Modelling,design and control of a light electric vehicle with hybrid energy storage system for Indian driving cycle

Abstract

This paper presents the modelling, design and power management of a hybrid energy storage system for a three-wheeled light electric vehicle under Indian driving conditions. The hybrid energy storage system described in this paper is characterized by effective coupling of Li-ion battery (primary energy source) and ultracapacitor (auxiliary source) interfaced with an efficient bi-directional converter. A design methodology related to vehicle modelling, choice of motor rating, converter design, sizing of Li-ion battery and ultracapacitor pack for the Indian driving cycle are presented. An improved real-time power-split management control strategy is proposed for proper power flow control of the hybrid energy storage system under various operating modes. The hybridized energy storage system with proposed control strategy improves the life of the battery and helps in effective utilization of the ultracapacitor. Furthermore, a relative comparison of the hybrid energy storage system with the battery energy storage system based on battery parameters and capital cost is also presented. Simulations are carried out in MATLAB/Simulink environment to verify the effectiveness of the proposed control strategy with modelled system components of three-wheeled light electric vehicle. A downscaled experimental prototype is built to validate the power-split between hybrid energy storage systems.

Keywords

Hybrid energy storage system light electric vehicle Indian driving cycle bi-directional converter energy management control strategy

Introduction

The adoption of electric vehicles (EVs) has been propelled with the objective of reducing the pollution and improving the fuel consumption.¹ In India, the NITI Aayog² has charted out a plan of fully progressing towards EVs by 2030, which in turn reduces the CO₂ emission by 37% and the energy demand by 64%. The environmental factors favour the choice of pure EVs in urban India. Of the different means of road transport available in urban India, the most preferred is the three-wheeled vehicle namely auto rickshaw.³ The conversion of this vehicle into a pure EV generates a lot of interest and necessitates a comprehensive analysis of various factors involved in the design and development of EVs to put forth new and efficient technologies.

The energy storage system (ESS) forms the core of the pure EV, and batteries are the most widely used ESSs. However, a pure EV employing battery energy storage system (BESS) suffers from drawbacks like limited driving range, reduced battery life and low power density. To overcome the above drawbacks, researchers have proposed the integration of ESSs.⁴ Ultracapacitors (UCs) and fuel cells are the most widely preferred storage system apart from battery for EV applications.

A fuel cell–based vehicle propulsion system combining proton-exchange membrane fuel cell (PEMFC) as the primary energy source and Ni–MH battery as an auxiliary source has been proposed.⁵ The technological challenges in the area of fuel cell vehicle include weight, volume and cost, which need to be addressed to achieve expected efficiency. To enhance the performance of fuel cell–based EVs, an auxiliary power source employing UCs has been suggested.^6,7 The results have been presented to indicate the merits of hybridization. The hybrid energy storage system (HESS) with battery/UC has been detailed.⁸ The hybridization creates a constant load profile for the battery and ensures good battery life. From the literature, it is evident that the hybrid storage system with battery/UC exhibits superior performance. The battery/UC interface demonstrates higher energy density for longer range, higher power density during acceleration and braking performance. In view of the above, the battery/UC hybrid configuration is considered in this work for three-wheeled EVs.

The primary focus of the design concepts for a three-wheeled EV has been on the integration of renewable energy sources with BESS and as an electric power assist to the internal combustion engine (ICE).^9–18 The comprehensive review of various design concepts of a three-wheeled vehicle is given in Table 1. The concept of integration of battery/UC configuration for a three-wheeled EV proposed in this work will open up new avenues of research with focus on weight, range, battery life and cost.

Table 1.

Various design concepts of a three-wheeled vehicle.

S. no.	Year	Energy source	Description
1.	2005	Battery and ICE	This hybrid power train is proposed as an alternative to the ICE vehicle. The results of power assisting and range extension modes are found to be satisfactory.⁹
2.	2009	Battery and ICE	Developed micro-hybridized Bajaj RE2 indicates 21% reduction of consumption and emissions with improved driver comfort.¹⁰
3.	2010	Solar and battery	Designed a solar-assisted auto rickshaw with a recharging infrastructure.¹¹
4.	2011	Fuel cell and battery	A fuel cell hybrid three-wheeler has been developed to analyse the performance of fuel cell and battery capacity.¹²
5.	2014	Solar and battery	A three-wheeled vehicle model using hybrid sources has been designed and developed to improve its performance.¹³
6.	2015	Wind and battery	A wind turbine–mounted vehicle has been designed and implemented to generate 200 W at a speed of 80 km/h.¹⁴
7.	2016	Solar and battery	The performance analysis of a micro-controller-based PV three-wheeler has been performed to reduce the discharging time of battery.¹⁵
8.	2016	Battery	A detailed comparison between the lead-acid and lithium-ion battery–powered three-wheeler EVs has been established.¹⁶
9.	2017	Solar, ICE and battery	A solar-based plug-in hybrid three-wheeler has been developed to observe its feasibility¹⁷
10.	2018	Wind, solar, ICE and battery	Solar and wind energy–assisted plug-in hybrid three-wheeler has been developed to reduce the dependency on grid electricity and fuel¹⁸

ICE: internal combustion engine; EVs: electric vehicles; PV: photovoltaic.

