The increasing popularity of electric vehicles (EVs) has been attributed to their low-carbon and environmentally friendly attributes. Given the depletion of fossil fuels and changes in climatic conditions due to air pollution, extensive research has been undertaken to develop EVs capable of matching or exceeding the performance of today’s internal combustion engines (ICEs). Transitioning from ICE vehicles to EVs can significantly reduce greenhouse gases over a vehicle’s lifetime. Across the different EV types, the widespread usage of batteries is due to their high power density and steady output voltage, making them excellent energy storage devices (ESD). The current downsides of battery-powered electric vehicles include long recharge times, the impact of additional strain on the grid, poor societal acceptance due to high initial costs, and a lack of adequate charging infrastructure. Even more problematic is their short driving range compared to standard ICE and fuel cell EVs. This situation is further compounded through battery degradation, which gradually reduces the capacity of a battery.
A combination of the primary degradation mechanism and the secondary degradation routes leads to an extra set of secondary degradation events such as pore obstruction, electrolyte breakdown, and electrolyte loss. Since the invention of Li-ion batteries, graphite has dominated anode materials due to its unrivaled combination of low cost, abundance, high energy density, power density, and extremely long cycle life. However, when graphite is chemically exfoliated in the presence of oxidants, graphite exfoliation occurs because oxygen atoms overcome the connection between graphene particles. Due to the significant change in volume caused by the Li-alloying process, electrodes have a short cycle life and need to be replaced frequently.
Over a limited period, lithium dendrites can grow on a lithium anode when Li-ions are deposited on the anode from a non-aqueous liquid electrolyte, such as the type used in Li-ion batteries. Dendrites are formed when Li-ion batteries are plated rather than alloyed with their anode, which can be either silicon or graphite. The production of dendrites, typical in solid and liquid electrolytes, accelerates electrolyte degradation, produces a thermal runway, and, among other consequences, results in an internal short circuit and capacity loss in the battery [11][12][13][14][15].Lithium plating on the graphite anode occurs as a side effect of Li-ions intercalation under severe charge circumstances, i.e., high charge rates and low charge temperatures. Such plated lithium is detrimental to the performance, durability, and safety of Li-ion batteries. Due to the closeness of graphite electrodes to Li-ions, graphite anodes are more vulnerable to lithium plating. On the other hand, hard carbons and lithium titanate anodes are less vulnerable to lithium plating [20][21][22]. A Li-ion battery is composed of an electrolyte and two electrodes. Each electrode is composed of an atomic framework that contains a small amount of mobile lithium. During battery charging or discharging, Li-ions are extracted from one electrode, migrate through the electrolyte, and are injected into the other electrode. At the same time, electrons move between the electrodes via an external metallic wire. When lithium is extracted or inserted, diffusion-induced stresses are applied to the electrodes, resulting in deformation and fracture [6]. The loss of structural integrity may decrease electric conductivity, in the process reducing the battery’s capacity [23][24]. These degradation pathways result in five distinct cell-level regimes: loss of Li-ion inventory, impedance change, stoichiometric drift, and loss of active material at both electrodes, marked operationally as capacity or as power fading.
Degradation Forms | Indications |
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Mechanical | Mechanical stress and deformation manifest in the form of external and internal stress. Automotive vibration in the form of Z-axis vibration of the cylindrical cells. |
Chemical | Lithium plating during overcharge, operation at a low temperature, and high discharge. |
Electrochemical | Side reactions, solvent dissolution, solid electrolyte layer growth and decomposition, and electrolyte oxidation. |
Electrochemical and mechanical | Fracture of particles in both electrodes and particle isolation. |
Thermal coupling | Reaction rate increases with higher temperature. |
Their rapid ascension can be attributed to their high power and energy densities, long cycle life, and improved efficiency. They have vast and diverse applications in energy systems, including bulk storage, peak shaving, frequency regulation, voltage support, and reserve capacity.
