# Intelligent Energy Management of a Multisource Power Supply for Electric Vehicle Application

Bachir Bendjedia, Nassim Rizoug, Moussa Boukhnifer, Farid Bouchafaa

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Bachir Bendjedia, Nassim Rizoug, Moussa Boukhnifer, Farid Bouchafaa. Intelligent Energy Management of a Multisource Power Supply for Electric Vehicle Application. 2017 IEEE Vehicle Power and Propulsion Conference (VPPC), Dec 2017, Belfort, France. pp.1-6, 10.1109/vppc.2017.8330922. [hal-04578045](https://hal.science/hal-04578045v1)

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# Intelligent Energy Management of a Multisource Power Supply for Electric Vehicle application

B. Bendjedia, N. Rizoug, M. Boukhnifer and F. Bouchafaa

**Abstract**—This paper focuses on an intelligent energy management of a hybrid source to supply a solar electric vehicle. A PhotoVoltaic Generator (PVG) is used as a primary source and the batteries are used as secondary/storage one. The main source (PVG) is connected to the DC bus via a boost converter which is controlled by a fuzzy logic based MPPT strategy to track the maximum power point of the photovoltaic (PV) system. The backup source (Battery) guarantees an uninterrupted power supply, assists the propulsion of the vehicle during transients and recover energy during regenerative braking. The Energy Management Strategy (EMS) is based on satisfying the load power requirement where a DC-DC buck-boost converter is used to control batteries bank. This approach is tested and validated by numerical simulations where it shows a good result in terms of robustness and dynamical performances.

**Keywords:** Fuzzy logic, GPV, Solar Electric Vehicle,

## I. INTRODUCTION

The Multisource power supply systems attracted the attention of various research institutions in last years. Its application progressed intensely in various industrial parts similar to the automobile, air craft, personal computers and telecommunications equipment. Also the power stations for isolated sites and grid connection applications. These systems can contain one two or more different nature sources, renewable such as the photovoltaic systems, the fuel cells, and storage like the batteries or the super capacitors and conventional ones.

PV systems can be hybridized with Energy Storage Systems (ESS) (e.g., batteries and/or super capacitors) to improve the performance of the PV system during transient and instantaneous peak power demands of an Electric Vehicle (EV) and to recover energy through regenerative braking [1]–[5].

Some prototypes of solar electric vehicles have been developed in last decade in Japan [2], [3] at Western Washington University [4], [5], at the Queensland University [3] and at the Industrial Electronics Laboratory of Toulouse [6]. The most problems funded for this type focuses specially in managing the energy between the two sources while respecting the imposed constraints of batteries charging/discharging rates, state of charge and available power on PVG [7]–[10]. Recently, it has been found in [11], that the classic P&O MPPT algorithm has some limitation in terms of dynamical performances and chattering phenomena.

B. Bendjedia, N. Rizoug, M. Boukhnifer are with the ESTACA Campus Paris-Saclay, 78180 Montigny-le-Bretonneux, Paris, France.

(corresponding author: e-mail: bachir.bendjedia@estaca.fr).

B. Bendjedia, F. Bouchafaa, are with the Electrical Engineering Department, USTHB University, Algiers, Algeria.

This paper proposes a solar electric vehicle study which is composed of a photovoltaic generator and a storage source (battery) to provide an electromechanical PMSM drive system (which ensure the wheel propulsion). The main improvement in this work focuses on optimal maximum power point tracking using intelligent fuzzy logic control in order to use classical P&O strategy used in previous works [12], [13], where the dynamic and static performances are enhanced. This approach contributes strongly on energy management efficiency and the elimination of the shattering problems in the PVG power tracking.

## II. PRESENTATION OF PVG/BATTERY HYBRID SOURCE AND SOLELHADA SEV

The solar electric vehicle, as shown in Fig. 1, uses a PV generator as the main power source and the ESS (e.g., batteries) as the secondary power source to assist the propulsion of the vehicle during transients and to recuperate energy during regenerative braking. In this configuration, the PV is connected to the DC bus through a boost Converter, whereas the battery pack is connected to it via a bidirectional DC/DC converter. The propulsion is ensured by a PMSM and three phase inverter linked to the DC bus.