The efficient utilization of HESS lies in the development of proper energy management system. Rule-based strategy,¹⁹ fuzzy logic–based algorithm,²⁰ filtering-based strategy²¹ and optimization-based techniques for optimal power flow management²² have been proposed. This paper discusses an improved real-time energy management strategy by integrating rule- and filtering-based technique for efficient power flow of battery/UC configuration.

This paper is organized into seven sections. The system description is introduced in section ‘System description’ followed by modelling of system components in section ‘Modelling of system components’. Section ‘Modes of operation and control strategy’ explains the operating modes and power-split management control strategy. The simulation results and discussions are presented in section ‘Simulation results and discussion’. Section ‘Experimental details and results’ shows the experimental verification and final section ‘Conclusion’ presents the conclusion.

System description

This work explores the concept of hybridizing battery and UC for a three-wheeled light electric vehicle (LEV). The specifications of the Bajaj RE three-wheeler have been considered for the analysis. The technical specifications of the conventional ICE-based three-wheeler is given in Table 2, and the corresponding specifications considered for LEVs are given in Table 3.

Table 2.

Technical specification of the Bajaj RE three-wheeler.

Mass of the vehicle(curb weight+payload weight)	678 kg (348 + 330 kg)
Maximum speed	65 km/h
Tyre	4.00–8.6 PR
Rolling resistancecoefficient, C_rr	0.015
Drag coefficient, C_d	0.44
Frontal area, A_d	2.09 m²
Gradeability	19%
Dimension (L × W × H)	2635 mm × 1300 mm ×1710 mm

Table 3.

Specification considered for the proposed work.

No. of passengers	4 (with luggage)
Range	60 km/charge
Maximum speed	42 km/h
Mass of the vehicle	With lead–acid battery	With Li-ionbattery
	798 kg	550 kg¹⁶

The block diagram of the electric drive system for the LEV considered in this work with dual ESSs (battery and UC) is shown in Figure 1. The system employs a semi-active parallel configuration²³ for interfacing battery and UC. Here, the battery acts as a main source, while the UC acts as an auxiliary source.

Figure 1.

Block diagram of electric drive system with dual energy sources.

In this configuration, the main source is directly connected to the DC link to maintain constant DC link voltage, and the auxiliary source is connected through a power electronic interface (PEI). This necessitates a highly efficient bi-directional DC/DC converter as an interface between the ESS.

As the proposed approach is destined for the Indian roads, the Indian driving cycle (IDC) by Automotive Research Association of India (ARAI), shown in Figure 2, is considered as per BIS code (IS 15886:2010). This is considered to size the ESS and evaluate the performance of energy management strategy and the adaptability of vehicle speed profile.²⁴ The specification of IDC is given in Table 4.

Figure 2.

Indian driving cycle (IDC).

Table 4.

Specification of IDC.

Total time (one cycle)	108 s
Distance	0.658 km
Maximum speed	42 km/h
Average speed	21.93 km/h
Maximum acceleration	0.65 m/s²
Maximum deceleration	–0.63 m/s²

Modelling of system components

The modelling approach of various system components involved in the realization of EV is essential to comprehend the design procedure and evaluate its performance.

Vehicle dynamic modelling

By using Newton’s law of motion, the vehicle is modelled as a load by considering various longitudinal forces acting on it,²⁵ which is depicted in Figure 3. The tractive force, F_t, required to drive the wheels by overcoming resistive force is given by

F_{t} = F_{rr} + F_{ad} + F_{hc} + F_{la} + F_{ω a}

(1)

where F_rr is the rolling resistance force, F_ad is the aerodynamic drag, F_hc is the hill climbing force, F_la is the linear acceleration force and F_ωa is the rotational acceleration force.

Figure 3.

Vehicle load model.

The extended form of equation (1) is given by

\begin{matrix} F_{t} = C_{rr} Mg \cos Ψ + \frac{1}{2} ρ A_{d} C_{d} υ^{2} + Mg \sin Ψ \\ + M \frac{d υ}{dt} + 0.1 F_{la} \end{matrix}

(2)

where C_rr is the rolling resistance coefficient, M is the mass of the vehicle (kg), g is the gravitational constant (9.81 m/s²), Ψ is the grade angle (degree), ρ is the density of air (1.22 kg/m³), A_d is the frontal area (m²), C_d is the drag coefficient and υ is the speed of vehicle (m/s).

The gradient of road (Ψ) contributes a major role in the responses of the vehicle model. For simplicity, the maximum gradient of the road is assumed as Ψ = 6° with a maximum speed of 15 km/h. Therefore, the various acting forces on the vehicle to create the instantaneous tractive force for propelling the vehicle on IDC are shown in Figure 4(a). Using the obtained instantaneous tractive forces and the speed of vehicle, the tractive power required at wheels is calculated as

Tractive power, P_{t} = F_{t} \times υ

(3)

Figure 4.

Vehicle model (a) distribution of various forces acting on vehicle, (b) distribution of tractive and motor power, (c) distribution of wheel and motor torque and (d) energy demand for one cycle of IDC.