Performance-based Energy Management Systems (EMS) emphasize the need to optimize the performance of an EMS in an EV by considering battery degradation and other aspects (associated parts and parameters) that can reduce overall costs. This optimization technique aims to find the variables that minimize or maximize the objective function while satisfying the constraints. The degradation cost can be reduced by resolving this objective function, which may involve single or multiobjective functions.
A real-time predictive energy management strategy (PEMS) for plug-in hybrid electric vehicles is proposed in [37] to coordinate fuel economy with battery lifetime. The engine-generator set (EGS), lithium-battery package, traction motor, and power inverter are the four components that make up the powertrain idea for a plug-in hybrid electric vehicle. The EGS and the battery are both connected to the power inverter. The output of the power inverter is then connected to the EV using the driveline axle and the traction motor. Based on the longitudinal vehicle dynamics, this control-oriented model of battery State of Charge (SoC) and State of Health (SoH for PEMS design was developed and constructed mathematically. The system for charging and discharging is represented by an equivalent circuit model with an internal resistance of the first order. This allows a variation in voltage and power flow to be established. A semi-empirical battery lifespan model based on a rechargeable lithium cell was used to quantify battery capacity loss. Among other features, the PEMS comprises a velocity predictor used to correctly anticipate future drive velocity, an SoC reference generator, and an online optimization technique. The velocity predictor uses the neural network technique based on radial basis functions. The control objective was to minimize fuel consumption, electricity, and battery degradation cost. In addition, a SoC tracking reference was introduced as part of the objective function. The physical constraints in the PHEV train include the engine’s power, generator, current drawn from the battery, and the state of charge. The cost minimization problem is formulated to resolve the model predictive control (MPC) problem [2]. Multivariable and constrained issues in nonlinear and multivariable systems can be resolved by MPC while maintaining high degrees of robustness and stability. A high level of stability is commonly required while regulating and controlling the systems. Hybridization systems, such as fuel cells and hybrid storage energy systems, can be utilized for any hybridized system, regardless of the storage device combination [38]. The equations consider the control target: fuel usage, electricity costs for battery charging and discharging, and the cost of corresponding battery degradation. The penalty-based continuation/generalized minimal residual (C/GMRES) algorithm determines the projected engine power command in real-time. This strategy is essential to alleviate the significant stress of the optimization procedure. The external penalty function instead of C/GMRES, which cannot handle inequality constraints, ensures that the powertrain’s physical inequality limits are not exceeded.The cost of installing a single battery to achieve the same benefits was calculated and compared using an offline technique based on a semi-empirical battery-degradation model. As a result of the envisaged connectivity, battery life for all three energy sources (engine, battery, and storage capacitor) is expected to be extended. To determine the concept’s practicality, the economic cost of the HESS system’s life cycle was evaluated and compared to that of a bus with a single battery. Given that obtaining the required electric range with a single battery of appropriate power capacity is impractical, the suggested technique would result in a more excellent engine, battery, and SC control due to the HESS, reduced battery deterioration, and overall cost saving through PHEBs. On the other hand, using a single battery bus would increase fuel consumption and exacerbate battery deterioration. The battery would have to be replaced several times to keep the EV running for its entire service life, resulting in substantial maintenance expenditure.
It is necessary to account for battery degradation when calculating the operating costs of a battery energy storage system because the life of electrochemical battery cells is highly sensitive to the number of charge and discharge cycles the battery undergoes. This, in turn, is directly affected by how the battery is maintained and operated. Batteries degrade in numerous ways, and current models of battery degradation either do not match published calculations or do not adequately depict the actual mechanism of battery degradation. Sizing guides and energy management (EM) benchmarks of the HESS of battery-SC arrangements deployed in EV applications are presented in [44]. The approach of HESS size optimization in order to minimize battery degradation and financial costs in EVs was explored. The optimal EM benchmarks that minimize battery degradation were likewise presented, irrespective of the EM technique implemented. By decoupling the EM problem from the issues surrounding the HESS sizing, the factors of battery degradation and HESS sizing, which are inconsequential to the specifications of batteries and SCs, as well as the design parameters of EV, were highlighted. The semi-empirical model used in this work follows the Arrhenius Law.Finding an ideal DOH requires achieving a balance of cost, fuel usage, and battery aging. The optimization goal was separated into three parts: equivalent hydrogen consumption per 100 km, the cost of the hybrid energy storage system (battery and PEMFC), and battery capacity degradation. The optimization constraint is a one-dimensional parameter consisting of the particle velocity and position. In terms of the DOH, several approaches were utilized to share the required motor power between PEMFC and battery. The battery took over when the FC could not supply the requirement from the motor. Battery life was prolonged, energy consumption optimized, and powertrain costs lowered based on individual requirements, the multiobjective optimization applied, and the proper hybridization degrees of the provided hybrid powertrain.