<figure><img src="image_2.png" /><figcaption>Fig. 1 The studied electric vehicle</figcaption></figure>

The dynamic model of the **SOLELHADA** solar vehicle is presented by the flowing equation [6]:

$$P_{Load} = \left( \frac{1}{2} * \rho_{air} * V_{t}^{2} * S * C_{x} + M * g * C_{r} + M * \frac{dv_{t}}{dt} \right) * V_{t} \quad (1)$$

Where:

- $V_t$ is the vehicle Speed ;

- M is the Vehicle mass;

- The Vehicle parameters are given as follow:

$$M=350 \text{ Kg}, S=12 \text{ m}^2 / C_x= 0.12, \rho_{air}=1.2 \text{ 1.2 Kg/m}^2, C_r = 52.26$$

The system energy source is guaranteed by 8 Panels of 150W (bp_sx150s), (1200W). The storage source is a Lead-Acid battery with a nominal voltage 72V, and a capacity of 12.5 Ah. The energy exchanges between the components are
done through a DC bus of 150V. A supervisor energy management module is used to control the energy flows.

### A. PV Model

A simple photovoltaic cell equivalent circuit model is shown in Fig. 2 [12][14]. The model consists of a current source $I_{ph}$ (represents cell photocurrent), a series resistance $R_s$ (the internal resistance of each cell) and a diode. The net output current of the photovoltaic cell is the difference between the photocurrent $I_{ph}$ and the diode current $I_D$ as described by the following equation [14]:

$$I_{pv} = I_{ph} - I_{D} = I_{ph} - I_{0} \left( \exp\left(\frac{q(V_{pv}+R_{s}I_{pv})}{mKT}\right) - 1 \right) (2)$$

Where $m$ is the diode ideality factor, $K$ is Boltzmann's constant, $q$ is electron charge, $V$ is the voltage applied across the cell and $I_0$ is the revers saturation current.

The cell photocurrent $I_{ph}$ linearly depends on solar irradiance and influenced by temperature:

$$I_{ph} = \frac{G}{G_n} (I_{ph\_n} + K_i (T - T_n)) \quad (3)$$

Where:

- $I_{ph\_n}$ is the cell photocurrent at nominal conditions (25°C and 1000 w/m²)

- $G$ is the illumination

- $G_n$ is the nominal illumination

- $T$ is the cell temperature

- $T_n$ is the nominal cell temperature

- $K_i$ is the short-circuit current/temperature coefficient

A practical PV panels consists of several PV solar cells connected in series and parallel. Therefore, equation (2) which presents a single PV cell should be amended into equation (3) to represent a PV panel [15].

$$I_{pv} = N_p I_{ph} - N_p I_0 \left( \exp\left(\frac{q\left(V_{pv} + R_s I_{pv} \frac{N_s}{N_p}\right)}{mKT}\right) - 1 \right) (4)$$

Where:

- $I_{pv}$ and $V_{pv}$ are respectively the current panel and the voltage from corner to corner of panel

- $N_p$ is the number of PV cells connected in parallel

- $N_s$ is the number of PV cells connected in series

- In the objective to obtain 1.2Kw, an association of the BP Solar SX 150s panels is used as:

$N_{series} = V_{dc}/2 * V_{mpp} = 150/2 * 69 \approx 2$ panels;

$N_{global} = P_{max}/P_{panel} = 1200/150 = 8$ panels;

$N_{parallel} = N_{global} / N_{series} = 8/2 = 4$ panels.

The Table. 1 shows the electric characteristics of the module BP Solar SX 150s, obtained from datasheet provided by manufacturer [12].

<figure><img src="image_3.png" /><figcaption>Fig. 2 Equivalent Circuit Model of PV cell</figcaption></figure>

<table><caption>Table.1 Electrical characteristics of 'bp sx 150s' solar panel</caption><tr><td>Maximum power ($P_{max}$)</td><td>150W</td></tr><tr><td>Operating Voltage at $P_{max}$ ($V_{mpp}$)</td><td>34.5 V</td></tr><tr><td>Operating Current at $P_{max}$ ($I_{mpp}$)</td><td>4.35 A</td></tr><tr><td>Temperature coefficient of $I_{sc}$</td><td>(0.065±0.015)%/°C</td></tr><tr><td>Temperature coefficient of $V_{oc}$</td><td>-(160±20) mV/°C</td></tr><tr><td>Power temperature coefficient</td><td>-(0.5±0.05)%/°C</td></tr></table>

### B. Battery Model

In this work, we have used the Battery dynamic model developed by [12], [14], [15]. The parameters of the used battery are summarized in the Table. 2.

