Sarah Lindqvist

Sarah Lindqvist

Sarah Lindqvist is a Battery Management Systems engineer with 12 years of experience in SOC/SOH estimation algorithms, protection circuit design, and cell balancing systems. She has led BMS development programmes for portable power stations, industrial UPS, and compact energy storage products across European and Asian markets.

Temperature-Corrected SOC Estimation in Reconfigurable Lithium-Ion Battery Packs: What Buyers Must Know

TL;DR Without temperature correction, OCV-based SOC estimation in reconfigurable lithium-ion battery packs produces errors of 3%–9% at the same measured voltage across a 10–40°C operating range — a gap large enough to trigger premature capacity derating or missed overcharge events.…

Read article →Temperature-Corrected SOC Estimation in Reconfigurable Lithium-Ion Battery Packs: What Buyers Must Know

Multi-Stage Transient Overvoltage Protection for Lithium-Ion Battery Modules: Circuit Design, Test Data & Sourcing Guide

TL;DR A three-stage cascaded passive protection circuit successfully clamped 1.2/50 μs impulse overvoltages ranging from 500 V to 4 kV down to a final port voltage of 15.2–26.4 V at the battery module terminals, with a self-breaking DC overvoltage threshold…

Read article →Multi-Stage Transient Overvoltage Protection for Lithium-Ion Battery Modules: Circuit Design, Test Data & Sourcing Guide

Grouped Active Cell Balancing for Second-Life LFP Packs: Performance Data and Procurement Thresholds

TL;DR A grouped bidirectional active balancing architecture using Buck-Boost circuits reduced SOC spread in a 12-cell retired LFP pack from 10.2% down to 2% during static equalization (98 min) and from 10.9% down to 1.94% during charge equalization (87 min)…

Read article →Grouped Active Cell Balancing for Second-Life LFP Packs: Performance Data and Procurement Thresholds

Intelligent Active-Passive Hybrid Equalization for Retired LiFePO₄ Battery Packs: BMS Qualification Guide

TL;DR A 16S LiFePO₄ pack of retired power cells achieved a discharge capacity restoration rate of 96.2% (22,789 mAh recovered from a 23,700 mAh baseline) across 121 charge-discharge cycles using an intelligent time-sharing active-passive hybrid equalization strategy. For buyers sourcing…

Read article →Intelligent Active-Passive Hybrid Equalization for Retired LiFePO₄ Battery Packs: BMS Qualification Guide

RLS-EKF SOC Estimation for Vanadium Redox Flow Batteries: Procurement Guide for MW-Scale Systems

TL;DR In controlled simulation testing, the RLS-EKF algorithm achieved a mean SOC prediction error of 0.00083 and an RMSE of 0.0011 under constant-current pulse charging — performance that holds up under dynamic trapezoidal discharge profiles as well. For buyers evaluating…

Read article →RLS-EKF SOC Estimation for Vanadium Redox Flow Batteries: Procurement Guide for MW-Scale Systems

CHB-PCS SoC Equalization: Technical Procurement Guide for Multi-String Battery Energy Storage

TL;DR In large-scale battery energy storage systems using cascaded H-bridge power conversion architecture, SoC imbalance across series-connected battery strings is the primary cause of premature capacity fade and thermal stress concentration — simulation data shows that uncontrolled inter-phase SoC deviation…

Read article →CHB-PCS SoC Equalization: Technical Procurement Guide for Multi-String Battery Energy Storage

SP2D Model-Based Lithium Plating Suppression for Fast-Charge BMS: Accuracy Thresholds and Cycle Life Validation

TL;DR An NCM prismatic cell (156 Ah nominal) charged using a simplified pseudo-two-dimensional (SP2D) model-controlled current profile reached the 4.3 V cutoff in 1895 seconds while keeping the anode potential above the 20 mV lithium plating threshold throughout — confirmed…

Read article →SP2D Model-Based Lithium Plating Suppression for Fast-Charge BMS: Accuracy Thresholds and Cycle Life Validation

Entropy-Based SOH Prediction for Lithium-Ion Battery Clusters: Real-Time Health Assessment Without Capacity Testing

TL;DR Entropy-based analysis of voltage drop and temperature distribution across cell clusters enables real-time health assessment without disassembling battery packs: information entropy values between 1.85–2.00 for voltage features and 1.51–1.53 for temperature features indicate consistent cell aging, while entropy deviations…

Read article →Entropy-Based SOH Prediction for Lithium-Ion Battery Clusters: Real-Time Health Assessment Without Capacity Testing

SOH Estimation for LiFePO₄ Packs: 1-Minute Voltage Sampling Achieves 0.37% MAPE

TL;DR The 1-minute voltage differential sampling method during constant-current charging below 3.41 V achieves 0.37% mean absolute percentage error in SOH prediction for LiFePO₄ packs, outperforming 3-minute and 5-minute intervals. For procurement teams managing large battery clusters, this precision directly…

Read article →SOH Estimation for LiFePO₄ Packs: 1-Minute Voltage Sampling Achieves 0.37% MAPE