Then, the motor power is calculated from the tractive power using

Motor power, P_{m} = \frac{P_{t}}{η_{t}}; P_{t} \geq 0

(4)

Motor power, P_{m} = P_{t} \times η_{t}; P_{t} < 0

(5)

where η_t is the transmission efficiency.

The distribution of motor and tractive power over a cycle of IDC is shown in Figure 4(b). The motor speed and the wheel speed are related by the gear ratio²⁶ (GR), which is expressed as

GR = \frac{N_{m} \times r_{w} \times π}{30 \times υ_{\max}} = 8.3

(6)

where N_m is the maximum motor speed (4200 r/min), r_w is the wheel radius (0.22 m) and υ_max is the maximum velocity of the vehicle (11.67 m/s).

The required wheel torque can be calculated from

T_{req} = F_{T} \times r_{w}

(7)

Thus, the relation between wheel torque and motor torque is obtained using equation (6), which is depicted in Figure 4(c). Therefore, the energy required for a single cycle of IDC is found as 156 kJ without considering regenerated power, shown in Figure 4(d). So, the assumed 60-km range requires 91.185 cycles of IDC, which demands 14.22 MJ of energy to power the motor, whereas the maximum energy demand reduces to 12.77 MJ for system that considers regenerated power.

Sizing of HESS

This section presents a design method for sizing the hybrid energy sources for the three-wheeled EV as per the IDC, that is, determine the number of battery cells and the number of UC cells. To dimension the energy sources, it is important to estimate the maximum power required from the HESS during various operating modes of IDC.²⁷

Power request for constant speed operation

The power requested during constant speed operation should be provided by the main energy source, Li-ion battery. The maximum constant speed occurs at the speed of 38 km/h in the IDC. So, the maximum cruising power of the motor is calculated as 1.78 kW. Therefore, the maximum power required from the battery is calculated as 2.46 kW by assuming motor and controller efficiency as 0.85%.

Power request for acceleration mode

The maximum acceleration request occurs at the standstill condition of 0–22 km/h in 8 s. Therefore the maximum power required from the HESS during acceleration is 5.25 kW. Since battery can provide 2.46-kW power, the remaining power required from the UC during acceleration is 3.1 kW by assuming converter efficiency as 0.9%.

Power request for road gradient

The power requested for road grade should be provided by the battery source. If the grade of road with inclination of 6° (10%) is assumed at a speed of 15 km/h, then the power requested is found as 2.73 kW using equations (1)–(4).

Power supplied during deceleration request

The maximum electrical power supplied during deceleration condition from maximum speed of 42–0 km/h is 1.76 kW in 21 s.

Based on the average and peak power requirement, estimation of battery and UC sizing are carried out for this vehicle.

Modelling of Li-ion battery

A proper Li-ion battery model is essential to predict the battery behaviour under various operating conditions and to estimate battery states, such as state of charge (SOC) and state of health.²⁸ An equivalent electrical circuit–based Li-ion model is shown in Figure 5. This model consists of open circuit voltage of the battery, V_OCV. Series resistor, R_O, represents the ohmic resistance drop, and the storage capacitance is represented by a series capacitor, C_S. The parallel RC branch accounts for the diffusion charge, where R_d represents diffusive resistance and C_d represents diffusive capacitance. N_bs represents the number of Li-ion cells in series. V_t is the terminal voltage of the battery as measured across its terminals, given by

V_{t} = V_{OCV} - V_{1} - V_{2} - V_{3}

(8)

Figure 5.

Equivalent electrical circuit–based Li-ion model.

The SOC of the battery is calculated using the integral equation as

SOC (t) = SO C_{initial} - \frac{1}{3600} \int_{t_{0}}^{t} i_{battery} (t) dt

(9)

The battery pack with the terminal voltage of 48 V is directly connected to the motor drive. The battery is assumed to operate with SOC from 30% to 100%, with an initial SOC of 80%. The energy demanded from battery for a hybridized system is reduced to 10 MJ, which is 2.78 kW h. As the allowable depth of discharge (DOD) is 70%, the energy capacity required is calculate as 3.61 kW h. The specification of the Li-ion battery bank is given in Table 5. Hence, for a 60-km range per charge, 60 cells (4 stacks of battery with 15 cells in series) are needed for 3.61 kW h of energy storage.

Table 5.

Specification of Li-ion battery bank.

3.2 V, 20 A h (IFP2265148) LiFePo₄ Prismatic battery bank
S. no.	Parameter	Value
1.	Voltage range	2.5–3.7 V
2.	Charging and dischargingcurrent	1 and 2 C
3.	DOD	70%
4.	Weight and cost	0.54 kg and US$20
5.	No. of cells in series (N_bs)	48/3.2 = 15
6.	Required capacity	3.51 kW h/48 = 73.125 A h
7.	No. of stacks in parallel (N_bp)	73.125 A h/20 ≈ 4

DOD: depth of discharge.

Modelling of UCs

A UC is modelled as a series RC circuit comprising the equivalent capacitance C_s and series Resistor R_s, shown in Figure 6. This series resistor, R_s, simulates energy loss during capacitor charging and discharging. The effect of cell-balancing intervention and energy loss due to capacitor self-discharge can be represented by the equivalent resistance R_p connected in parallel to the series RC circuit.²⁹

Figure 6.