A vehicle model proposed in ref. [46] was based on an existing vehicle that was initially a pure electric automobile. However, a proton exchange membrane FC was added to increase its autonomy. When creating the objective function and limitations, a model that illustrates dynamic behavior was included. Following that, an energy management system was evaluated to aid in measuring the damage caused to the battery during any profile, specifically the current profile, taking into account fuel economy and battery degradation. After completing the previous modeling, the high-efficiency hydrogen fuel cell used in the car was modeled with clear accommodation of the boost converter, which is helpful for power flow management between the fuel cell and the DC bus. The equivalent consumption minimization technique, an online EMS, was utilized with a comprehensive vehicle model to solve the local optimization problem. The control input or command flow for the vehicle can be computed. A weight factor ranging from 0 to 1 is significant in altering the performance of the suggested technique from maximum fuel economy to maximum battery economy. The objective function was specified to accommodate fuel consumption and battery usage, and a single weight factor was also introduced. The control input, the change in fuel cell power, and the state vector are all limitations. The state vectors are the SoC, the battery current, and the power connected to the fuel cell. In addition, an additional optimal strategy, though offline in nature and based on the dynamic programming strategy, was also employed.A new notion of traveled distance between two consecutive recharging events (CRE) was developed to characterize battery capacity degradation based on an analysis of large-scale electric taxi global positioning satellite (GPS) data. A box-plot-based statistical analysis method was provided using historical CRE readings from electric taxis to better understand battery aging and its effects on performance. This method was chosen because BMS data is unavailable in the public domain. The data in the experiments came from over four years of real-world EV taxi GPS data, which was used to evaluate battery performance and degradation in real-world EV operation. The researchtudy showed that external circumstances, such as temperature, road conditions, and charging rate, will impact battery degradation in real-life EV use.
The availability of a large volume of data has cleared the pathway for using Big Data analytics in the EMS, especially the BMS. In [56], the capabilities of Big Data analytics in BMS applications were addressed, emphasizing the properties of Big Data in intelligent BMS, Big Data software frameworks, sources, and infrastructure. A feasible semi-empirical mathematical evaluation model, the extended wear density function (WDF), to be used to determine the remaining battery life, was proposed in [57]. In addition to analyzing the data, the authors developed an enhanced WDF model to create a more practical WDF. A transformation was performed to convert the measured operating temperature, current, and operating SoC values into coefficients for the extended WDF. The proposed data platform saves the measured data along with the parameters of the batteries. It is used to train the extended WDF model, which estimates battery degradation based on new experimental data with the same characteristics as the training data. The simulation results verify that the proposed platform's extended WDF and data structure are accurate.Reference | Type of EV | Description | Pros | Limitations |
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[67] | FCEV | The cost minimization considers hydrogen consumption, hybrid cost of battery and fuel cell, and battery degradation while using PSO to find optimal DoH. | PSO used allows a fast convergence rate for an optimal solution. It can thus be employed to solve non-linear and multiobjective optimization problems. |
The DoH is designed for each objective variable, i.e., prolonging battery life, reducing system cost, or reducing fuel consumption. Despite this, it has not been demonstrated whether it is possible to handle all the objective variables due to their varied weights. |
[46] | FCEV | Heuristic EMS strategy. | The heuristic strategy might provide acceptable performance and lower the computational burden in real-time applications. | The solution might not be optimal. The decision made might be inaccurate. |
[43] | PHEV | A semi-empirical model is used. The 2D PMP algorithm/strategy minimizes the system’s life-cycle cost. | The bus service chosen for modeling the driving cycle and charging patterns around a fixed route, following the same route daily, makes the model simpler than using a passenger vehicle with an ever-changing route. The PMP strategy includes one more state variable and can be used in a real-time control situation. Computational costs are reduced due to the 2D PMP algorithm. |
