorithm-Based Parameter Optimization for HEV Powertrain and Its Control Strategy.Int. J. Automotive Technology 9: 53-69.
- Yeniay O (2005) Penalty Function Methods for Constrained Optimization with Genetic Algorithms. Math and Comp. Applications 10: 45-56.

Select your language of interest to view the total content in your interested language

- ACS Nano
- AI Algorithm
- Advanced Materials
- Algorithm
- Android Technology
- Artificial Intelligence
- Artificial Intelligence Studies
- Artificial Intelligence and Philosophy
- Artificial neural networks
- Automated Mining
- Automated Reasoning and Inference
- Automation
- Behavior-based systems
- Big data
- Bioinformatics Modeling
- Biomechanics
- Biomechanics and Robotics
- Biosensor
- Biostatistics: Current Trends
- Case-based reasoning
- Cloud
- Cloud Computation
- Cognitive Aspects of AI
- Commonsense Reasoning
- Computational Neuroscience
- Computational Sciences
- Computer Artificial Intelligence
- Computer Hardware
- Computer Science
- Computer-aided design (CAD)
- Constraint Processing
- Cryptography
- Data Communication
- Data Mining Current Research
- Data Mining and Analysis
- Data Security
- Data Storage
- Development Process
- Digital Image Processing
- Distributed Sensor Networks
- Electronic Engineering
- Evolution of social network
- Evolutionary Optimisation
- Evolutionary algorithm
- Evolutionary algorithm in datamining
- Evolutionary computation
- Evolutionary science
- Findings on Machine Learning
- Fuzzy Logic
- Handover
- Hardware Networking
- Heuristic Search
- High-Level Computer Vision
- Human Centered
- Human-Robot Interaction
- Hybrid soft computing
- IT Management
- Industrial Robotics
- Information Systems
- Information Technology
- Intelligent Interfaces
- Intelligent Robotics
- Internet Communication Technology
- Knowledge modelling
- Lovotics
- Machine Learninng
- Mathematical Modeling
- Medical Device
- Medical Robotics
- Mobile Communication
- Mobile Device
- Mobile Repots
- Mobile Robot System
- Motion Sensors
- Multi Objective Programming
- Nano/Micro Robotics
- Network Algorthims
- Network Security
- Networks Systems Technology
- Neural Network
- Neural Networks
- Neurorobotics
- Ontology Engineering
- Optical Communication
- Project development
- Real Time
- Robotic Rehabilitation
- Robotics
- Robotics In Medical
- Robotics Research
- Robotics for Application
- Robotics for Mechanism
- Routing Protocol
- Sensing and Perception
- Sensor Network Technology
- Sensor Technology
- Sensors and Actuators
- Simulation
- Social Robots
- Soft Computing
- Software Architecture
- Software Component
- Software Quality
- Studies on Computational Biology
- Swarm Robotics
- Swarm intelligence
- Systems Biology
- Telerobotics
- Web Service
- Wireless Sensor Networks
- Wireless Technology
- ZIPBEE Protocol
- swarm intelligence and robotics

- 4th Global Summit and Expo on Multimedia &
**Artificial Intelligence**

July 19-21, 2018 Rome, Italy - International Conference on
**Artificial Intelligence**,**Robotics**& IoT

August 21-22, 2018 Paris, France - 6th World Convention on
**Robots**and Deep Learning

September 10-11, 2018 Singapore City, Singapore - International Conference on Mechatronics &
**Robotics**

October 15-16, 2018 Helsinki, Finland

- Total views:
**11924** - [From(publication date):

August-2015 - May 24, 2018] - Breakdown by view type
- HTML page views :
**8149** - PDF downloads :
**3775**

Peer Reviewed Journals

International Conferences 2018-19