Electrical equivalent of UC.

The specification of UC bank is given in Table 6. Twelve numbers of UC cells are connected in series to achieve a maximum voltage of 32 V in the low-voltage (LV) side. The minimum capacitance requirement of UC for the maximum speed of the vehicle can be calculated from relation

\begin{matrix} \frac{1}{2} C_{UC} [V_{UC_\max}^{2} - V_{UC_\min}^{2}] \times No . of series cell \\ = \frac{1}{2} m ϑ_{\max}^{2} \\ C_{UC} = 1141 F \end{matrix}

where C_UC is the capacitance of UC; V_{UC_max} is the maximum UC voltage, 32 V; and V_{UC_min} is the minimum UC voltage, 16 V.

Table 6.

Specification of UC bank.

2.7 V, 1200 F Maxwell UC bank
S. no.	Parameter	Value
1.	Internal resistance	0.58 mΩ
2.	Stored energy	1.22 W h
3.	Continuous andmaximum current	70 and 930 A
4.	Weight and cost	0.26 kg and US$58
5.	No. of cells in series (N_US)	32/2.7 ≈ 12
6.	No. of UC stacks inparallel (N_UP)	C_Total = C_UC × (N_UP/N_US) ≈ 1
7.	Allowable voltage range	1.35–2.7 V

Therefore, UC with 1200-F capacitance is chosen. Since, UC should provide 3.1-kW power during initial acceleration request, the average current of UC is calculated as

i_{UC (acc)} = \frac{[\frac{3.1 \times 10^{3}}{32} + \frac{3.1 \times 10^{3}}{16}]}{2} = 145.31 A

So, the total capacitance of the UC bank is calculated as

dv = i_{UC} \times \frac{dt}{C_{Total}} + i_{UC} \times R_{Total}

(10)

C_{Total} = 77.55 F

where dv is the voltage difference, 32–16 V; i_UC is the average UC current, 145.31 A; dt is the time difference, 8 s; C_Total is the total capacitance of UC; and R_Total is the total internal resistance of UC, 8.7 mΩ.

The maximum charging power of UC during deceleration is −1.76 kW. So, the average current of UC during regeneration request is −82.5 A Refer appendix I.

The average power demand of the vehicle is provided by the Li-ion battery for the entire range (60 km), and the peak power demand is provided by the UC. Thus, the peak current stress shared by UC prolongs the life span of battery.

Motor modelling

In this research work, brushless direct current (BLDC) motor is preferred as it is intended for LEVs. It exhibits better performance in terms of efficiency, fast response, simple structure, high torque and easier maintenance.^24–26,30 Also, a suitable motor drive needs to be interfaced with the DC-link. The plot of motor torque versus motor speed and motor power versus motor speed for IDC provides the designing details needed for a three-wheeled EV motor, shown in Figure 7.

Figure 7.

(a) Motor speed–torque characteristic and (b) motor speed–power characteristic.

The maximum power of the motor is 5.5 kW. But, a motor can be overrated almost 1.75 or 2 times for few seconds.³¹ So, a 3-kW BLDC motor has been chosen to meet the power requirement at acceleration and during grading of road. Also, the design details of BLDC motor such as rated speed and rated torque corresponding to the rated power of the motor are obtained as 2700 r/min and 14.5 N m, respectively.

Design of bi-directional buck–boost DC/DC converter

The survey of literature prefers the buck–boost half-bridge topology as the suitable bi-directional converter (BDC) for battery/UC interface. This BDC topology is preferred for a system that has size and cost constraint,^32,33 shown in Figure 8.

Figure 8.

Bi-directional buck–boost converter topology.

It works as a boost converter when discharging power from UC to DC-link and as a buck converter when charging power from DC-link to UC, thus providing bilateral power flow and controlled regenerated power to the UC. When T1 is ON, the converter acts as a boost converter and inductor and L stores the energy. When T1 is OFF, the stored energy of the inductor is transferred to load through diode D. When T2 is ON, the converter acts in buck mode, and it takes power from DC link to UC. When T2 is OFF, the current freewheels through the diode D2. The high-voltage side (DC-link) and low-voltage (LV) side (UC) has voltage relationships

V_{dc - link} = \frac{1}{1 - d_{boost}} V_{UC}

(11)

The converter is designed to have a low side voltage of 16–32 V, a high side voltage of 44–54 V, maximum power rating of the converter of 3 kW and a steady-state switching frequency of 20 kHz. The ripple requirements are 10% for the inductor current and 4.5% for the capacitor voltage Refer appendix I. The average current of the inductor is given by

I_{L (avg)} = \frac{I_{load}}{1 - d_{boost}} = 94.16 A

(12)

The critical inductance of the converter can be calculated as

L_{critical} = \frac{V_{UC} d_{boost}}{Δ I_{L} f_{s}} = 70 μ H

(13)

The inductance of the converter should be larger than or equal to 70 μH. The low-voltage (LV) side input capacitance and the high-voltage side output capacitance can be calculated as

C_{L} = \frac{V_{UC} d_{boost}}{8 {Lf}_{s}^{2}} = 60 μ F

(14)

C_{H} = \frac{(1 - d_{boost}) (d_{boost} I_{L (avg)})}{Δ V_{H} f_{s}} = 470 μ F

(15)

Modes of operation and control strategy

Operating modes

A typical Indian drive cycle has different modes of operation, which is given by

Starting and acceleration mode;

Constant speed or cruising mode;

Deceleration or braking mode.