The mechanisms governing degradation are complex, nonlinear, and strongly interrelated. They are susceptible to varying operational conditions; thus, practical analysis is complex. Variation in electrodes, electrolytes, and manufacturing processes significantly increases the difficulty level. Not all predominant mechanisms are considered in the study |
[58] | FCEV | Powertrain system modeling with proton exchange membrane FC and Li-ion battery power sources is designed and modeled. Subsequently, a prioritized experience replay Deep Q-Network algorithm is applied. | The SoC in the analysis is kept at 0.7, resulting in less battery power. With the help of continuous training, the actions selected can bring about better rewards and stability in the system. |
With the increasing number of layers comes the increased complexity associated with the training process. |
[37] | PHEV | RBF-Neural network plus C/GMRES algorithm used for cost minimization. | Driving velocity can be predicted based on the algorithm used. Therefore, the potential for practical use is enormous. The C/GMRES algorithm is used to mitigate the burden associated with real-time optimization. |
The extra penalty method used in handling inequality constraints, heuristic in nature, will trade-off precision for speed. |
[44] | All EV types | HESS sizing is used to minimize battery degradation and financial cost using the DP approach. | This approach makes the decoupling of EMS from the HESS sizing problem realizable. Therefore, it is possible to minimize battery degradation regardless of the HESS size. | The decoupling of EMS from the HESS makes it impossible to investigate co-dependence and co-existing variables that EM and HESS share. The solution provided due to the isolation of each system might not be globally optimal. |
[47] | FCEV | A deterministic DP algorithm uses a fuel cell degradation model based on electrochemical surface area (ECSA) loss and a battery capacity model (minimizing fuel consumption and maximizing fuel cell lifetime plus battery). | Other frameworks for fuel cell degradation mechanisms can be incorporated whenever available. Since the EMS is rule-based, it is easy to implement. Even though only one mechanism of the fuel cell was considered (degradation of the platinum catalyst), the possibility of converting other degradation mechanisms found in transient load into ECSA allows a more dynamic and accurate representation. |
Drive cycles are representative of the typical urban transit system. This system does not accommodate the variability associated with personal cars that use different routes. |
[50] | BEV | Two stochastic stage systems plus ANN are supplied with historical time series lag (Software based: GAMS + KNITRO Solver). | KNITRO Solver supports a wide range of linear and non-linear problems. The low level of SoC employed in the design limits the degradation rate. |
When ANN produces a probing solution, it does not indicate why and how. This approach reduces trust and the ability to replicate such results within the network. There is no specific rule for determining the structure of ANN. The appropriate network structure is achieved through experience and trial and error. |
[63] | HEV | Cost minimization using adaptive EMS based on dynamic source resistance splitting plus heuristic optimization using the quantum butterfly optimization algorithm. | It is simple to implement since it accommodates dynamic source conditions (battery and ultracapacitor). Through optimal sizing, the energy and power requirements are satisfied. The stress on the battery packs is alleviated; thus, the battery life is extended. High peak charging and discharging is avoided. With this, the incidence of current drain is reduced. |
Even though an electrical model was investigated, it has been established that electrochemical models are advantageous as an accurate representation of what occurs in cells is revealed. |
[65] | FCEV | Fuzzy logic-controlled EMS relies on the genetic algorithm. The cost of the battery, fuel degradation, and fuel consumption were considered part of the objective function. | The EMS system is formulated as an optimization problem, allowing the fuzzy controller to be tuned to objective functions. | Cycling ageing has been considered while avoiding calendar ageing. The rule-based EMS might not be optimal for driving scenarios experienced on the road. A single SoC value was used; it was unclear whether it was the minimum or maximum value. |