- The voltage battery is termed by:

$$V_{batt} = E_0 - R_i - K \frac{q}{q-it} \quad it \quad A \cdot \exp(-B \cdot it) \quad K \frac{q}{it-q} i^* \quad (5)$$

Where:

- $V_{batt}$ is the battery voltage (V)

- $E_0$ is battery constant voltage (V)

- $R$ is the internal resistance ($\Omega$)

- $i$ is the battery current (A)

- $K$ = polarization constant (V/Ah) or polarization resistance ($\Omega$/Ah)

- $Q$ = battery capacity (Ah)

- $it = \int idt$ is the actual battery charge (Ah)

- $A$ is the exponential zone amplitude (V)

- $B$ is the exponential zone time constant inverse ($Ah)^{-1}$

- $i^*$ Is the filtered current (A)

### III. DC/DC CONVERTER CONTROL AND ENERGY MANAGEMENT

It is necessary to maintain power balance between sources and load with satisfying the required load power and charge or discharge batteries. A control of DC/DC converters ensures this task by adjusting the different duty cycles.

#### A. Batteries Bidirectional converter control

The DC bus voltage control will be provided by the associated battery pack converter. Control consists of two cascade loops including an external (loop voltage) develops the battery pack reference current $I_{b\_ref}$ to be controlled by the inner current loop [6]. The controller imposes a behavior loop voltage with a slow dynamics compared to the current loop.

<table><caption>Table.2 Battery parameters</caption><tr><td>Rated Capacity ($Q_{nom}$)</td><td>12.5 Ah</td></tr><tr><td>Nominal voltage</td><td>72 V</td></tr><tr><td>Internal resistance (R)</td><td>0.04 Ω</td></tr><tr><td>Fully charged voltage</td><td>52.26 V</td></tr><tr><td>Nominal discharge current</td><td>2.4 A</td></tr></table>
## B. PVG Boost Control (MPPT using Fuzzy Logic Control)

The DC/DC converter always matches the PVG output characteristic to the load input characteristic, so that maximum power is extracted from the solar cell arrays. Many different types of maximum power point tracking strategies to control the boost and PVG systems have been used [14], [16].

The Perturb and Observe (P&O) method is a widely used to MPPT. This last has a limitation regarding its performances in dynamics especially. However, it is needed for a vehicle application that sources must have the best dynamics. For this reason, Fuzzy Logic Control (FLC) is chosen and used to enhance the dynamic responses of PV system.

The proposed FLC is shown in Fig.3, it consists of two inputs and one output. The two FLC inputs variables are the error (E) and change of error (CE) that expressed respectively by equation (6) and (7).

$$E(j) = \frac{P_{PV}(j) - P_{PV}(j-1)}{V_{PV}(j) - V_{PV}(j-1)} \quad (6)$$

$$CE(j) = E(j) - E(j-1) \quad (7)$$

<figure><img src="image_4.png" /><figcaption>Fig. 3: Fuzzy controller diagram</figcaption></figure>

Where Ppv, Vpv are the power of the PVG and PVG voltage respectively at the instant j. E (j) indicates the load operating point (on the left or on the right of the maximum power point on the P-V characteristic) (see Fig.4). The duty cycle D used to track the maximum power point is used to generate a PWM signal for the boost converter.

<figure><img src="image_5.png" /><figcaption>Fig. 4: MPPT Algorithm with ΔP&ΔV sign on the P-V characteristic</figcaption></figure>

## ❖ Fuzzification:

The Fuzzification is the process of converting the system actual inputs values E and CE into linguistic fuzzy sets using fuzzy membership function. These variables are expressed in terms of five linguistic variables (such as ZE (zero), PB (positive big), PS (positive small), NB (negative big), NS (negative small)) using basic fuzzy subsets as shown in the Fig.5.

## ❖ Rule base & inference engine

Fuzzy rule base is a collection of if-then rules that contain all the information for the controlled parameters. It is set according to professional experience and the operation of the system control. The fuzzy rule algorithm includes 25 fuzzy control rules listed in Table. 3.

## ❖ Defuzzification

Defuzzification of the inference engine, which evaluates the rules based on a set of control actions for a given fuzzy inputs set. This operation converts the inferred fuzzy control action into a numerical value at the output by forming the union of the outputs resulting from each rule.