Staring and acceleration

When the demanded power is greater than rated power of battery P_demand > P_batt_(max), UC assists battery through the bi-directional DC/DC converter without violating the limit V_uc > V_uc_(min) and SOC_UC > SOC_UC_(min), and the BDC works as a boost converter, providing boosted voltage to the DC-link.

Constant speed operation

When P_demand ≤ P_batt_(max), battery alone provides the demand power requirement. In this mode, the BDC works as a buck converter only to charge the ultracapacior from battery when the SOC of UC falls below minimum. Otherwise, no operation takes place in the interface circuit.

Deceleration or braking

The regenerated power in the DC link during braking has been controlled to flow back to any of the HESS (battery or UC), depending on its SOC status which is described below:

The UC gets charged when V_UC < V_UC_(max) and SOC_UC < SOC_UC_(max). Battery gets charged only when V_UC ≥ V_UC_(max) and SOC_UC ≥ SOC_UC_(max).

Most of the drive cycle power requirements are met by the battery, depending on its voltage and SOC conditions. Transient power requirements are met by the UC in addition to battery. Thus, the entire drive cycle requirement can be suitably achieved by using a UC in parallel with battery. The coordination of battery and UC in different modes of operation is depicted in Figure 9. The above mentioned various modes of operation are implemented in the power management system as rules.

Figure 9.

Different modes of operation: (a) acceleration mode; (b, c) constant speed mode; and (d, e) braking mode.

Power-split management system

To maximize the benefits of the Li-ion/UC HESS, proper management of the power flow from and into the system is essential.³³ The significance of the power flow management and various energy management schemes has been described in literature.^19–22 As discerned from the literature, the filtering method and rule-based strategy have been mostly preferred for real-time applications.³²

The filtering method does not use heuristic knowledge to consider the energy capacity of HESS, therefore results in improper sizing of ESSs with the separation of high- and low-frequency component.²¹ But the rule-based algorithm uses the set of rules based on prior knowledge for the management of ESSs.¹⁹ Therefore, an effective power-split strategy incorporating filtering technique and deterministic rules has been proposed for effective control of HESS. This proposed strategy combines the advantages of rule-based algorithm and the frequency separation strategy, thus providing smoother battery current.

The flowchart of the control strategy adopting rules and conditions is given in Figure 10. The power management system reads the inputs such as the power demand from the HESS (P_demand), SOC of battery (SOC_batt), SOC of UC (SOC_UC), battery power (P_batt) and UC power (P_UC) at every time instant for the implication of power-split mechanism. A low-pass filter with cut-off frequency f_c = 0.024 Hz is used to separate the high- and low-frequency component. The equality and inequality constraints are checked for the violation of limits. Power demand, P_demand, is used as a key factor to determine the mode of operation, that is, braking, acceleration or constant speed mode. If the P_demand is equal to or greater than zero, then the mode of operation can either be an acceleration or constant speed mode, which is generally called as motoring mode.

Figure 10.

Flowchart of power-split management system.

In the motoring mode, P_demand is again compared with the maximum power of the battery, P_battery_(max). If the P_demand exceeds the maximum power of the battery, then the demanded power sharing should be done by both battery and UC. The battery supplies its maximum power and the UC supplies the rest by balancing the power demand during the acceleration mode. If the summation of maximum power of the UC and battery is less than the demanded power, then the maximum power rating of the battery should be increased by increasing the maximum limit of battery current. If not, the condition of P_demand > P_batt_(max) is satisfied, then the control loop enters the constant speed mode of operation. In this mode, the SOC and voltage of UC are checked for its minimum value. If it is lesser than the minimum value, the battery provides the demanded power and power to the UC. Thus, the battery pack provides the average power demand, and the peak power demand is provided by UC.

For braking mode of operation (P_demand < 0), if SOC_UC < SOC_UC_(max) and V_UC < V_UC_(max), then the demand power is the regenerated power, which appears across the motor terminal and is fed back to the UC through the BDC. If the above constraint is not met, then the regenerated power flows to the battery. Thus, the power-split strategy adopting set of rules are used to control the power flow for the load requirement. So, depending on the modes of operation, the required power of battery and UC are converted as reference current control signal for battery and UC. The reference current of battery and UC are obtained as follows

I_{ref} = \frac{P_{UC}}{V_{UC}}

(16)

I_{batt_reg} = \frac{P_{demand}}{V_{batt}} - I_{ref}

(17)

These reference currents are compared with the actual currents and processed through controller for the generation of pulse signals. These pulse signals are used to control the operation of BDC to work either as buck or boost converter.

