Energy Storage Systems for Urban-Driven Electric Vehicles: Comparison
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Please note this is a comparison between Version 2 by Fanny Huang and Version 1 by Mohamed S. Hassan.

The prevalence of electric vehicles (EVs) is of a great significance to help reduce greenhouse gas emissions. Boosting the performance of energy management systems (EMSs) of electric vehicles (EVs) helps encourage their mass adoption by addressing range anxiety concerns. 

  • electric vehicle
  • hybrid energy storage system
  • Li-ion battery
  • supercapacitors

1. Introduction

The prevalence of electric vehicles (EVs) is of a great significance to help reduce greenhouse gas emissions and reduce the dependencies on fossil fuels that are expected to become scarce in the next few decades [1]. Nevertheless, among the major factors obstructing mass adoption of EVs are the driving range limitations and the concerns on EV battery durability and lifetime [2,3][2][3]. The driving range of EVs is limited by the maximum capacity of their energy storage units. However, increasing the driving range by enlarging the capacity of the battery bank translates into higher EV prices, acknowledging that the battery size and capacity are the highest contributors to the cost of EVs [4]. On the other hand, dynamic wireless EV charging (DWC) systems enable battery capacity reduction through offering on-the-move charging [5,6,7][5][6][7]. However, they suffer from inherent misalignments that decrease the power transfer efficiency and cause fluctuations in the received energy, thereby degrading the performance and lifetime of the EV battery [8,9,10][8][9][10]. Accordingly, alternative solutions are investigated to improve the efficiency of the EV EMS, improve its battery lifetime, and reduce the EV operational costs by decreasing the frequency of battery replacement [11]. Among these solutions is the integration of supercapacitors (SC) to form a hybrid energy storage system (HESS), and developing efficient battery–SC-based EMSs to regulate the power flow between the two energy storage units and the EV motor.
SCs are electrolytic capacitors that are characterized by large capacitance values and higher power densities compared to the Li-ion batteries typically used in EVs. The higher power density allows SCs to release significantly higher power in shorter time intervals. This makes them suitable for addressing the EV motor requirements during urban driving patterns with frequent accelerations and decelerations that cause large fluctuations in the power demand [12]. At times when the current demand by the load is high, this current can be supplied by the SC to reduce the thermal stress on the EV battery and prolong its lifetime [13]. This improves the EV EMS performance and reduces its operational costs by reducing the degradation rate of the EV battery, and hence, reducing the frequency of battery replacement. The internal resistance of an SC is also significantly smaller than that of a Li-ion battery of similar energy storage capacity, which translates into lower power losses [14].

2. Battery–SC Connectivity

Different SC connectivity solutions are proposed in the literature, to increase the operational efficiency and improve the voltage supply capabilities of the EV energy storage system [15,16][15][16]. One battery–SC connection configuration known as passive paralleling is proposed in Ref. [17]. In this method, the hybridization is formed without any power electronic circuits, which causes the SC to deplete significantly faster than the Li-ion battery. To overcome this issue, a DC/DC converter is used to connect the SC to the battery in Refs. [18,19][18][19] in a semi-active topology, to control the voltage of the SC using the duty cycle of the DC/DC converter. However, in this topology, the battery bank is directly connected to the DC bus and its voltage cannot be controlled. This is addressed in Ref. [20] by swapping the positions of the energy sources in the configuration discussed in Ref. [18]. Semi-active topologies are also utilized in Refs. [21,22][21][22] where only the battery is connected to the DC/DC converter while the SC is connected directly to the DC link. In this way, the voltage of the battery can be maintained lower or higher than the SC voltage. The work in Ref. [23] compares the two connection topologies discussed in Refs. [18,20][18][20] to decide whether the battery or the SC should be connected to the DC bus through the DC/DC converters. In their assessment, the authors reveal that connecting the SC to the DC link through a DC/DC converter is better to enable the SC to supply larger current peaks.
The authors in Ref. [24] assert that the HESS should have both sources connected to the DC bus through DC/DC converters, to enable better control of their energy flow. However, this suffers from higher power losses as two DC/DC converters are utilized, which decreases the overall energy exchange efficiency. In Ref. [25], the authors compare the passive paralleling topology in Ref. [17] with two improved topologies, namely, a three-level converter and a half-controlled converter. However, the two topologies utilize extra switches, and hence, require extra gating signals which increases their complexity, while the battery remains directly connected to the DC bus. Ref. [26] proposes a configuration in which the low-voltage side is held by the battery pack and is interfaced by a power diode with the DC link held by the supercapacitor bank. With this topology, the battery bank is expected to operate with a lower voltage than the SC to keep the diode reverse biased. However, as the SC discharges, its voltage decreases and the diode becomes forward biased, which splits the output battery current between the motor and the SC for recharging.

3. HESS Energy Management Systems

Extensive research has been conducted to develop efficient EMSs for integrated battery–SC HESSs in EVs. The deterministic rule-based method is proposed in Refs. [27,28,29][27][28][29] in the form of if-else paradigms that are developed based on heuristic human experience. In these works, the authors set a threshold on the current demanded by the EV motor. Below the threshold, the EV battery is in operation, whereas the SC is activated when the current demand exceeds the threshold to supply the needed power. Other rule-based HESS energy management algorithms are proposed in Refs. [30[30][31],31], in which the threshold is set on the power delivered to the motor instead of its current demand. In this way, both the voltage and the current requirements of the motor are acknowledged.
In the aforementioned rule-based algorithms, the the current and/or power thresholds are determined in advance, using the expected power demand for pre-known driving cycles. This limitation is addressed in Ref. [32] by proposing an adaptive power split strategy that tracks the motor load profile in real-time to determine the level of variation in the power demand and split the power supply between the battery and the SC accordingly. Another real-time power split approach is utilized in Ref. [33], using fuzzy logic controllers to detect the load variation frequency. A fuzzy rule-based HESS EMS is presented in Ref. [34] with optimized fuzzy membership function to accurately determine the power thresholds. In addition, real-time current sensing is adopted in Ref. [35] to control the maximum current supplied by the battery to the EV motor and prevent excessive battery discharge.
Different optimization techniques can also be integrated with a rule-based algorithm to enable efficient power splitting in HESSs. In Refs. [36,37][36][37], the search space for power provisioning between the battery and the SC is first restricted by a set of rules, within which the optimal operating point is determined in real-time using metaheuristic optimization techniques, such as particle swarm optimization (PSO). Other real-time optimization solutions are also proposed in Refs. [16,38][16][38] using model predictive control (MPC) to determine the optimal power split between the EV battery and the SC bank under unknown driving cycles. Adaptive Pontryagin’s minimum principle (PMP) is also utilized in Refs. [39,40][39][40] for efficient and less computationally expensive optimization of the power provisioning in HESS. In contrast, offline, global optimal EMSs are proposed in Refs. [19,41,42][19][41][42] using dynamic programming (DP), yet they require prior knowledge of the driving cycle and a large computational capability of the EMS controller.
In Ref. [21], a multi-objective optimization problem is formulated to determine the optimal sizing of the battery and the SC banks required to implement a rule-based energy management algorithm in a semi-active HESS, while minimizing the cost of the HESS and extending the EV driving range. Ref. [22] also presents an optimal battery–SC sizing model for a semi-active HESS, but uses the power requirements of different driving cycles, instead of predefined EMS rules, to determine the model constraints.

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