<figure><img src="image_6.png" /><figcaption>Fig. 5: Membership function of E, CE and D</figcaption></figure>

<table><caption>Table. 3: FLC Rules base</caption><thead><tr><th>CE / E</th><th>NB</th><th>NS</th><th>ZE</th><th>PS</th><th>PB</th></tr></thead><tbody><tr><th>NB</th><td>ZE</td><td>ZE</td><td>PB</td><td>PB</td><td>PB</td></tr><tr><th>NS</th><td>ZE</td><td>ZE</td><td>PS</td><td>PS</td><td>PS</td></tr><tr><th>ZE</th><td>PS</td><td>ZE</td><td>ZE</td><td>ZE</td><td>NS</td></tr><tr><th>PS</th><td>NS</td><td>NS</td><td>NS</td><td>ZE</td><td>ZE</td></tr><tr><th>PB</th><td>NB</td><td>NB</td><td>NB</td><td>ZE</td><td>ZE</td></tr></tbody></table>
<figure><img src="image_7.png" /><figcaption>-a-</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>-b-</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>-c-</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>-d-</figcaption></figure>

<caption>Fig. 6: FLC MPPT Simulation results: -a- Duty cycle, -b- PVG current, -c- PVG Power, -d- PVG Power according to the voltage</caption>

The illumination is variable and the temperature is supposed to be constants (T=25°C). Fig. 6 shows the simulation results for the FLC MPPT.

An illustration of the relationship between the duty cycle and GPV output power demonstrates the effectiveness of the FLC controller. It is shown that FLC can meet requirement of fast voltage and current responses and small voltage and current overshoots.

### C. Energy management

The battery current reference is produced by the DC bus voltage loop control using a PI controller (see Fig. 7). When the load power increases or the PVG decreases, the battery current increases to ensure a regulated DC bus and to satisfy the load energy demand.

In case of the PVG power is higher than the load power, the batteries could be charged by the photovoltaic power excess, in this case the battery current reference becomes negative to accomplish this task.

<figure><img src="image_11.png" /><figcaption>Fig. 7: DC Bus Control scheme</figcaption></figure>

## IV. SIMULATION RESULTS

In the objective to show the performances of the proposed control approach, a simulation of the global system under Matlab/Simulink was tested.

Fig. 8, and Fig. 9 illustrate the simulation results under standards conditions and constant illumination.

### Discussion

The Fig. 8.a shows the cycle profile which consists of a part of NEDC cycle. Then using this cycle and the dynamic, we can obtain the current load profile as shown in Fig. 8.b.

In the Fig. 9.a, It's shown that between (130s and 150s), the load is supplied by both PV generator and the batteries sources, because the PVG power (green) is lower than then load power (blue). In this time the battery current (red) (see Fig. 10) becomes positive to ensure the required power to the load.

In the power flow Fig. 9.b, it's significant that between (65s and 85 s), the PV Generator supplies the load fully and the batteries sources recuperate the difference of power between the principal source and the load. This mode is described by that the PVG power (green) is higher than principal source power (blue), (See Fig. 10).

From the DC bus regulation (Fig. 9.c), it's notable that the Vdc is controlled at the desired voltage (150 V), and it's affected slowly at each load variation, we can improve this problem by using robust controller for DC bus control to ensure robustness against these variations.

In this stage, a variable illumination, constant temperature (T=25°C) are used to test the SEV performances.
<figure><img src="image_12.png" /><figcaption>Fig. 8: NEDC Urban Profile and Load current profile</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 9: Sources currents, Dc Bus voltage profiles, Power profiles under standards conditions</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 10: Illumination Profile, Power profiles and DC Bus voltage, and Sources Currents under variable illumination</figcaption></figure>

<figure><img src="image_15.png" /></figure>

Discussion

Under variable illumination, it's shown in Fig. 10, that the PVG power follows the point of maximum power. The MPPT proposed control algorithm changes the converter duty ratio correctly to reach the MPP.

In this time, the PVG supplies the load totally and charge the batteries by the difference of power, because its power exceeds the required load power.
It is notable that at each illumination variation the PVG power follows the maximum power point. In other hand, the load power is affected immediately by illumination variation but the storage system (Batteries) contributes in the system stability by recovering the power difference at each illumination variation.

The Vdc is controlled at the desired voltage (150V), and it's affected slowly by each illumination profile variation.

## V. CONCLUSION

In this paper, a hybrid system is proposed to follow correctly the urban profile (NEDC) phases. In the first part, modeling, control and energy management strategy of the ESV have been detailed. The main source is a PVG and the secondary one is an electrochemical battery.

Depend on the power available on the PV Generator which be influenced directly by the illumination conditions, two scenarios are presented:

- The PVG supply load and charge batteries,

- The load supplied by both sources when its power is higher than PVG maximum power.

The simulation results show that the fuzzy logic MPPT provide excellent performance and improve the efficiency of the hybrid system.

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