Simulation results and discussion

Simulations are performed to verify the feasibility of the proposed control strategy with the designed and modelled system components, explained in section ‘System description’. The overall layout and control of the proposed EV system is shown in Figure 11. IDC is considered as the reference speed profile, and the BLDC motor is controlled to track the IDC.

Figure 11.

Overall schematic and control of the proposed system.

First, the battery-alone system is considered with Li-ion battery as an energy source. The power demanded by the vehicle is supplied by the battery source alone, provides power during motoring and receives power during regeneration. Initial SOC of battery is assumed as 80% with the maximum DC-link voltage. The battery current, its SOC and the distribution of motor and battery power are shown in Figure 12.

Figure 12.

Battery-alone system: (a) battery SOC, (b) battery current and (c) distribution of motor and battery power.

The maximum current drawn from the battery is 140 A, and the SOC drops from 80% to 77.8% for one cycle of IDC. By hybridizing both battery and UC as energy source and employing proposed control strategy, the main source battery is restricted to provide only the average low-frequency power component, and the auxiliary source UC is controlled to provide high-frequency power component that occurs only during transient demand. UC is allowed to draw the regenerated power during deceleration, depending on the SOC condition of battery and UC.

The motor accelerates from t = 17 s to t = 25 s to reach the 22-km/h speed, which demands more power than the maximum limit of battery. So, UC, in addition to battery, supplies to meet the demanded power, thus limits the current drawn from the battery to 50 A to ensure its long run. Between t = 25 s and t = 29 s, the speed profile falls to 13 km/h, thus results in deceleration, provides regenerated power to the UC as its SOC is below 95%. Between t = 29 s and t = 31 s, IDC maintains constant speed profile of 13 km/h. So, battery alone satisfies the required power demand. Thus, the process continues for the entire drive cycle which has numerous acceleration, deceleration and constant speed in its speed profile.

Figure 13 shows the obtained responses of battery and UC for hybridized system with proposed control strategy. From the figure, it is revealed that the maximum battery current of the hybridized system is less, thus removing stress from the battery to extend its life period. The UC voltage variation is maintained between its minimum value 16 V and maximum value 32 V for its efficient operation. The initial SOC of UC is 88%. Because of frequent acceleration and deceleration in drive cycle, the SOC falls and rises frequently and reaches 78% at the end of 108 s.

Figure 13.

Hybridized system: (a) battery current, (b) UC current, (c) UC voltage, (d) UC SOC, (e) distribution of current and (f) distribution of power.

The total power demand of the vehicle is satisfied by both the energy sources by providing power flow during various modes of operation by meeting all the rules and constraints. The energy provided by battery and UC over a cycle of IDC is shown in Figure 14.

Figure 14.

Energy provided by battery and UC.

The performance of the proposed control strategy is compared with conventional strategies in terms of various performance parameters and tabulated in Table 7. The root mean square (RMS) current of battery is found to be less in proposed system which reduces the degradation of battery and improves its lifetime. The maximum energy demanded from the hybridized system using proposed control strategy is found to be less than the systems with other control techniques. Figure 15 shows the comparison plot of battery SOC in battery-alone system and hybridized system.

Table 7.

Comparison of proposed strategy with other control strategies.

S. no.	Parameters	Battery-alone system	Filtering strategy	Rule based	Proposed strategy
1.	Battery RMS current (A)	41.3	31.5	30	28
2.	Max. battery current (A)	150	90	58	50
3.	Max. energy by battery (kJ)	160	148.8	120	110
4.	Max. energy by UC (kJ)	Nil	15	20	25
5.	UC SOC difference (%)	Nil	Initial: 87 Final: 80 DOD: 50	Initial: 87 Final: 60 DOD: 40	Initial: 87 Final: 78 DOD: 50

RMS: root mean square; UC: ultracapacitor; SOC: state of charge; DOD: depth of discharge.

Figure 15.

Comparison plot of battery SOC for BESS and HESS.

The control of vehicle speed during all modes of operation (motoring, constant speed and braking) should be maintained for the proper EV operation. The PI controller (driver model) helps in accurate tracking of the desired speed profile. Thus, the desired and actual speed profiles of the vehicle are shown in Figure 16. Figure 17 shows the comparison chart of hybridized (battery + UC) ESS and battery-alone system with and without regeneration capability interms of various parameters. It is found from Figure 17 that the parameters like maximum energy demand (E_max), battery capacity (A h), number of parallel branches of battery (N_bp), total number of battery cells (N_batt_cells) and the total weight of the ESS (TW_ESS) of the battery-alone energy storage system (BESS) without regeneration are higher than the BESS with regeneration and HESS with a proposed strategy.

Figure 16.

Vehicle speed plot.

Figure 17.

Comparison chart between BESS and HESS for various parameters.

Also, it is found from Figure 18 that the battery cost (C_batt) of HESS is found to be less, but the capital cost of ESS (CC_ESS) is higher than BESS. The cost of the converter per kilowatt gets added to the HESS and thus increases the capital cost of the HESS. If the replacement cost, operational cost and maintenance cost of batteries are considered, then the total cost of HESS is not more than BESS. The HESS with proposed control strategy reduces the RMS current of battery and offers efficient power train operation. Therefore, it improves the life span of the battery thus reducing the replacement cost, maintenance and operational cost.

Figure 18.

Capital cost comparison between BESS and HESS.

Experimental details and results

In order to validate the proposed system with its power-split control strategy for HESS, a small-scale experimental platform was built and is shown in Figure 19. The main components of the experimental platform are listed in Table 8. Due to non-feasibility, Li-ion battery bank and UC banks are replaced with lead–acid batteries. The experimental setup is mainly used to validate the power-split between two energy sources to offer reduced current stress. The main source battery bank is composed of four 12-V, 18-A h cell (4*12 = 48 V) lead–acid batteries in series. The minimum allowable voltage of the battery bank is 23 V, while the maximum voltage is 52 V.

Figure 19.

Experimental setup.

Table 8.

System components for experimental verification.

S. no.	Components	Rating	Quantity
1.	Lead–acid battery	12 V/18 A h	4
1.	Lead–acid battery	12 V/1.2 A h	2
2.	BLDC motor	1 kW/48 V	1
3.	Inductor	2.5 mH	1
4.	Capacitor	1000 µF/100 V	2
5.	MOSFET	IRFP450	2
6.	Traction inverter	48 V/40 A	1
7.	Arduino controller		1

BLDC: brushless direct current.

Instead of UC, lead–acid battery of 12 V/1.2 A h is used to provide transient current. The highest voltage of the auxiliary source bank is 27 V, while the minimum voltage is 18 V. The throttle controls the speed and direction of the rotation of the BLDC motor. The speed profile is obtained by feeding the hall and plotted using PC, shown in Figure 20. BDC provides the bi-directional power flow to/from the DC-link. The observed experimental plots during motoring conditions are shown in Figure 21. During the acceleration mode, both the auxiliary and main battery provides the demanded current and BDC works as a boost converter.

Figure 20.

Speed profile plot obtained from hall signal.

Figure 21.

Experimental plot:(a) main battery current for boost and buck pulses, (b) auxiliary battery current for corresponding boost and buck pulses, (c) main and auxiliary battery voltage, (d) auxiliary source current and corresponding hall signal during boost operation, (e) BLDC motor current and phase voltage and (f) hall signals.

For the constant speed mode, only the main source battery provides the demanded current and the charging current for auxiliary source, by making the BDC to work as a buck converter. Figure 21(a) and (b) shows the current of main battery and auxiliary battery during buck and boost mode of BDC operation. The main battery voltage and auxiliary battery voltages are shown in Figure 21(c). Figure 21(d) shows the auxiliary source current for accelerated condition.

The experimental plots of BLDC motor current, phase voltage and corresponding hall signals are shown in Figure 21(e) and (f). Thus, the experimental results validate the simulation results for different operating modes in motoring condition.

Conclusion

A design methodology of a HESS employing Li-ion battery/UC for a three-wheeled LEV with effective power-split strategy is proposed in this paper. An improved real-time power-split control strategy for the effective utilization of HESS has been proposed and compared with other conventional approaches. It is obtained from the results that the RMS current of battery has been reduced with effective exploitation of UC, thus enhancing the lifes-pan of battery. The superiority of HESS relative to BESS has also been established through comparison of various parameters. Simulation results are presented in support of the findings. Due to non-feasibility, lead–acid batteries are employed as energy sources in hardware prototype to verify the power-split management. The Li-ion batteries and UC will be considered for future work.

Footnotes

Appendix 1 Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

S Devi Vidhya

References

Katić

Dumnić

Čorba

, et al. Electrification of the vehicle propulsion system – an overview. Facta Universit Ser: Electron Energ 2014; 27(2): 299–316.

NITI Aayog, http://www.niti.gov.in/

Mani

Pai

Aggarwal

. Sustainable urban transport in India: role of the auto-rickshaw sector, 2012, https://wrirosscities.org/research/publication/sustainable-urban-transport-india-role-auto-rickshaw-sector

Khaligh

. 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 T Veh Technol 2010; 59(6): 2806–2814.

Garcia

Fernandez

Garcia

, et al. Energy management system of fuel-cell-battery hybrid tramway. IEEE T Ind Electron 2010; 57(12): 4013–4023.

Thounthong

Pierfederici

Martin

J-P

, et al. Modeling and control of fuel cell/supercapacitor hybrid source based on differential flatness control. IEEE T Veh Technol 2010; 59(6): 2700–2710.

Thounthong

Chunkag

Sethakul

, et al. Comparative study of fuel-cell vehicle hybridization with battery or supercapacitor storage device. IEEE T Veh Technol 2009; 58(8): 3892–3904.

Cao

Emadi

. A new battery/ultracapacitor hybrid energy storage system for electric, hybrid, and plug-in hybrid electric vehicles. IEEE T Power Electr 2012; 27(1): 122–132.

Vezzini

Sharan

Umanand

. Low-pollution three-wheeler autorickshaw with power-assist series hybrid and novel variable DC-link voltage system. J Indian I Sci 2005; 85: 105–118.

10.

Hofman

Van der Tas

Ooms

, et al. Development of a micro-hybrid system for a three-wheeled motor taxi. World Electr Veh J 2009; 3(3): 572–580.

11.

Mulhall

Lukic

Wirasingha

, et al. Solar-assisted electric auto rickshaw three-wheeler. IEEE T Veh Technol 2010; 59: 2298–2307.

12.

Abu mallouh

Surgenor

Denman

, et al. Analysis and validation of a Powertrain System Analysis Toolkit model of a fuel cell hybrid rickshaw. Int J Energ Res 2011; 35(15): 1389–1398.

13.

Chowdhury

Rahman

Azad

. Power conversion for environment friendly electrically assisted rickshaw using photovoltaic technology in Bangladesh. In: Proceedings of the 2015 IEEE transportation electrification conference and expo (ITEC), Dearborn, MI, 14–17 June 2015. New York: IEEE.

14.

Awal

Jusoh

Sakib

, et al. Design and implementation of vehicle mounted wind turbine. ARPN J Eng Appl Sci 2015; 10: 8699–8706.

15.

Siddique

ARM

Sakib

Tahsien

, et al. Performance evaluation of a microcontroller-based solar powered auto-rickshaw. In: Proceedings of the 2014 international conference on electrical engineering and information & communication technology (ICEEICT), Dhaka, Bangladesh, 10–12 April 2014. New York: IEEE.

16.

Sreejith

Rajagopal

. An insight into motor and battery selections for three-wheeler electric vehicle. In: Proceedings of the 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES), Delhi, India, 4–6 July 2016, pp. 1–6. New York: IEEE.

17.

Mallik

Arefin

Rashid

, et al. Solar based plugged-in hybrid engine driven rickshaw (auto-rickshaw) & its feasibility analysis for Bangladesh. In: Proceedings of the 3rd international conference on mechanical, industrial and materials engineering (ICMIME), Rajshahi, Bangladesh, 28–30 December 2017.

18.

Arefin

Mallik

Asfaquzzaman

. Renewable energy–assisted hybrid three-wheeler: a numerical investigation. Adv Mech Eng 2018; 10(12): 1–13.

19.

Gokce

Ozdemir

. A rule based power split strategy for battery/ultracapacitor energy storage systems in hybrid electric vehicles. Int J Electrochem Sc 2016; 11: 1228–1246.

20.

Yin

Zhou

, et al. An adaptive fuzzy logic-based energy management strategy on battery/ultracapacitor hybrid electric vehicles. IEEE T Transport Electrific 2016; 2(3): 300–311.

21.

Araújo

de Castro

Pinto

, et al. Combined sizing and energy management in EVs with batteries and supercapacitors. IEEE T Veh Technol 2014; 63(7): 3062–3076.

22.

Shen

Khaligh

. A supervisory energy management control strategy in a battery/ultracapacitor hybrid energy storage system. IEEE T Transport Electrific 2015; 1(3): 223–231.

23.

Song

Hou

, et al. The battery-supercapacitor hybrid energy storage system in electric vehicle applications: a case study. Energy 2018; 154: 433–441.

24.

Sreejith

Rajagopal

Singh

. Modelling and analysis of PMBLDC motor based three wheeler EV for closed loop optimum operation. In: Proceedings of the 2016 IEEE international conference on power electronics, drives and energy systems (PEDES), Trivandrum, India, 14–17 December 2016. New York: IEEE.

25.

Bobba

Rajagopal

. Modeling and analysis of hybrid energy storage systems used in electric vehicles. In: Proceedings of the 2012 IEEE international conference on power electronics, drives and energy systems (PEDES), Bengaluru, India, 16–19 December 2012, pp. 1–6. New York: IEEE.

26.

Mishra

Saha

Ikkurti

. Selection of propulsion motor and suitable gear ratio for driving electric vehicle on Indian city roads. In: Proceedings of the 2013 IEEE international conference on energy efficient technologies for sustainability (ICEETS’13), Nagercoil, India, 10–12 April 2013, pp. 692–698. New York: IEEE.

27.

Pereirinha

Trovão

. Multiple energy sources hybridization: the future of electric vehicles? In: Stević

(ed.) New generation of electric vehicles. London: IntechOpen Limited, 2012, pp. 237–264.

28.

Wang

Liu

Pan

, et al. Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator. Energy 2017; 121: 739–750.

29.

Dixon

Nakashima

Arcos

, et al. Electric vehicle using a combination of ultracapacitors and ZEBRA battery. IEEE T Ind Electron 2010; 57(3): 943–949.

30.

Krishnan

. Electric motor drives: modeling, analysis, and control. Upper Saddle River, NJ: Prentice Hall, 2001.

31.

Mishra

Saha

Ikkurti

. A methodology for selection of optimum power rating of propulsion motor of three wheeled electric vehicle on Indian Drive Cycle (IDC). Int J Theor Appl Res Mech Eng 2013; 2(1): 95–100.

32.

Camara

Gualous

Gustin

, et al. Design and new control of DC/DC converters to share energy between supercapacitors and batteries in hybrid vehicles. IEEE T Veh Technol 2008; 57(5): 2721–2735.

33.

Geetha

Subramani

. A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles. Int J Energ Res 2017; 41(13): 1817–1834.